Evaluating land surface phenology from the Advanced Himawari Imager using observations from MODIS and the Phenological Eyes Network

Yan, Dong; Zhang, Xiaoyang; Nagai, Shin; Yu, Yunyue; Akitsu, Tomoko; Nasahara, Kenlo Nishida; Ide, Reiko; Maeda, Tsuneaki

doi:10.1016/j.jag.2019.02.011

Cited by 33 publications

(38 citation statements)

References 60 publications

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“…Furthermore, we showed that the sun-angle effect can introduce 7% and 10% uncertainty in estimating NDVI max and IntNDVI, and a reduced uncertainty for EVI. This result has important implications as both the VI max and IntVI have been used extensively for estimating vegetation primary productivity (or crop yield and rangeland forage production) [81][82][83][84][85], for land carbon uptake modelling [86,87], and for food security assessment for famine early-warning systems [88,89]. Our findings therefore stress the need to consider proper corrections of the sun-angle effect to achieve more reliable use of vegetation indices in a variety of applications.…”

Section: Sun-angle Effect On Retrievals Of Vegetation Phenology and Pmentioning

confidence: 72%

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

Huete

Tran

et al. 2020

Remote Sensing

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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.

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Section: Sun-angle Effect On Retrievals Of Vegetation Phenology and Pmentioning

confidence: 72%

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

Huete

Tran

et al. 2020

Remote Sensing

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“…Geostationary satellite sensors, such as the SEVIRI and Himawari-8, that provide multiple images at a sub-daily resolution, could help in further improving the identification of cloud-free data for accurate monitoring of land surface phenology [154,155]. Studies [155][156][157][158] evaluating the performance of geostationary datasets to construct time series of vegetation indices and estimate different LSP metrics have reported an increase of more than 50% cloud-free data in comparison to data from MODIS and VIIRS. Though improvements in SOS were marginal, the EOS estimated from the geostationary satellites were within days of the observed in situ dates, whereas MODIS-retrieved dates deviated by up to a month.…”

Section: Gap Filling Techniques For Phenological Research Using Sentimentioning

confidence: 99%

Status of Phenological Research Using Sentinel-2 Data: A Review

2020

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Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

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“…Very recently, Yan et al . 28 reported the first application of Himawari-8 AHI two-band Enhanced Vegetation Index (EVI2) time series data to land surface phenology in Northern and Central Japan. They found that AHI EVI2 higher temporal resolution data only helped improve the characterization of spring phenology in comparison to the MODIS counterpart.…”

Section: Introductionmentioning

confidence: 99%

Improved Characterisation of Vegetation and Land Surface Seasonal Dynamics in Central Japan with Himawari-8 Hypertemporal Data

Miura

Nagai

Takeuchi

et al. 2019

Sci Rep

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Spectral vegetation index time series data, such as the normalized difference vegetation index (NDVI), from moderate resolution, polar-orbiting satellite sensors have widely been used for analysis of vegetation seasonal dynamics from regional to global scales. The utility of these datasets is often limited as frequent/persistent cloud occurrences reduce their effective temporal resolution. In this study, we evaluated improvements in capturing vegetation seasonal changes with 10-min resolution NDVI data derived from Advanced Himawari Imager (AHI), one of new-generation geostationary satellite sensors. Our analysis was focused on continuous monitoring sites, representing three major ecosystems in Central Japan, where in situ time-lapse digital images documenting sky and surface vegetation conditions were available. The very large number of observations available with AHI resulted in improved NDVI temporal signatures that were remarkably similar to those acquired with in situ spectrometers and captured seasonal changes in vegetation and snow cover conditions in finer detail with more certainty than those obtained from Visible Infrared Imaging Radiometer Suite (VIIRS), one of the latest polar-orbiting satellite sensors. With the ability to capture in situ-quality NDVI temporal signatures, AHI “hypertemporal” data have the potential to improve spring and autumn phenology characterisation as well as the classification of vegetation formations.

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Evaluating land surface phenology from the Advanced Himawari Imager using observations from MODIS and the Phenological Eyes Network

Cited by 33 publications

References 60 publications

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

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

Status of Phenological Research Using Sentinel-2 Data: A Review

Improved Characterisation of Vegetation and Land Surface Seasonal Dynamics in Central Japan with Himawari-8 Hypertemporal Data

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