Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

Abstract

Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is an important monitoring technology for the soil moisture content (SMC) of agroecological systems in arid regions. This technology develops precision farming and agricultural informatization. However, hyperspectral data are generally used in data mining. In this study, UAV-based hyperspectral imaging data with a resolution o 4 cm and totaling 70 soil samples (0–10 cm) were collected from farmland (2.5 × 104 m2) near Fukang City, Xinjiang Uygur Autonomous Region, China. Four estimation strategies were tested: the original image (strategy I), first- and second-order derivative methods (strategy II), the fractional-order derivative (FOD) technique (strategy III), and the optimal fractional order combined with the optimal multiband indices (strategy IV). These strategies were based on the eXtreme Gradient Boost (XGBoost) algorithm, with the aim of building the best estimation model for agricultural SMC in arid regions. The results demonstrated that FOD technology could effectively mine information (with an absolute maximum correlation coefficient of 0.768). By comparison, strategy IV yielded the best estimates out of the methods tested (R2val = 0.921, RMSEP = 1.943, and RPD = 2.736) for the SMC. The model derived from the order of 0.4 within strategy IV worked relatively well among the different derivative methods (strategy I, II, and III). In conclusion, the combination of FOD technology and the optimal multiband indices generated a highly accurate model within the XGBoost algorithm for SMC estimation. This research provided a promising data mining approach for UAV-based hyperspectral imaging data.

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

“…The enriched spectral information captured by hyperspectral data can obscure valuable information regarding some target variables, which is why many scholars note that preprocessing hyperspectral data is obligatory [31,35,36]. Fractional order discretization (FOD) is one of the frequently applied and more efficient methods for preprocessing hyperspectral data [37].…”

Spatial-Temporal Variation in Paddy Evapotranspiration in Subtropical Climate Regions Based on the SEBAL Model: A Case Study of the Ganfu Plain Irrigation System, Southern China

“…To evaluate the prediction accuracy of the model for ET, the coefficient of determination (R 2 ), root-meansquare error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE) were chosen to analyze the ET derived by the model, and the calculation process is shown in Equations ( 20)- (22). A reliable model usually has high R 2 and NSE values and low RMSE values [51,52].…”

Soil Moisture a Posteriori Measurements Enhancement Using Ensemble Learning

“…Several investigators report the excellent performance of Extra Trees and Random Forest algorithm when dealing with moisture measurements [14][15][16]. According to others, a good way of obtaining low error estimations is the application of Extreme Gradient Boosting [17,18]. Other algorithms, like Adaptive Boosting (AdaBoost) for water parameters' estimations [19] or Light Gradient Boosting Machines for evapotranspiration modeling [20], are also recommended.…”