Revisiting the use of red and near-infrared reflectances in vegetation studies and numerical climate models

Authors

Garik Gutman, NASAFollow
Sergii Skakun, University of Maryland at College Park
Anatoly Gitelson, University of Nebraska-LincolnFollow

Document Type

Article

Date of this Version

6-22-2021

Citation

Science of Remote Sensing 4 (2021) 100025

doi:10.1016/j.srs.2021.100025

Comments

CC BY-NC-ND

Abstract

Surface reflectance data acquired in red and near-infrared spectra by remote sensing sensors are traditionally applied to construct various vegetation indices (VIs), which are related to vegetation biophysical parameters. Most VIs use pre-defined weights (usually equal to 1) for the red and NIR reflectance values, therefore constraining particular weights for red and NIR during the VI design phase, and potentially limiting capabilities of the VI to explain an independent variable. In this paper, we propose an approach to estimate biophysical variables, such as Leaf Area Index (LAI), Canopy Chlorophyll Content (CCC) and Fraction of Photosynthetically Active Radiation (FPAR) absorbed by green vegetation, represented as linear combinations of the red and NIR reflectances with weights determined empirically from observations and radiative transfer model (PROSAIL) simulations. The proof of concept is first tested on available close-range observations over maize and soybean crops in Nebraska, USA. The empirical results compare well with those from PROSAIL model simulations. The proposed LAI model is then used with data from Landsat 8, Sentinel-2 and Planet/Dove, and the results are validated with in situ LAI measurements in Ukraine. We show that the weights on red and NIR reflectances are vegetation-specific and stable in time. The approach is further tested on crops and forests in the conterminous USA and on a global scale using MODIS LAI and FPAR products as proxies for “ground observations”. These LAI and FPAR, however, are not independently measured but derived from the corresponding remotely sensed reflectances, which precludes recommending a final set of the weights/coefficients for the users, and, thus, should be considered mostly for demonstrating the concept. The results for crop types, other than maize and soybean, and for all forests are conceptual and need to be tested with real ground data. It was, however, encouraging to see that the derived maps of coefficients/weights exhibit regular patterns over the globe compatible with those of vegetation classes and crop types. Tedious and thorough work on compiling available in situ measurements on various crops and forests needs to be accomplished prior to large-scale applications, and the method needs to be further tested and proven that it works at a large scale. The proposed parameterization may be attractive for global studies of various sub-classes of vegetation, once the parameter coefficients are established, validated, tabulated and their stability verified. Ultimately, this approach may provide quantification of vegetation traits for the past decades and be a useful asset for climate models that include satellite-derived land cover classifications and vegetation variables for simulating surface fluxes. This is a conceptual paper, with a proof-of-concept supported by observations over two crops, for which we had close-range observations. It is not a technical note, which would provide users with a recommended set of coefficients for global applications. Our intent was to develop a paradigm, which could ultimately be useful in global models.