The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Taifeng Dong - One of the best experts on this subject based on the ideXlab platform.
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estimating Crop Biomass using leaf area index derived from landsat 8 and sentinel 2 data
Isprs Journal of Photogrammetry and Remote Sensing, 2020Co-Authors: Taifeng Dong, Heather Mcnairn, Jiangui Liu, Budong Qian, Jane Liu, Rong Wang, Qi Jing, Catherine Champagne, Jarrett Powers, Yichao ShiAbstract:Abstract The availability of Landsat 8 and Sentinel-2 has led to a steady increase in both temporal and spatial resolution of satellite data, offering new opportunities for large-scale Crop condition monitoring and Crop yield mapping. This study investigated the potential of using Landsat 8 and Sentinel-2 data from the harmonized Landsat 8 and Sentinel-2 (HLS) products for Crop Biomass estimation for six Crops in Manitoba, Canada. Crop Biomass was estimated using remotely sensed leaf area index (LAI) to reparametrize a simple Crop growth model. The results showed that the LAI of six different Crops can be estimated using a generic relationship between LAI and red-edge based vegetation indices (VIs, e.g., modified simple ratio red-edge (MSRRE) and red-edge normalized difference VI (NDVIRE)) for the Multispectral Instrument (MSI) of Sentinel-2. For the Operational Land Imager of Landsat 8 without the red-edge band, LAI can be best estimated using a VI derived from Near-infrared (NIR) and short-wave infrared (SWIR) bands (Normalized Difference Water Index, NDWI1). Above-ground dry Biomass of these six Crops was more accurately estimated from the assimilation of LAI derived from both satellites (R2 (the coefficient of determination) = 0.81, RMSE (the root-mean-square-error) = 135.4 g/m2, nRMSE (the normalized RMSE) = 37.9%, RPD (the ratio of percent deviation) = 2.26) than that of LAI derived from MSI-data (R2 = 0.80, RMSE = 136.7 g/m2, nRMSE = 38.3%, RPD = 2.23) or that from LAI derived from OLI-data (R2 = 0.68, RMSE = 191.0 g/m2, nRMSE = 53.5%, RPD = 1.16). Further analysis showed that these three assimilation cases (MSI and OLI; MSI alone; OLI alone) with a different number of LAI observations resulted in differences in parameter optimization, particularly the parameters relevant to Crop phenology and Biomass partitioning. Both Crop growth stage (e.g., the emergence date for Crop growth) and leaf dry Biomass estimated from the assimilation of LAI derived from MSI and OLI, or MSI alone, produced the most accurate estimates. These results are likely attributed to the improved temporal coverage associated with Sentinel-2 and the availability of a red-edge band on this sensor.
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deriving maximum light use efficiency from Crop growth model and satellite data to improve Crop Biomass estimation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Taifeng Dong, Jiali Shang, Budong Qian, Qi Jing, Holly Croft, Jingming Chen, Jinfei Wang, Ted Huffman, Pengfei ChenAbstract:Maximum light use efficiency ( ${\text{LUE}}_{\rm{max}}$ ) is an important parameter in Biomass estimation models (e.g., the Production Efficiency Models (PEM)) based on remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of deriving spatially variable Crop ${\text{LUE}}_{\rm{max}}$ from satellite remote sensing data. ${\text{LUE}}_{\rm{max}}$ at the plot level was retrieved first by assimilating field measured green leaf area index and Biomass into a Crop model (the Simple Algorithm for Yield estimate model), and was then correlated with a few Landsat-8 vegetation indices (VIs) to develop regression models. ${\text{LUE}}_{\rm{max}}$ was then mapped using the best regression model from a VI. The influence factors on ${\text{LUE}}_{\rm{max}}$ variability were also assessed. Contrary to a fixed ${\text{LUE}}_{\rm{max}}$ , our results suggest that ${\text{LUE}}_{\rm{max}}$ is affected by environmental stresses, such as leaf nitrogen deficiency. The strong correlation between the plot-level ${\text{LUE}}_{\rm{max}}$ and VIs, particularly the two-band enhanced vegetation index for winter wheat ( Triticum aestivum ) and the green chlorophyll index for maize ( Zea mays ) at the milk stage, provided a potential to derive ${\text{LUE}}_{\rm{max}}$ from remote sensing observations. To evaluate the quality of ${\text{LUE}}_{\rm{max}}$ derived from remote sensing data, Biomass of winter wheat and maize was compared with that estimated using a PEM model with a constant ${\text{LUE}}_{\rm{max}}$ and the derived variable ${\text{LUE}}_{\rm{max}}$ . Significant improvements in Biomass estimation accuracy were achieved (by about 15.0% for the normalized root-mean-square error) using the derived variable ${\text{LUE}}_{\rm{max}}$ . This study offers a new way to derive ${\text{LUE}}_{\rm{max}}$ for a specific PEM and to improve the accuracy of Biomass estimation using remote sensing.
