Satellite canopy water content from Sentinel-2, Landsat-8 and MODIS: Principle, algorithm and assessment
(2025) In Remote Sensing of Environment 326.- Abstract
- In spite of the efforts made for canopy water content (CWC) mapping in the community, including vegetation water proxy from microwave-based vegetation optical depth (VOD) as well as optical-based indices, there is still no operational CWC product from optical satellites up to now. To fill this gap, this study proposes a unified algorithm for CWC mapping at both decametric and coarse spatial resolution from several widely used optical satellites. Based on machine learning trained on radiative transfer model simulations, we comprehensively parameterized the distribution of the canopy and vegetation input variables (i.e., leaf traits and soil background) of the PROSAIL model, by relying on the largest open integrated global plant and soil... (More)
- In spite of the efforts made for canopy water content (CWC) mapping in the community, including vegetation water proxy from microwave-based vegetation optical depth (VOD) as well as optical-based indices, there is still no operational CWC product from optical satellites up to now. To fill this gap, this study proposes a unified algorithm for CWC mapping at both decametric and coarse spatial resolution from several widely used optical satellites. Based on machine learning trained on radiative transfer model simulations, we comprehensively parameterized the distribution of the canopy and vegetation input variables (i.e., leaf traits and soil background) of the PROSAIL model, by relying on the largest open integrated global plant and soil spectral databases. We investigated the impact of diverse band combinations as well as the inclusion of optical indices for CWC estimation using RTMs. The performances of this algorithm were first evaluated at decametric resolution based on ground measurements distributed over five ground campaigns corresponding to diverse climate and biome types. The retrieved CWC from Sentinel-2 and Landsat-8 exhibits satisfactory performance, with coefficient R of 0.81 and RMSE of 0.046 g/cm2. We then evaluated CWC at 500 m resolution from MODIS by comparing it with Landsat-8 and Sentinel-2 aggregated values over a globally distributed selection of LANDVAL sites, representative of the existing biome types combined with a range of precipitation, soil moisture and vegetation density conditions. The MODIS CWC global maps show reasonable seasonal and spatial patterns compared to multi-frequencies microwave-based VOD, and improvements compared to the conventionally and extensively used optical indices such as NDWI. The CWC product developed in this study is expected to provide new insights for global or regional vegetation water variations monitoring from optical satellites, with the strength of high spatial resolution compared to the microwave passive VOD (i.e., 20-500 m vs 22.5 km). These two products could be further combined for more accurate global vegetation water and biomass mapping in the future to improve our understanding of carbon uptake and hydrological applications. (Less)
- Abstract (Swedish)
- In spite of the efforts made for canopy water content (CWC) mapping in the community, including vegetation water proxy from microwave-based vegetation optical depth (VOD) as well as optical-based indices, there is still no operational CWC product from optical satellites up to now. To fill this gap, this study proposes a unified algorithm for CWC mapping at both decametric and coarse spatial resolution from several widely used optical satellites. Based on machine learning trained on radiative transfer model simulations, we comprehensively parameterized the distribution of the canopy and vegetation input variables (i.e., leaf traits and soil background) of the PROSAIL model, by relying on the largest open integrated global plant and soil... (More)
- In spite of the efforts made for canopy water content (CWC) mapping in the community, including vegetation water proxy from microwave-based vegetation optical depth (VOD) as well as optical-based indices, there is still no operational CWC product from optical satellites up to now. To fill this gap, this study proposes a unified algorithm for CWC mapping at both decametric and coarse spatial resolution from several widely used optical satellites. Based on machine learning trained on radiative transfer model simulations, we comprehensively parameterized the distribution of the canopy and vegetation input variables (i.e., leaf traits and soil background) of the PROSAIL model, by relying on the largest open integrated global plant and soil spectral databases. We investigated the impact of diverse band combinations as well as the inclusion of optical