The Experts below are selected from a list of 4869 Experts worldwide ranked by ideXlab platform
Xavier Pons - One of the best experts on this subject based on the ideXlab platform.
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Radiometric Correction of Landsat-8 and Sentinel-2A Scenes Using Drone Imagery in Synergy with Field Spectroradiometry
Remote Sensing, 2018Co-Authors: Joan-cristian Padró, Lluís Pesquer, Francisco-javier Muñoz, Luis Ángel Ávila, Xavier PonsAbstract:The main objective of this research is to apply unmanned aerial system (UAS) data in synergy with field spectroradiometry for the accurate Radiometric Correction of Landsat-8 (L8) and Sentinel-2 (S2) imagery. The central hypothesis is that imagery acquired with multispectral UAS sensors that are well calibrated with highly accurate field measurements can fill in the scale gap between satellite imagery and conventional in situ measurements; this can be possible by sampling a larger area, including difficult-to-access land covers, in less time while simultaneously providing good Radiometric quality. With this aim and by using near-coincident L8 and S2 imagery, we applied an upscaling workflow, whereby: (a) UAS-acquired multispectral data was empirically fitted to the reflectance of field measurements, with an extensive set of Radiometric references distributed across the spectral domain; (b) drone data was resampled to satellite grids for comparison with the Radiometrically corrected L8 and S2 official products (6S-LaSRC and Sen2Cor-SNAP, respectively) and the CorRad-MiraMon algorithm using pseudo-invariant areas, such as reflectance references (PIA-MiraMon), to examine their overall accuracy; (c) then, a subset of UAS data was used as reflectance references, in combination with the CorRad-MiraMon algorithm (UAS-MiraMon), to Radiometrically correct the matching bands of UAS, L8, and S2; and (d) Radiometrically corrected L8 and S2 scenes obtained with UAS-MiraMon were intercompared (intersensor coherence). In the first upscaling step, the results showed a good correlation between the field spectroRadiometric measurements and the drone data in all evaluated bands (R2 > 0.946). In the second upscaling step, drone data indicated good agreement (estimated from root mean square error, RMSE) with the satellite official products in visible (VIS) bands (RMSEVIS < 2.484%), but yielded poor results in the near-infrared (NIR) band (RMSENIR > 6.688% was not very good due to spectral sensor response differences). In the third step, UAS-MiraMon indicated better agreement (RMSEVIS < 2.018%) than the other satellite Radiometric Correction methods in visible bands (6S-LaSRC (RMSE < 2.680%), Sen2Cor-SNAP (RMSE < 2.192%), and PIA-MiraMon (RMSE < 3.130%), but did not achieve sufficient results in the NIR band (RMSENIR < 7.530%); this also occurred with all other methods. In the intercomparison step, the UAS-MiraMon method achieved an excellent intersensor (L8-S2) coherence (RMSEVIS < 1%). The UAS-sampled area involved 51 L8 (30 m) pixels, 143 S2 (20 m) pixels, and 517 S2 (10 m) pixels. The drone time needed to cover this area was only 10 min, including areas that were difficult to access. The systematic sampling of the study area was achieved with a pixel size of 6 cm, and the raster nature of the sampling allowed for an easy but rigorous resampling of UAS data to the different satellite grids. These advances improve human capacities for conventional field spectroradiometry samplings. However, our study also shows that field spectroradiometry is the backbone that supports the full upscaling workflow. In conclusion, the synergy between field spectroradiometry, UAS sensors, and Landsat-like satellite data can be a useful tool for accurate Radiometric Corrections used in local environmental studies or the monitoring of protected areas around the world.
