Estimating Vegetation Water Content of Corn and Soybean Using Different Polarization Ratios Based on L- and S-Band Radar Data

文献类型： 外文期刊

第一作者： Ma, Jianwei

作者： Ma, Jianwei;Huang, Shifeng;Li, Jiren;Li, Xiaotao;Sun, Yayong;Song, Xiaoning;Leng, Pei

作者机构：

关键词： Passive and active L-and S-band sensor (PALS);polarization ratio;vegetation water content (VWC)

期刊名称：IEEE GEOSCIENCE AND REMOTE SENSING LETTERS （ 影响因子：3.966； 五年影响因子：3.986 ）

ISSN： 1545-598X

年卷期： 2017 年 14 卷 3 期

页码：

收录情况： SCI

摘要： Vegetation water content (VWC) is an important parameter of agriculture and forestry. In this letter, specific polarization ratios were evaluated for estimating VWC of corn and soybean. Backscattering coefficients (sigma(hh), sigma(vv), sigma(vh) and sigma(hv)), polarization ratios (sigma(hh)/sigma(vv), sigma(vv)/sigma(vh), and sigma(hh)/sigma(hv)), and the radar vegetation index derived from L-band (1.26 GHz) and S-band (3.15 GHz) radar data of the passive and active L-and S-band sensor (PALS) in Soil Moisture Experiments 2002 were implemented to develop various linear relationship models with field VWC measurements for corn and soybean, respectively. L-band shh/svv was found to be most correlated with corn VWC (R = 0.81), while for soybean, L-band shh/shv was the best parameter to estimate VWC with an R of 0.90. Based upon these analyses, prediction equations for the estimation of corn and soybean VWC using the polarization ratios were developed. Results indicated that L-band shh/svv was able to estimate corn VWC with a root mean square error (RMSE) of 0.53 kg/m(2) and a mean absolute relative error (MARE) of 11.48%. As for soybean, L-band shh/shv was capable of estimating soybean VWC with an RMSE of 0.12 kg/m(2) and an MARE of 13.33%. The main reason for these differences is most likely due to the disparate structure features and VWC distribution of corn and soybean. This letter proposes an effective method for acquiring VWC in regional areas, and it is also considered to be a powerful supplement for the current methods based on optical remotely sensed data.

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