Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data

Chenjie Lin, Yueming Hu, Zhenhua Liu, Yiping Peng, Lu Wang and Dailiang Peng
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Chenjie Lin: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Yueming Hu: Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China
Zhenhua Liu: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Yiping Peng: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Lu Wang: Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China
Dailiang Peng: Aerospace Information Research Institute, China Academy of Sciences, Beijing 100094, China

Agriculture, 2022, vol. 12, issue 1, 1-13

Abstract: Efficient monitoring of cultivated land quality (CLQ) plays a significant role in cultivated land protection. Soil spectral data can reflect the state of cultivated land. However, most studies have used crop spectral information to estimate CLQ, and there is little research on using soil spectral data for this purpose. In this study, soil hyperspectral data were utilized for the first time to evaluate CLQ. We obtained the optimal spectral variables from dry soil spectral data using a gradient boosting decision tree (GBDT) algorithm combined with the variance inflation factor (VIF). Two estimation algorithms (partial least-squares regression (PLSR) and back-propagation neural network (BPNN)) with 10-fold cross-validation were employed to develop the relationship model between the optimal spectral variables and CLQ. The optimal algorithms were determined by the degree of fit (determination coefficient, R 2 ). In order to estimate CLQ at the regional scale, HuanJing-1A Hyperspectral Imager (HJ-1A HSI) data were transformed into dry soil spectral data using the linkage model of original soil spectral reflectance to dry soil spectral reflectance. This study was conducted in the Guangdong Province, China and the Conghua district within the same province. The results showed the following: (1) the optimal spectral variables selected from the dry soil spectral variables were 478 nm, 502 nm, 614 nm, 872 nm, 966 nm, 1007 nm, and 1796 nm. (2) The BPNN was the optimal model, with an R 2 (C) of 0.71 and a normalized root mean square error (NRMSE) of 12.20%. (3) The results showed the R 2 of the regional-scale CLQ estimation based on the proposed method was 0.05 higher, and the NRMSE was 0.92% lower than that of the CLQ map obtained using the traditional method. Additionally, the NRMSE of the regional-scale CLQ estimation base on dry soil spectral variables from HJ-1A HSI data was 2.00% lower than that of the model base on the original HJ-1A HSI data.

Keywords: soil spectrum; cultivated land quality; HJ-1A imagery; Guangdong (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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