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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Shrnutí: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.
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ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2024.3393828