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Radiometric Correction of Simultaneously Acquired Landsat-7/Landsat-8 and Sentinel-2A Imagery Using Pseudoinvariant Areas (PIA): Contributing to the Landsat Time Series Legacy
Remote Sensing, 2017Co-Authors: Joan-cristian Padró, Xavier Pons, David Aragonés, Ricardo Díaz-delgado, D. García, Javier Bustamante, Lluís Pesquer, Cristina Domingo-marimon, Oscar Gonzalez-guerrero, Jordi CristóbalAbstract:The use of Pseudoinvariant Areas (PIA) makes it possible to carry out a reasonably robust and automatic Radiometric Correction for long time series of remote sensing imagery, as shown in previous studies for large data sets of Landsat MSS, TM, and ETM+ imagery. In addition, they can be employed to obtain more coherence among remote sensing data from different sensors. The present work validates the use of PIA for the Radiometric Correction of pairs of images acquired almost simultaneously (Landsat-7 (ETM+) or Landsat-8 (OLI) and Sentinel-2A (MSI)). Four pairs of images from a region in SW Spain, corresponding to four different dates, together with field spectroradiometry measurements collected at the time of satellite overpass were used to evaluate a PIA-based Radiometric Correction. The results show a high coherence between sensors (r2 = 0.964) and excellent correlations to in-situ data for the MiraMon implementation (r2 > 0.9). Other methodological alternatives, ATCOR3 (ETM+, OLI, MSI), SAC-QGIS (ETM+, OLI, MSI), 6S-LEDAPS (ETM+), 6S-LaSRC (OLI), and Sen2Cor-SNAP (MSI), were also evaluated. Almost all of them, except for SAC-QGIS, provided similar results to the proposed PIA-based approach. Moreover, as the PIA-based approach can be applied to almost any image (even to images lacking of extra atmospheric information), it can also be used to solve the robust integration of data from new platforms, such as Landsat-8 or Sentinel-2, to enrich global data acquired since 1972 in the Landsat program. It thus contributes to the program’s continuity, a goal of great interest for the environmental, scientific, and technical community.
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automatic and improved Radiometric Correction of landsat imagery using reference values from modis surface reflectance images
International Journal of Applied Earth Observation and Geoinformation, 2014Co-Authors: Xavier Pons, Lluís Pesquer, Jordi Cristóbal, Oscar GonzalezguerreroAbstract:Abstract Radiometric Correction is a prerequisite for generating high-quality scientific data, making it possible to discriminate between product artefacts and real changes in Earth processes as well as accurately produce land cover maps and detect changes. This work contributes to the automatic generation of surface reflectance products for Landsat satellite series. Surface reflectances are generated by a new approach developed from a previous simplified Radiometric (atmospheric + topographic) Correction model. The proposed model keeps the core of the old model (incidence angles and cast-shadows through a digital elevation model [DEM], Earth–Sun distance, etc.) and adds new characteristics to enhance and automatize ground reflectance retrieval. The new model includes the following new features: (1) A fitting model based on reference values from pseudoinvariant areas that have been automatically extracted from existing reflectance products (Terra MODIS MOD09GA) that were selected also automatically by applying quality criteria that include a geostatistical pattern model. This guarantees the consistency of the internal and external series, making it unnecessary to provide extra atmospheric data for the acquisition date and time, dark objects or dense vegetation. (2) A spatial model for atmospheric optical depth that uses detailed DEM and MODTRAN simulations. (3) It is designed so that large time-series of images can be processed automatically to produce consistent Landsat surface reflectance time-series. (4) The approach can handle most images, acquired now or in the past, regardless of the processing system, with the exception of those with extremely high cloud coverage. The new methodology has been successfully applied to a series of near 300 images of the same area including MSS, TM and ETM+ imagery as well as to different formats and processing systems (LPGS and NLAPS from the USGS; CEOS from ESA) for different degrees of cloud coverage (up to 60%) and SLC-off. Reflectance products have been validated with some example applications: time series robustness (for a pixel in a pseudoinvariant area, deviations are only 1.04% on average along the series), spectral signatures generation (visually coherent with the MODIS ones, but more similar between dates), and classification (up to 4 percent points better than those obtained with the original manual method or the CDR products). In conclusion, this new approach, that could also be applied to other sensors with similar band configurations, offers a fully automatic and reasonably good procedure for the new era of long time-series of spatially detailed global remote sensing data.
