Abstract
The upcoming satellite mission EnMAP offers the opportunity to retrieve information on the seasonal development of vegetation parameters on a regional scale based on hyperspectral data. This study aims to investigate whether an analysis method for the retrieval of leaf area index (LAI), developed and validated on the 4 m resolution scale of six airborne datasets covering the 2012 growing period, is transferable to the spaceborne 30 m resolution scale of the future EnMAP mission. The widely used PROSAIL model is applied to generate look-up-table (LUT) libraries, by which the model is inverted to derive LAI information. With the goal of defining the impact of different selection criteria in the inversion process, different techniques for the LUT based inversion are tested, such as several cost functions, type and amount of artificial noise, number of considered solutions and type of averaging method. The optimal inversion procedure (Laplace, median, 4% inverse multiplicative noise, 350 out of 100,000 averages) is identified by validating the results against corresponding in-situ measurements (n = 330) of LAI. Finally, the best performing LUT inversion (R2 = 0.65, RMSE = 0.64) is adapted to simulated EnMAP data, generated from the airborne acquisitions. The comparison of the retrieval results to upscaled maps of LAI, previously validated on the 4 m scale, shows that the optimized retrieval method can successfully be transferred to spaceborne EnMAP data.
Highlights
In order to ensure the supply of the future world population with agricultural goods, a sustainable increase in agricultural productivity and an associated reduction of the yield gap [1] is mandatory
This study focuses leaf area index (LAI) as it is a significant parameter describing the size of the producing layer and it is important for the estimation of foliage cover, as well as for forecasting crop growth and yield [11,12]
The assessment of the inversion loop results led to the identification of an optimized configuration showing the highest accuracy of estimated parameters when compared to the observed in-situ measurements
Summary
In order to ensure the supply of the future world population with agricultural goods (food, fiber and energy), a sustainable increase in agricultural productivity and an associated reduction of the yield gap [1] is mandatory. Preparing for the use of spaceborne hyperspectral data, which potentially is collected globally, an overall scientific objective should be to derive this information without depending on prior information in the form of in-situ data. This is mostly due to the fact that an implementation of corresponding field measurements, as it has regularly been observed with spatially and temporally limited airborne spectroscopy campaigns (e.g., [8,9,10]), would not be viable in the context of the satellite-based EnMAP. This study focuses leaf area index (LAI) as it is a significant parameter describing the size of the producing layer and it is important for the estimation of foliage cover, as well as for forecasting crop growth and yield [11,12]
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