Abstract
This paper proposes a combined approach wherein the optical, near-infrared, and thermal infrared data from the Landsat 8 satellite and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) data are fused for soil moisture mapping under sparse sampling conditions, based on the Bayesian maximum entropy (BME) framework. The study was conducted in three stages. First, based on the maximum entropy principle of the information theory, a Lagrange multiplier was introduced to construct general knowledge, representing prior knowledge. Second, a principal component analysis (PCA) was conducted to extract three principal components from the multi-source data mentioned above, and an innovative and operable discrete probability method based on a fuzzy probability matrix was used to approximate the probability relationship. Thereafter, soft data were generated on the basis of the weight coefficients and coordinates of the soft data points. Finally, by combining the general knowledge with the prior information, hard data (HD), and soft data (SD), we completed the soil moisture mapping based on the Bayesian conditioning rule. To verify the feasibility of the combined approach, the ordinary kriging (OK) method was taken as a comparison. The results confirmed the superiority of the soil moisture map obtained using the BME framework. The map revealed more detailed information, and the accuracies of the quantitative indicators were higher compared with that for the OK method (the root mean squared error (RMSE) = 0.0423 cm3/cm3, mean absolute error (MAE) = 0.0399 cm3/cm3, and Pearson correlation coefficient (PCC) = 0.7846), while largely overcoming the overestimation issue in the range of low values and the underestimation issue in the range of high values. The proposed approach effectively fused inexpensive and easily available multi-source data with uncertainties and obtained a satisfactory mapping accuracy, thus demonstrating the potential of the BME framework for soil moisture mapping using multi-source data.
Highlights
The soil moisture in the first 0–5 cm of a soil layer plays an important role in the exchange of energy and substances between the atmosphere and the land
The Bayesian maximum entropy (BME) framework has a demonstrated performance in effectively integrating multi-source data for the spatial mapping of the land surface parameter and has been successfully applied to soil properties, its applications to soil moisture mapping are limited, with studies often focusing on only one type of auxiliary data [12,23]
Combined with the general knowledge under the constraint of maximum entropy principle generated in the prior stage, the soil moisture mapping was carried out based on the Bayesian conditioning rule
Summary
The soil moisture in the first 0–5 cm of a soil layer plays an important role in the exchange of energy and substances between the atmosphere and the land. Conventional in situ experiments can provide highly precise and reliable soil moisture data, it is almost impossible to sample soil moisture at a regional scale depending entirely on in situ experiments. This is because only sparse samples can be obtained given the high cost and considerable time required for this approach [6,7,8]. Because of the absence of auxiliary data, the OK method has limited accuracy and is significantly affected by the distribution and density of the samples used [15,16,17]. There remain restrictions and constraints in the application process, auxiliary data can help improve the prediction accuracy of the soil properties [12,27,28,29]
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