Using an improved radiative transfer model to estimate leaf area index, fractional vegetation cover and leaf inclination angle from Himawari-8 geostationary satellite data

Abstract

Quantitative vegetation structural parameters such as leaf area index (LAI), fractional vegetation cover (FVC), and leaf inclination angle (LIA) are important for controlling biophysical processes, such as carbon dynamics and transpiration. The generation of remote sensing vegetation structural products using geostationary satellite data may allow for near real-time monitoring of vegetation change and associated biophysical processes. However, operational algorithms for retrieving the vegetation structure from geostationary satellite imagery are rare. Herein we developed a bidirectional model of reflectance and difference vegetation index (DVI) which requires LAI and other vegetation parameters as inputs, allowing these parameters to be estimated via an optimization scheme. The developed radiative transfer model specifically considers the high-frequency and multi-angle features of geostationary satellite data to separate the sun-angle related variables from the sun-angle independent variables. This parameterization facilitates the retrieval of vegetation structural products by reducing the number of variables while maintaining the generality of the model. The inversion of this physical radiative transfer model produced daily LAI and FVC with a spatial resolution of 1 km from the bidirectional reflectance factor (BRF) of Himawari-8 high-frequency observations for Australia. In contrast to most other readily available LAI products, this approach to generating Himawari-8 LAI did not rely on MODIS LAI or land cover data. Compared with field-measured data, the RMSE of Himawari-8 LAI was 1.009 and the bias was −0.354, and for FVC the RMSE was 0.132 and the bias was −0.014; these were more accurate than MODIS LAI and GLASS LAI, and GEOV3 FVC, respectively. The intercomparison of these products showed that the Himawari-8 LAI and FVC products performed well having realistic spatio-temporal distributions. For the first time, a mean leaf inclination angle (MLIA) product was generated only using satellite data. Similarity was found between the spatial patterns of MLIA and the land cover map over Australia. Independent validation data showed that the uncertainty of MLIA was generally less than 10°. The high-frequency nature of geostationary satellite imagery coupled with the radiative transfer model developed herein enables the derived vegetation structural products to facilitate improved monitoring of both short-term (i.e., daily to weekly) and long-term (i.e., seasonal to annual) vegetation dynamics.