阿克苏河流域土壤湿度反演与监测研究

以新疆阿克苏河流域为研究区,以GF-1 WFV和Landsat8 OLI两种高分辨率遥感影像为数据源,结合102个不同深度层的土壤湿度实测数据,选择垂直干旱指数(PDI)、改进型垂直干旱指数(MPDI)和植被调整垂直干旱指数(VAPDI),对土壤湿度指数反演的效果进行比较和验证。结果表明,两种数据源下的PDI、MPDI、VAPDI与土壤湿度实测含水量的决定系数较高,尤其是0—10 cm的相关性最强,平均决定系数达到0.68,说明基于光学遥感影像近红外和红光波段反射率构建的反演指数对近地表层土壤湿度信息更敏感,但对地下较深层次的土壤湿度反演精度略低;MPDI和VAPDI能够在一定程度上克服混合像元对土壤湿度光谱信息的影响,反演的精度要比PDI高,尤其是高植被覆盖度区,采用垂直植被指数(PVI)修正的VAPDI反演精度最佳;基于两种遥感数据源的土壤湿度空间异质性基本一致,但空间分辨率较高的GF-1 WFV模拟的土壤湿度空间分异更加精细和明显。研究结果可为"一带一路"背景下干旱半干旱地区大范围和动态监测土壤湿度、开展定量节水灌溉等提供理论基础和实践参考。

Soil moisture retrieval and monitoring in the Aksu River basin

As a basic parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on vegetation growth, agricultural production, and the healthy function of regional ecosystems. Monitoring of soil moisture by remote sensing plays a significant role in the dynamic characterization and management of surface heat balance, water evapotranspiration, and soil moisture in agricultural production. In order to verify the applicability of GF-1 data to the rapid acquisition of agricultural parameters in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. All data were sampled from the vegetated area of the Aksu River basin in July 2016. GF-1 WFV images, Landsat8 OLI images, as well as measured soil moisture data were used to retrieve the perpendicular drought index (PDI), modified perpendicular drought index (MPDI), and vegetation adjusted perpendicular drought index (VAPDI). The results showed that, first, the determinant coefficients of correlation analyses of PDI, MPDI, VAPDI, and measured soil moisture in the 0-10, 10-20, and 20-30 cm depth layers based on GF-1 WFV images and Landsat8 OLI images, were good. In the 0-10 cm depth layer, the average determination coefficient was 0.68, all models met the accuracy requirements of soil moisture inversion. Inversion indices based on NIR-Red spectral space derived from optical remote sensing images were more sensitive to soil moisture information near the surface layer, but the accuracy of soil moisture retrieval for deeper layers was slightly lower in the study area. Second, in the area of moderate vegetation coverage, MPDI and VAPDI had higher inversion accuracy than PDI; to a certain extent, they overcame the influence of mixed pixels in soil moisture spectral information. In the area of high vegetation coverage, VAPDI modified by PVI was not susceptible to vegetation saturation, and thus had higher inversion accuracy. Third, the spatial heterogeneity of soil moisture retrieved by the remote sensing types was similar. However, GF-1 WFV images were more sensitive to changes in soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for dynamic monitoring of surface soil moisture and large-scale water-saving irrigation projects in the arid and semi-arid regions, under the Belt & Road initiative.