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

College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu 730000, China
School of Life Sciences, University of Technology Sydney, Ultimo NSW 2007, Australia
School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi 10000, Vietnam
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(8), 1339;
Received: 31 March 2020 / Revised: 21 April 2020 / Accepted: 21 April 2020 / Published: 23 April 2020
(This article belongs to the Special Issue Earth Monitoring from A New Generation of Geostationary Satellites)
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. View Full-Text
Keywords: geostationary land application; BRDF; vegetation index; phenology; vegetation productivity geostationary land application; BRDF; vegetation index; phenology; vegetation productivity
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MDPI and ACS Style

Ma, X.; Huete, A.; Tran, N.N.; Bi, J.; Gao, S.; Zeng, Y. Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8. Remote Sens. 2020, 12, 1339.

AMA Style

Ma X, Huete A, Tran NN, Bi J, Gao S, Zeng Y. Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8. Remote Sensing. 2020; 12(8):1339.

Chicago/Turabian Style

Ma, Xuanlong, Alfredo Huete, Ngoc Nguyen Tran, Jian Bi, Sicong Gao, and Yelu Zeng. 2020. "Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8" Remote Sensing 12, no. 8: 1339.

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