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implications of interpreting tropical dry forest succession after Radiometric Correction of chris proba images
International Geoscience and Remote Sensing Symposium, 2012Co-Authors: V E Garciamillan, Xavier Pons, G A Sanchezazofeifa, G C Malvarez, G More, M YamanakaAbstract:In the present paper, we evaluate the effect of cast shadows in the classification of three successional stages of tropical forest in Mexico using hyperspectral and multiangular Chris/Proba images. A simple algorithm based on the cosine of the angle of solar incidence on the terrain is applied to correct the effect of topography on Chris/Proba's reflectances. Previous to the Correction of topographic effects, Chris/Proba images were atmospherically corrected in BEAM software. Vegetation maps of the study site were generated using non-parametric decision trees, defining four mam classes: late, intermediate and early stages of succession within a tropical dry forest, and riparian forests. By the comparison of resultant vegetation maps before and after the Correction of topography in Chris/Proba's spectral data, we found that late stage of succession and riparian forests are overestimated in non-corrected images while intermediate and early stages of succession are underestimated. Errors in classification are more important in extreme Chris/Proba's angles of observation. Thus, the Radiometric Correction of topography is necessary for an accurate classification of succession in tropical dry forests.
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IGARSS - Implications of interpreting tropical dry forest succession after Radiometric Correction of Chris/ Proba images
2012 IEEE International Geoscience and Remote Sensing Symposium, 2012Co-Authors: V.e. Garcia-millan, Xavier Pons, G C Malvarez, G More, G.a. Sanchez-azofeifa, M YamanakaAbstract:In the present paper, we evaluate the effect of cast shadows in the classification of three successional stages of tropical forest in Mexico using hyperspectral and multiangular Chris/Proba images. A simple algorithm based on the cosine of the angle of solar incidence on the terrain is applied to correct the effect of topography on Chris/Proba's reflectances. Previous to the Correction of topographic effects, Chris/Proba images were atmospherically corrected in BEAM software. Vegetation maps of the study site were generated using non-parametric decision trees, defining four mam classes: late, intermediate and early stages of succession within a tropical dry forest, and riparian forests. By the comparison of resultant vegetation maps before and after the Correction of topography in Chris/Proba's spectral data, we found that late stage of succession and riparian forests are overestimated in non-corrected images while intermediate and early stages of succession are underestimated. Errors in classification are more important in extreme Chris/Proba's angles of observation. Thus, the Radiometric Correction of topography is necessary for an accurate classification of succession in tropical dry forests.
Jun Pan - One of the best experts on this subject based on the ideXlab platform.
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CPU/GPU near real-time preprocessing for ZY-3 satellite images: Relative Radiometric Correction, MTF compensation, and geoCorrection
ISPRS Journal of Photogrammetry and Remote Sensing, 2014Co-Authors: Liuyang Fang, Mi Wang, Jun PanAbstract:Abstract ZY-3 is the first high-accuracy civil stereo-mapping optical satellite of China. It greatly improves China’s optical satellite image resolution with a boom in data volume, calling for new challenges in processing real-time applications. On the other hand, using central processing unit (CPU)/graphic processing unit (GPU) to resolve data-intensive remote sensing problems becomes a hot issue. In this paper, we present an approach for CPU/GPU near real-time preprocessing of ZY-3 satellite images, focusing on three key processors: relative Radiometric Correction (RRC), modulation transfer function compensation (MTFC), and geoCorrection (GC). First, basic GPU implementation issues are addressed to make the processors capable of processing with GPU. Second, three effective GPU specific optimizations are applied for further improvement of the GPU performance. Furthermore, to fully exploit the CPU’s computing horsepower within the system, a CPU/GPU workload distribution scheme is proposed, in which CPU undertakes partial computation to share the workloads of GPU. The experimental result shows that our approach achieved an overall 48.84-fold speedup ratio in ZY-3 nadir image preprocessing (the corresponding run time is 11.60 s for one image), which is capable of meeting the requirement of near real-time response to the applications that follow. In addition, with the supportability of IEEE 754–2008 floating-point standard in the Fermi type GPU, preprocessing ZY-3 images with our CPU/GPU processors could maintain the quality of image preprocess as done traditionally with CPU processors.
