The Experts below are selected from a list of 55116 Experts worldwide ranked by ideXlab platform

Prasad S. Thenkabail - One of the best experts on this subject based on the ideXlab platform.

  • a 30 m landsat derived Cropland extent product of australia and china using random forest machine learning algorithm on google earth engine cloud computing platform
    Isprs Journal of Photogrammetry and Remote Sensing, 2018
    Co-Authors: Prasad S. Thenkabail, P Teluguntla, J. Xiong, Murali Krishna Gumma, Russell G. Congalton, Kamini Yadav, A Oliphant, Alfredo Huete
    Abstract:

    Mapping high resolution (30-m or better) Cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such Cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level Cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived Cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-Croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m Cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m Cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the Cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for Cropland class. Total Cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The Cropland extent product further demonstrated the ability to estimate sub-national Cropland areas accurately by providing an R2 value of 0.85 when compared with province-wise Cropland areas of China. The study provides a paradigm-shift on how Cropland maps are produced using multi-date remote sensing. These products can be browsed at www.Croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282.

  • CropRef: Reference Datasets and techniques to improve global Cropland mapping
    2017
    Co-Authors: J. Xiong, Prasad S. Thenkabail, P Teluguntla, Murali Krishna Gumma, Russell G. Congalton, Kamini Yadav, R Massey, A Oliphant, C Smith
    Abstract:

    Global timely, accurate, and cost-effective Cropland mapping is a prerequisite for agriculture monitoring and application. Recently, the world’s first global 30-m Cropland product was produced through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) supported global food security-support analysis data (GFSAD) project (https://Croplands.org/app/map). However, on average, in over 50 discrete segments of the world errors of omissions and commissions of GFSAD Cropland extent product was around 20%. One of the major reasons for these errors is due to lack of sufficient, spatially well distributed in-situ data for the development of these products. To address this issue, we built a web application (CropRef) to help collect crowdsourced geoTagged Cropland samples (https://Croplands.org/app/data/search). The system allows users to interactively query and browse the geo-referenced statistical data in the form of maps and to subsequently download them for the regions of interest from any place in the world. Our system (CropRef) also integrates online and mobile applications, very high spatial resolution satellite imagery (sub-meter to 5-m) available from Google Earth, as well as various forms of data collected through crowdsourcing as a mechanism for validating and improving globally relevant spatial information on agriculture. Through its growing network of volunteers and a number of successful data collection campaigns, over 100,000 samples of Croplands versus non-Croplands have been collected around the globe. This paper provides an overview of the main features of CropRef, and then using a series of examples, illustrates how the crowdsourced data collected through CropRef have been used to improve information on knowledge extraction and consequential global Cropland mapping. Validating land-cover maps at the global scale is a significant challenge. We also built a global reference dataset for validating 30 m-resolution global land-cover maps in the GFSAD30 project. The dataset has been carefully improved through several rounds of interpretation and verification by different image interpreters, and checked by an expert quality controller. Certainty in interpretation was measured by majority of interpreters agreeing on a class that is also accepted by expert quality controller. The tool and dataset are located at Croplands.org.

  • nominal 30 m Cropland extent map of continental africa by integrating pixel based and object based algorithms using sentinel 2 and landsat 8 data on google earth engine
    Remote Sensing, 2017
    Co-Authors: J. Xiong, Prasad S. Thenkabail, P Teluguntla, Murali Krishna Gumma, Russell G. Congalton, Adam J. Oliphant, Kamini Yadav, James C Tilton, Noel Gorelick
    Abstract:

    A satellite-derived Cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global Cropland extent maps, indicating Cropland and non-Cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, Cropland fallows, as well as for assessment of Cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the Cropland extent map have cascading effects on all higher-level Cropland products. However, precise and accurate Cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping Croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for Cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015–2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January–June 2016 and period 2: July–December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-Cropland training samples (sample size = 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available Cropland maps as well as with LULC maps to show spatial similarity. Finally, the Cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94%, with a producer’s accuracy of 85.9% (or omission error of 14.1%), and user’s accuracy of 68.5% (commission error of 31.5%) for the Cropland class. The total net Cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015. The online product, referred to as the Global Food Security-support Analysis Data @ 30-m for the African Continent, Cropland Extent product (GFSAD30AFCE) is distributed through the NASA’s Land Processes Distributed Active Archive Center (LP DAAC) as (available for download by 10 November 2017 or earlier): https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001 and can be viewed at https://Croplands.org/app/map. Causes of uncertainty and limitations within the crop extent product are discussed in detail.