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deriving maximum light use efficiency from Crop growth model and satellite data to improve Crop Biomass estimation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Taifeng Dong, Jiali Shang, Jiangui Liu, Budong Qian, Qi Jing, Holly Croft, Jingming Chen, Jinfei Wang, Ted Huffman, Pengfei ChenAbstract:Maximum light use efficiency ( ${\text{LUE}}_{\rm{max}}$ ) is an important parameter in Biomass estimation models (e.g., the Production Efficiency Models (PEM)) based on remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of deriving spatially variable Crop ${\text{LUE}}_{\rm{max}}$ from satellite remote sensing data. ${\text{LUE}}_{\rm{max}}$ at the plot level was retrieved first by assimilating field measured green leaf area index and Biomass into a Crop model (the Simple Algorithm for Yield estimate model), and was then correlated with a few Landsat-8 vegetation indices (VIs) to develop regression models. ${\text{LUE}}_{\rm{max}}$ was then mapped using the best regression model from a VI. The influence factors on ${\text{LUE}}_{\rm{max}}$ variability were also assessed. Contrary to a fixed ${\text{LUE}}_{\rm{max}}$ , our results suggest that ${\text{LUE}}_{\rm{max}}$ is affected by environmental stresses, such as leaf nitrogen deficiency. The strong correlation between the plot-level ${\text{LUE}}_{\rm{max}}$ and VIs, particularly the two-band enhanced vegetation index for winter wheat ( Triticum aestivum ) and the green chlorophyll index for maize ( Zea mays ) at the milk stage, provided a potential to derive ${\text{LUE}}_{\rm{max}}$ from remote sensing observations. To evaluate the quality of ${\text{LUE}}_{\rm{max}}$ derived from remote sensing data, Biomass of winter wheat and maize was compared with that estimated using a PEM model with a constant ${\text{LUE}}_{\rm{max}}$ and the derived variable ${\text{LUE}}_{\rm{max}}$ . Significant improvements in Biomass estimation accuracy were achieved (by about 15.0% for the normalized root-mean-square error) using the derived variable ${\text{LUE}}_{\rm{max}}$ . This study offers a new way to derive ${\text{LUE}}_{\rm{max}}$ for a specific PEM and to improve the accuracy of Biomass estimation using remote sensing.
Pengfei Chen - One of the best experts on this subject based on the ideXlab platform.
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deriving maximum light use efficiency from Crop growth model and satellite data to improve Crop Biomass estimation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Taifeng Dong, Jiali Shang, Budong Qian, Qi Jing, Holly Croft, Jingming Chen, Jinfei Wang, Ted Huffman, Pengfei ChenAbstract:Maximum light use efficiency ( ${\text{LUE}}_{\rm{max}}$ ) is an important parameter in Biomass estimation models (e.g., the Production Efficiency Models (PEM)) based on remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of deriving spatially variable Crop ${\text{LUE}}_{\rm{max}}$ from satellite remote sensing data. ${\text{LUE}}_{\rm{max}}$ at the plot level was retrieved first by assimilating field measured green leaf area index and Biomass into a Crop model (the Simple Algorithm for Yield estimate model), and was then correlated with a few Landsat-8 vegetation indices (VIs) to develop regression models. ${\text{LUE}}_{\rm{max}}$ was then mapped using the best regression model from a VI. The influence factors on ${\text{LUE}}_{\rm{max}}$ variability were also assessed. Contrary to a fixed ${\text{LUE}}_{\rm{max}}$ , our results suggest that ${\text{LUE}}_{\rm{max}}$ is affected by environmental stresses, such as leaf nitrogen deficiency. The strong correlation between the plot-level ${\text{LUE}}_{\rm{max}}$ and VIs, particularly the two-band enhanced vegetation index for winter wheat ( Triticum aestivum ) and the green chlorophyll index for maize ( Zea mays ) at the milk stage, provided a potential to derive ${\text{LUE}}_{\rm{max}}$ from remote sensing observations. To evaluate the quality of ${\text{LUE}}_{\rm{max}}$ derived from remote sensing data, Biomass of winter wheat and maize was compared with that estimated using a PEM model with a constant ${\text{LUE}}_{\rm{max}}$ and the derived variable ${\text{LUE}}_{\rm{max}}$ . Significant improvements in Biomass estimation accuracy were achieved (by about 15.0% for the normalized root-mean-square error) using the derived variable ${\text{LUE}}_{\rm{max}}$ . This study offers a new way to derive ${\text{LUE}}_{\rm{max}}$ for a specific PEM and to improve the accuracy of Biomass estimation using remote sensing.
