scholarly journals Evaluating the Performance of Satellite-Derived Vegetation Indices for Estimating Gross Primary Productivity Using FLUXNET Observations across the Globe

2019 ◽  
Vol 11 (15) ◽  
pp. 1823 ◽  
Author(s):  
Xiaojuan Huang ◽  
Jingfeng Xiao ◽  
Mingguo Ma
Keyword(s):  
Primary Productivity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Gross Primary Productivity ◽  
Surface Reflectance ◽  
Monthly Scale ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Annual Scale

Satellite-derived vegetation indices (VIs) have been widely used to approximate or estimate gross primary productivity (GPP). However, it remains unclear how the VI-GPP relationship varies with indices, biomes, timescales, and the bidirectional reflectance distribution function (BRDF) effect. We examined the relationship between VIs and GPP for 121 FLUXNET sites across the globe and assessed how the VI-GPP relationship varied among a variety of biomes at both monthly and annual timescales. We used three widely-used VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and 2-band EVI (EVI2) as well as a new VI - NIRV and used surface reflectance both with and without BRDF correction from the moderate resolution imaging spectroradiometer (MODIS) to calculate these indices. The resulting traditional (NDVI, EVI, EVI2, and NIRV) and BRDF-corrected (NDVIBRDF, EVIBRDF, EVI2BRDF, and NIRV, BRDF) VIs were used to examine the VI-GPP relationship. At the monthly scale, all VIs were moderate or strong predictors of GPP, and the BRDF correction improved their performance. EVI2BRDF and NIRV, BRDF had similar performance in capturing the variations in tower GPP as did the MODIS GPP product. The VIs explained lower variance in tower GPP at the annual scale than at the monthly scale. The BRDF-correction of surface reflectance did not improve the VI-GPP relationship at the annual scale. The VIs had similar capability in capturing the interannual variability in tower GPP as MODIS GPP. VIs were influenced by temperature and water stresses and were more sensitive to temperature stress than to water stress. VIs in combination with environmental factors could improve the prediction of GPP than VIs alone. Our findings can help us better understand how the VI-GPP relationship varies among indices, biomes, and timescales and how the BRDF effect influences the VI-GPP relationship.

scholarly journals Using Solar-Induced Chlorophyll Fluorescence Observed by OCO-2 to Predict Autumn Crop Production in China

2019 ◽  
Vol 11 (14) ◽  
pp. 1715 ◽  
Author(s):  
Jin Wei ◽  
Xuguang Tang ◽  
Qing Gu ◽  
Min Wang ◽  
Mingguo Ma ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Crop Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Close Correlation ◽  
Estimation Accuracy ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

The remote sensing of solar-induced chlorophyll fluorescence (SIF) has attracted considerable attention as a new monitor of vegetation photosynthesis. Previous studies have revealed the close correlation between SIF and terrestrial gross primary productivity (GPP), and have used SIF to estimate vegetation GPP. This study investigated the relationship between the Orbiting Carbon Observatory-2 (OCO-2) SIF products at two retrieval bands (SIF757, SIF771) and the autumn crop production in China during the summer of 2015 on different timescales. Subsequently, we evaluated the performance to estimate the autumn crop production of 2016 by using the optimal model developed in 2015. In addition, the OCO-2 SIF was compared with the moderate resolution imaging spectroradiometer (MODIS) vegetation indices (VIs) (normalized difference vegetation index, NDVI; enhanced vegetation index, EVI) for predicting the crop production. All the remotely sensed products exhibited the strongest correlation with autumn crop production in July. The OCO-2 SIF757 estimated autumn crop production best (R2 = 0.678, p < 0.01; RMSE = 748.901 ten kilotons; MAE = 567.629 ten kilotons). SIF monitored the crop dynamics better than VIs, although the performances of VIs were similar to SIF. The estimation accuracy was limited by the spatial resolution and discreteness of the OCO-2 SIF products. Our findings demonstrate that SIF is a feasible approach for the crop production estimation and is not inferior to VIs, and suggest that accurate autumn crop production forecasts while using the SIF-based model can be obtained one to two months before the harvest. Furthermore, the proposed method can be widely applied with the development of satellite-based SIF observation technology.

Why Is Remote Sensing of Amazon Forest Greenness So Challenging?

