# Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN
## 1 Introduction  
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]. Thus, rapid and accurate quantification of CLQ is critical. Traditionally, the assessment of CLQ is usually conducted using field measurements, which is time-consuming and costly. More importantly, 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]. Current studies on CLQ evaluation using satellite imagery can be divided into two categories: traditional CLQ evaluation methods and pressure-state-response (PSR) based approaches. In the former group of CLQ evaluation methods, remote sensing data were only utilized to obtain some traditional indicators of CLQ, such as soil properties. One typical example is the study of [PERSON] et al. (2012) in which Landsat TM images were used to derive soil organic matter, soil acidity, soil texture, and then generate the estimates of CLQ based on gradation regulations on the quality of farmland in China [13]. Instead of soil fertilizer variables, [PERSON] et al. (2012) utilized normalized difference vegetation index from Landsat TM imagery to evaluate CLQ [14]. However, the evaluation efficiency of CLQ using satellite image-driven evaluation methods is limited because the use of the methods is dependent on field measurements. While, of the PSR based methods, CLQ is directly evaluated using remote sensing spectral indicators. For example, [PERSON] et al. (2010) developed a linear model for evaluating CLQ based on predictors, including slope, sandy area ratio in a pixel, and modified soil-adjusted vegetation index [4; 15]. [PERSON] et al. (2019) generated the spatial distribution of CLQ estimates based on the Genetic Algorithm-Based Back Propagation Neural Network (GA-BPNN) model. The authors utilized five remote sensing data derived predictors, including Slope, Vegetation Index, Temperature Vegetation Dryness Index, Road Accessibility, and Patch Fractal Dimension, and found that CLQ was significantly and nonlinearly correlated with the spectral predictors [16]. [PERSON] et al. (2018) developed a frequent pattern-growth algorithm for improving the evaluation efficiency of CLQ [17]. At present, there have been no reports about CLQ evaluation using gross primary production (GPP). Some scholars have used GPP to evaluate cultivated land productivity. [PERSON] et al. (2018) explored the estimation of cultivated land productivity using the mean GPP from 2000 to 2018 and analyzed the change trend and amplitude of cultivated land productivity, implying that GPP provided the potential to evaluate CLQ [18]. Although the studies demonstrated the possibility of rapidly evaluating CLQ, the spectral responses between CLQ and remote sensing indicators were ignored. Moreover, the prediction accuracy of CLQ is affected by the selected spectral indicators in the PSR framework due to the limitations of image spatial and spectral resolutions. This study aimed to propose an accurate spectral response model of CLQ based on GPP spectral indicators from the MODIS-GPPs from 2011 to 2015 at different growth stages of late rice and the corresponding temporal CLQ for mapping CLQ. Here, CLQ is defined as the farmland utilization quality grade and represents the degree of anthropogenic use and natural conditions of cultivated land [19]. The Empirical Bayes Kriging (EBK) interpolation was first employed to perform spatial downscaling transformation of the MODIS GPP images from 500 m spatial resolution to 30 m. The accurate spectral model based on GA-BPNN was then developed and validated by comparing it with a linear partial least squares regression (PLSR) and a non-linear support vector regression (SVR). The comparison of the models was made in the study area mentioned next.