The study on dynamic monitoring soil water contents using remote sensing optical method
利用遥感光谱法进行农田土壤水分遥感动态监测

Remote sensing provides information on the land surface. Therefore, linkages must be established if these data are to be used in ground water and recharge analyses. Keys to this process are the use of remote sensing techniques that provide information on soil moisture and water-balance models that tie these observations to the recharge. The soil water content, variational law and utilization rate of soil water for crop were studied from our experiments in 1997~1998. Soil water in 0~50cm, spectral and TM/NOAA data were observed from 1997 to 1998 in DingXi county, Gansu. Based on remote sensing data and field soil moisture, a study on dynamic monitoring soil water contents had been done with the help of remote sensing optimal methods. As the vegetation interferes with estimating soil water content, the vegetation information is necessary for estimating the yield of crop. The vegetation coverage can reflect crop yields, but its precision is low because it can not reflect not only the canopy density of branches and leaves, but also one on top of another. Considering the needs for removing the interference of vegetation with soil water contents and extracting soil water information and estimating crop yield, we were put forward a new concept of optical vegetation coverage and set up a let of remote sensing estimating soil water models and calculated related parameters using TM/NOAA data. The correlative spectral models and soil water content distributed maps were made between remote sensing data and soil water from ground by RS and GIS in the paper. The results showed that there existed an obvious correlation between the soil moistures and spectral vegetation indices of TM and NOAA/AVHRR (p<0.05), when the vegetation interfered with soil moisture by RS were discarded from mixed data, and remote sensing monitoring models of soil moisture were made by remote sensing optical method. In 0~20cm soil, the estimating soil moisture accuracy was above 90% by the models and the actual estimating accuracy was above 72.3% from the ground. In 20~50cm soil, the estimating soil moisture accuracy was above 80% by the models and the actual estimating accuracy was 60% observed from the ground by the optical vegetation coverage models.