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cpu gpu near real time preprocessing for zy 3 satellite images relative Radiometric Correction mtf compensation and geoCorrection
Isprs Journal of Photogrammetry and Remote Sensing, 2014Co-Authors: Liuyang Fang, Mi Wang, Jun PanAbstract:Abstract ZY-3 is the first high-accuracy civil stereo-mapping optical satellite of China. It greatly improves China’s optical satellite image resolution with a boom in data volume, calling for new challenges in processing real-time applications. On the other hand, using central processing unit (CPU)/graphic processing unit (GPU) to resolve data-intensive remote sensing problems becomes a hot issue. In this paper, we present an approach for CPU/GPU near real-time preprocessing of ZY-3 satellite images, focusing on three key processors: relative Radiometric Correction (RRC), modulation transfer function compensation (MTFC), and geoCorrection (GC). First, basic GPU implementation issues are addressed to make the processors capable of processing with GPU. Second, three effective GPU specific optimizations are applied for further improvement of the GPU performance. Furthermore, to fully exploit the CPU’s computing horsepower within the system, a CPU/GPU workload distribution scheme is proposed, in which CPU undertakes partial computation to share the workloads of GPU. The experimental result shows that our approach achieved an overall 48.84-fold speedup ratio in ZY-3 nadir image preprocessing (the corresponding run time is 11.60 s for one image), which is capable of meeting the requirement of near real-time response to the applications that follow. In addition, with the supportability of IEEE 754–2008 floating-point standard in the Fermi type GPU, preprocessing ZY-3 images with our CPU/GPU processors could maintain the quality of image preprocess as done traditionally with CPU processors.
Lluís Pesquer - One of the best experts on this subject based on the ideXlab platform.
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Radiometric Correction of Landsat-8 and Sentinel-2A Scenes Using Drone Imagery in Synergy with Field Spectroradiometry
Remote Sensing, 2018Co-Authors: Joan-cristian Padró, Lluís Pesquer, Francisco-javier Muñoz, Luis Ángel Ávila, Xavier PonsAbstract:The main objective of this research is to apply unmanned aerial system (UAS) data in synergy with field spectroradiometry for the accurate Radiometric Correction of Landsat-8 (L8) and Sentinel-2 (S2) imagery. The central hypothesis is that imagery acquired with multispectral UAS sensors that are well calibrated with highly accurate field measurements can fill in the scale gap between satellite imagery and conventional in situ measurements; this can be possible by sampling a larger area, including difficult-to-access land covers, in less time while simultaneously providing good Radiometric quality. With this aim and by using near-coincident L8 and S2 imagery, we applied an upscaling workflow, whereby: (a) UAS-acquired multispectral data was empirically fitted to the reflectance of field measurements, with an extensive set of Radiometric references distributed across the spectral domain; (b) drone data was resampled to satellite grids for comparison with the Radiometrically corrected L8 and S2 official products (6S-LaSRC and Sen2Cor-SNAP, respectively) and the CorRad-MiraMon algorithm using pseudo-invariant areas, such as reflectance references (PIA-MiraMon), to examine their overall accuracy; (c) then, a subset of UAS data was used as reflectance references, in combination with the CorRad-MiraMon algorithm (UAS-MiraMon), to Radiometrically correct the matching bands of UAS, L8, and S2; and (d) Radiometrically corrected L8 and S2 scenes obtained with UAS-MiraMon were intercompared (intersensor coherence). In the first upscaling step, the results showed a good correlation between the field spectroRadiometric measurements and the drone data in all evaluated bands (R2 > 0.946). In the second upscaling step, drone data indicated good agreement (estimated from root mean square error, RMSE) with the satellite official products in visible (VIS) bands (RMSEVIS < 2.484%), but yielded poor results in the near-infrared (NIR) band (RMSENIR > 6.688% was not very good due to spectral sensor response differences). In the third step, UAS-MiraMon indicated better agreement (RMSEVIS < 2.018%) than the other satellite Radiometric Correction methods in visible bands (6S-LaSRC (RMSE < 2.680%), Sen2Cor-SNAP (RMSE < 2.192%), and PIA-MiraMon (RMSE < 3.130%), but did not achieve sufficient results in the NIR band (RMSENIR < 7.530%); this also occurred with all other methods. In the intercomparison step, the UAS-MiraMon method achieved an excellent intersensor (L8-S2) coherence (RMSEVIS < 1%). The UAS-sampled area involved 51 L8 (30 m) pixels, 143 S2 (20 m) pixels, and 517 S2 (10 m) pixels. The drone time needed to cover this area was only 10 min, including areas that were difficult to access. The systematic sampling of the study area was achieved with a pixel size of 6 cm, and the raster nature of the sampling allowed for an easy but rigorous resampling of UAS data to the different satellite grids. These advances improve human capacities for conventional field spectroradiometry samplings. However, our study also shows that field spectroradiometry is the backbone that supports the full upscaling workflow. In conclusion, the synergy between field spectroradiometry, UAS sensors, and Landsat-like satellite data can be a useful tool for accurate Radiometric Corrections used in local environmental studies or the monitoring of protected areas around the world.
