Soil Moisture Estimation Using Citizen Observatory Data, Microwave Satellite Imagery, and Environmental Covariates

Abstract

Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R2) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.

Estimation of soil moisture using environmental covariates and machine learning algorithms in Cathedral Peak Catchment, South Africa

“…A study by Sanuade et al (2020) revealed that the SVM model was most suited for the study area situated within the University of Ibadan campus in Nigeria; these results are markedly better than what is achieved in the current study; however, the improved estimation may have been enabled by a smaller catchment area and a denser sampling network (i.e., 75 sampling points in 0.0253 km 2 ). The GLM displayed a good performance compared to Kibirige and Dobos (2020), who used an MLR approach. While the error metrics obtained for the current study are satisfactory and share commonalities with the previous literature, the comparison is difficult given the vastly different catchment land uses, areas, and climatic conditions.…”

“…The aspect (Figure 5) is the cardinal direction a slope faces. This is an important parameter as it influ-ences the terrain's exposure to sunlight, thus forming variable microclimate regions across neighboring slopes (Kibirige & Dobos, 2020). For instance, north-facing slopes often receive less exposure to sunlight, and therefore more moisture can be observed owing to a cooler microclimate.…”

“…Sentinel 1B image ground range detected product was selected because the product consists of SAR data that have been multi-looked detected, multi-looked, and projected to ground range using an Earth ellipsoid model as opposed to a single look complex (Kibirige & Dobos, 2020). Thereafter, the image is subjected to an accurate selection of orbit state vectors, thermal and noise removal, calibration, speckle filtering, terrain correction, conversion of an image into dB using Equation ( 5) and finally, a raster image for each day containing the mean σ(dB) in VV and VH polarizations (Figure 10).…”

Evaluation of Sentinel-1 Satelite-based Soil Moisture Products for Runoff Modelling with Karst Formation Characteristics

“…The study utilised a microwave backscatter model to compute the groundwater content. The data is accessible on the European Space Agency (ESA) website (https://scihub.copernicus.eu/provided) (Asmuß et al 2018;Kibirige and Dobos 2020). The data used (refer to Table 1) are obtained from the Sentinel-1A C-band SAR image, which records backscatter signals day and night, regardless of lighting and weather conditions.…”

Digital Soil Mapping Approach to Estimate Soil Plasticity Using Georeferenced Technical Data of Tractors

“…This high density elevation point data can be interpolated or converted to any raster datasets of 1 meter scale raster resolution. This DEM can later be used to model the surface and subsurface water flow and nutrient translocation within the field, and it is also a very efficient predictor for digital soil mapping approaches [8][9][10][11][12][13][14]. Plant condition characterization is using proximal and remote sensing datasets and produce vegetation indices and other indirect information to describe the biomass and the crop "wellness" around the field.…”