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

JI Cuicui; LUO Yixia; LI Xiaosong; XU Jinhong; YANG Xuemei; CHEN Maolin

doi:10.11834/jrs.20231207

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Retrieving sparse non-photosynthetic vegetation fractional cover by Sentinel-1 and Sentinel-2
Vol. 27, Issue 12, Pages: 2873-2881(2023)
Received：16 April 2021，

Published：07 December 2023
DOI： 10.11834/jrs.20231207
稿件说明：

移动端阅览

姬翠翠，骆义峡，李晓松，徐金鸿，杨雪梅，陈茂霖.2023.Sentinel-1和Sentinel-2协同反演稀疏非光合植被覆盖度.遥感学报，27（12）： 2873-2881 DOI： 10.11834/jrs.20231207.

Ji C C，Luo Y X，Li X S，Xu J H，Yang X M and Chen M L. 2023. Retrieving sparse non-photosynthetic vegetation fractional cover by Sentinel-1 and Sentinel-2. National Remote Sensing Bulletin， 27（12）：2873-2881 DOI： 10.11834/jrs.20231207.

摘要

精准定量反演光合植被（PV）和非光合植被（NPV）覆盖度对了解植被碳循环过程至关重要，同时，获取的非光合植被覆盖度信息也为土地沙漠化及植被转化机制研究提供重要信息。本文以甘肃省民勤县为研究区、Sentinel-1B IW GRD和Sentinel-2A为数据源，采用线性指数模型（LIM）和随机森林模型（RFM），基于控制变量法开展微波与光学遥感数据协同反演NPV覆盖度的方法研究，并参考野外获取样地真实性检验数据，将均方根误差（RMSE）和相对均方根误差（RMSE，%）作为指标评价反演结果精度。结果表明：（1）与仅采用Sentinel-2光学遥感数据相比，Sentinel-1和Sentinel-2协同反演NPV能够明显提高NPV覆盖度的估算精度；（2）由Sentinel-1和Sentinel-2获取植被指数构建的RFM在NPV覆盖度估算上较LIM精度更高，RFM和LIM估算NPV的RMSE分别为0.0149和0.0153，估算精度提高了1.4%；（3）垂直水平极化（VH）和垂直极化（VV）两种极化方式参与建立RFM可有效提高NPV覆盖度的估算精度，尤其VH极化对非光合植被信息探测更为敏感，较VV模型估算精度提高了5.1%；（4）加入表征土壤信息的比值土壤指数（RSI）有效减少了土壤对NPV覆盖度估算影响，提高了NPV覆盖度估算精度。综上，微波和光学遥感数据结合是提高NPV覆盖度估算精度的有效方法，同时，土壤作为独立重要指标参与模型计算对提高NPV覆盖度估算具有重要意义。

Abstract

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 (

NPV

) 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

NPV

of the linear index model are 0.0149 and 0.0153

respectively. Obviously

the accuracy of

NPV

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

NPV

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

NPV

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.

关键词

Keywords

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