基于可见光-短波红外波谱反射率的裸土土壤含水量反演建模

土壤是地表能够生长植物的疏松土层。它由不同矿物质、有机物质、空气、水分、微生物等组成，其中土壤含水量变化较大，利用遥感技术快速准确的反演土壤含水量具有重要意义。针对反演土壤含水量受土壤类型影响较大的问题，提出构建归一化光谱斜率吸收指数（normalized spectral slope and absorption index，NSSAI），用于削弱土壤类型对土壤水分反演影响。其建模方法如下：首先在野外采取不同类型的土壤样本，制备不同土壤含水量的土样，利用ASD光谱仪采集不同含水量下的土壤反射率光谱，然后根据各类土壤在不同含水量下的光谱响应规律，提取短波红外水分的光谱吸收特征和可见光波段的土壤光谱斜率，综合两者在降低土壤类型影响土壤含水量反演精度的各自优势，构建归一化光谱斜率吸收指数，进一步建立该指数与土壤含水量之间的线性拟合关系来计算土壤含水量。实验结果表明，与传统利用单一土壤水分光谱吸收特征参数及其变形的方法相比，基于NSSAI构建的土壤含水量反演模型拟合精度较高（相关系数为0.93），在土壤含水量较低和较高的情况下都能有效排除土壤类型差异造成的影响，提高了裸土土壤含水量的反演精度。

Bare Soil Moisture Inversion Model Based on Visible-Shortwave Infrared Reflectance

Soil is the loose solum of land surface that can support plants. It consists of minerals, organics, atmosphere, moisture, microbes, et al. Among its complex compositions, soil moisture varies greatly. Therefore, the fast and accurate inversion of soil moisture by using remote sensing is very crucial. In order to reduce the influence of soil type on the retrieval of soil moisture, this paper proposed a normalized spectral slope and absorption index named NSSAI to estimate soil moisture. The modeling of the new index contains several key steps: Firstly, soil samples with different moisture level were artificially prepared, and soil reflectance spectra was consequently measured using spectroradiometer produced by ASD Company. Secondly, the moisture absorption spectral feature located at shortwave wavelengths and the spectral slope of visible wavelengths were calculated after analyzing the regular spectral feature change patterns of different soil at different moisture conditions. Then advantages of the two features at reducing soil types’ effects was synthesized to build the NSSAI. Thirdly, a linear relationship between NSSAI and soil moisture was established. The result showed that NSSAI worked better (correlation coefficient is 0.93) than most of other traditional methods in soil moisture extraction. It can weaken the influences caused by soil types at different moisture levels and improve the bare soil moisture inversion accuracy.