Abstract
This paper proposes a combined approach comprising a set of methods for the high-precision mapping of soil moisture in a study area located in Jiangsu Province of China, based on the Chinese C-band synthetic aperture radar data of GF-3 and high spatial-resolution optical data of GF-1, in situ experimental datasets and background knowledge. The study was conducted in three stages: First, in the process of eliminating the effect of vegetation canopy, an empirical vegetation water content model and a water cloud model with localized parameters were developed to obtain the bare soil backscattering coefficient. Second, four commonly used models (advanced integral equation model (AIEM), look-up table (LUT) method, Oh model, and the Dubois model) were coupled to acquire nine soil moisture retrieval maps and algorithms. Finally, a simple and effective optimal solution method was proposed to select and combine the nine algorithms based on classification strategies devised using three types of background knowledge. A comprehensive evaluation was carried out on each soil moisture map in terms of the root-mean-square-error (RMSE), Pearson correlation coefficient (PCC), mean absolute error (MAE), and mean bias (bias). The results show that for the nine individual algorithms, the estimated model constructed using the AIEM (mv1) was significantly more accurate than those constructed using the other models (RMSE = 0.0321 cm³/cm³, MAE = 0.0260 cm³/cm³, and PCC = 0.9115), followed by the Oh model (m_v5) and LUT inversion method under HH polarization (mv2). Compared with the independent algorithms, the optimal solution methods have significant advantages; the soil moisture map obtained using the classification strategy based on the percentage content of clay was the most satisfactory (RMSE = 0.0271 cm³/cm³, MAE = 0.0225 cm³/cm³, and PCC = 0.9364). This combined method could not only effectively integrate the optical and radar satellite data but also couple a variety of commonly used inversion models, and at the same time, background knowledge was introduced into the optimal solution method. Thus, we provide a new method for the high-precision mapping of soil moisture in areas with a complex underlying surface.
Highlights
The soil moisture is a main component of the earth system, with the first 0–5 cm of the soil layer playing an important role in the exchange of substances and energy between the land and atmosphere; it is an important parameter in the fields of agriculture, meteorology, and hydrology [1,2,3,4,5,6]
The results of all the categories were combined to form a soil moisture map of the entire study area; the three soil moisture maps based on the three sets of background knowledge were compared, and one with the best precision was selected as the optimal solution soil moisture map
Three optimal solution classification strategies were proposed as follows: (1) Based on the four main land use types in the study area, a statistical analysis was made on the accuracy of the nine soil moisture inversion maps, and the best inversion method for each type was selected; (2) Based on the percentage of clay, nine soil moisture inversion accuracies were calculated corresponding to the five clay percentage values (14, 26, 28, 29, and 37%), and the
Summary
The soil moisture is a main component of the earth system, with the first 0–5 cm of the soil layer playing an important role in the exchange of substances and energy between the land and atmosphere; it is an important parameter in the fields of agriculture, meteorology, and hydrology [1,2,3,4,5,6]. The microwave remote sensing method, owing to the synergy between microwaves and other information acquired from the electromagnetic radiation spectrum, has a good application prospect for estimating the soil moisture of bare soil and vegetation-covered areas [2,14,18]. In addition to radar system parameters, the backscattering coefficient obtained by active microwave remote sensing is mainly determined by the soil dielectric constant, surface roughness parameters, and vegetation canopy water content [26]. The vegetation canopy water content is a vital input parameter in the above models; its value can be obtained by establishing empirical models based on optical remote sensing data. The four processed bands of the GF-1 data were used to calculate the vegetation index during the in-situ experiments
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