Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8

Abstract

Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20°, 40°, 60°) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time.

Seasonal Comparisons of Himawari-8 AHI and MODIS Vegetation Indices over Latitudinal Australian Grassland Sites

“…Overall, reflectances were highest during mid-day periods near solar noon, while VI values were lowest near solar noon. Similar diurnal VI patterns have been reported over partial grass canopy covers [23,32]. The results from the dry grassland sites studied here may not apply to other biomes.…”

“…The directional diurnal patterns may further vary with canopy architecture, and leaf and soil optical properties. Over structurally complex land cover types, such as woodlands and forests, diurnal patterns will further be influenced by shaded and sunlit forest canopy components and intercrown shadowing [25,32,53,56].…”

“…Solar zenith angles at solar noon varied by 10 • across the four sites, while relative azimuth angles were nearly invariant. These seasonal and diurnal SZA variations may potentially drive significant variations in surface reflectance and VIs [31,32]. Three vegetation indices (VIs) were computed and analyzed, including the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the two-band enhanced vegetation index (EVI2) [33][34][35].…”

Reviews and syntheses: Ongoing and emerging opportunities to improve environmental science using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites

“…The most widely used environmental stressors include functions to describe temperature and moisture stress on LUE. Multiple approaches for estimating GPP from space exist based on the LUE approach, with differences arising from the spatiotemporal resolution of the inputs, the meteorological data used, incorporating the impacts of CO 2 fertilization, environmental scalars used for adjusting LUE, and the treatment of LUE as a constant or specific to biome, plant functional type, or photosynthetic pathway (McCallum et al, 2009;Robinson et al, 2018;Sims et al, 2006;Xiao et al, 2019).…”

Reviews and syntheses: Ongoing and emerging opportunities to improve environmental science using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites

“…Compared to polar-orbiting satellites, the large number of diurnal observations from geostationary satellites capture greater variation in sun-angle geometries. This increased variability allows for better BRDF adjustments and improved investigations about the impact of the SZA on the vegetation indices used for extracting phenological transitions (Ma et al, 2020). Time series of LAI, NDVI, and the two-band Enhanced Vegetation Index (EVI2) from SEVIRI, AHI, and GOCI show increased observations during cloudy conditions and improved estimates of phenological cycles and transitions (Guan et al, 2014;Miura et al, 2019;Yan et al, 2016;Yeom et al, 2018;.…”