Abstract
The detection of global land change via satellite observation is a major challenge in improving the understanding of global environmental change. In this study, we develop a new vegetation index which can be used as a proxy for the fractions of tree canopy and short vegetation, based on the simple linear regression between microwave vegetation optical depth (VOD) and optical leaf area index (LAI). Although we use no high-resolution reference data, the newly developed vegetation index successfully detects global land change which has been reported by previous estimations based on high-resolution reference data. We find that the relationship between VOD and LAI is non-stationary and the temporal change in the VOD-LAI relationship is an important signal for detecting global change in the terrestrial ecosystem.
Highlights
Detected by Fusion of Microwave andThe detection of global change in terrestrial ecosystems is crucially important for understanding global environmental change
We reveal that the simple linear regression between microwave Vegetation Optical Depth (VOD) and optical Leaf Area Index (LAI) provides a useful proxy of the fractions of tree canopy and short vegetation
We propose a new vegetation index by combining microwave VOD and optical LAI
Summary
Detected by Fusion of Microwave andThe detection of global change in terrestrial ecosystems is crucially important for understanding global environmental change. They successfully detected long-term global land change. To distinguish tree canopy (in which woody biomass is dominant in the total aboveground biomass) and short vegetation canopy (in which photosynthetically active biomass is dominant) in optical satellite observations, complex supervised machine learning with high spatial resolution reference data such as LANDSAT observation is usually required (e.g., [3]). To overcome this limitation, microwave remote sensing has been recognized as an alternative approach to monitor global change in the terrestrial ecosystem. Microwave Vegetation Optical Depth (VOD) is sensitive to vegetation water content, which includes information on both leaf and woody biomass (see [5,6] for the detailed description of VOD and the retrieval algorithms)
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