Validation and inter-comparison of the FY-3B/MERSI LAI product with GLOBMAP and MYD15A2H

Abstract

The leaf area index (LAI) describes the structure of vegetation and is a key variable for Earth system process modelling and ecohydrological models at regional and global scales. The Medium-Resolution Spectral Imager (MERSI) onboard China's new generation of polar-orbiting meteorological satellite series FengYun-3 (FY-3) can provide continuous and global observations of the land surface. Therefore, it could be a potential data source for global LAI retrieval. In this study, a LAI product was generated from FY-3B/MERSI data using the GLOBCARBON LAI algorithm. Cross-calibration of the FY-3B/MERSI spectral response function with Land Remote-Sensing Satellite (Landsat) Thematic Mapper (TM), allowed to correct the influence of the spectral response function difference on the inversion results. Field measurements of LAI and scale-converted LAI reference images provided by the ImagineS project were used to validate and inter-compare the FY-3B/MERSI LAI product with two widely used medium-resolution LAI products, GLOBMAP LAI product and Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product (MYD15A2 H). The results demonstrate that FY-3B/MERSI LAI has the lowest uncertainty of the three products. The low uncertainty of FY-3B/MERSI LAI in shrub-grass mixed areas (root mean square error, RMSE = 0.07), in part, by the generally underestimated LAI value. For deciduous broadleaf forest, of the three products tested, FY-3B/MERSI LAI is closest to the LAI obtained from the reference image (coefficient of determination, R 2 = 0.70) and yields the lowest uncertainty (RMSE = 0.81). GLOBMAP (R 2 = 0.58), which uses the same algorithm, and surface cover data as FY-3B/MERSI LAI, significantly overestimates the LAI. This overestimation may partly due to the use of a relatively lower clumping index. MYD15A2H shows a relatively weak correlation with the reference data (R 2 = 0.25) and a higher uncertainty (RMSE = 1.45). For mature crops, all three LAI products display systematic underestimation of LAI. FY-3B/MERSI LAI yields the greatest underestimation (about 50%), followed by GLOBMAP (about 35%) and MYD15A2H (about 15%). Our inter-comparison of the three LAI products demonstrates that all have higher correlation for low LAI values. FY-3B/MERSI shows similar capabilities and quality to those of the MODIS sensor with respect to the top of atmosphere observations. However, different atmospheric correction processes may ARTICLE HISTORY

Fractional Vegetation Cover Estimation Algorithm for FY-3B Reflectance Data Based on Random Forest Regression Method

“…The band-13 (red) and band-16 (NIR) reflectance values were used as the input data for FVC estimating. Beforehand, a satellite monitoring analysis and remote-sensing application tool (SMART) were used to conduct the radiative calibration and geolocation operations for FY-3 reflectance by the National Satellite Meteorological Centers (NSMC) [34]. Furthermore, the top of atmosphere (TOA) reflectance of the FY-3B data were converted to land surface reflectance data based on the fast line-of-sight atmospheric analysis of spectral hypercube (FLAASH) model from the Environment for Visualizing Images (ENVI) software.…”

“…Particularly, the MERSI contains 20 spectral bands, and the spectral coverage is 0.40~12.5µm, which provides rich land surface observation data [33]. Table 2 shows the information of the FY-3B/MERSI spectral bands and specifications [34]. The band-13 (red) and band-16 (NIR) reflectance values were used as the input data for FVC estimating.…”