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Radiometric Correction of Simultaneously Acquired Landsat-7/Landsat-8 and Sentinel-2A Imagery Using Pseudoinvariant Areas (PIA): Contributing to the Landsat Time Series Legacy
Remote Sensing, 2017Co-Authors: Joan-cristian Padró, Xavier Pons, David Aragonés, Ricardo Díaz-delgado, D. García, Javier Bustamante, Lluís Pesquer, Cristina Domingo-marimon, Oscar Gonzalez-guerrero, Jordi CristóbalAbstract:The use of Pseudoinvariant Areas (PIA) makes it possible to carry out a reasonably robust and automatic Radiometric Correction for long time series of remote sensing imagery, as shown in previous studies for large data sets of Landsat MSS, TM, and ETM+ imagery. In addition, they can be employed to obtain more coherence among remote sensing data from different sensors. The present work validates the use of PIA for the Radiometric Correction of pairs of images acquired almost simultaneously (Landsat-7 (ETM+) or Landsat-8 (OLI) and Sentinel-2A (MSI)). Four pairs of images from a region in SW Spain, corresponding to four different dates, together with field spectroradiometry measurements collected at the time of satellite overpass were used to evaluate a PIA-based Radiometric Correction. The results show a high coherence between sensors (r2 = 0.964) and excellent correlations to in-situ data for the MiraMon implementation (r2 > 0.9). Other methodological alternatives, ATCOR3 (ETM+, OLI, MSI), SAC-QGIS (ETM+, OLI, MSI), 6S-LEDAPS (ETM+), 6S-LaSRC (OLI), and Sen2Cor-SNAP (MSI), were also evaluated. Almost all of them, except for SAC-QGIS, provided similar results to the proposed PIA-based approach. Moreover, as the PIA-based approach can be applied to almost any image (even to images lacking of extra atmospheric information), it can also be used to solve the robust integration of data from new platforms, such as Landsat-8 or Sentinel-2, to enrich global data acquired since 1972 in the Landsat program. It thus contributes to the program’s continuity, a goal of great interest for the environmental, scientific, and technical community.