2012 ◽  
Vol 16 (7) ◽  
pp. 1-14 ◽  
Author(s):  
Arindam Samanta ◽  
Sangram Ganguly ◽  
Eric Vermote ◽  
Ramakrishna R. Nemani ◽  
Ranga B. Myneni
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Atmospheric Effects ◽  
Amazon Forest ◽  
Surface Reflectance ◽  
Wet Season ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Reflectance Data

Abstract The prevalence of clouds and aerosols and their impact on satellite-measured greenness levels of forests in southern and central Amazonia are explored in this article using 10 years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) greenness data: normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). During the wet season (October–March), cloud contamination of greenness data is pervasive; nearly the entire region lacks uncorrupted observations. Even in the dry season (July–September), nearly 60%–66% of greenness data are corrupted, mainly because of biomass burning aerosol contamination. Under these conditions, spectrally varying residual atmospheric effects in surface reflectance data introduce artifacts into greenness indices; NDVI is known to artificially decrease, whereas EVI, given its formulation and use of blue channel surface reflectance data, shows artificial enhancement, which manifests as large patches of enhanced greenness. These issues render remote sensing of Amazon forest greenness a challenging task.

scholarly journals Evaluating the Performance of Sentinel-3A OLCI Land Products for Gross Primary Productivity Estimation Using AmeriFlux Data

2020 ◽  
Vol 12 (12) ◽  
pp. 1927 ◽  
Author(s):  
Zhijiang Zhang ◽  
Lin Zhao ◽  
Aiwen Lin
Keyword(s):  
Primary Productivity ◽  
Land Surface ◽  
Vegetation Index ◽  
Meteorological Data ◽  
Gross Primary Productivity ◽  
Carbon Cycles ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Relationship

Accurate and reliable estimation of gross primary productivity (GPP) is of great significance in monitoring global carbon cycles. The fraction of absorbed photosynthetically active radiation (FAPAR) and vegetation index products of the Moderate Resolution Imaging Spectroradiometer (MODIS) are currently the most widely used data in evaluating GPP. The launch of the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite provides the FAPAR and the OLCI Terrestrial Chlorophyll Index (OTCI) products with higher temporal resolution and smoother spatial distribution than MODIS, having the potential to monitor terrain GPP. OTCI is one of the red-edge indices and is particularly sensitive to canopy chlorophyll content related to GPP. The purpose of the study is to evaluate the performance of OLCI FAPAR and OTCI for the estimation of GPP across seven biomes in 2017–2018. To this end, OLCI FAPAR and OTCI products in combination with insitu meteorological data were first integrated into the MODIS GPP algorithm and in three OTCI-driven models to simulate GPP. The modeled GPP (GPPOLCI-FAPAR and GPPOTCI) were then compared with flux tower GPP (GPPEC) for each site. Furthermore, the GPPOLCI-FAPAR and GPP derived from the MODIS FAPAR (GPPMODIS-FAPAR) were compared. Results showed that the performance of GPPOLCI-FAPAR was varied in different sites, with the highest R2 of 0.76 and lowest R2 of 0.45. The OTCI-driven models that include APAR data exhibited a significant relationship with GPPEC for all sites, and models using only OTCI provided the most varied performance, with the relationship between GPPOTCI and GPPEC from strong to nonsignificant. Moreover, GPPOLCI-FAPAR (R2 = 0.55) performed better than GPPMODIS-FAPAR (R2 = 0.44) across all biomes. These results demonstrate the potential of OLCI FAPAR and OTCI products in GPP estimation, and they also provide the basis for their combination with the soon-to-launch Fluorescence Explorer satellite and their integration with the Sentinel-3 land surface temperature product into light use models for GPP monitoring at regional and global scales.

scholarly journals An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products

2021 ◽  
Vol 13 (4) ◽  
pp. 719
Author(s):  
Xiuxia Li ◽  
Shunlin Liang ◽  
Huaan Jin
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Continuous Data ◽  
Landsat Data ◽  
Area Index ◽  
Free Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Resolution Data

Leaf area index (LAI) and normalized difference vegetation index (NDVI) are key parameters for various applications. However, due to sensor tradeoff and cloud contaminations, these data are often temporally intermittent and spatially discontinuous. To address the discontinuities, this study proposed a method based on spectral matching of 30 m discontinuous values from Landsat data and 500 m temporally continuous values from Moderate-resolution Imaging Spectroradiometer (MODIS) data. Experiments have proven that the proposed method can effectively yield spatiotemporally continuous vegetation products at 30 m spatial resolution. The results for three different study areas with NDVI and LAI showed that the method performs well in restoring the time series, fills in the missing data, and reasonably predicts the images. Remarkably, the proposed method could address the issue when no cloud-free data pairs are available close to the prediction date, because of the temporal information “borrowed” from coarser resolution data. Hence, the proposed method can make better use of partially obscured images. The reconstructed spatiotemporally continuous data have great potential for monitoring vegetation, agriculture, and environmental dynamics.

scholarly journals Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor

2016 ◽  
Vol 51 (7) ◽  
pp. 858-868
Author(s):  
Marcos Cicarini Hott ◽  
Luis Marcelo Tavares de Carvalho ◽  
Mauro Antonio Homem Antunes ◽  
Polyanne Aguiar dos Santos ◽  
Tássia Borges Arantes ◽  
...  
Keyword(s):  
Vegetative Growth ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Growth ◽  
Growth Index ◽  
Data Series ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Very High