  • automated Cropland mapping of continental africa using google earth engine cloud computing
    Isprs Journal of Photogrammetry and Remote Sensing, 2017
    Co-Authors: J. Xiong, Prasad S. Thenkabail, P Teluguntla, Murali Krishna Gumma, Russell G. Congalton, Justin Poehnelt, Kamini Yadav, David Thau
    Abstract:

    Abstract The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine Cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated Cropland mapping algorithm (ACMA) that captures extensive knowledge on the Croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference Cropland layers or RCLs (Cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and Croplands versus Cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived Cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net Cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross Cropland areas. Of the 260 Mha of net Cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net Cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net Cropland area without Cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived Cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) Croplands increased by 1 Mha/yr, and (b) Cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based Cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) Cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results.

  • fallow land algorithm based on neighborhood and temporal anomalies fanta to map planted versus fallowed Croplands using modis data to assist in drought studies leading to water and food security assessments
    Giscience & Remote Sensing, 2017
    Co-Authors: Cynthia S A Wallace, Prasad S. Thenkabail, Jesus R Rodriguez, Melinda K Brown
    Abstract:

    An important metric to monitor for optimizing water use in agricultural areas is the amount of Cropland left fallowed, or unplanted. Fallowed Croplands are difficult to model because they have many expressions; for example, they can be managed and remain free of vegetation or be abandoned and become weedy if the climate for that season permits. We used 250 m, 8-day composite Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index data to develop an algorithm that can routinely map Cropland status (planted or fallowed) with over 75% user’s and producer’s accuracies. The Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) compares the current greenness of a cultivated pixel to its historical greenness and to the greenness of all cultivated pixels within a defined spatial neighborhood, and is therefore transportable across space and through time. This article introduces FANTA and applies it to California from 2001 to 2015 as a case study for use in data-poo...

P Teluguntla - One of the best experts on this subject based on the ideXlab platform.

  • a 30 m landsat derived Cropland extent product of australia and china using random forest machine learning algorithm on google earth engine cloud computing platform
    Isprs Journal of Photogrammetry and Remote Sensing, 2018
    Co-Authors: Prasad S. Thenkabail, P Teluguntla, J. Xiong, Murali Krishna Gumma, Russell G. Congalton, Kamini Yadav, A Oliphant, Alfredo Huete
    Abstract:

    Mapping high resolution (30-m or better) Cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such Cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level Cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived Cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-Croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m Cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m Cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the Cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for Cropland class. Total Cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The Cropland extent product further demonstrated the ability to estimate sub-national Cropland areas accurately by providing an R2 value of 0.85 when compared with province-wise Cropland areas of China. The study provides a paradigm-shift on how Cropland maps are produced using multi-date remote sensing. These products can be browsed at www.Croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282.

  • CropRef: Reference Datasets and techniques to improve global Cropland mapping
    2017
    Co-Authors: J. Xiong, Prasad S. Thenkabail, P Teluguntla, Murali Krishna Gumma, Russell G. Congalton, Kamini Yadav, R Massey, A Oliphant, C Smith
    Abstract:

    Global timely, accurate, and cost-effective Cropland mapping is a prerequisite for agriculture monitoring and application. Recently, the world’s first global 30-m Cropland product was produced through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) supported global food security-support analysis data (GFSAD) project (https://Croplands.org/app/map). However, on average, in over 50 discrete segments of the world errors of omissions and commissions of GFSAD Cropland extent product was around 20%. One of the major reasons for these errors is due to lack of sufficient, spatially well distributed in-situ data for the development of these products. To address this issue, we built a web application (CropRef) to help collect crowdsourced geoTagged Cropland samples (https://Croplands.org/app/data/search). The system allows users to interactively query and browse the geo-referenced statistical data in the form of maps and to subsequently download them for the regions of interest from any place in the world. Our system (CropRef) also integrates online and mobile applications, very high spatial resolution satellite imagery (sub-meter to 5-m) available from Google Earth, as well as various forms of data collected through crowdsourcing as a mechanism for validating and improving globally relevant spatial information on agriculture. Through its growing network of volunteers and a number of successful data collection campaigns, over 100,000 samples of Croplands versus non-Croplands have been collected around the globe. This paper provides an overview of the main features of CropRef, and then using a series of examples, illustrates how the crowdsourced data collected through CropRef have been used to improve information on knowledge extraction and consequential global Cropland mapping. Validating land-cover maps at the global scale is a significant challenge. We also built a global reference dataset for validating 30 m-resolution global land-cover maps in the GFSAD30 project. The dataset has been carefully improved through several rounds of interpretation and verification by different image interpreters, and checked by an expert quality controller. Certainty in interpretation was measured by majority of interpreters agreeing on a class that is also accepted by expert quality controller. The tool and dataset are located at Croplands.org.

  • nominal 30 m Cropland extent map of continental africa by integrating pixel based and object based algorithms using sentinel 2 and landsat 8 data on google earth engine
    Remote Sensing, 2017
    Co-Authors: J. Xiong, Prasad S. Thenkabail, P Teluguntla, Murali Krishna Gumma, Russell G. Congalton, Adam J. Oliphant, Kamini Yadav, James C Tilton, Noel Gorelick
    Abstract:

    A satellite-derived Cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global Cropland extent maps, indicating Cropland and non-Cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, Cropland fallows, as well as for assessment of Cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the Cropland extent map have cascading effects on all higher-level Cropland products. However, precise and accurate Cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping Croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for Cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015–2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January–June 2016 and period 2: July–December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-Cropland training samples (sample size = 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available Cropland maps as well as with LULC maps to show spatial similarity. Finally, the Cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94%, with a producer’s accuracy of 85.9% (or omission error of 14.1%), and user’s accuracy of 68.5% (commission error of 31.5%) for the Cropland class. The total net Cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015. The online product, referred to as the Global Food Security-support Analysis Data @ 30-m for the African Continent, Cropland Extent product (GFSAD30AFCE) is distributed through the NASA’s Land Processes Distributed Active Archive Center (LP DAAC) as (available for download by 10 November 2017 or earlier): https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001 and can be viewed at https://Croplands.org/app/map. Causes of uncertainty and limitations within the crop extent product are discussed in detail.

  • automated Cropland mapping of continental africa using google earth engine cloud computing
    Isprs Journal of Photogrammetry and Remote Sensing, 2017
    Co-Authors: J. Xiong, Prasad S. Thenkabail, P Teluguntla, Murali Krishna Gumma, Russell G. Congalton, Justin Poehnelt, Kamini Yadav, David Thau
    Abstract:

    Abstract The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine Cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated Cropland mapping algorithm (ACMA) that captures extensive knowledge on the Croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference Cropland layers or RCLs (Cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and Croplands versus Cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived Cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net Cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross Cropland areas. Of the 260 Mha of net Cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net Cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net Cropland area without Cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived Cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) Croplands increased by 1 Mha/yr, and (b) Cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based Cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) Cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results.

  • Spectral matching techniques (SMTs) and automated Cropland classification algorithms (ACCAs) for mapping Croplands of Australia using MODIS 250-m time-series (2000–2015) data
    International Journal of Digital Earth, 2017
    Co-Authors: P Teluguntla, Prasad S. Thenkabail, J. Xiong, Murali Krishna Gumma, Russell G. Congalton, Adam J. Oliphant, Justin Poehnelt, Kamini Yadav, Mahesh N. Rao, R Massey
    Abstract:

    Mapping Croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, Croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated Cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine Cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive Croplands, rich history of agriculture, and yet nonexistent routine yearly generated Cropland products using multi-temporal remote sensing. This research produced three distinct Cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) Cropland extent/areas versus Cropland fallow areas, (2) irrigated versus rainfed Croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference Cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015. A comparison between the ACCA-derived Cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa = 0.814) with six classes: (a) producer’s accuracies varying between 72% and 90% and (b) user’s accuracies varying between 79% and 90%. ACPs for the individual years 2000–2013 and 2015 (ACP2000–ACP2013, ACP2015) showed very strong similarities with several other studies. The extent and vigor of the Australian Croplands versus Cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015, thus highlighting the value of the study in food security analysis. The ACCA algorithm and the Cropland products are released through http://Croplands.org/app/map and http://geography.wr.usgs.gov/science/Croplands/algorithms/australia_250m.html

Murali Krishna Gumma - One of the best experts on this subject based on the ideXlab platform.

  • a 30 m landsat derived Cropland extent product of australia and china using random forest machine learning algorithm on google earth engine cloud computing platform
    Isprs Journal of Photogrammetry and Remote Sensing, 2018
    Co-Authors: Prasad S. Thenkabail, P Teluguntla, J. Xiong, Murali Krishna Gumma, Russell G. Congalton, Kamini Yadav, A Oliphant, Alfredo Huete
    Abstract:

    Mapping high resolution (30-m or better) Cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such Cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level Cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived Cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-Croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m Cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m Cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the Cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for Cropland class. Total Cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The Cropland extent product further demonstrated the ability to estimate sub-national Cropland areas accurately by providing an R2 value of 0.85 when compared with province-wise Cropland areas of China. The study provides a paradigm-shift on how Cropland maps are produced using multi-date remote sensing. These products can be browsed at www.Croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282.

  • CropRef: Reference Datasets and techniques to improve global Cropland mapping
    2017
    Co-Authors: J. Xiong, Prasad S. Thenkabail, P Teluguntla, Murali Krishna Gumma, Russell G. Congalton, Kamini Yadav, R Massey, A Oliphant, C Smith
    Abstract:

    Global timely, accurate, and cost-effective Cropland mapping is a prerequisite for agriculture monitoring and application. Recently, the world’s first global 30-m Cropland product was produced through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) supported global food security-support analysis data (GFSAD) project (https://Croplands.org/app/map). However, on average, in over 50 discrete segments of the world errors of omissions and commissions of GFSAD Cropland extent product was around 20%. One of the major reasons for these errors is due to lack of sufficient, spatially well distributed in-situ data for the development of these products. To address this issue, we built a web application (CropRef) to help collect crowdsourced geoTagged Cropland samples (https://Croplands.org/app/data/search). The system allows users to interactively query and browse the geo-referenced statistical data in the form of maps and to subsequently download them for the regions of interest from any place in the world. Our system (CropRef) also integrates online and mobile applications, very high spatial resolution satellite imagery (sub-meter to 5-m) available from Google Earth, as well as various forms of data collected through crowdsourcing as a mechanism for validating and improving globally relevant spatial information on agriculture. Through its growing network of volunteers and a number of successful data collection campaigns, over 100,000 samples of Croplands versus non-Croplands have been collected around the globe. This paper provides an overview of the main features of CropRef, and then using a series of examples, illustrates how the crowdsourced data collected through CropRef have been used to improve information on knowledge extraction and consequential global Cropland mapping. Validating land-cover maps at the global scale is a significant challenge. We also built a global reference dataset for validating 30 m-resolution global land-cover maps in the GFSAD30 project. The dataset has been carefully improved through several rounds of interpretation and verification by different image interpreters, and checked by an expert quality controller. Certainty in interpretation was measured by majority of interpreters agreeing on a class that is also accepted by expert quality controller. The tool and dataset are located at Croplands.org.

