The Experts below are selected from a list of 55560 Experts worldwide ranked by ideXlab platform
Bob Macmillan - One of the best experts on this subject based on the ideXlab platform.
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retrieval and mapping of Soil Texture based on land surface diurnal temperature range data from modis
PLOS ONE, 2015Co-Authors: Decai Wang, Ganlin Zhang, Mingsong Zhao, Xianzhang Pan, Yuguo Zhao, Bob MacmillanAbstract:Numerous studies have investigated the direct retrieval of Soil properties, including Soil Texture, using remotely sensed images. However, few have considered how Soil properties influence dynamic changes in remote images or how Soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional Soil Texture based on the hypothesis that the rate of change of land surface temperature is related to Soil Texture, given the assumption of similar starting Soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle Soil size fractions at the Soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate Soil Texture. The spatial distribution of Soil Texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of Soil Texture. This study demonstrates the potential for digitally mapping regional Soil Texture variations in flat areas using readily available MODIS data.
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retrieval and mapping of Soil Texture based on land surface diurnal temperature range data from modis
PLOS ONE, 2015Co-Authors: Decai Wang, Ganlin Zhang, Mingsong Zhao, Yuguo Zhao, Decheng Li, Bob MacmillanAbstract:Numerous studies have investigated the direct retrieval of Soil properties, including Soil Texture, using remotely sensed images. However, few have considered how Soil properties influence dynamic changes in remote images or how Soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional Soil Texture based on the hypothesis that the rate of change of land surface temperature is related to Soil Texture, given the assumption of similar starting Soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle Soil size fractions at the Soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate Soil Texture. The spatial distribution of Soil Texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of Soil Texture. This study demonstrates the potential for digitally mapping regional Soil Texture variations in flat areas using readily available MODIS data.
Decai Wang - One of the best experts on this subject based on the ideXlab platform.
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retrieval and mapping of Soil Texture based on land surface diurnal temperature range data from modis
PLOS ONE, 2015Co-Authors: Decai Wang, Ganlin Zhang, Mingsong Zhao, Xianzhang Pan, Yuguo Zhao, Bob MacmillanAbstract:Numerous studies have investigated the direct retrieval of Soil properties, including Soil Texture, using remotely sensed images. However, few have considered how Soil properties influence dynamic changes in remote images or how Soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional Soil Texture based on the hypothesis that the rate of change of land surface temperature is related to Soil Texture, given the assumption of similar starting Soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle Soil size fractions at the Soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate Soil Texture. The spatial distribution of Soil Texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of Soil Texture. This study demonstrates the potential for digitally mapping regional Soil Texture variations in flat areas using readily available MODIS data.
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retrieval and mapping of Soil Texture based on land surface diurnal temperature range data from modis
PLOS ONE, 2015Co-Authors: Decai Wang, Ganlin Zhang, Mingsong Zhao, Yuguo Zhao, Decheng Li, Bob MacmillanAbstract:Numerous studies have investigated the direct retrieval of Soil properties, including Soil Texture, using remotely sensed images. However, few have considered how Soil properties influence dynamic changes in remote images or how Soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional Soil Texture based on the hypothesis that the rate of change of land surface temperature is related to Soil Texture, given the assumption of similar starting Soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle Soil size fractions at the Soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate Soil Texture. The spatial distribution of Soil Texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of Soil Texture. This study demonstrates the potential for digitally mapping regional Soil Texture variations in flat areas using readily available MODIS data.
Joseph A Santanello - One of the best experts on this subject based on the ideXlab platform.
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using remotely sensed estimates of Soil moisture to infer Soil Texture and hydraulic properties across a semi arid watershed
Remote Sensing of Environment, 2007Co-Authors: Joseph A Santanello, Christa D Peterslidard, Matthew Garcia, David Mocko, Michael A Tischler, Susan M Moran, David P ThomaAbstract:Near-surface Soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the Soil are not easy to quantify or predict because of the difficulty in accurately representing Soil Texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing Soil properties from a unique perspective based on components originally developed for operational estimation of Soil moisture for mobility assessments. Estimates of near-surface Soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the Soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed Soil moisture were minimized by adjusting the Soil Texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating within a continuous range of widely applicable Soil properties such as sand, silt, and clay percentages rather than applying rigid Soil Texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting Soils could then be assessed. In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and Soil drying. The resultant Soil properties were applied to an extended time period demonstrating the improvement in simulated Soil moisture over that using default or county-level Soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new Soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful Soil information using simple parameter estimation with few but appropriately timed remote sensing retrievals.
