Enhancing Global Surface Soil Moisture Estimation From ESA CCI and SMAP Product With a Conditional Variational Autoencoder

High-quality soil moisture (SM) estimation is crucial for various applications, including drought monitoring, environmental assessment, and agricultural management. Advances in remote sensing technology have enabled the retrieval of near real-time Earth surface SM using both active and passive senso...

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Vydáno v:	IEEE journal of selected topics in applied earth observations and remote sensing Ročník 17; s. 9337 - 9359
Hlavní autoři:	Shi, Changjiang, Zhang, Zhijie, Xiong, Shengqing, Zhang, Wanchang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Agricultural management Algorithms Benchmarks Climate change Conditional variational autoencoder (CVAE) Correlation coefficient Correlation coefficients Data models Drought Earth surface Environmental assessment Environmental Impact Assessment Environmental management Environmental monitoring ESA climate change initiative (CCI) Land cover Land surface Microwave theory and techniques Moisture Moisture content product reconstruction Remote sensing Retrieval Satellite orbits Sensors SMAP L4 Soil moisture Soil quality Soil surfaces Spatial discrimination Spatial resolution surface soil moisture (SM) Temporal resolution
ISSN:	1939-1404, 2151-1535
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Abstract	High-quality soil moisture (SM) estimation is crucial for various applications, including drought monitoring, environmental assessment, and agricultural management. Advances in remote sensing technology have enabled the retrieval of near real-time Earth surface SM using both active and passive sensors. However, the ESA climate change initiative (CCI) SM product, which combines data from multiple sensors, sacrifices spatial-temporal resolution and coverage due to satellite orbit constraints and retrieval algorithms. To address this issue, an SM reconstruction approach based on a conditional variational autoencoder model was developed, leveraging the high spatial resolution of SMAP L4 data and the accuracy of CCI fused products across different land cover types. This method resulted in the creation of a global three-day SM product at 0.0625<inline-formula><tex-math notation="LaTeX">^{\circ }</tex-math></inline-formula> spanning from 2015 to 2021. The reconstructed SM product underwent rigorous validation against global core SM sites and sparse observation networks. The evaluation employed multiple metrics, including the global unbiased root mean square error (ubRMSE) and correlation coefficient (CC). The validation yielded results, with ubRMSE values of approximately 0.029 and 0.071 m<inline-formula><tex-math notation="LaTeX">^{3}</tex-math></inline-formula>/m<inline-formula><tex-math notation="LaTeX">^{3}</tex-math></inline-formula>, and CC values of around 0.863 and 0.743 for core SM sites and sparse observation networks. This reconstructed product offers global coverage and enhanced accuracy compared to existing benchmarks.
AbstractList	High-quality soil moisture (SM) estimation is crucial for various applications, including drought monitoring, environmental assessment, and agricultural management. Advances in remote sensing technology have enabled the retrieval of near real-time Earth surface SM using both active and passive sensors. However, the ESA climate change initiative (CCI) SM product, which combines data from multiple sensors, sacrifices spatial-temporal resolution and coverage due to satellite orbit constraints and retrieval algorithms. To address this issue, an SM reconstruction approach based on a conditional variational autoencoder model was developed, leveraging the high spatial resolution of SMAP L4 data and the accuracy of CCI fused products across different land cover types. This method resulted in the creation of a global three-day SM product at 0.0625[Formula Omitted] spanning from 2015 to 2021. The reconstructed SM product underwent rigorous validation against global core SM sites and sparse observation networks. The evaluation employed multiple metrics, including the global unbiased root mean square error (ubRMSE) and correlation coefficient (CC). The validation yielded results, with ubRMSE values of approximately 0.029 and 0.071 m[Formula Omitted]/m[Formula Omitted], and CC values of around 0.863 and 0.743 for core SM sites and sparse observation networks. This reconstructed product offers global coverage and enhanced accuracy compared to existing benchmarks. High-quality soil moisture (SM) estimation is crucial for various applications, including drought monitoring, environmental assessment, and agricultural management. Advances in remote sensing technology have enabled the retrieval of near real-time Earth surface SM using both active and passive sensors. However, the ESA climate change initiative (CCI) SM product, which combines data from multiple sensors, sacrifices spatial-temporal resolution and coverage due to satellite orbit constraints and retrieval algorithms. To address this issue, an SM reconstruction approach based on a conditional variational autoencoder model was developed, leveraging the high spatial resolution of SMAP L4 data and the accuracy of CCI fused products across different land cover types. This method resulted in the creation of a global three-day SM product at 0.0625<tex-math notation="LaTeX">$^{\circ }$</tex-math> spanning from 2015 to 2021. The reconstructed SM product underwent rigorous validation against global core SM sites and sparse observation networks. The evaluation employed multiple metrics, including the global unbiased root mean square error (ubRMSE) and correlation coefficient (CC). The validation yielded results, with ubRMSE values of approximately 0.029 and 0.071 m<tex-math notation="LaTeX">$^{3}$</tex-math>/m<tex-math notation="LaTeX">$^{3}$</tex-math>, and CC values of around 0.863 and 0.743 for core SM sites and sparse observation networks. This reconstructed product offers global coverage and enhanced accuracy compared to existing benchmarks. High-quality soil moisture (SM) estimation is crucial for various applications, including drought monitoring, environmental assessment, and agricultural management. Advances in remote sensing technology have enabled the retrieval of near real-time Earth surface SM using both active and passive sensors. However, the ESA climate change initiative (CCI) SM product, which combines data from multiple sensors, sacrifices spatial-temporal resolution and coverage due to satellite orbit constraints and retrieval algorithms. To address this issue, an SM reconstruction approach based on a conditional variational autoencoder model was developed, leveraging the high spatial resolution of SMAP L4 data and the accuracy of CCI fused products across different land cover types. This method resulted in the creation of a global three-day SM product at 0.0625<inline-formula><tex-math notation="LaTeX">^{\circ }</tex-math></inline-formula> spanning from 2015 to 2021. The reconstructed SM product underwent rigorous validation against global core SM sites and sparse observation networks. The evaluation employed multiple metrics, including the global unbiased root mean square error (ubRMSE) and correlation coefficient (CC). The validation yielded results, with ubRMSE values of approximately 0.029 and 0.071 m<inline-formula><tex-math notation="LaTeX">^{3}</tex-math></inline-formula>/m<inline-formula><tex-math notation="LaTeX">^{3}</tex-math></inline-formula>, and CC values of around 0.863 and 0.743 for core SM sites and sparse observation networks. This reconstructed product offers global coverage and enhanced accuracy compared to existing benchmarks.
Author	Xiong, Shengqing Zhang, Wanchang Shi, Changjiang Zhang, Zhijie
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SubjectTerms	Accuracy Agricultural management Algorithms Benchmarks Climate change Conditional variational autoencoder (CVAE) Correlation coefficient Correlation coefficients Data models Drought Earth surface Environmental assessment Environmental Impact Assessment Environmental management Environmental monitoring ESA climate change initiative (CCI) Land cover Land surface Microwave theory and techniques Moisture Moisture content product reconstruction Remote sensing Retrieval Satellite orbits Sensors SMAP L4 Soil moisture Soil quality Soil surfaces Spatial discrimination Spatial resolution surface soil moisture (SM) Temporal resolution
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Title	Enhancing Global Surface Soil Moisture Estimation From ESA CCI and SMAP Product With a Conditional Variational Autoencoder
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