Abstract
Many applications, including crop growth and yield monitoring, require accurate long-term time series of leaf area index (LAI) at high spatiotemporal resolution with a quantification of the associated uncertainties. We propose an LAI retrieval approach based on a combination of the LAINet observation system, the Consistent Adjustment of the Climatology to Actual Observations (CACAO) method, and Gaussian process regression (GPR). First, the LAINet wireless sensor network provides temporally continuous field measurements of LAI. Then, the CACAO approach generates synchronous reflectance data at high spatiotemporal resolution (30-m and 8-day) from the fusion of multitemporal MODIS and high spatial resolution Landsat satellite imagery. Finally, the GPR machine learning regression algorithm retrieves the LAI maps and their associated uncertainties. A case study in a cropland site in China showed that the accuracy of LAI retrievals is 0.36 (12.7%) in terms of root mean square error and R2 = 0.88 correlation with ground measurements as evaluated over the entire growing season. This paper demonstrates the potential of the joint use of newly developed software and hardware technologies in deriving concomitant LAI and uncertainty maps with high spatiotemporal resolution. It will contribute to precision agriculture, as well as to the retrieval and validation of LAI products.
Highlights
The leaf area index (LAI) describes the surface area available per unit ground surface for energy and mass exchanges between vegetation and the atmosphere
In a previous work [8], we proposed a framework for the retrieval of high spatiotemporal LAI products based on the combined use of wireless sensor network (WSN) and data blending (DB) technologies
We proposed a framework to generate long-term time series of LAI and their associated uncertainty maps with high spatiotemporal resolution
Summary
The leaf area index (LAI) describes the surface area available per unit ground surface for energy and mass exchanges between vegetation and the atmosphere. It plays a key role in several surface processes, including photosynthesis, respiration, and evapotranspiration. Spatiotemporal continuous LAI data is critical for climate model validation [1], regional to global scale carbon budget estimation [2], disturbance detection [3], and many other applications. The ill-posed nature of the inversion problem—several sets of input variables can yield very similar spectra [18,19]—hampers the application of the physical and hybrid methods [18], so empirical methods are still popular, especially in regional-scale studies
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