The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data

Shanshan Liu; Yiping Peng; Ziqing Xia; Yueming Hu; Guangxing Wang; A-Xing Zhu; Zhenhua Liu

doi:10.3390/s19235127

The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data

Sensors (Basel). 2019 Nov 22;19(23):5127. doi: 10.3390/s19235127.

Authors

Shanshan Liu¹, Yiping Peng¹, Ziqing Xia¹, Yueming Hu^{1

2

3

4}, Guangxing Wang^{1

5}, A-Xing Zhu^{1

6}, Zhenhua Liu¹

Affiliations

¹ College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China.
² Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China.
³ Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China.
⁴ Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China.
⁵ Department of Geography and Environmental Resources, College of Liberal Arts, Southern Illinois University Carbondale (SIUC), Carbondale, IL 62901, USA.
⁶ Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA.

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.

Keywords: GA-BPNN model; cultivated land quality; spectral indices.

Abstract

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