Abstract
Automated, image based methods for the retrieval of vegetation biophysical and biochemical variables are often hampered by the lack of a priori knowledge about land cover and phenology, which makes the retrieval a highly underdetermined problem. This study addresses this problem by presenting a novel approach, called CRASh, for the concurrent retrieval of leaf area index, leaf chlorophyll content, leaf water content and leaf dry matter content from high resolution solar reflective earth observation data. CRASh, which is based on the inversion of the combined PROSPECT+SAILh radiative transfer model (RTM), explores the benefits of combining semi-empirical and physically based approaches. The approach exploits novel ways to address the underdetermined problem in the context of an automated retrieval from mono-temporal high resolution data. To regularize the inverse problem in the variable domain, RTM inversion is coupled with an automated land cover classification. Model inversion is based on a two step lookup table (LUT) approach: First, a range of possible solutions is selected from a previously calculated LUT based on the analogy between measured and simulated reflectance. The final solution is determined from this subset by minimizing the difference between the variables used to simulate the spectra contained in the reduced LUT and a first guess of the solution. This first guess of the variables is derived from predictive semi-empirical relationships between classical vegetation indices and the single variables. Additional spectral regularization is obtained by the use of hyperspectral data. Results show that estimates obtained with CRASh are significantly more accurate than those obtained with a tested conventional RTM inversion and semi-empirical approach. Accuracies obtained in this study are comparable to the results obtained by various authors for better constrained inversions that assume more a priori information. The completely automated and image-based nature of the approach facilitates its use in operational chains for upcoming high resolution airborne and spaceborne imaging spectrometers.
Highlights
In agriculture, remote sensing in the reflective optical domain is often used as a cost-effective method to determine spatial and temporal variations in canopy state variables such as leaf area index (LAI) and leaf chlorophyll content
The relationships between vegetation index (VI) and biochemical and physical variables are often established by the use of canopy radiative transfer models (RTMs), which explicitly describe the interactions between solar radiation and the elements constituting the canopy using physical laws
Apart from the variable ranges and statistical distributions used to simulate the lookup table (LUT) for the respective land cover classes, no further a priori information on variables is available in an automated approach where each input data set is regarded independently
Summary
Remote sensing in the reflective optical domain is often used as a cost-effective method to determine spatial and temporal variations in canopy state variables such as leaf area index (LAI) and leaf chlorophyll content. To be valid over a wide range of species, canopy conditions, and view/sun configurations, these relationships need to be recalibrated for each specific situation, which is a tedious and costly task [14,15,16] For this reason, the relationships between VIs and biochemical and physical variables are often established by the use of canopy radiative transfer models (RTMs), which explicitly describe the interactions between solar radiation and the elements constituting the canopy using physical laws. Its performance is discussed with regard to a broadly used conventional RTM inversion approach
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