Abstract:

It is difficult to monitor land surface soil moisture in high temporal and spatial resolution within a wide range for lack of ground observation data when the satellite is passing over. To solve this problem, a new integrated approach termed as “soil moisture retrieval with combined active and passive microwave remote sensing observation” was proposed. AMSR-E soil moisture product is compensated as “high temporal resolution observation control data” and soil moisture benchmark is retrieved together with ASAR alternating polarization mode data. Then both of them are integrated to build up a co-inversion model for soil moisture retrieval. This approach applies to areas where the land surface roughness is small and vegetation index (NDVI) is low. The approach is evaluated in Weibei Upland of Shaanxi Province. According to the regression analysis based on AIEM (advanced integrated equation model), the correlation coefficient between compensated AMSR-E soil moisture and downscaled ASAR backscattering coefficient was approximately 0.81. Verification analysis with the in-situ data of Fengxiang County in the study area shows that the soil moisture retrieved with combined active and passive microwave remote sensing observation displays a correlation coefficient of 0.92, and the root mean square errors (RMSE) of the soil volumetric moisture is 0.025. It indicates that the approach is credible and the soil moisture retrieval results could be used in simulating regional crop growth under water-limited environments.

Key words: soil moisture, AMSR-E, ASAR, crop growth simulation model, AIEM

摘要：

在缺乏卫星过境时地面同步观测数据的情况下, 大范围高时空分辨率的土壤水分监测存在一定的困难。针对这一问题, 提出一种不依赖地面土壤水分同步观测数据的主、被动微波协同反演逐日高空间分辨率的土壤水分观测新方法。该方法将补偿后的 AMSR-E 土壤水分作为“高时间分辨率土壤水分观测控制值”, 以此计算逐日土壤水分变化量, 并结合 ASAR 交替极化模式数据, 反演高空间分辨率的土壤水分基准日期值, 然后基于两者建立土壤水分协同反演模型。该模型适用于地势比较平坦、地表粗糙度较小且无植被覆盖或植被覆盖度较低的区域。在陕西省渭北台塬西部地区的试验结果表明: 该方法参数拟合的决定系数约为0.81; 反演得到的土壤水分与凤翔县农业气象站地面实测土壤湿度数据对比, 两者的决定系数为 0.92, 土壤体积含水量的均方根误差为0.025。反演结果可用于水分限制条件下作物生长模拟。

关键词: 土壤水分, AMSR-E, ASAR, 作物生长模拟模型, AIEM