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Article

Spatiotemporal Change Analysis of Soil Moisture Based on Downscaling Technology in Africa

by 1,2,†, 2,3,†, 2,4,5,*,†, 2 and 5
1
School of Physics and Electronic Engineering, Ningxia University, Yinchuan 750021, China
2
Hulunbeir Grassland Ecosystem Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
3
Faculty of Geographical and Environmental Science, University of Khartoum, Khartoum 11111, Sudan
4
School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250100, China
5
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth Research, Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: José María María Senciales-González and Jan Wesseling
Water 2022, 14(1), 74; https://doi.org/10.3390/w14010074
Received: 29 September 2021 / Revised: 22 December 2021 / Accepted: 28 December 2021 / Published: 2 January 2022
(This article belongs to the Special Issue Remote Sensing Application on Soil Moisture)
Evaluating the long-term spatiotemporal variability in soil moisture (SM) over Africa is crucial for understanding how crop production is affected by drought or flooding. However, the lack of continuous and stable long-term series and high-resolution soil moisture records impedes such research. To overcome the inconsistency of different microwave sensors (Advanced Microwave Scanning Radiometer-EOS, AMSR-E; Soil Moisture and Ocean Salinity, SMOS; and Advanced Microwave Scanning Radiometer 2, AMSR2) in measuring soil moisture over time and depth, we built a time series reconstruction model to correct SM, and then used a Spatially Weighted Downscaling Model to downscale the SM data from three different sensors to a 1 km spatial resolution. The verification of the reconstructed data shows that the product has high accuracy, and can be used for application and analysis. The spatiotemporal trends of SM in Africa were examined for 2003–2017. The analysis indicated that soil moisture is declining in Africa as a whole, and it is notably higher in central Africa than in other subregions. The most significant decrease in SM was observed in the savanna zone (slope < −0.08 m3 m−3 and P < 0.001), followed by South Africa and Namibia (slope < −0.07 m3 m−3 and P < 0.01). Seasonally, the most significant downward trends in SM were observed during the spring, mainly over eastern and central Africa (slope < −0.07 m3 m−3, R < −0.58 and P < 0.001). The analysis of spatiotemporal changes in soil moisture can help improve the understanding of hydrological cycles, and provide benchmark information for drought management in Africa. View Full-Text
Keywords: downscaling; soil moisture; spatially weighted downscaling model (SWDM); Africa; vegetation temperature condition index (VTCI) downscaling; soil moisture; spatially weighted downscaling model (SWDM); Africa; vegetation temperature condition index (VTCI)
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MDPI and ACS Style

Yuan, Z.; NourEldeen, N.; Mao, K.; Qin, Z.; Xu, T. Spatiotemporal Change Analysis of Soil Moisture Based on Downscaling Technology in Africa. Water 2022, 14, 74. https://doi.org/10.3390/w14010074

AMA Style

Yuan Z, NourEldeen N, Mao K, Qin Z, Xu T. Spatiotemporal Change Analysis of Soil Moisture Based on Downscaling Technology in Africa. Water. 2022; 14(1):74. https://doi.org/10.3390/w14010074

Chicago/Turabian Style

Yuan, Zijin, Nusseiba NourEldeen, Kebiao Mao, Zhihao Qin, and Tongren Xu. 2022. "Spatiotemporal Change Analysis of Soil Moisture Based on Downscaling Technology in Africa" Water 14, no. 1: 74. https://doi.org/10.3390/w14010074

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