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deriving maximum light use efficiency from Crop growth model and satellite data to improve Crop Biomass estimation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Taifeng Dong, Jiali Shang, Jiangui Liu, Budong Qian, Qi Jing, Holly Croft, Jingming Chen, Jinfei Wang, Ted Huffman, Pengfei ChenAbstract:Maximum light use efficiency ( ${\text{LUE}}_{\rm{max}}$ ) is an important parameter in Biomass estimation models (e.g., the Production Efficiency Models (PEM)) based on remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of deriving spatially variable Crop ${\text{LUE}}_{\rm{max}}$ from satellite remote sensing data. ${\text{LUE}}_{\rm{max}}$ at the plot level was retrieved first by assimilating field measured green leaf area index and Biomass into a Crop model (the Simple Algorithm for Yield estimate model), and was then correlated with a few Landsat-8 vegetation indices (VIs) to develop regression models. ${\text{LUE}}_{\rm{max}}$ was then mapped using the best regression model from a VI. The influence factors on ${\text{LUE}}_{\rm{max}}$ variability were also assessed. Contrary to a fixed ${\text{LUE}}_{\rm{max}}$ , our results suggest that ${\text{LUE}}_{\rm{max}}$ is affected by environmental stresses, such as leaf nitrogen deficiency. The strong correlation between the plot-level ${\text{LUE}}_{\rm{max}}$ and VIs, particularly the two-band enhanced vegetation index for winter wheat ( Triticum aestivum ) and the green chlorophyll index for maize ( Zea mays ) at the milk stage, provided a potential to derive ${\text{LUE}}_{\rm{max}}$ from remote sensing observations. To evaluate the quality of ${\text{LUE}}_{\rm{max}}$ derived from remote sensing data, Biomass of winter wheat and maize was compared with that estimated using a PEM model with a constant ${\text{LUE}}_{\rm{max}}$ and the derived variable ${\text{LUE}}_{\rm{max}}$ . Significant improvements in Biomass estimation accuracy were achieved (by about 15.0% for the normalized root-mean-square error) using the derived variable ${\text{LUE}}_{\rm{max}}$ . This study offers a new way to derive ${\text{LUE}}_{\rm{max}}$ for a specific PEM and to improve the accuracy of Biomass estimation using remote sensing.
Qi Jing - One of the best experts on this subject based on the ideXlab platform.
