Soil Moisture Retrieval and Spatiotemporal Pattern Analysis Using Sentinel-1 Data of Dahra, Senegal

Abstract

Abstract:The spatiotemporal pattern of soil moisture is of great significance for the understanding of the water exchange between the land surface and the atmosphere. The two-satellite constellation of the Sentinel-1 mission provides C-band synthetic aperture radar (SAR) observations with high spatial and temporal resolutions, which are suitable for soil moisture monitoring. In this paper, we aim to assess the capability of pattern analysis based on the soil moisture retrieved from Sentinel-1 time-series data of Dahra in Senegal. The look-up table (LUT) method is used in the retrieval with the backscattering coefficients that are simulated by the advanced integrated equation Model (AIEM) for the soil layer and the Michigan microwave canopy scattering (MIMICS) model for the vegetation layer. The temporal trend of Sentinel-1A soil moisture is evaluated by the ground measurements from the site at Dahra, with an unbiased root-mean-squared deviation (ubRMSD) of 0.053 m 3 /m 3 , a mean average deviation (MAD) of 0.034 m 3 /m 3 , and an R value of 0.62. The spatial variation is also compared with the existing microwave products at a coarse scale, which confirms the reliability of the Sentinel-1A soil moisture. The spatiotemporal patterns are analyzed by empirical orthogonal functions (EOF), and the geophysical factors that are affecting soil moisture are discussed. The first four EOFs of soil moisture explain 77.2% of the variance in total and the primary EOF explains 66.2%, which shows the dominant pattern at the study site. Soil texture and the normalized difference vegetation index are more closely correlated with the primary pattern than the topography and temperature in the study area. The investigation confirms the potential for soil moisture retrieval and spatiotemporal pattern analysis using Sentinel-1 images.

What Rainfall Does Not Tell Us—Enhancing Financial Instruments with Satellite-Derived Soil Moisture and Evaporative Stress

“…Remotely sensed soil moisture and evapotranspiration datasets exhibit complementary strengths and weaknesses because their retrieval is based on different sensors, retrieval mechanisms, and algorithmic approaches. Microwave-based soil-moisture remote-sensing techniques struggle with obtaining valid retrievals under dense vegetation because measurements are not only sensitive to the dielectric properties of water molecules in the soil (used to estimate soil moisture), but also to characteristics related to surface roughness, vegetation cover, and topography [19,20]. While high vegetation density is not problematic for the Evaporative Stress Index (ESI) [21], thermal infrared-based evapotranspiration estimates used in the ESI are strongly affected by cloud cover [22,23].…”

Sentinel-1 Backscatter Analysis and Radiative Transfer Modeling of Dense Winter Wheat Time Series

“…Lately, more and more studies using microwave time series data to estimate the soil moisture of agricultural areas-especially for the crop type winter wheat-have been conducted [19][20][21]. Often, only a single satellite orbit constellation and, therefore, data from one satellite with the same acquisition geometry are used [19,[22][23][24]. Occasionally, the time series used consists of data from the same satellite but related to different orbits and, thus, various azimuth or/and incidence angles [20,23,[25][26][27].…”

Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data

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