一种改进的土壤水分微波遥感反演模型

利用微波遥感数据反演地表土壤水分有着较好的物理基础,可实现大范围土壤水分状况的遥感监测。本文基于被动微波传感器AMSR-E的X波段数据,将土壤水分值分解成基准值和日变化量两个部分,并分别建立反演模型,同时引入降雨修正因子来进一步提高土壤水分的估算精度;利用IDL语言实现了我们所研发的模型,并集成为新疆土壤水分遥感反演系统模块之一;利用Watch Dog2400与传统铝盒采样获取的新疆地面土壤水分数据,提取适合的模型经验参数,并对模型结果进行精度评价。结果表明,经改进的模型反演得到的新疆土壤水分结果比美国冰雪数据中心的土壤水分产品在精度上有显著提高:均方根误差由8.4%降低为4.25%;所研发的软件模块可为相关应用部门提供快速的大范围土壤水分监测产品。

Improved soil moisture retrieval model from remotely sensed microwave data

Retrieving land surface soil water content from remotely sensed passive microwave data has a good physical basis. Thus, it can provide dynamic monitoring of large-range soil moisture condition. The Advanced Microwave Scanning Radiometer for EOS (AMSR-E) data were used to derive soil moisture base value and daily variation for each image pixel, respectively, to build an inversion model for retrieving soil moisture information. A precipitation impact factor was proposed and incorporated into the modeling process to improve the accuracy of soil moisture retrieval. The IDL language was used to implement the proposed model as software modules of the System of Xinjiang Soil Moisture Inversion from Remotely Sensed Data. The in-situ measured soil moisture data by the WatchDog 2400 instrument and Loss-on-Drying method were used to derive empirical parameters for the regressive model that are suited to the conditions in Xinjiang, and to verify the proposed model output. The results show that, with reference to the data of in-situ measurements, our improved model can achieve better estimation of Xinxiang's soil moisture than the soil moisture products of US National Snow & Ice Data Center (NSIDC). The RMSE is improved from 8.4% to 4.25%. The software modules developed in this study can provide a tool for quick soil moisture monitoring in a large area such as Xinjiang.