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estimating Crop Biomass using leaf area index derived from landsat 8 and sentinel 2 data
Isprs Journal of Photogrammetry and Remote Sensing, 2020Co-Authors: Taifeng Dong, Heather Mcnairn, Jiangui Liu, Budong Qian, Jane Liu, Rong Wang, Qi Jing, Catherine Champagne, Jarrett Powers, Yichao ShiAbstract:Abstract The availability of Landsat 8 and Sentinel-2 has led to a steady increase in both temporal and spatial resolution of satellite data, offering new opportunities for large-scale Crop condition monitoring and Crop yield mapping. This study investigated the potential of using Landsat 8 and Sentinel-2 data from the harmonized Landsat 8 and Sentinel-2 (HLS) products for Crop Biomass estimation for six Crops in Manitoba, Canada. Crop Biomass was estimated using remotely sensed leaf area index (LAI) to reparametrize a simple Crop growth model. The results showed that the LAI of six different Crops can be estimated using a generic relationship between LAI and red-edge based vegetation indices (VIs, e.g., modified simple ratio red-edge (MSRRE) and red-edge normalized difference VI (NDVIRE)) for the Multispectral Instrument (MSI) of Sentinel-2. For the Operational Land Imager of Landsat 8 without the red-edge band, LAI can be best estimated using a VI derived from Near-infrared (NIR) and short-wave infrared (SWIR) bands (Normalized Difference Water Index, NDWI1). Above-ground dry Biomass of these six Crops was more accurately estimated from the assimilation of LAI derived from both satellites (R2 (the coefficient of determination) = 0.81, RMSE (the root-mean-square-error) = 135.4 g/m2, nRMSE (the normalized RMSE) = 37.9%, RPD (the ratio of percent deviation) = 2.26) than that of LAI derived from MSI-data (R2 = 0.80, RMSE = 136.7 g/m2, nRMSE = 38.3%, RPD = 2.23) or that from LAI derived from OLI-data (R2 = 0.68, RMSE = 191.0 g/m2, nRMSE = 53.5%, RPD = 1.16). Further analysis showed that these three assimilation cases (MSI and OLI; MSI alone; OLI alone) with a different number of LAI observations resulted in differences in parameter optimization, particularly the parameters relevant to Crop phenology and Biomass partitioning. Both Crop growth stage (e.g., the emergence date for Crop growth) and leaf dry Biomass estimated from the assimilation of LAI derived from MSI and OLI, or MSI alone, produced the most accurate estimates. These results are likely attributed to the improved temporal coverage associated with Sentinel-2 and the availability of a red-edge band on this sensor.
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deriving maximum light use efficiency from Crop growth model and satellite data to improve Crop Biomass estimation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Taifeng Dong, Jiali Shang, Budong Qian, Qi Jing, Holly Croft, Jingming Chen, Jinfei Wang, Ted Huffman, Pengfei ChenAbstract:Maximum light use efficiency ( ${\text{LUE}}_{\rm{max}}$ ) is an important parameter in Biomass estimation models (e.g., the Production Efficiency Models (PEM)) based on remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of deriving spatially variable Crop ${\text{LUE}}_{\rm{max}}$ from satellite remote sensing data. ${\text{LUE}}_{\rm{max}}$ at the plot level was retrieved first by assimilating field measured green leaf area index and Biomass into a Crop model (the Simple Algorithm for Yield estimate model), and was then correlated with a few Landsat-8 vegetation indices (VIs) to develop regression models. ${\text{LUE}}_{\rm{max}}$ was then mapped using the best regression model from a VI. The influence factors on ${\text{LUE}}_{\rm{max}}$ variability were also assessed. Contrary to a fixed ${\text{LUE}}_{\rm{max}}$ , our results suggest that ${\text{LUE}}_{\rm{max}}$ is affected by environmental stresses, such as leaf nitrogen deficiency. The strong correlation between the plot-level ${\text{LUE}}_{\rm{max}}$ and VIs, particularly the two-band enhanced vegetation index for winter wheat ( Triticum aestivum ) and the green chlorophyll index for maize ( Zea mays ) at the milk stage, provided a potential to derive ${\text{LUE}}_{\rm{max}}$ from remote sensing observations. To evaluate the quality of ${\text{LUE}}_{\rm{max}}$ derived from remote sensing data, Biomass of winter wheat and maize was compared with that estimated using a PEM model with a constant ${\text{LUE}}_{\rm{max}}$ and the derived variable ${\text{LUE}}_{\rm{max}}$ . Significant improvements in Biomass estimation accuracy were achieved (by about 15.0% for the normalized root-mean-square error) using the derived variable ${\text{LUE}}_{\rm{max}}$ . This study offers a new way to derive ${\text{LUE}}_{\rm{max}}$ for a specific PEM and to improve the accuracy of Biomass estimation using remote sensing.
