基于机载极化雷达技术的农作物覆盖区土壤水分估算

从研究区辅助数据中获得植被覆盖区域农作物含水量，并对“水云模型”的参数进行了修改。从总的极化雷达后向散射中去除了植被影响；再应用简化的裸露地表模型和多极化数据特征消除了地表粗糙度的影响，从而能够通过多极化雷达数据得到地表土壤水分。经过文测数据验证表明农作物覆盖地表土壤水分变化的估算算法还需要更进一步发展和改进以提高反演精度，并探讨了应用极化雷达技术估算植被覆盖区土壤水分的方法。

Estimation of Soil Moisture Content in Vegetated Areas Using Multi-polarization Airborne SAR

In this paper,the estimation of soil moisture content in vegetated areas is researched.Firstly,the data of water content of crops in the vegetated areas in the study area are derived by using the ancillary data,the vegetation effects on the estimation of soil moisture content are eliminated from the total polarization radar backscattering by using the modified water-cloud model,and then the effects of surface roughness are eliminated by using the simplified bare surface model and multi-polarization airSAR data.Thus,the soil moisture contents in vegetated areas are estimated by using the multi-polarization radar data.The results show that the estimation of soil moisture content in vegetated areas,proposed in this paper,should be further developed.The analysis reveals that the errors in estimating soil moisture contents in the paper are mainly from following aspects: a.In the water-cloud model,the parameters used to describe vegetation are different from the vegetation structures,but some parameters are used in the estimation of soil moisture content only;b.The water-cloud model is too simple;c.The simplified bare surface model is used to describe the backscattering.It is considered that the estimation of soil moisture content in the vegetated areas is complicated due to the dispersion of vegetation canopy,so it will be the emphasis in the future that how to effectively eliminate the effects of vegetation canopy on the estimation of soil moisture contents and how to increase the accuracy of estimating soil moisture contents by using radar remote sensing technology,or how to directly estimate soil moisture contents by using the multi-polarization,multi-waveband and multi-angle radar data without using other ancillary data.