• 遥感科学与应用技术 •

### 微波与光学遥感协同反演旱区地表土壤水分研究

1. 1. 长安大学地球科学与资源学院,西安 710054
2. 旱区地下水文与生态效应教育部重点实验室,西安 710054
• 收稿日期:2016-01-16 修回日期:2016-02-24 出版日期:2016-06-10 发布日期:2016-06-10
• 作者简介:

作者简介：孔金玲(1964-),女,博士,教授,主要从事定量遥感研究。E-mail: jlkong@163.com

• 基金资助:
国家自然科学基金项目(41272246、41371220);教育部科学技术研究重点项目(108183);中央高校基本科研业务费专项资金项目(2013G3272013)

### Inversion of Soil Moisture in Arid Area Based on Microwave and Optical Remote Sensing Data

KONG Jinling1,2,*(), LI Jingjing1, ZHEN Peipei1, YANG Xiaotian1, YANG Jing1, WU Zhechao1

1. 1. School of Earth Science and Resources, Chang'an University, Xi'an 710054, China
2. Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Chang'an University, Education Ministry, Xi′an 710054, China
• Received:2016-01-16 Revised:2016-02-24 Online:2016-06-10 Published:2016-06-10
• Contact: KONG Jinling E-mail:jlkong@163.com

Abstract:

Soil moisture is a key factor in the dynamics of hydrological cycle, and it also plays an important role in the ecological environment, especially in terms of an arid area. Microwave remote sensing technology is an effective technique that has been used to extract the soil moisture. However, the impact that vegetation imposes on the process and result of soil moisture retrieval is so influential that cannot be ignored. Therefore, it is necessary to establish a soil moisture retrieval model for the arid area with vegetation being taken into consideration. By taking the Uxin Banner of Inner Mongolia as a case study, the main objective of this paper is to develop different soil moisture retrieval models that are suitable for surfaces that are covered by sparse vegetation in the arid area based on Radarsat-2 and TM remote sensing data. The NDVI and NDWI indices extracted from the TM data have been used to calculate the vegetation water content. And subsequently, the impact of vegetation to the backscattering is removed by applying the water-cloud model. Furthermore, according to the characteristics of the vegetation in the study area, an improved algorithm based on the AIEM model is proposed to retrieve the soil moisture under different surface roughness parameters and polarization modes (VV and HH). Different from the existing algorithms which only utilize the backscattering coefficient within a single treatment for the soil moisture inversion, the improved algorithm comprehensively utilizes the backscattering coefficient before and after applying the correction which is conducted by the water-cloud model. After comparing the inversion results with the field in situ data, the results show that the improved soil moisture inversion algorithm based on the vegetation characteristics has presented a better adaptability. The soil moisture inversion model $Mvσvv1lh$ (this inversion model removes the vegetation influence by using NDVI under the VV polarization mode) is more suitable for the soil moisture inversion in arid area while considering the influence of sparse vegetation.