Quantifying the Impact of NDVIsoil Determination Methods and NDVIsoil Variability on the Estimation of Fractional Vegetation Cover in Northeast China

Remote Sensing

Yanling Ding³  Xingming Zheng⁴  Kai Zhao⁴  Xiaoping Xin²  Huanjun Liu⁴  Sangram Ganguly¹  Compton Tucker¹  Parth Sarathi Roy¹ 

[1] School of Geographical Sciences, Northeast Normal University, Changchun 130024, ChinaInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Science, Beijing 130102, China;School of Geographical Sciences, Northeast Normal University, Changchun 130024, China;Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;

关键词: fractional vegetation cover;    NDVI_soil;    dimidiate pixel model;    HWSD;    soil background;   

DOI  :  10.3390/rs8010029

来源: mdpi

【 摘 要 】

Fractional vegetation cover (FVC) is one of the most critical parameters in monitoring vegetation status. Accurate estimates of FVC are crucial to the use in land surface models. The dimidiate pixel model is the most widely used method for retrieval of FVC. The normalized difference vegetation index (NDVI) of bare soil endmember (NDVI_soil) is usually assumed to be invariant without taking into account the spatial variability of soil backgrounds. Two NDVI_soil determining methods were compared for estimating FVC. The first method used an invariant NDVI_soil for the Northeast China. The second method used the historical minimum NDVI along with information on soil types to estimate NDVI_soil for each soil type. We quantified the influence of variations of NDVI_soil derived from the second method on FVC estimation for each soil type and compared the differences in FVC estimated by these two methods. Analysis shows that the uncertainty in FVC estimation introduced by NDVI_soil variability can exceed 0.1 (root mean square error—RMSE), with the largest errors occurring in vegetation types with low NDVI. NDVI_soil with higher variation causes greater uncertainty on FVC. The difference between the two versions of FVC in Northeast China, is about 0.07 with an RMSE of 0.07. Validation using fine-resolution FVC reference maps shows that the second approach yields better estimates of FVC than using an invariant NDVI_soil value. The accuracy of FVC estimates is improved from 0.1 to 0.07 (RMSE), on average, in the croplands and from 0.04 to 0.03 in the grasslands. Soil backgrounds have impacts not only on NDVI_soil but also on other VI_soil. Further focus will be the selection of optimal vegetation indices and the modeling of the relationships between VI_soil and soil properties for predicting VI_soil.

【 授权许可】

CC BY   
© 2016 by the authors; licensee MDPI, Basel, Switzerland.