Abstract
Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation are two of the essential biophysical variables used in most global models of climate, hydrology, biogeochemistry, and ecology. Most LAI/FPAR products are retrieved from non-geostationary satellite observations. Long revisit times and cloud/cloud shadow contamination lead to temporal and spatial gaps in such LAI/FPAR products. For more effective use in monitoring of vegetation phenology, climate change impacts, disaster trend etc., in a timely manner, it is critical to generate LAI/FPAR with less cloud/cloud shadow contamination and at higher temporal resolution—something that is feasible with geostationary satellite data. In this paper, we estimate the geostationary Himawari-8 Advanced Himawari Imager (AHI) LAI/FPAR fields by training artificial neural networks (ANNs) with Himawari-8 normalized difference vegetation index (NDVI) and moderate resolution imaging spectroradiometer (MODIS) LAI/FPAR products for each biome type. Daily cycles of the estimated AHI LAI/FPAR products indicate that these are stable at 10-min frequency during the day. Comprehensive evaluations were carried out for the different biome types at different spatial and temporal scales by utilizing the MODIS LAI/FPAR products and the available field measurements. These suggest that the generated Himawari-8 AHI LAI/FPAR fields were spatially and temporally consistent with the benchmark MODIS LAI/FPAR products. We also evaluated the AHI LAI/FPAR products for their potential to accurately monitor the vegetation phenology—the results show that AHI LAI/FPAR products closely match the phenological development captured by the MODIS products.
Highlights
Leaf area index (LAI), defined as one-sided green leaf area per unit ground area in broadleaf canopies and as the projected needle leaf area in coniferous canopies [1,2], characterizes the vegetation structure and functioning [3]
A number of LAI/fraction of photosynthetically active radiation (FPAR) products have been successfully developed from satellite data, such as coarse-resolution products for the moderate resolution imaging spectroradiometer (MODIS) [10,11,12], Visible Infrared Imaging Radiometer Suite (VIIRS) [13,14,15], and SPOT-Vegetation [16,17,18], fine-resolution retrievals from Sentinel-2 [19,20], Landsat [21,22], and downscaling products combining coarse-resolution and fine-resolution data [23,24,25]
We evaluated the Advanced Himawari Imager (AHI) LAI/FPAR products for monitoring vegetation phenology by utilizing MODIS LAI/FPAR and vegetation index products
Summary
Leaf area index (LAI), defined as one-sided green leaf area per unit ground area in broadleaf canopies and as the projected needle leaf area in coniferous canopies [1,2], characterizes the vegetation structure and functioning [3]. A number of LAI/FPAR products have been successfully developed from satellite data, such as coarse-resolution products for the moderate resolution imaging spectroradiometer (MODIS) [10,11,12], Visible Infrared Imaging Radiometer Suite (VIIRS) [13,14,15], and SPOT-Vegetation [16,17,18], fine-resolution retrievals from Sentinel-2 [19,20], Landsat [21,22], and downscaling products combining coarse-resolution and fine-resolution data [23,24,25] All of these products were generated from non-geostationary satellite observations. Current geostationary satellites provide an opportunity to generate consistent LAI/FPAR at much higher temporal frequency and with many more cloud-free observations
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