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deriving maximum light use efficiency from Crop growth model and satellite data to improve Crop Biomass estimation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Taifeng Dong, Jiali Shang, Jiangui Liu, Budong Qian, Qi Jing, Holly Croft, Jingming Chen, Jinfei Wang, Ted Huffman, Pengfei ChenAbstract:Maximum light use efficiency ( ${\text{LUE}}_{\rm{max}}$ ) is an important parameter in Biomass estimation models (e.g., the Production Efficiency Models (PEM)) based on remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of deriving spatially variable Crop ${\text{LUE}}_{\rm{max}}$ from satellite remote sensing data. ${\text{LUE}}_{\rm{max}}$ at the plot level was retrieved first by assimilating field measured green leaf area index and Biomass into a Crop model (the Simple Algorithm for Yield estimate model), and was then correlated with a few Landsat-8 vegetation indices (VIs) to develop regression models. ${\text{LUE}}_{\rm{max}}$ was then mapped using the best regression model from a VI. The influence factors on ${\text{LUE}}_{\rm{max}}$ variability were also assessed. Contrary to a fixed ${\text{LUE}}_{\rm{max}}$ , our results suggest that ${\text{LUE}}_{\rm{max}}$ is affected by environmental stresses, such as leaf nitrogen deficiency. The strong correlation between the plot-level ${\text{LUE}}_{\rm{max}}$ and VIs, particularly the two-band enhanced vegetation index for winter wheat ( Triticum aestivum ) and the green chlorophyll index for maize ( Zea mays ) at the milk stage, provided a potential to derive ${\text{LUE}}_{\rm{max}}$ from remote sensing observations. To evaluate the quality of ${\text{LUE}}_{\rm{max}}$ derived from remote sensing data, Biomass of winter wheat and maize was compared with that estimated using a PEM model with a constant ${\text{LUE}}_{\rm{max}}$ and the derived variable ${\text{LUE}}_{\rm{max}}$ . Significant improvements in Biomass estimation accuracy were achieved (by about 15.0% for the normalized root-mean-square error) using the derived variable ${\text{LUE}}_{\rm{max}}$ . This study offers a new way to derive ${\text{LUE}}_{\rm{max}}$ for a specific PEM and to improve the accuracy of Biomass estimation using remote sensing.
Budong Qian - One of the best experts on this subject based on the ideXlab platform.
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estimating Crop Biomass using leaf area index derived from landsat 8 and sentinel 2 data
Isprs Journal of Photogrammetry and Remote Sensing, 2020Co-Authors: Taifeng Dong, Heather Mcnairn, Jiangui Liu, Budong Qian, Jane Liu, Rong Wang, Qi Jing, Catherine Champagne, Jarrett Powers, Yichao ShiAbstract:Abstract The availability of Landsat 8 and Sentinel-2 has led to a steady increase in both temporal and spatial resolution of satellite data, offering new opportunities for large-scale Crop condition monitoring and Crop yield mapping. This study investigated the potential of using Landsat 8 and Sentinel-2 data from the harmonized Landsat 8 and Sentinel-2 (HLS) products for Crop Biomass estimation for six Crops in Manitoba, Canada. Crop Biomass was estimated using remotely sensed leaf area index (LAI) to reparametrize a simple Crop growth model. The results showed that the LAI of six different Crops can be estimated using a generic relationship between LAI and red-edge based vegetation indices (VIs, e.g., modified simple ratio red-edge (MSRRE) and red-edge normalized difference VI (NDVIRE)) for the Multispectral Instrument (MSI) of Sentinel-2. For the Operational Land Imager of Landsat 8 without the red-edge band, LAI can be best estimated using a VI derived from Near-infrared (NIR) and short-wave infrared (SWIR) bands (Normalized Difference Water Index, NDWI1). Above-ground dry Biomass of these six Crops was more accurately estimated from the assimilation of LAI derived from both satellites (R2 (the coefficient of determination) = 0.81, RMSE (the root-mean-square-error) = 135.4 g/m2, nRMSE (the normalized RMSE) = 37.9%, RPD (the ratio of percent deviation) = 2.26) than that of LAI derived from MSI-data (R2 = 0.80, RMSE = 136.7 g/m2, nRMSE = 38.3%, RPD = 2.23) or that from LAI derived from OLI-data (R2 = 0.68, RMSE = 191.0 g/m2, nRMSE = 53.5%, RPD = 1.16). Further analysis showed that these three assimilation cases (MSI and OLI; MSI alone; OLI alone) with a different number of LAI observations resulted in differences in parameter optimization, particularly the parameters relevant to Crop phenology and Biomass partitioning. Both Crop growth stage (e.g., the emergence date for Crop growth) and leaf dry Biomass estimated from the assimilation of LAI derived from MSI and OLI, or MSI alone, produced the most accurate estimates. These results are likely attributed to the improved temporal coverage associated with Sentinel-2 and the availability of a red-edge band on this sensor.