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using remotely sensed estimates of Soil moisture to infer Soil Texture and hydraulic properties across a semi arid watershed
Remote Sensing of Environment, 2007Co-Authors: Joseph A Santanello, Christa D Peterslidard, Matthew Garcia, David Mocko, Michael A Tischler, Susan M Moran, David P ThomaAbstract:Near-surface Soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the Soil are not easy to quantify or predict because of the difficulty in accurately representing Soil Texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing Soils from a unique perspective based on components originally developed for operational estimation of Soil moisture for mobility assessments. Estimates of near-surface Soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the Soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed Soil moisture were minimized by adjusting the Soil Texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating a continuous range of widely applicable Soil properties such as sand, silt, and clay percentages rather than applying rigid Soil Texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting Soils could then be assessed. In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and Soil drying. The resultant Soil properties were applied to an extended time period demonstrating the improvement in simulated Soil moisture over that using default or county-level Soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new Soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful Soils information using simple parameter estimation with few but appropriately timed remote sensing retrievals.
Ganlin Zhang - One of the best experts on this subject based on the ideXlab platform.
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high resolution and three dimensional mapping of Soil Texture of china
Geoderma, 2020Co-Authors: Feng Liu, Ganlin Zhang, Yuguo Zhao, Xiaodong Song, Jinling Yang, Fei YangAbstract:Abstract The lack of detailed three-dimensional Soil Texture information largely restricts many applications in agriculture, hydrology, climate, ecology and environment. This study predicted 90 m resolution spatial variations of sand, silt and clay contents at a national extent across China and at multiple depths 0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm. We used 4579 Soil profiles collected from a national Soil series inventory conducted recently and currently available environmental covariates. The covariates characterized environmental factors including climate, parent materials, terrain, vegetation and Soil conditions. We constructed random forest models and employed a parallel computing strategy for the predictions of Soil Texture fractions based on its relationship with the environmental factors. Quantile regression forest was used to estimate the uncertainty of the predictions. Results showed that the predicted maps were much more accurate and detailed than the conventional linkage maps and the SoilGrids250m product, and could well represent spatial variation of Soil Texture across China. The relative accuracy improvement was around 245–370% relative to the linkage maps and 83–112% relative to the SoilGrids250m product with regard to the R2, and it was around 24–26% and 14–19% respectively with regard to the RMSE. The wide range between 5% lower and 95% upper prediction limits may suggest that there was a substantial room to improve current predictions. Besides, we found that climate and terrain factors are major controllers for spatial patterns of Soil Texture in China. The heat and water-driven physical and chemical weathering and wind-driven erosion processes primarily shape the pattern of clay content. The terrain, wind and water-driven deposition, erosion and transportation sorting processes of Soil particles primarily shape the pattern of silt. The findings provide clues for modeling future Soil evolution and for national Soil security management under the background of global and regional environmental changes.
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retrieval and mapping of Soil Texture based on land surface diurnal temperature range data from modis
PLOS ONE, 2015Co-Authors: Decai Wang, Ganlin Zhang, Mingsong Zhao, Xianzhang Pan, Yuguo Zhao, Bob MacmillanAbstract:Numerous studies have investigated the direct retrieval of Soil properties, including Soil Texture, using remotely sensed images. However, few have considered how Soil properties influence dynamic changes in remote images or how Soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional Soil Texture based on the hypothesis that the rate of change of land surface temperature is related to Soil Texture, given the assumption of similar starting Soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle Soil size fractions at the Soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate Soil Texture. The spatial distribution of Soil Texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of Soil Texture. This study demonstrates the potential for digitally mapping regional Soil Texture variations in flat areas using readily available MODIS data.
