Spatial downscaling and gap-filling of SMAP soil moisture to high resolution using MODIS surface variables and machine learning approaches over ShanDian River Basin, China

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Nadeem, Adeel Ahmad, Zha, Yuanyuan ORCID: 0000-0003-4323-0730, Shi, Liangsheng ORCID: 0000-0003-0446-0488, Ali, Shoaib ORCID: 0000-0002-6377-6610, Wang, Xi, Zafar, Zeeshan ORCID: 0000-0002-1575-9242, Afzal, Zeeshan and Tariq, Muhammad Atiq Ur Rehman ORCID: 0000-0002-0226-7310 (2023) Spatial downscaling and gap-filling of SMAP soil moisture to high resolution using MODIS surface variables and machine learning approaches over ShanDian River Basin, China. Remote Sensing, 15 (3). ISSN 2072-4292


High-resolution soil moisture (SM) information is essential for regional to global hydrological and agricultural applications. The Soil Moisture Active Passive (SMAP) offers daily global composites of SM at coarse-resolution 9 and 36 km, with data gaps limiting its local application to depict SM distribution in detail. To overcome the aforementioned problem, a downscaling and gap-filling novel approach was adopted, using random forest (RF) and artificial neural network (ANN) algorithms to downscale SMAP SM data, using land-surface variables from moderate-resolution imaging spectroradiometer (MODIS) onboard Aqua and Terra satellites from the years 2018 to 2019. Firstly, four combinations (RF+Aqua, RF+Terra, ANN+Aqua, and ANN+Terra) were developed. Each combination downscaled SMAP SM at a high resolution (1 km). These combinations were evaluated by using error matrices and in situ SM at different scales in the ShanDian River (SDR) Basin. The combination RF+Terra showed a better performance, with a low averaged unbiased root mean square error (ubRMSE) of 0.034 (Formula presented.) / (Formula presented.) and high averaged correlation (R) of 0.54 against the small-, medium-, and large-scale in situ SM. Secondly, the impact of various land covers was examined by using downscaled SMAP and in situ SM. Vegetation attenuation makes woodland more error-prone and less correlated than grassland and farmland. Finally, the RF+Terra and ANN+Terra combinations were selected for their higher accuracy in gap filling of downscaled SMAP SM. The gap-filled downscaled SMAP SM results were compared spatially with China Land Data Assimilation System (CLDAS) SM and in situ SM. The RF+Terra combination outcomes were more humid than ANN+Terra combination results in the SDR basin. Overall, the RF+Terra combination gap-filled data showed high R (0.40) and less ubRMSE (0.064 (Formula presented.) / (Formula presented.)) against in situ SM, which was close to CLDAS SM. This study showed that the proposed RF- and ANN-based downscaling methods have a potential to improve the spatial resolution and gap-filling of SMAP SM at a high resolution (1 km).

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Item type Article
DOI 10.3390/rs15030812
Official URL
Subjects Current > FOR (2020) Classification > 4005 Civil engineering
Current > FOR (2020) Classification > 4611 Machine learning
Current > Division/Research > Institute for Sustainable Industries and Liveable Cities
Keywords SMAP soil moisture, artificial neural network, random forest, downscaling, gap-filling, ShanDian River Basin
Citations in Scopus 1 - View on Scopus
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