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deriving maximum light use efficiency from Crop growth model and satellite data to improve Crop Biomass estimation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Taifeng Dong, Jiali Shang, Budong Qian, Qi Jing, Holly Croft, Jingming Chen, Jinfei Wang, Ted Huffman, Pengfei ChenAbstract:Maximum light use efficiency ( ${\text{LUE}}_{\rm{max}}$ ) is an important parameter in Biomass estimation models (e.g., the Production Efficiency Models (PEM)) based on remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of deriving spatially variable Crop ${\text{LUE}}_{\rm{max}}$ from satellite remote sensing data. ${\text{LUE}}_{\rm{max}}$ at the plot level was retrieved first by assimilating field measured green leaf area index and Biomass into a Crop model (the Simple Algorithm for Yield estimate model), and was then correlated with a few Landsat-8 vegetation indices (VIs) to develop regression models. ${\text{LUE}}_{\rm{max}}$ was then mapped using the best regression model from a VI. The influence factors on ${\text{LUE}}_{\rm{max}}$ variability were also assessed. Contrary to a fixed ${\text{LUE}}_{\rm{max}}$ , our results suggest that ${\text{LUE}}_{\rm{max}}$ is affected by environmental stresses, such as leaf nitrogen deficiency. The strong correlation between the plot-level ${\text{LUE}}_{\rm{max}}$ and VIs, particularly the two-band enhanced vegetation index for winter wheat ( Triticum aestivum ) and the green chlorophyll index for maize ( Zea mays ) at the milk stage, provided a potential to derive ${\text{LUE}}_{\rm{max}}$ from remote sensing observations. To evaluate the quality of ${\text{LUE}}_{\rm{max}}$ derived from remote sensing data, Biomass of winter wheat and maize was compared with that estimated using a PEM model with a constant ${\text{LUE}}_{\rm{max}}$ and the derived variable ${\text{LUE}}_{\rm{max}}$ . Significant improvements in Biomass estimation accuracy were achieved (by about 15.0% for the normalized root-mean-square error) using the derived variable ${\text{LUE}}_{\rm{max}}$ . This study offers a new way to derive ${\text{LUE}}_{\rm{max}}$ for a specific PEM and to improve the accuracy of Biomass estimation using remote sensing.
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deriving maximum light use efficiency from Crop growth model and satellite data to improve Crop Biomass estimation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Taifeng Dong, Jiali Shang, Jiangui Liu, Budong Qian, Qi Jing, Holly Croft, Jingming Chen, Jinfei Wang, Ted Huffman, Pengfei ChenAbstract:Maximum light use efficiency ( ${\text{LUE}}_{\rm{max}}$ ) is an important parameter in Biomass estimation models (e.g., the Production Efficiency Models (PEM)) based on remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of deriving spatially variable Crop ${\text{LUE}}_{\rm{max}}$ from satellite remote sensing data. ${\text{LUE}}_{\rm{max}}$ at the plot level was retrieved first by assimilating field measured green leaf area index and Biomass into a Crop model (the Simple Algorithm for Yield estimate model), and was then correlated with a few Landsat-8 vegetation indices (VIs) to develop regression models. ${\text{LUE}}_{\rm{max}}$ was then mapped using the best regression model from a VI. The influence factors on ${\text{LUE}}_{\rm{max}}$ variability were also assessed. Contrary to a fixed ${\text{LUE}}_{\rm{max}}$ , our results suggest that ${\text{LUE}}_{\rm{max}}$ is affected by environmental stresses, such as leaf nitrogen deficiency. The strong correlation between the plot-level ${\text{LUE}}_{\rm{max}}$ and VIs, particularly the two-band enhanced vegetation index for winter wheat ( Triticum aestivum ) and the green chlorophyll index for maize ( Zea mays ) at the milk stage, provided a potential to derive ${\text{LUE}}_{\rm{max}}$ from remote sensing observations. To evaluate the quality of ${\text{LUE}}_{\rm{max}}$ derived from remote sensing data, Biomass of winter wheat and maize was compared with that estimated using a PEM model with a constant ${\text{LUE}}_{\rm{max}}$ and the derived variable ${\text{LUE}}_{\rm{max}}$ . Significant improvements in Biomass estimation accuracy were achieved (by about 15.0% for the normalized root-mean-square error) using the derived variable ${\text{LUE}}_{\rm{max}}$ . This study offers a new way to derive ${\text{LUE}}_{\rm{max}}$ for a specific PEM and to improve the accuracy of Biomass estimation using remote sensing.
