1. The effect of seasonal and spatial variations in solar zenith angle (SZA) on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) remains largely unknown.
2. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity, and VI sensitivity to SZA also varied among sites and phenological stages.
3. Future studies are needed to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science, as well as evaluate the suitability of different BRDF models for normalizing sun-angle across a broad spectrum of vegetation structure, phenological stages, and geographic locations.
The article titled "利用Himawari-8观测和建模的太阳角对遥感物候的影响" provides an analysis of the impact of seasonal and spatial variations in solar zenith angle (SZA) on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). The study area is located in southeastern Australia, encompassing forest, woodland, and grassland sites. The article highlights that satellite remote sensing of vegetation at regional to global scales involves considerable variations in SZA across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown.
The article provides a detailed analysis of the impact of SZA on NDVI and EVI sensitivity, as well as on phenological metrics such as start and end of growing season. The results show 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 normalized to different SZAs.
The article acknowledges that commonly used satellite products are not generally normalized to a constant sun-angle across space and time, which can result in uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series. However, it does not provide any counterarguments or explore alternative approaches for dealing with this issue.
One potential bias in this article is its focus on a specific study area in southeastern Australia. While the results may be applicable to other regions with similar vegetation types and climate conditions, the article does not provide any evidence to support this assumption. Additionally, the article does not explore the potential impact of other factors such as cloud cover and atmospheric conditions on the retrieval of phenology from satellite remote sensing data.
Overall, the article provides valuable insights into the impact of SZA on retrieving vegetation phenology from satellite remote sensing data. However, it would benefit from a more comprehensive analysis of potential biases and limitations, as well as alternative approaches for dealing with sun-angle variations in satellite remote sensing applications.