利用改进的辐射传输模型估算Himawari-8同步卫星资料的叶面积指数、植被覆盖度和叶片倾角

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Yaoyao Chen, Xihan Mu, Tim R. McVicar, Yuanyuan Wang, Yuhan Guo, Kai Yan, Yongkang Lai, Donghui Xie, Guangjian Yan
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引用次数: 0

摘要

叶面积指数(LAI)、植被覆盖度(FVC)和叶片倾角(LIA)等植被结构参数对控制碳动态和蒸腾等生物物理过程具有重要意义。利用地球静止卫星数据生成遥感植被结构产品,可实现对植被变化和相关生物物理过程的近实时监测。然而,从地球同步卫星图像中检索植被结构的操作算法很少。在此,我们建立了一个双向反射率和植被差异指数(DVI)模型,该模型需要LAI和其他植被参数作为输入,并通过优化方案对这些参数进行估计。所建立的辐射传输模型专门考虑了静止卫星数据的高频多角度特征,将太阳角相关变量与太阳角自变量分离开来。这种参数化在保持模型通用性的同时,减少了变量的数量,有利于植被结构产品的检索。利用Himawari-8高频观测资料的双向反射因子(BRF)反演该物理辐射传输模式,得到了澳大利亚的日LAI和FVC,空间分辨率为1 km。与大多数其他现成的LAI产品相比,这种生成Himawari-8 LAI的方法不依赖于MODIS LAI或土地覆盖数据。与野外实测数据相比,Himawari-8 LAI的RMSE为1.009,偏差为- 0.354;FVC的RMSE为0.132,偏差为- 0.014;其精度分别高于MODIS LAI、GLASS LAI和GEOV3 FVC。对比结果表明,hima -8 LAI和FVC产品具有较好的时空分布特征。首次利用卫星数据生成平均叶片倾角(MLIA)产品。MLIA的空间格局与澳大利亚土地覆盖图相似。独立验证数据表明,MLIA的不确定度一般小于10°。地球静止卫星图像的高频特性与本文开发的辐射传输模型相结合,使衍生的植被结构产品能够促进对短期(即每日至每周)和长期(即季节性至年度)植被动态的改进监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using an improved radiative transfer model to estimate leaf area index, fractional vegetation cover and leaf inclination angle from Himawari-8 geostationary satellite data
Quantitative vegetation structural parameters such as leaf area index (LAI), fractional vegetation cover (FVC), and leaf inclination angle (LIA) are important for controlling biophysical processes, such as carbon dynamics and transpiration. The generation of remote sensing vegetation structural products using geostationary satellite data may allow for near real-time monitoring of vegetation change and associated biophysical processes. However, operational algorithms for retrieving the vegetation structure from geostationary satellite imagery are rare. Herein we developed a bidirectional model of reflectance and difference vegetation index (DVI) which requires LAI and other vegetation parameters as inputs, allowing these parameters to be estimated via an optimization scheme. The developed radiative transfer model specifically considers the high-frequency and multi-angle features of geostationary satellite data to separate the sun-angle related variables from the sun-angle independent variables. This parameterization facilitates the retrieval of vegetation structural products by reducing the number of variables while maintaining the generality of the model. The inversion of this physical radiative transfer model produced daily LAI and FVC with a spatial resolution of 1 km from the bidirectional reflectance factor (BRF) of Himawari-8 high-frequency observations for Australia. In contrast to most other readily available LAI products, this approach to generating Himawari-8 LAI did not rely on MODIS LAI or land cover data. Compared with field-measured data, the RMSE of Himawari-8 LAI was 1.009 and the bias was −0.354, and for FVC the RMSE was 0.132 and the bias was −0.014; these were more accurate than MODIS LAI and GLASS LAI, and GEOV3 FVC, respectively. The intercomparison of these products showed that the Himawari-8 LAI and FVC products performed well having realistic spatio-temporal distributions. For the first time, a mean leaf inclination angle (MLIA) product was generated only using satellite data. Similarity was found between the spatial patterns of MLIA and the land cover map over Australia. Independent validation data showed that the uncertainty of MLIA was generally less than 10°. The high-frequency nature of geostationary satellite imagery coupled with the radiative transfer model developed herein enables the derived vegetation structural products to facilitate improved monitoring of both short-term (i.e., daily to weekly) and long-term (i.e., seasonal to annual) vegetation dynamics.
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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