Soil salinization is a serious restrictive factor affecting sustainable agricultural development. In order to explore the effect of Fractional Vegetation Cover (FVC), we monitored soil salinization in sites different vegetation coverage in Jiefangzha Irrigation District in Inner Mongolia using satellite remote sensing. From May to August 2018, we carried out field sampling at different depths in each month, and calculated FVC and spectral covariates using GF‐1 satellite images in the corresponding sampling period. Based on the FVC division criteria for Inner Mongolia, we took the following steps: (a) setting up a control treatment A (the full data with undivided FVC, TA) and experimental treatments B (bare land, TB), C (mid‐low FVC, TC), D (mid FVC, TD) and E (high FVC, TE); (b) conducting the Best Subset Selection (BSS) for all spectral covariates at different depths of each treatment; and (c) constructing the Soil Salt Content (SSC) inversion models using partial least square regression (PLSR), Cubist, and Extreme Learning Machine (ELM). The results indicated that (a) classifying FVC could improve the stability and predictive ability of the models; (b) the performance of the three modeling methods were different (Cubist was the best, ELM next and PLSR the poorest); (c) the optimal inversion models for TB, TC and TE were constructed by Cubist at 0–20, 0–40 and 0–20 cm, and for TD was constructed by ELM at 0–60 cm, respectively. The results can provide references for soil salinization prevention and agricultural production in Jiefangzha Irrigation District and other areas with the similar vegetation cover.

中文翻译：

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遥感估算不同植被覆盖度下的土壤盐分

土壤盐渍化是影响可持续农业发展的严重限制因素。为了探索部分植被覆盖（FVC）的效果，我们使用卫星遥感监测了内蒙古解放闸灌区不同植被覆盖地点的土壤盐渍化。从2018年5月至2018年8月，我们每月在不同深度进行野外采样，并在相应的采样周期内使用GF-1卫星图像计算FVC和频谱协变量。根据内蒙古的FVC划分标准，我们采取了以下步骤：（a）设置对照处理A（未划分FVC，TA的完整数据）和实验处理B（裸地，TB），C（中度-低FVC，TC），D（中FVC，TD）和E（高FVC，TE）；（b）对每种治疗不同深度的所有光谱协变量进行最佳子集选择（BSS）；（c）使用偏最小二乘回归（PLSR），立体派和极限学习机（ELM）构建土壤盐含量（SSC）反演模型。结果表明：（a）分类FVC可以提高模型的稳定性和预测能力；（b）三种建模方法的性能有所不同（立体主义者最好，ELM次之，PLSR最差）；（c）TB，TC和TE的最佳反演模型由Cubist在0–20、0–40和0–20 cm建立，而TD的反演模型由ELM在0–60 cm建立。研究结果可为解放闸灌区及植被相似的其他地区的土壤盐渍化防治与农业生产提供参考。