Spatial Downscaling of Soil Moisture Product to Generate High-Resolution Data: A Multi-Source Approach over Heterogeneous Landscapes in Kenya
Soil moisture (SM) estimates are essential for drought monitoring, hydrological modeling, and climate resilience planning applications. While satellite and model-derived SM products effectively capture SM dynamics, their coarse spatial resolutions (~10–36 km) hinder their ability to represent SM var...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/10/1763 |
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| Summary: | Soil moisture (SM) estimates are essential for drought monitoring, hydrological modeling, and climate resilience planning applications. While satellite and model-derived SM products effectively capture SM dynamics, their coarse spatial resolutions (~10–36 km) hinder their ability to represent SM variability in heterogeneous landscapes influenced by local factors. This study proposes a novel downscaling framework that employs an Artificial Neural Network (ANN) on a cloud-computing platform to improve the spatial resolution and representation of multi-source SM datasets. A data analysis was conducted by integrating Google Earth Engine (GEE) with the computing capabilities of the python language through Google Colab. The framework downscaled Soil Moisture Active Passive (SMAP), European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation (ERA5-Land), and Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) at 500 m for Kenya, East Africa. This was achieved by leveraging ten input variables comprising elevation, slope, surface albedo, vegetation, soil texture, land surface temperatures (day and night), evapotranspiration, and geolocations. The coarse SM datasets exhibited spatiotemporal consistency, with a standard deviation below 0.15 m<sup>3</sup>/m<sup>3</sup>, capturing over 95% of the variability in the original data. Validation against in situ SM data at the station confirmed the framework’s reliability, achieving an average UbRMSE of less than 0.04 m<sup>3</sup>/m<sup>3</sup> and a correlation coefficient (r) over 0.52 for each downscaled dataset. Overall, the framework improved significantly in r values from 0.48 to 0.64 for SMAP, 0.47 to 0.63 for ERA5-Land, and 0.60 to 0.69 for FLDAS. Moreover, the performance of FLDAS and its downscaled version across all climate zone is consistent. Despite the uncertainties among the datasets, the framework effectively improved the representation of SM variability spatiotemporally. These results demonstrate the framework’s potential as a reliable tool for enhancing SM applications, particularly in regions with complex environmental conditions. |
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| ISSN: | 2072-4292 |