Retrieving sparse non-photosynthetic vegetation fractional cover by Sentinel-1 and Sentinel-2

被引：0
|
作者：
Ji C. [1 ,2 ]
Luo Y. [1 ]
Li X. [2 ,3 ]
Xu J. [1 ]
Yang X. [4 ]
Chen M. [1 ]
机构：
[1] School of Civil Engineering, Chongqing Jiaotong University, Chongqing
[2] International Research Center of Big Data for Sustainable Development Goals, Beijing
[3] Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing
[4] Tourism School, Lanzhou University of Arts and Science, Lanzhou
来源：
National Remote Sensing Bulletin | 2023年 / 27卷 / 12期
基金：
中国国家自然科学基金;
关键词：
linear index model; Minqin County in Gansu province; non-photosynthetic vegetation; random forest regression model; Sentinel-1; Sentinel-2; VV and VH polarization;
D O I：
10.11834/jrs.20231207
中图分类号：
学科分类号：
摘要：
Quantitatively estimating the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation (NPV), and bare soil plays an important role in establishing carbon dynamics models. Accurately obtaining the fractional cover of NPV provides the important information for the study of land desertification and vegetation transformation mechanisms. Although some progress has been made in obtaining NPV fractional cover (fNPV) by optical remote sensing in previous studies, many interfering factors and difficulties are still present. We will attempt to combine microwave and optical remote sensing information to obtain NPV fractional cover for further improving the accuracy of the fractional cover estimation of NPV. In this study, we used Minqin County in Gansu Province as the research area, and we employed Sentinel-1B IW GRD and Sentinel-2A as data sources. The experiments employed the control variable method with the linear index model and the random forest regression (RFR) model to conduct the fractional cover estimation of NPV by using microwave and optical remote sensing data. Then, the estimated endmember fractions were validated with reference to fraction measurements. In addition, the Root Mean Square Error (RMSE) and Relative Root Mean Square Error (RMSE%) were employed as indicators to evaluate the inversion accuracy. Results show that (1) using cooperative Sentinel-1 and Sentinel-2 remote sensing data to estimate the fractional cover of NPV can effectively improve the estimated accuracy compared with using Sentinel-2 data alone. (2) The RFR model is an effective method for the fractional cover estimation of sparse NPV, and its estimation accuracy is higher than that of the linear index model. The validation RMSE of the random forest model and the estimated fNPV of the linear index model are 0.0149 and 0.0153, respectively. Obviously, the accuracy of fNPV estimation increases by 1.4% when using the RFR model instead of the linear index model. (3) The VH and VV polarization bands of Sentinel-1 data can effectively detect the characteristics of NPV. Especially, VH band is more sensitive to NPV, and its estimation accuracy is improved by 5.1% compared with that of VV band. (4) The accuracy of fNPV estimation can be improved when soil index is considered in each model, which illustrates that incorporating the soil characteristic information in the models is important for NPV extraction. Overall, the combination of Sentinel-1 and Sentinel-2 remote sensing data can effectively improve the accuracy of the fractional cover estimation of NPV by employing the RFR model. VV and VH polarization modes are sensitive to NPV vegetation detection, especially VH polarization mode. The accuracy of NPV extraction can be further improved by considering the soil index, which reflects the soil characteristics. Therefore, the combination of microwave and optical remote sensing data is an effective method to improve the accuracy of fNPV estimation. Incorporating polarization information with vegetation structure information and soil parameters with soil characteristic information is important for improving the accuracy of the fractional cover estimation of NPV. © 2023 Science Press. All rights reserved.
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页码：2873 / 2881
页数：8
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