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retrieval and mapping of Soil Texture based on land surface diurnal temperature range data from modis
PLOS ONE, 2015Co-Authors: Decai Wang, Ganlin Zhang, Mingsong Zhao, Yuguo Zhao, Decheng Li, Bob MacmillanAbstract:Numerous studies have investigated the direct retrieval of Soil properties, including Soil Texture, using remotely sensed images. However, few have considered how Soil properties influence dynamic changes in remote images or how Soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional Soil Texture based on the hypothesis that the rate of change of land surface temperature is related to Soil Texture, given the assumption of similar starting Soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle Soil size fractions at the Soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate Soil Texture. The spatial distribution of Soil Texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of Soil Texture. This study demonstrates the potential for digitally mapping regional Soil Texture variations in flat areas using readily available MODIS data.
Yuguo Zhao - One of the best experts on this subject based on the ideXlab platform.
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high resolution and three dimensional mapping of Soil Texture of china
Geoderma, 2020Co-Authors: Feng Liu, Ganlin Zhang, Yuguo Zhao, Xiaodong Song, Jinling Yang, Fei YangAbstract:Abstract The lack of detailed three-dimensional Soil Texture information largely restricts many applications in agriculture, hydrology, climate, ecology and environment. This study predicted 90 m resolution spatial variations of sand, silt and clay contents at a national extent across China and at multiple depths 0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm. We used 4579 Soil profiles collected from a national Soil series inventory conducted recently and currently available environmental covariates. The covariates characterized environmental factors including climate, parent materials, terrain, vegetation and Soil conditions. We constructed random forest models and employed a parallel computing strategy for the predictions of Soil Texture fractions based on its relationship with the environmental factors. Quantile regression forest was used to estimate the uncertainty of the predictions. Results showed that the predicted maps were much more accurate and detailed than the conventional linkage maps and the SoilGrids250m product, and could well represent spatial variation of Soil Texture across China. The relative accuracy improvement was around 245–370% relative to the linkage maps and 83–112% relative to the SoilGrids250m product with regard to the R2, and it was around 24–26% and 14–19% respectively with regard to the RMSE. The wide range between 5% lower and 95% upper prediction limits may suggest that there was a substantial room to improve current predictions. Besides, we found that climate and terrain factors are major controllers for spatial patterns of Soil Texture in China. The heat and water-driven physical and chemical weathering and wind-driven erosion processes primarily shape the pattern of clay content. The terrain, wind and water-driven deposition, erosion and transportation sorting processes of Soil particles primarily shape the pattern of silt. The findings provide clues for modeling future Soil evolution and for national Soil security management under the background of global and regional environmental changes.
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retrieval and mapping of Soil Texture based on land surface diurnal temperature range data from modis
PLOS ONE, 2015Co-Authors: Decai Wang, Ganlin Zhang, Mingsong Zhao, Xianzhang Pan, Yuguo Zhao, Bob MacmillanAbstract:Numerous studies have investigated the direct retrieval of Soil properties, including Soil Texture, using remotely sensed images. However, few have considered how Soil properties influence dynamic changes in remote images or how Soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional Soil Texture based on the hypothesis that the rate of change of land surface temperature is related to Soil Texture, given the assumption of similar starting Soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle Soil size fractions at the Soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate Soil Texture. The spatial distribution of Soil Texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of Soil Texture. This study demonstrates the potential for digitally mapping regional Soil Texture variations in flat areas using readily available MODIS data.
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retrieval and mapping of Soil Texture based on land surface diurnal temperature range data from modis
PLOS ONE, 2015Co-Authors: Decai Wang, Ganlin Zhang, Mingsong Zhao, Yuguo Zhao, Decheng Li, Bob MacmillanAbstract:Numerous studies have investigated the direct retrieval of Soil properties, including Soil Texture, using remotely sensed images. However, few have considered how Soil properties influence dynamic changes in remote images or how Soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional Soil Texture based on the hypothesis that the rate of change of land surface temperature is related to Soil Texture, given the assumption of similar starting Soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle Soil size fractions at the Soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate Soil Texture. The spatial distribution of Soil Texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of Soil Texture. This study demonstrates the potential for digitally mapping regional Soil Texture variations in flat areas using readily available MODIS data.