indices for CWC estimation using RTMs. The performances of this algorithm were first evaluated at decametric resolution based on ground measurements distributed over five ground campaigns corresponding to diverse climate and biome types. The retrieved CWC from Sentinel-2 and Landsat-8 exhibits satisfactory performance, with coefficient R of 0.81 and RMSE of 0.046 g/cm2. We then evaluated CWC at 500 m resolution from MODIS by comparing it with Landsat-8 and Sentinel-2 aggregated values over a globally distributed selection of LANDVAL sites, representative of the existing biome types combined with a range of precipitation, soil moisture and vegetation density conditions. The MODIS CWC global maps show reasonable seasonal and spatial patterns compared to multi-frequencies microwave-based VOD, and improvements compared to the conventionally and extensively used optical indices such as NDWI. The CWC product developed in this study is expected to provide new insights for global or regional vegetation water variations monitoring from optical satellites, with the strength of high spatial resolution compared to the microwave passive VOD (i.e., 20-500 m vs 22.5 km). These two products could be further combined for more accurate global vegetation water and biomass mapping in the future to improve our understanding of carbon uptake and hydrological applications. (Less)
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- 2025-08-15
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- Remote Sensing of Environment
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- 326
- article number
- 114801
- publisher
- Elsevier
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- scopus:105004989074
- ISSN
- 0034-4257
- DOI
- 10.1016/j.rse.2025.114801
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- English
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@article{12946aa4-1295-4744-aecc-c6c11ab8ca30,
abstract = {{In spite of the efforts made for canopy water content (CWC) mapping in the community, including vegetation water proxy from microwave-based vegetation optical depth (VOD) as well as optical-based indices, there is still no operational CWC product from optical satellites up to now. To fill this gap, this study proposes a unified algorithm for CWC mapping at both decametric and coarse spatial resolution from several widely used optical satellites. Based on machine learning trained on radiative transfer model simulations, we comprehensively parameterized the distribution of the canopy and vegetation input variables (i.e., leaf traits and soil background) of the PROSAIL model, by relying on the largest open integrated global plant and soil spectral databases. We investigated the impact of diverse band combinations as well as the inclusion of optical indices for CWC estimation using RTMs. The performances of this algorithm were first evaluated at decametric resolution based on ground measurements distributed over five ground campaigns corresponding to diverse climate and biome types. The retrieved CWC from Sentinel-2 and Landsat-8 exhibits satisfactory performance, with coefficient R of 0.81 and RMSE of 0.046 g/cm2. We then evaluated CWC at 500 m resolution from MODIS by comparing it with Landsat-8 and Sentinel-2 aggregated values over a globally distributed selection of LANDVAL sites, representative of the existing biome types combined with a range of precipitation, soil moisture and vegetation density conditions. The MODIS CWC global maps show reasonable seasonal and spatial patterns compared to multi-frequencies microwave-based VOD, and improvements compared to the conventionally and extensively used optical indices such as NDWI. The CWC product developed in this study is expected to provide new insights for global or regional vegetation water variations monitoring from optical satellites, with the strength of high spatial resolution compared to the microwave passive VOD (i.e., 20-500 m vs 22.5 km). These two products could be further combined for more accurate global vegetation water and biomass mapping in the future to improve our understanding of carbon uptake and hydrological applications.}},
author = {{Ma, Hongliang and Weiss, Marie and Malik, Daria and Berthelot, Beatrice and Yebra, Marta and Nolan, Rachel and Mialon, Arnaud and Zeng, Jiangyuan and Quan, Xingwen and Tagesson, Torbern and Olioso, Albert and Baret, Frederic}},
issn = {{0034-4257}},
language = {{eng}},
month = {{08}},
publisher = {{Elsevier}},
series = {{Remote Sensing of Environment}},
title = {{Satellite canopy water content from Sentinel-2, Landsat-8 and MODIS: Principle, algorithm and assessment}},
url = {{http://dx.doi.org/10.1016/j.rse.2025.114801}},
doi = {{10.1016/j.rse.2025.114801}},
volume = {{326}},
year = {{2025}},
}