期刊
INTERNATIONAL JOURNAL OF REMOTE SENSING
卷 41, 期 23, 页码 9206-9228出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2020.1798549
关键词
-
资金
- National Key R&D Program of China [2019YFA0607102]
- Natural Science Foundation of China [41971232]
- strategic Priority Research Program of the Chinese Academy of Sciences
- Pan-Third Pole Environment Study for a Green Silk Road (Pan-TPE) [XDA20040301]
- State Key Laboratory of Resources and Environmental Information System
Solar-Induced chlorophyll Fluorescence (SIF) is associated with vegetation canopy photosynthesis and is potentially used to retrieve Gross Primary Productivity (GPP). However, the coarse resolutions of the currently available SIF satellite data limit their applications. To expand the applicability of the SIF dataset, a framework was developed to disaggregate the Global Ozone Monitoring Experiment-2 (GOME-2) SIF dataset, which was based on statistical relationships between SIF and remotely sensed measurements of the Normalized Difference Vegetation Index (NDVI), the fraction of absorbed photosynthetically active radiation (f(PAR)), the soil moisture index and Land Surface Temperature (LST). The statistical relationships were established within a zone ofnx npixels (n is an element of[1, 25]) with a moving window technique. The regression function established withinnx npixels with the smallest Root Mean Square Error (RMSE) and highest coefficient of determination (R-2) was selected for downscaling regression. Compared with the fixed window technique (n= 5) and theglobal regression model, the moving window technique presented low residuals and highR(2)values. Validated with flux-tower eddy covariance measurements, the GPP retrieved within the downscaled SIF data shows the potential to improve vegetation GPP prediction, and the downscaled SIF could trace the seasonal phenology of evergreen forests.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据