Publications

Zhang, YH; Fang, HL; Wang, Y; Li, SJ (2021). Variation of intra-daily instantaneous FAPAR estimated from the geostationary Himawari-8 AHI data. AGRICULTURAL AND FOREST METEOROLOGY, 307, 108535.

Abstract
The fraction of absorbed photosynthetically active radiation (FAPAR) quantifies the efficiency of light absorption by vegetation. The intra-daily variation in FAPAR is important for monitoring vegetation growth but current studies to estimate the intra-daily FAPAR from remote sensing are limited. This study proposes to estimate the intra-daily variations of the direct, diffuse, and total FAPARs on clear days from the geostationary Himawari-8 AHI data. A look-up table inversion method was developed based on simulations of a radiative transfer model that couples a soil reflectance (GSV, General Spectral Vectors), canopy (PROSAIL), and atmospheric models (6SV, Second Simulation of a Satellite Signal in the Solar Spectrum). Field measurements were conducted over paddy rice fields in Northeast China from June 22 to August 25, 2019, to obtain continuous FAPAR measurements using an AccuPAR ceptometer and digital hemispherical photography. The instantaneous FAPAR values were analyzed and evaluated with field measurements and MODIS and GEOV2 FAPAR products. The results show that the retrieved AHI FAPAR values are consistent with the field measurements (R-2 > 0.75). The AHI, MODIS, and GEOV2 FAPAR values display similar spatial and temporal patterns over the growing season (R-2 > 0.66). The intra-daily variations in the total and direct FAPAR values agree with the field measurements, and both show a "bowl-shaped" pattern with the smallest value found at noon. The diffuse FAPAR remains stable before 16:00 local solar time (LST). The instantaneous total and direct FAPAR values at 10:00 - 10:30 LST usually assumed to be daily values underestimate the daily values, while the values at 9:30 and 14:30 LST can better represent the daily FAPAR. As a future study, global intra-daily instantaneous and daily FAPAR products could be generated by combining all geostationary satellite data.

DOI:
10.1016/j.agrformet.2021.108535

ISSN:
0168-1923