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 (R2 = 0.38 and RMSE = 87.97) and SVR (R2 = 0.64 and RMSE = 64.38), the GA-BPNN model (R2 = 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.
Highlights
Cultivated land quality (CLQ) has significant influence on agricultural production and resident living [1,2,3]
0.59 and normalized RMSE (NRMSE) of 11.19% [16], the Genetic Algorithm-Based Back Propagation Neural Network (GA-back propagation neural network (BPNN)) model proposed in this study shows stronger ability for cultivated land quality (CLQ) evaluation with R2 = 0.69 and NRMSE = 8.59%, implying that the gross primary production (GPP) spectral indicator provides a direct and effective means for estimating CLQ
This study attempted to obtain an accurate spectral model for evaluation of CLQ based on the GPP spectral indicator at four important growth stages of late rice phenology by comparison of partial least squares regression (PLSR), support vector regression (SVR), and genetic algorithm optimization (GA)-BPNN models using the measurements of CLQ from 294 training samples and the corresponding GPP data from MOD17 products
Summary
Cultivated land quality (CLQ) has significant influence on agricultural production and resident living [1,2,3]. The CLQ often changes dramatically under conditions of human disturbances or environments [4]. Rapid and accurate quantification of CLQ is critical. The assessment of CLQ is usually conducted using field measurements, which is time-consuming and costly. This method lacks the ability to generate spatial distributions of CLQ [5,6,7]. Using remotely sensed data offers the potential of obtaining accurate and spatially explicit estimates of CLQ with low cost and has attracted the attention of scholars [8,9,10,11,12]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
