Abstract
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.
Highlights
Soil moisture (SM) changes, as a vital factor in climate change, play a significant role in the environment [1,2,3]
Moderate Resolution Imaging Spectroradiometer (MODIS) products were used in this work, including the 16-day composite Normalized Difference Vegetation Index (NDVI) product (MYD13A3) and the 1-km, 8-day composite land surface temperature (LST) product (MYD11A2), which were used as input parameters for the vegetation temperature condition index (VTCI) model
The spatiotemporal analysis indicated that soil moisture is declining in Africa as a whole, and it is notably higher in central Africa than in other subregions
Summary
Soil moisture (SM) changes, as a vital factor in climate change, play a significant role in the environment [1,2,3]. The SM data obtained from these microwave radiometers are very effective for drought monitoring and hydrological investigations [29], they have coarse spatial resolutions (10–40 km), and are difficult to use at regional scales for certain research models [30]. Downscaling techniques based on the higher resolution of visible/thermal infrared data can provide more accurate SM data, and have been widely employed to reflect the spatial heterogeneity of low-spatial resolution data [11,33] This downscaling approach originated from the ‘universal triangle’ concept [34,35], which describes the correlation between SM, vegetation indices, and land surface temperature (LST) [36]. DAHRA PBO_H2O (https://www.catds.fr/Products/Available-products-from-CEC-SM/SMOS-IC, accessed on 18 January 2019)
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