# Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data
## 1 Introduction  
Due to rapid socio-economic development and urbanization, the amount of cultivated land in China has been decreasing. Cultivated land quality (CLQ) is an agriculture land quality grade that represents the soil fertility and natural conditions of cultivated land [1, 2]. Cultivated land quality evaluation is crucial to ensuring the sustainability of cultivated land [3, 4]. Traditional CLQ evaluations have primarily relied on field investigations and laboratory analyses, although these methods are not well suited for real-time dynamic monitoring of CLQ [5, 6, 7].  
With the steady growth of remote sensing, several scholars have utilized remote sensing techniques to evaluate CLQ in recent years [8; 9; 10]. The methods can be divided into two categories: the quasi-satellite-driven CLQ evaluation method and the satellite-driven CLQ evaluation method. In the quasi-satellite-driven CLQ evaluation method, the indicators were divided into two components by the traditional CLQ assessment system (GB/T 28407-2012): some indicators were acquired from field investigations and laboratory analyses, while others were obtained from remote sensing data. For instance, [PERSON] et al. (2012) obtained the soil organic matter (SOM) content and soil pH from field measurements and developed a model to relate the information to Landsat TM5 data to establish a CLQ evaluation method [11]. [PERSON] et al. (2012) established a CLQ evaluation indicator system using soil nutrient content and topsoil thickness obtained from field measurements and MODIS data [12]. However, the other indicators in the method cannot be obtained from remote sensing data, making rapid and timely monitoring of CLQ difficult or impossible.  
In satellite-driven CLQ evaluation methods, vegetation indicators are used to estimate CLQ. For example, [PERSON] et al. (2020) used the empirical Bayesian kriging (EBK) algorithm to scale down the MODIS gross primary productivity (GPP) data of rice in different growth periods and constructed a CLQ evaluation model based on GPP [13]. [PERSON] et al. (2018) established a CLQ evaluation model based on the normalized difference vegetation index (NDVI) from Landsat data modified by grain yield data [14]. These methods focused on the relationship between the spectral response of vegetation and CLQ, overcoming the limitations of the quasi-satellite-driven CLQ evaluation method. However, the spectral response of the crop affects the CLQ estimation, and different crops with different spectral responses will result in different CLQ estimates, resulting in potentially large errors.  
In contrast, soil spectral data can provide CLQ estimates unaffected by the crop spectral response. In recent years, hyperspectral data have been increasingly used to obtain the physical and chemical characteristics of soil [15; 16]. The goal of this study is to develop a novel method to estimate CLQ using soil spectral data [10; 13]. The objectives are to (1) determine the optimal spectral variables describing the correlation between soil spectral data and CLQ; (2) establish a CLQ evaluation model based on the optimal spectral variables; (3) expand the model to regional-scale CLQ estimation using HuanJing-1A Hyperspectral Imager (HJ-1A HSI) data.