Abstract
The latest Geostationary (GEO) Operational Environmental Satellite-16 (GOES-16) equipped with Advanced Baseline Imager (ABI) has comparable spectral and spatial resolution as low earth orbiting (LEO) sensors [i.e., the Moderate Resolution Imaging Spectroradiometer (MODIS)], but with up-to-the-minute image acquisition capability. This enables greater opportunities to generate two essential climate variables-Leaf area index (LAI) and the fraction of photosynthetically active radiation (FPAR) absorbed by vegetation with more cloud-free observations and at much higher frequency. The improved GEO LAI/FPAR products will increase the capacity for monitoring highly dynamic ecosystems in a timely manner. However, the radiative transfer (RT)-based MODIS operational algorithm cannot be directly applied to GOES-16 ABI data due to different sensor characteristics. Fortunately, it has been shown theoretically and practically, that the RT-based algorithm can be transplanted to any other optical sensors by optimizing the sensor-specific parameters-the single scattering albedo (SSA) and relative stabilized precision (RSP). We built the RT-based ABI-specific lookup tables (LUTs) using a global optimizing algorithm (SCE-UA) that can quickly find the optimal solution. SCE-UA optimizes the SSAs and RSPs in the LUTs by minimizing the difference between ABI and MODIS retrievals and maximizing the main algorithm execution rate. Our efforts indicate that these strategies of parametric optimization is able to decrease the discrepancy between the ABI and MODIS LAI/FPAR products. Comprehensive evaluations were conducted to evaluate ABI retrievals. These indirect inter-comparisons suggest a spatiotemporal consistency between ABI and the benchmark MODIS products, while direct validation with field measurements increases confidence in their accuracy. The proposed approach is applicable to any other optical sensors for LAI/FPAR estimation, especially, GEO sensors (i.e., Himawari-8, Geo-KOMPSAT-2A, FengYun-4 etc.).
Highlights
The newest version of these satellites, such as FengYun-4A [14], Himawari-8 [15], Geostationary Korea Multipurpose Satellite (Geo-KOMPSAT-2A) [16], and Geostationary Operational Environmental Satellite-16 (GOES-16) [17], are equipped with sensors offering images at sub hour acquisition interval, while keeping similar spectral and spatial resolutions compared to low earth orbiting (LEO) sensors (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS), and VIIIRS etc.) The advantage of hyper temporal resolution greatly increases the probability of cloud-free observations [18] and expand on the land applications based on LEO sensors [19, 20]
The intercomparision with MODIS shows agreement between Advanced Baseline Imager (ABI) and MODIS Leaf area index (LAI)/fraction of photosynthetically active radiation (FPAR) in terms of both spatial and temporal scales. These results suggest that the GEO sensor outperforms LEO sensor in spatiotemporal completeness of LAI/FPAR products and the proposed approach of parametric optimization can adapt MODIS LAI/FPAR algorithm to other optical sensors, like ABI
The latest GEO satellite GOES-16 is equipped with sensors able to measure reflectance at comparable spatial and spectral resolutions as LEO satellites like MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS), but with the advantage of unprecedented high frequency
Summary
Several standard LAI/FPAR products have been generated using various types of satellite data, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR [6], Visible Infrared Imaging Radiometer Suite (VIIRS) LAI/FPAR [7], LAI/FPAR 3g [8], Global Land Surface Satellites (GLASS) LAI [9], and GEOLAND2 Version 1 (GEOV1) LAI/FPAR [10], etc. These products have been widely used to monitor vegetation phenology, capture impacts of climate change and natural disaster etc.[5, 11, 12]. The newest version of these satellites, such as FengYun-4A [14], Himawari-8 [15], Geostationary Korea Multipurpose Satellite (Geo-KOMPSAT-2A) [16], and Geostationary Operational Environmental Satellite-16 (GOES-16) [17], are equipped with sensors offering images at sub hour acquisition interval, while keeping similar spectral and spatial resolutions compared to LEO sensors (i.e., MODIS, and VIIIRS etc.) The advantage of hyper temporal resolution greatly increases the probability of cloud-free observations [18] and expand on the land applications based on LEO sensors [19, 20]
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