Sensitivity of Seven MODIS Vegetation Indices to BRDF Effects during the Amazonian Dry Season

Petri, Caio Arlanche; Galvão, Lênio Soares

doi:10.3390/rs11141650

Cited by 23 publications

(12 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MOD13Q1 has NDVI, EVI, and four spectral bands, which are blue (459-479 nm), red (620 -670 nm), near infrared (NIR) (841-876), and shortwave NIR (SWIR-2) (2105-2155 nm). LSWI is calculated by LSWI = (NIR-SWIR-2)/ (NIR + SWIR-2) [54]. Band SWIR with a resolution of 500 m was resampled to get a resolution of 250 m, which is the same as that of other bands and the vegetation index.…”

Section: B Data and Processingmentioning

confidence: 99%

Classification of Paddy Rice Using a Stacked Generalization Approach and the Spectral Mixture Method Based on MODIS Time Series

Zhang

Liu

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Paddy rice is a major stable food, accounting for about 20% world's food supply. And the rice paddy, an important artificial wetland type, plays an important role in the regional ecological environment. This study proposes a stacked generalization and spectral mixture approach to map paddy rice using coarse spatial resolution images [Moderate Resolution Imaging Spectralradiometer, (MODIS)]. By this method, the time series MODIS enhanced vegetation index images, phenological variables, land surface water index, elevation, and slope images are all employed to produce the optimal feature combination, which is then used to map paddy rice by the stacking algorithm. The validation experiment using the data of the Dongting Lake area showed that the proposed method can improve the overall accuracy of single classifiers, including the support vector machine, random forest, k-nearestneigbor (kNN), extreme gradient boosting (XGB), and decision tree. Stacking (XGB) achieves the highest overall accuracy (90.3%) and Kappa coefficient (0.86), which are 2.8% and 0.03 higher than that of using the single kNN classifier. Furthermore, its user accuracies for distinguishing double-cropping rice and single-season rice are 92.5% and 90.0%, respectively. In terms of the paddy rice classification accuracy, the stacking model is also superior to single classifiers. Moreover, the MODIS-derived rice map obtained by the stacked generalization approach and the spectral mixture method area has a large determination coefficient (R 2 = 0.9975) with the government statistic data. The results demonstrate the potential of the proposed method in using coarse spatial resolution images for large-scale paddy rice mapping. Index Terms-Moderate Resolution Imaging Spectralradiometer (MODIS) time series, paddy rice, phonological variables, spectral mixture, stack generalization. I. INTRODUCTION P ADDY rice feeds 1/5 of the world's people. The demand for paddy rice is increasing due to the fast-growing global population and cause cultivate land and water shortage and biodiversity damage [1]-[5]. As an important cultivated wetland, rice paddy consumes a large amount of fresh water and Manuscript

show abstract

Section: B Data and Processingmentioning

confidence: 99%

Classification of Paddy Rice Using a Stacked Generalization Approach and the Spectral Mixture Method Based on MODIS Time Series

Zhang

Liu

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Sun-angle variation can alter surface reflectance and vegetation indices (VIs) [15][16][17][18][19][20][21], especially for three-dimensionally complex vegetated surfaces such as forest and woodland [22]. In the meantime, for any given location observational sun-angle of commonly used spaceborne sensors such as Landsat, MODIS, and Advanced Very High Resolution Radiometer (AVHRR) can vary considerably across time [9,10].…”

Section: Introductionmentioning

confidence: 99%

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

Huete

Tran

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

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.

show abstract

“…Although we detect generally consistent significant advancing trends in spring phenology based on both NDVI3g and NDVIm datasets, there are still large uncertainties in the magnitude and/or sign of trends between different products ( Peng et al., 2017 ; Moon et al., 2021 ; Ma et al., 2022 ). These inconsistencies could stem from the following factors: biological meaning (leaf emergence or plant photosynthesis), extraction methods ( Cong et al., 2012 ), spatial and temporal resolution (different levels of mixed pixel effect, and different observation frequency) ( Zhang et al., 2003 ; Zhang et al., 2009 ; Melaas et al., 2013 ; Shen et al., 2014 ; Tian et al., 2020 ; Tian et al., 2021 ), the BRDF effect (solar illumination angle and satellite view angle) ( Morton et al., 2014 ; Ma et al., 2019 ; Petri and Galvao, 2019 ; Ma et al., 2020 ; Norris and Walker, 2020 ; Lu et al., 2022 ), and effects due to atmospheric (aerosols, clouds, and hazes) ( Chen et al., 2004 ; Cai et al., 2017 ) or snow ( Wang et al., 2013 ). For example, Yu et al.…”

Section: Discussionmentioning

confidence: 99%

Continued spring phenological advance under global warming hiatus over the Pan-Third Pole

Yan

Xu²,

Wang³

et al. 2022

Front. Plant Sci.

View full text Add to dashboard Cite

The global surface temperature has witnessed a warming hiatus in the first decade of this century, but how this slowing down of warming will impact spring phenology over Pan-Third Pole remains unclear. Here, we combined multiple satellite-derived vegetation indices with eddy covariance datasets to evaluate the spatiotemporal changes in spring phenological changes over the Pan-Third Pole. We found that the spring phenology over Pan-Third Pole continues to advance at the rate of 4.8 days decade-1 during the warming hiatus period, which is contrasted to a non-significant change over the northern hemisphere. Such a significant and continued advance in spring phenology was mainly attributed to an increase in preseason minimum temperature and water availability. Moreover, there is an overall increasing importance of precipitation on changes in spring phenology during the last four decades. We further demonstrated that this increasingly negative correlation was also found across more than two-thirds of the dryland region, tentatively suggesting that spring phenological changes might shift from temperature to precipitation-controlled over the Pan-Third Pole in a warmer world.

show abstract

Sensitivity of Seven MODIS Vegetation Indices to BRDF Effects during the Amazonian Dry Season

Cited by 23 publications

References 42 publications

Classification of Paddy Rice Using a Stacked Generalization Approach and the Spectral Mixture Method Based on MODIS Time Series

Classification of Paddy Rice Using a Stacked Generalization Approach and the Spectral Mixture Method Based on MODIS Time Series

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

Continued spring phenological advance under global warming hiatus over the Pan-Third Pole

Contact Info

Product

Resources

About