Adam J Vanbergen - One of the best experts on this subject based on the ideXlab platform.
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Soil biota, carbon cycling and Crop plant Biomass responses to biochar in a temperate mesocosm experiment
Plant and Soil, 2019Co-Authors: Sarah A. Mccormack, David W. Hopkins, M. Glória Pereira, Richard D Bardgett, Nick Ostle, Adam J VanbergenAbstract:Background and aimsBiochar addition to soil is a carbon capture and storage option with potential to mitigate rising atmospheric CO_2 concentrations, yet the consequences for soil organisms and linked ecosystem processes are inconsistent or unknown. We tested biochar impact on soil biodiversity, ecosystem functions, and their interactions, in temperate agricultural soils.MethodsWe performed a 27-month factorial experiment to determine effects of biochar, soil texture, and Crop species treatments on microbial Biomass (PFLA), soil invertebrate density, Crop Biomass and ecosystem CO_2 flux in plant-soil mesocosms.ResultsOverall soil microbial Biomass, microarthropod abundance and Crop Biomass were unaffected by biochar, although there was an increase in fungal-bacterial ratio and a positive relationship between the 16:1ω5 fatty acid marker of AMF mass and collembolan density in the biochar-treated mesocosms. Ecosystem CO_2 fluxes were unaffected by biochar, but soil carbon content of biochar-treated mesocosms was significantly lower, signifying a possible movement/loss of biochar or priming effect.ConclusionsCompared to soil texture and Crop type, biochar had minimal impact on soil biota, Crop production and carbon cycling. Future research should examine subtler effects of biochar on biotic regulation of ecosystem production and if the apparent robustness to biochar weakens over greater time spans or in combination with other ecological perturbations.
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soil biota carbon cycling and Crop plant Biomass responses to biochar in a temperate mesocosm experiment
Plant and Soil, 2019Co-Authors: Sarah A. Mccormack, David W. Hopkins, Richard D Bardgett, Nick Ostle, Gloria M Pereira, Adam J VanbergenAbstract:Biochar addition to soil is a carbon capture and storage option with potential to mitigate rising atmospheric CO2 concentrations, yet the consequences for soil organisms and linked ecosystem processes are inconsistent or unknown. We tested biochar impact on soil biodiversity, ecosystem functions, and their interactions, in temperate agricultural soils. We performed a 27-month factorial experiment to determine effects of biochar, soil texture, and Crop species treatments on microbial Biomass (PFLA), soil invertebrate density, Crop Biomass and ecosystem CO2 flux in plant-soil mesocosms. Overall soil microbial Biomass, microarthropod abundance and Crop Biomass were unaffected by biochar, although there was an increase in fungal-bacterial ratio and a positive relationship between the 16:1ω5 fatty acid marker of AMF mass and collembolan density in the biochar-treated mesocosms. Ecosystem CO2 fluxes were unaffected by biochar, but soil carbon content of biochar-treated mesocosms was significantly lower, signifying a possible movement/loss of biochar or priming effect. Compared to soil texture and Crop type, biochar had minimal impact on soil biota, Crop production and carbon cycling. Future research should examine subtler effects of biochar on biotic regulation of ecosystem production and if the apparent robustness to biochar weakens over greater time spans or in combination with other ecological perturbations.