利用遥感光谱法进行农田土壤水分遥感动态监测

自 1 997年 4月至 1 998年 1 0月 ,在甘肃省定西县进行了大面积 0～ 5 0 cm土层农田土壤水分按每 1 5 d本底资料实际观测 ,对此间收到的 5幅 TM与 7幅 NOAA卫片数据资料进行了加工处理 ,并对地面光谱资料也进行了观测。在光谱反演与光谱和土壤水分相关性分析基础上 ,利用遥感技术和地理信息系统 ,初步建立了典型试验区 ( 3× 3km2 )遥感信息与土壤含水量之间的遥感光谱相关监测模型 ,做出了观测区土壤水分含量分布图和得到了大面积农田土壤水分宏观动态监测结果 ,并同地面实测土壤水分进行了精度校正。研究结果表明 ,文中提出的“光学植被盖度”概念 ,对土壤水分遥感监测研究是有益的 ,利用遥感光谱法和数学统计方法求出了有关物理参数 ,初步建立了 TM与 NOAA光谱水分监测模型 ,其模型监测 0～2 0 cm土层含水量的精度达到 90 %以上 ,实际监测土壤水分精度达到 72 .3% ;在遥感监测 2 0～ 5 0 cm土层土壤含水量中 ,利用遥感模型监测土壤水分精度达到 80 %以上 ,实际遥感监测精度达到 60 %左右 ,其结果可有效指导干旱半干旱雨养农业区春耕时间和动态监测大面积土壤墒情 ,可为农业生产提供科学依据。另外 ,经地面大量观测表明 ,一般来说 ,当土壤含水量为田间最大持水量的 5 5 %～ 85 %时 ,从生长状况和经济

The study on dynamic monitoring soil water contents using remote sensing optical method

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.