  • nominal 30 m Cropland extent map of continental africa by integrating pixel based and object based algorithms using sentinel 2 and landsat 8 data on google earth engine
    Remote Sensing, 2017
    Co-Authors: J. Xiong, Prasad S. Thenkabail, P Teluguntla, Murali Krishna Gumma, Russell G. Congalton, Adam J. Oliphant, Kamini Yadav, James C Tilton, Noel Gorelick
    Abstract:

    A satellite-derived Cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global Cropland extent maps, indicating Cropland and non-Cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, Cropland fallows, as well as for assessment of Cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the Cropland extent map have cascading effects on all higher-level Cropland products. However, precise and accurate Cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping Croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for Cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015–2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January–June 2016 and period 2: July–December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-Cropland training samples (sample size = 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available Cropland maps as well as with LULC maps to show spatial similarity. Finally, the Cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94%, with a producer’s accuracy of 85.9% (or omission error of 14.1%), and user’s accuracy of 68.5% (commission error of 31.5%) for the Cropland class. The total net Cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015. The online product, referred to as the Global Food Security-support Analysis Data @ 30-m for the African Continent, Cropland Extent product (GFSAD30AFCE) is distributed through the NASA’s Land Processes Distributed Active Archive Center (LP DAAC) as (available for download by 10 November 2017 or earlier): https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001 and can be viewed at https://Croplands.org/app/map. Causes of uncertainty and limitations within the crop extent product are discussed in detail.

  • automated Cropland mapping of continental africa using google earth engine cloud computing
    Isprs Journal of Photogrammetry and Remote Sensing, 2017
    Co-Authors: J. Xiong, Prasad S. Thenkabail, P Teluguntla, Murali Krishna Gumma, Russell G. Congalton, Justin Poehnelt, Kamini Yadav, David Thau
    Abstract:

    Abstract The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine Cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated Cropland mapping algorithm (ACMA) that captures extensive knowledge on the Croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference Cropland layers or RCLs (Cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and Croplands versus Cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived Cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net Cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross Cropland areas. Of the 260 Mha of net Cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net Cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net Cropland area without Cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived Cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) Croplands increased by 1 Mha/yr, and (b) Cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based Cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) Cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results.

  • Spectral matching techniques (SMTs) and automated Cropland classification algorithms (ACCAs) for mapping Croplands of Australia using MODIS 250-m time-series (2000–2015) data
    International Journal of Digital Earth, 2017
    Co-Authors: P Teluguntla, Prasad S. Thenkabail, J. Xiong, Murali Krishna Gumma, Russell G. Congalton, Adam J. Oliphant, Justin Poehnelt, Kamini Yadav, Mahesh N. Rao, R Massey
    Abstract:

    Mapping Croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, Croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated Cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine Cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive Croplands, rich history of agriculture, and yet nonexistent routine yearly generated Cropland products using multi-temporal remote sensing. This research produced three distinct Cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) Cropland extent/areas versus Cropland fallow areas, (2) irrigated versus rainfed Croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference Cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015. A comparison between the ACCA-derived Cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa = 0.814) with six classes: (a) producer’s accuracies varying between 72% and 90% and (b) user’s accuracies varying between 79% and 90%. ACPs for the individual years 2000–2013 and 2015 (ACP2000–ACP2013, ACP2015) showed very strong similarities with several other studies. The extent and vigor of the Australian Croplands versus Cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015, thus highlighting the value of the study in food security analysis. The ACCA algorithm and the Cropland products are released through http://Croplands.org/app/map and http://geography.wr.usgs.gov/science/Croplands/algorithms/australia_250m.html

J. Xiong - One of the best experts on this subject based on the ideXlab platform.

  • a 30 m landsat derived Cropland extent product of australia and china using random forest machine learning algorithm on google earth engine cloud computing platform
    Isprs Journal of Photogrammetry and Remote Sensing, 2018
    Co-Authors: Prasad S. Thenkabail, P Teluguntla, J. Xiong, Murali Krishna Gumma, Russell G. Congalton, Kamini Yadav, A Oliphant, Alfredo Huete
    Abstract:

    Mapping high resolution (30-m or better) Cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such Cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level Cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived Cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-Croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m Cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m Cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the Cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for Cropland class. Total Cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The Cropland extent product further demonstrated the ability to estimate sub-national Cropland areas accurately by providing an R2 value of 0.85 when compared with province-wise Cropland areas of China. The study provides a paradigm-shift on how Cropland maps are produced using multi-date remote sensing. These products can be browsed at www.Croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282.

