Abstract
Rapid and efficient assessment of cultivated land quality (CLQ) using remote sensing technology is of great significance for protecting cultivated land. However, it is difficult to obtain accurate CLQ estimates using the current satellite-driven approaches in the pressure-state-response (PSR) framework, owing to the limitations of linear models and CLQ spectral indices. In order to improve the estimation accuracy of CLQ, this study used four evaluation models (the traditional linear model; partial least squares regression, PLSR; back propagation neural network, BPNN; and BPNN with genetic algorithm optimization, GA-BPNN) to evaluate CLQ for determining the accurate evaluation model. In addition, the optimal satellite-derived indicator in the land state index was selected among five vegetation indices (the normalized vegetation index, NDVI; enhanced vegetation index, EVI; modified soil-adjusted vegetation index, MSAVI; perpendicular vegetation index, PVI; and soil-adjusted vegetation index, SAVI) to improve the prediction accuracy of CLQ. This study was conducted in Conghua District of Guangzhou, Guangdong Province, China, based on Gaofen-1 (GF-1) data. The prediction accuracies from the traditional linear model, PLSR, BPNN, and GA-BPNN were compared using observations. The results demonstrated that (1) compared with other models (the traditional linear model: R2 = 0.14 and RMSE = 91.53; PLSR: R2 = 0.33 and RMSE = 74.58; BPNN: R2 = 0.50 and RMSE = 61.75), the GA-BPNN model based on EVI in the land state index provided the most accurate estimates of CLQ, with the R2 of 0.59 and root mean square error (RMSE) of 56.87, indicating a nonlinear relationship between CLQ and the prediction indicator; and (2) the GA-BPNN-based evaluation approach of CLQ in the PSR framework was driven to map CLQ of the study area using the GF-1 data, leading to an RMSE of 61.44 at the regional scale, implying that the GA-BPNN-based evaluation approach has the potential to map CLQ over large areas. This study provides an important reference for the high-accuracy prediction of CLQ based on remote sensing technology.
Highlights
Cultivated land is the most basic capital good of agricultural production [1,2]
It can be seen that enhanced vegetation index (EVI) exhibited the greatest correlation coefficients with soil organic matter (SOM) and total nitrogen (TN) (0.88 and 0.90, respectively), indicating EVI as the optimal indicator for the evaluation of cultivated land quality (CLQ)
It is agreed that estimating and mapping CLQ using satellite-derived approaches based on the PSR framework is quick and effective, yet very challenging owing to spectral indicator accuracy, modeling methods, and model transferability, which will affect the accuracy of CLQ evaluation
Summary
Cultivated land is the most basic capital good of agricultural production [1,2]. The area of cultivated land in China covers 134.9 × 106 hm , accounting for 19.71% of the land area. The rapid urbanization and industrialization in China have resulted in a strong demand for evaluating cultivated land quality (CLQ) to ensure national food security. The evaluation of CLQ represents natural conditions and the degree of anthropogenic use of cultivated land resources, involving the quantification and grading of CLQ indices [3,4,5]. It is very important to quickly and effectively evaluate CLQ. Traditional estimations of CLQ are based on field measurements, which are time-consuming and costly [6]. Remote sensing technology provides a unique means for the rapid evaluation of CLQ in a time-efficient and low-cost manner. Substantial research has been conducted for the evaluation of CLQ based on remote sensing data [7,8,9,10]
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