Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN

Mingbang Zhu, Shanshan Liu, Ziqing Xia, Guangxing Wang, Yueming Hu and Zhenhua Liu
Additional contact information
Mingbang Zhu: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Shanshan Liu: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Ziqing Xia: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Guangxing Wang: Department of Geography and Environmental Resources, Southern Illinois University Carbondale (SIUC), Carbondale, IL 62901, USA
Yueming Hu: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Zhenhua Liu: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2020, vol. 10, issue 8, 1-16

Abstract: Rapid and accurate evaluation of cultivated land quality (CLQ) using remotely sensed images plays an important role for national food security and social stability. Current approaches for evaluating CLQ do not consider spectral response relationships between CLQ and spectral indicators based on crop growth stages. This study aimed to propose an accurate spectral model to evaluate CLQ based on late rice phenology. In order to increase the accuracy of evaluation, the Empirical Bayes Kriging (EBK) interpolation was first performed to scale down gross primary production (GPP) products from a 500 m spatial resolution to 30 m. As an indicator, the ability of MODIS-GPPs from critical growth stages (tillering, jointing, heading, and maturity stages) was then investigated by combining Pearson correlation analysis and variance inflation factor (VIF) to select the phases of CLQ evaluation. Finally, a linear Partial Least Squares Regression (PLSR) and two nonlinear models, including Support Vector Regression (SVR) and Genetic Algorithm-Based Back Propagation Neural Network (GA-BPNN), were driven to develop an accurate spectral model of evaluating CLQ based on MODIS-GPPs. The models were tested and compared in the Conghua and Zengcheng districts of Guangzhou City, Guangdong, China. The results showed that based on field measured GPP data, the validation accuracy of 30 m spatial resolution MODIS GPP products with a root mean square error (RMSE) of 7.43 and normalized RMSE (NRMSE) of 1.59% was higher than that of the 500 m MODIS GPP products, indicating that the downscaled 30 m MODIS GPP products by EBK were more appropriate than the 500 m products. Compared with PLSR (R 2 = 0.38 and RMSE = 87.97) and SVR (R 2 = 0.64 and RMSE = 64.38), the GA-BPNN model (R 2 = 0.69 and RMSE = 60.12) was more accurate to evaluate CLQ, implying a non-linear relationship of CLQ with the GPP spectral indicator. This is the first study to improve the accuracy of estimating CLQ using the rice growth stage GPP-driven spectral model by GA-BPNN and can thus advance the literature in this field.

Keywords: CLQ; GA-BPNN; GPP-driven spectral model; rice phenology; EBK (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2077-0472/10/8/318/pdf (application/pdf)
https://www.mdpi.com/2077-0472/10/8/318/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:10:y:2020:i:8:p:318-:d:393258

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().