  • CropRef: Reference Datasets and techniques to improve global Cropland mapping
    2017
    Co-Authors: J. Xiong, Prasad S. Thenkabail, P Teluguntla, Murali Krishna Gumma, Russell G. Congalton, Kamini Yadav, R Massey, A Oliphant, C Smith
    Abstract:

    Global timely, accurate, and cost-effective Cropland mapping is a prerequisite for agriculture monitoring and application. Recently, the world’s first global 30-m Cropland product was produced through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) supported global food security-support analysis data (GFSAD) project (https://Croplands.org/app/map). However, on average, in over 50 discrete segments of the world errors of omissions and commissions of GFSAD Cropland extent product was around 20%. One of the major reasons for these errors is due to lack of sufficient, spatially well distributed in-situ data for the development of these products. To address this issue, we built a web application (CropRef) to help collect crowdsourced geoTagged Cropland samples (https://Croplands.org/app/data/search). The system allows users to interactively query and browse the geo-referenced statistical data in the form of maps and to subsequently download them for the regions of interest from any place in the world. Our system (CropRef) also integrates online and mobile applications, very high spatial resolution satellite imagery (sub-meter to 5-m) available from Google Earth, as well as various forms of data collected through crowdsourcing as a mechanism for validating and improving globally relevant spatial information on agriculture. Through its growing network of volunteers and a number of successful data collection campaigns, over 100,000 samples of Croplands versus non-Croplands have been collected around the globe. This paper provides an overview of the main features of CropRef, and then using a series of examples, illustrates how the crowdsourced data collected through CropRef have been used to improve information on knowledge extraction and consequential global Cropland mapping. Validating land-cover maps at the global scale is a significant challenge. We also built a global reference dataset for validating 30 m-resolution global land-cover maps in the GFSAD30 project. The dataset has been carefully improved through several rounds of interpretation and verification by different image interpreters, and checked by an expert quality controller. Certainty in interpretation was measured by majority of interpreters agreeing on a class that is also accepted by expert quality controller. The tool and dataset are located at Croplands.org.

  • nominal 30 m Cropland extent map of continental africa by integrating pixel based and object based algorithms using sentinel 2 and landsat 8 data on google earth engine
    Remote Sensing, 2017
    Co-Authors: J. Xiong, Prasad S. Thenkabail, P Teluguntla, Murali Krishna Gumma, Russell G. Congalton, Adam J. Oliphant, Kamini Yadav, James C Tilton, Noel Gorelick
    Abstract:

    A satellite-derived Cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global Cropland extent maps, indicating Cropland and non-Cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, Cropland fallows, as well as for assessment of Cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the Cropland extent map have cascading effects on all higher-level Cropland products. However, precise and accurate Cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping Croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for Cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015–2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January–June 2016 and period 2: July–December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-Cropland training samples (sample size = 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available Cropland maps as well as with LULC maps to show spatial similarity. Finally, the Cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94%, with a producer’s accuracy of 85.9% (or omission error of 14.1%), and user’s accuracy of 68.5% (commission error of 31.5%) for the Cropland class. The total net Cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015. The online product, referred to as the Global Food Security-support Analysis Data @ 30-m for the African Continent, Cropland Extent product (GFSAD30AFCE) is distributed through the NASA’s Land Processes Distributed Active Archive Center (LP DAAC) as (available for download by 10 November 2017 or earlier): https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001 and can be viewed at https://Croplands.org/app/map. Causes of uncertainty and limitations within the crop extent product are discussed in detail.

  • automated Cropland mapping of continental africa using google earth engine cloud computing
    Isprs Journal of Photogrammetry and Remote Sensing, 2017
    Co-Authors: J. Xiong, Prasad S. Thenkabail, P Teluguntla, Murali Krishna Gumma, Russell G. Congalton, Justin Poehnelt, Kamini Yadav, David Thau
    Abstract:

    Abstract The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine Cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated Cropland mapping algorithm (ACMA) that captures extensive knowledge on the Croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference Cropland layers or RCLs (Cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and Croplands versus Cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived Cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net Cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross Cropland areas. Of the 260 Mha of net Cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net Cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net Cropland area without Cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived Cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) Croplands increased by 1 Mha/yr, and (b) Cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based Cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) Cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results.