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automatic and improved Radiometric Correction of landsat imagery using reference values from modis surface reflectance images
International Journal of Applied Earth Observation and Geoinformation, 2014Co-Authors: Xavier Pons, Lluís Pesquer, Jordi Cristóbal, Oscar GonzalezguerreroAbstract:Abstract Radiometric Correction is a prerequisite for generating high-quality scientific data, making it possible to discriminate between product artefacts and real changes in Earth processes as well as accurately produce land cover maps and detect changes. This work contributes to the automatic generation of surface reflectance products for Landsat satellite series. Surface reflectances are generated by a new approach developed from a previous simplified Radiometric (atmospheric + topographic) Correction model. The proposed model keeps the core of the old model (incidence angles and cast-shadows through a digital elevation model [DEM], Earth–Sun distance, etc.) and adds new characteristics to enhance and automatize ground reflectance retrieval. The new model includes the following new features: (1) A fitting model based on reference values from pseudoinvariant areas that have been automatically extracted from existing reflectance products (Terra MODIS MOD09GA) that were selected also automatically by applying quality criteria that include a geostatistical pattern model. This guarantees the consistency of the internal and external series, making it unnecessary to provide extra atmospheric data for the acquisition date and time, dark objects or dense vegetation. (2) A spatial model for atmospheric optical depth that uses detailed DEM and MODTRAN simulations. (3) It is designed so that large time-series of images can be processed automatically to produce consistent Landsat surface reflectance time-series. (4) The approach can handle most images, acquired now or in the past, regardless of the processing system, with the exception of those with extremely high cloud coverage. The new methodology has been successfully applied to a series of near 300 images of the same area including MSS, TM and ETM+ imagery as well as to different formats and processing systems (LPGS and NLAPS from the USGS; CEOS from ESA) for different degrees of cloud coverage (up to 60%) and SLC-off. Reflectance products have been validated with some example applications: time series robustness (for a pixel in a pseudoinvariant area, deviations are only 1.04% on average along the series), spectral signatures generation (visually coherent with the MODIS ones, but more similar between dates), and classification (up to 4 percent points better than those obtained with the original manual method or the CDR products). In conclusion, this new approach, that could also be applied to other sensors with similar band configurations, offers a fully automatic and reasonably good procedure for the new era of long time-series of spatially detailed global remote sensing data.
Andrew Robson - One of the best experts on this subject based on the ideXlab platform.
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Assessing Radiometric Correction Approaches for Multi-Spectral UAS Imagery for Horticultural Applications
Remote Sensing, 2018Co-Authors: Stuart Phinn, Kasper Johansen, Andrew RobsonAbstract:Multi-spectral imagery captured from unmanned aerial systems (UAS) is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS-based data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based Radiometric Correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different Radiometric Corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant Radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate bidirectional reflectance distribution function (BRDF) Correction. Future UAS-based horticultural crop monitoring can benefit from the proposed solutions to Radiometric Corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties.
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Assessing Radiometric Correction Approaches for Multi-Spectral UAS Imagery for Horticultural Applications
2018Co-Authors: Stuart Phinn, Kasper Johansen, Andrew RobsonAbstract:UAS-based multi-spectral imagery is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based Radiometric Correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different Radiometric Corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant Radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate BRDF Correction. Future UAS based horticultural crop monitoring can benefit from the proposed solutions to Radiometric Corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties.
Joan-cristian Padró - One of the best experts on this subject based on the ideXlab platform.
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Radiometric Correction of Landsat-8 and Sentinel-2A Scenes Using Drone Imagery in Synergy with Field Spectroradiometry