  • Spectral matching techniques (SMTs) and automated Cropland classification algorithms (ACCAs) for mapping Croplands of Australia using MODIS 250-m time-series (2000–2015) data
    International Journal of Digital Earth, 2017
    Co-Authors: P Teluguntla, Prasad S. Thenkabail, J. Xiong, Murali Krishna Gumma, Russell G. Congalton, Adam J. Oliphant, Justin Poehnelt, Kamini Yadav, Mahesh N. Rao, R Massey
    Abstract:

    Mapping Croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, Croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated Cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine Cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive Croplands, rich history of agriculture, and yet nonexistent routine yearly generated Cropland products using multi-temporal remote sensing. This research produced three distinct Cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) Cropland extent/areas versus Cropland fallow areas, (2) irrigated versus rainfed Croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference Cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015. A comparison between the ACCA-derived Cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa = 0.814) with six classes: (a) producer’s accuracies varying between 72% and 90% and (b) user’s accuracies varying between 79% and 90%. ACPs for the individual years 2000–2013 and 2015 (ACP2000–ACP2013, ACP2015) showed very strong similarities with several other studies. The extent and vigor of the Australian Croplands versus Cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015, thus highlighting the value of the study in food security analysis. The ACCA algorithm and the Cropland products are released through http://Croplands.org/app/map and http://geography.wr.usgs.gov/science/Croplands/algorithms/australia_250m.html

Russell G. Congalton - One of the best experts on this subject based on the ideXlab platform.

  • a 30 m landsat derived Cropland extent product of australia and china using random forest machine learning algorithm on google earth engine cloud computing platform
    Isprs Journal of Photogrammetry and Remote Sensing, 2018
    Co-Authors: Prasad S. Thenkabail, P Teluguntla, J. Xiong, Murali Krishna Gumma, Russell G. Congalton, Kamini Yadav, A Oliphant, Alfredo Huete
    Abstract:

    Mapping high resolution (30-m or better) Cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such Cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level Cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived Cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-Croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m Cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m Cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the Cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for Cropland class. Total Cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The Cropland extent product further demonstrated the ability to estimate sub-national Cropland areas accurately by providing an R2 value of 0.85 when compared with province-wise Cropland areas of China. The study provides a paradigm-shift on how Cropland maps are produced using multi-date remote sensing. These products can be browsed at www.Croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282.

  • CropRef: Reference Datasets and techniques to improve global Cropland mapping
    2017
    Co-Authors: J. Xiong, Prasad S. Thenkabail, P Teluguntla, Murali Krishna Gumma, Russell G. Congalton, Kamini Yadav, R Massey, A Oliphant, C Smith
    Abstract:

    Global timely, accurate, and cost-effective Cropland mapping is a prerequisite for agriculture monitoring and application. Recently, the world’s first global 30-m Cropland product was produced through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) supported global food security-support analysis data (GFSAD) project (https://Croplands.org/app/map). However, on average, in over 50 discrete segments of the world errors of omissions and commissions of GFSAD Cropland extent product was around 20%. One of the major reasons for these errors is due to lack of sufficient, spatially well distributed in-situ data for the development of these products. To address this issue, we built a web application (CropRef) to help collect crowdsourced geoTagged Cropland samples (https://Croplands.org/app/data/search). The system allows users to interactively query and browse the geo-referenced statistical data in the form of maps and to subsequently download them for the regions of interest from any place in the world. Our system (CropRef) also integrates online and mobile applications, very high spatial resolution satellite imagery (sub-meter to 5-m) available from Google Earth, as well as various forms of data collected through crowdsourcing as a mechanism for validating and improving globally relevant spatial information on agriculture. Through its growing network of volunteers and a number of successful data collection campaigns, over 100,000 samples of Croplands versus non-Croplands have been collected around the globe. This paper provides an overview of the main features of CropRef, and then using a series of examples, illustrates how the crowdsourced data collected through CropRef have been used to improve information on knowledge extraction and consequential global Cropland mapping. Validating land-cover maps at the global scale is a significant challenge. We also built a global reference dataset for validating 30 m-resolution global land-cover maps in the GFSAD30 project. The dataset has been carefully improved through several rounds of interpretation and verification by different image interpreters, and checked by an expert quality controller. Certainty in interpretation was measured by majority of interpreters agreeing on a class that is also accepted by expert quality controller. The tool and dataset are located at Croplands.org.

  • nominal 30 m Cropland extent map of continental africa by integrating pixel based and object based algorithms using sentinel 2 and landsat 8 data on google earth engine
    Remote Sensing, 2017
    Co-Authors: J. Xiong, Prasad S. Thenkabail, P Teluguntla, Murali Krishna Gumma, Russell G. Congalton, Adam J. Oliphant, Kamini Yadav, James C Tilton, Noel Gorelick
    Abstract:

    A satellite-derived Cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global Cropland extent maps, indicating Cropland and non-Cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, Cropland fallows, as well as for assessment of Cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the Cropland extent map have cascading effects on all higher-level Cropland products. However, precise and accurate Cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping Croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for Cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015–2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January–June 2016 and period 2: July–December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-Cropland training samples (sample size = 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available Cropland maps as well as with LULC maps to show spatial similarity. Finally, the Cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94%, with a producer’s accuracy of 85.9% (or omission error of 14.1%), and user’s accuracy of 68.5% (commission error of 31.5%) for the Cropland class. The total net Cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015. The online product, referred to as the Global Food Security-support Analysis Data @ 30-m for the African Continent, Cropland Extent product (GFSAD30AFCE) is distributed through the NASA’s Land Processes Distributed Active Archive Center (LP DAAC) as (available for download by 10 November 2017 or earlier): https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001 and can be viewed at https://Croplands.org/app/map. Causes of uncertainty and limitations within the crop extent product are discussed in detail.

  • automated Cropland mapping of continental africa using google earth engine cloud computing
    Isprs Journal of Photogrammetry and Remote Sensing, 2017
    Co-Authors: J. Xiong, Prasad S. Thenkabail, P Teluguntla, Murali Krishna Gumma, Russell G. Congalton, Justin Poehnelt, Kamini Yadav, David Thau
    Abstract:

    Abstract The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine Cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated Cropland mapping algorithm (ACMA) that captures extensive knowledge on the Croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference Cropland layers or RCLs (Cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and Croplands versus Cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived Cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net Cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross Cropland areas. Of the 260 Mha of net Cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net Cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net Cropland area without Cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived Cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) Croplands increased by 1 Mha/yr, and (b) Cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based Cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) Cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results.

  • Spectral matching techniques (SMTs) and automated Cropland classification algorithms (ACCAs) for mapping Croplands of Australia using MODIS 250-m time-series (2000–2015) data
    International Journal of Digital Earth, 2017
    Co-Authors: P Teluguntla, Prasad S. Thenkabail, J. Xiong, Murali Krishna Gumma, Russell G. Congalton, Adam J. Oliphant, Justin Poehnelt, Kamini Yadav, Mahesh N. Rao, R Massey
    Abstract:

    Mapping Croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, Croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated Cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine Cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive Croplands, rich history of agriculture, and yet nonexistent routine yearly generated Cropland products using multi-temporal remote sensing. This research produced three distinct Cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) Cropland extent/areas versus Cropland fallow areas, (2) irrigated versus rainfed Croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference Cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015. A comparison between the ACCA-derived Cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa = 0.814) with six classes: (a) producer’s accuracies varying between 72% and 90% and (b) user’s accuracies varying between 79% and 90%. ACPs for the individual years 2000–2013 and 2015 (ACP2000–ACP2013, ACP2015) showed very strong similarities with several other studies. The extent and vigor of the Australian Croplands versus Cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015, thus highlighting the value of the study in food security analysis. The ACCA algorithm and the Cropland products are released through http://Croplands.org/app/map and http://geography.wr.usgs.gov/science/Croplands/algorithms/australia_250m.html