Remote Sensing, 2018Co-Authors: Joan-cristian Padró, Lluís Pesquer, Francisco-javier Muñoz, Luis Ángel Ávila, Xavier PonsAbstract:The main objective of this research is to apply unmanned aerial system (UAS) data in synergy with field spectroradiometry for the accurate Radiometric Correction of Landsat-8 (L8) and Sentinel-2 (S2) imagery. The central hypothesis is that imagery acquired with multispectral UAS sensors that are well calibrated with highly accurate field measurements can fill in the scale gap between satellite imagery and conventional in situ measurements; this can be possible by sampling a larger area, including difficult-to-access land covers, in less time while simultaneously providing good Radiometric quality. With this aim and by using near-coincident L8 and S2 imagery, we applied an upscaling workflow, whereby: (a) UAS-acquired multispectral data was empirically fitted to the reflectance of field measurements, with an extensive set of Radiometric references distributed across the spectral domain; (b) drone data was resampled to satellite grids for comparison with the Radiometrically corrected L8 and S2 official products (6S-LaSRC and Sen2Cor-SNAP, respectively) and the CorRad-MiraMon algorithm using pseudo-invariant areas, such as reflectance references (PIA-MiraMon), to examine their overall accuracy; (c) then, a subset of UAS data was used as reflectance references, in combination with the CorRad-MiraMon algorithm (UAS-MiraMon), to Radiometrically correct the matching bands of UAS, L8, and S2; and (d) Radiometrically corrected L8 and S2 scenes obtained with UAS-MiraMon were intercompared (intersensor coherence). In the first upscaling step, the results showed a good correlation between the field spectroRadiometric measurements and the drone data in all evaluated bands (R2 > 0.946). In the second upscaling step, drone data indicated good agreement (estimated from root mean square error, RMSE) with the satellite official products in visible (VIS) bands (RMSEVIS < 2.484%), but yielded poor results in the near-infrared (NIR) band (RMSENIR > 6.688% was not very good due to spectral sensor response differences). In the third step, UAS-MiraMon indicated better agreement (RMSEVIS < 2.018%) than the other satellite Radiometric Correction methods in visible bands (6S-LaSRC (RMSE < 2.680%), Sen2Cor-SNAP (RMSE < 2.192%), and PIA-MiraMon (RMSE < 3.130%), but did not achieve sufficient results in the NIR band (RMSENIR < 7.530%); this also occurred with all other methods. In the intercomparison step, the UAS-MiraMon method achieved an excellent intersensor (L8-S2) coherence (RMSEVIS < 1%). The UAS-sampled area involved 51 L8 (30 m) pixels, 143 S2 (20 m) pixels, and 517 S2 (10 m) pixels. The drone time needed to cover this area was only 10 min, including areas that were difficult to access. The systematic sampling of the study area was achieved with a pixel size of 6 cm, and the raster nature of the sampling allowed for an easy but rigorous resampling of UAS data to the different satellite grids. These advances improve human capacities for conventional field spectroradiometry samplings. However, our study also shows that field spectroradiometry is the backbone that supports the full upscaling workflow. In conclusion, the synergy between field spectroradiometry, UAS sensors, and Landsat-like satellite data can be a useful tool for accurate Radiometric Corrections used in local environmental studies or the monitoring of protected areas around the world.
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Radiometric Correction of Simultaneously Acquired Landsat-7/Landsat-8 and Sentinel-2A Imagery Using Pseudoinvariant Areas (PIA): Contributing to the Landsat Time Series Legacy
Remote Sensing, 2017Co-Authors: Joan-cristian Padró, Xavier Pons, David Aragonés, Ricardo Díaz-delgado, D. García, Javier Bustamante, Lluís Pesquer, Cristina Domingo-marimon, Oscar Gonzalez-guerrero, Jordi CristóbalAbstract:The use of Pseudoinvariant Areas (PIA) makes it possible to carry out a reasonably robust and automatic Radiometric Correction for long time series of remote sensing imagery, as shown in previous studies for large data sets of Landsat MSS, TM, and ETM+ imagery. In addition, they can be employed to obtain more coherence among remote sensing data from different sensors. The present work validates the use of PIA for the Radiometric Correction of pairs of images acquired almost simultaneously (Landsat-7 (ETM+) or Landsat-8 (OLI) and Sentinel-2A (MSI)). Four pairs of images from a region in SW Spain, corresponding to four different dates, together with field spectroradiometry measurements collected at the time of satellite overpass were used to evaluate a PIA-based Radiometric Correction. The results show a high coherence between sensors (r2 = 0.964) and excellent correlations to in-situ data for the MiraMon implementation (r2 > 0.9). Other methodological alternatives, ATCOR3 (ETM+, OLI, MSI), SAC-QGIS (ETM+, OLI, MSI), 6S-LEDAPS (ETM+), 6S-LaSRC (OLI), and Sen2Cor-SNAP (MSI), were also evaluated. Almost all of them, except for SAC-QGIS, provided similar results to the proposed PIA-based approach. Moreover, as the PIA-based approach can be applied to almost any image (even to images lacking of extra atmospheric information), it can also be used to solve the robust integration of data from new platforms, such as Landsat-8 or Sentinel-2, to enrich global data acquired since 1972 in the Landsat program. It thus contributes to the program’s continuity, a goal of great interest for the environmental, scientific, and technical community.