Abstract: The objective of this work was to analyze the development of grasslands in Zona da Mata, in the state of Minas Gerais, Brazil, between 2000 and 2013, using a parameter based on the growth index of the normalized difference vegetation index (NDVI) from the moderate resolution imaging spectroradiometer (Modis) data series. Based on temporal NDVI profiles, which were used as indicators of edaphoclimatic conditions, the growth index (GI) was estimated for 16-day periods throughout the spring season of 2012 to early 2013, being compared with the average GI from 2000 to 2011, used as the reference period. Currently, the grassland areas in Zona da Mata occupy approximately 1.2 million hectares. According to the used methods, 177,322 ha (14.61%) of these grassland areas have very low vegetative growth; 577,698 ha (45.96%) have low growth; 433,475 ha (35.72%) have balanced growth; 39,980 ha (3.29%) have high growth; and 5,032 ha (0.41%) have very high vegetative growth. The grasslands had predominantly low vegetative growth during the studied period, and the NDVI/Modis series is a useful source of data for regional assessments.

scholarly journals Variation of MODIS reflectance and vegetation indices with viewing geometry and soybean development

2012 ◽  
Vol 84 (2) ◽  
pp. 263-274 ◽  
Author(s):  
Fábio M. Breunig ◽  
Lênio S. Galvão ◽  
Antônio R. Formaggio ◽  
José C.N. Epiphanio
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Constant Factor ◽  
The Other ◽  
Large Field ◽  
Phenological Stages ◽  
Water Index ◽  
Growing Seasons ◽  
Moderate Resolution Imaging Spectroradiometer

Directional effects introduce a variability in reflectance and vegetation index determination, especially when large field-of-view sensors are used (e.g., Moderate Resolution Imaging Spectroradiometer - MODIS). In this study, we evaluated directional effects on MODIS reflectance and four vegetation indices (Normalized Difference Vegetation Index - NDVI; Enhanced Vegetation Index - EVI; Normalized Difference Water Index - NDWI1640 and NDWI2120) with the soybean development in two growing seasons (2004-2005 and 2005-2006). To keep the reproductive stage for a given cultivar as a constant factor while varying viewing geometry, pairs of images obtained in close dates and opposite view angles were analyzed. By using a non-parametric statistics with bootstrapping and by normalizing these indices for angular differences among viewing directions, their sensitivities to directional effects were studied. Results showed that the variation in MODIS reflectance between consecutive phenological stages was generally smaller than that resultant from viewing geometry for closed canopies. The contrary was observed for incomplete canopies. The reflectance of the first seven MODIS bands was higher in the backscattering. Except for the EVI, the other vegetation indices had larger values in the forward scattering direction. Directional effects decreased with canopy closure. The NDVI was lesser affected by directional effects than the other indices, presenting the smallest differences between viewing directions for fixed phenological stages.

Improved fuel moisture prediction in non-native tropical Megathyrsus maximus grasslands using Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived vegetation indices

2017 ◽  
Vol 26 (5) ◽  
pp. 384
Author(s):  
L. M. Ellsworth ◽  
A. P. Dale ◽  
C. M. Litton ◽  
T. Miura
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Native Grass ◽  
Tropical Ecosystems ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Live Fuel Moisture ◽  
Moderate Resolution Imaging Spectroradiometer

The synergistic impacts of non-native grass invasion and frequent human-derived wildfires threaten endangered species, native ecosystems and developed land throughout the tropics. Fire behaviour models assist in fire prevention and management, but current models do not accurately predict fire in tropical ecosystems. Specifically, current models poorly predict fuel moisture, a key driver of fire behaviour. To address this limitation, we developed empirical models to predict fuel moisture in non-native tropical grasslands dominated by Megathyrsus maximus in Hawaii from Terra Moderate-Resolution Imaging Spectroradiometer (MODIS)-based vegetation indices. Best-performing MODIS-based predictive models for live fuel moisture included the two-band Enhanced Vegetation Index (EVI2) and Normalized Difference Vegetation Index (NDVI). Live fuel moisture models had modest (R2=0.46) predictive relationships, and outperformed the commonly used National Fire Danger Rating System (R2=0.37) and the Keetch–Byram Drought Index (R2=0.06). Dead fuel moisture was also best predicted by a model including EVI2 and NDVI, but predictive capacity was low (R2=0.19). Site-specific models improved model fit for live fuel moisture (R2=0.61), but limited extrapolation. Better predictions of fuel moisture will improve fire management in tropical ecosystems dominated by this widespread and problematic non-native grass.

scholarly journals Development of a remote sensing-based rice yield forecasting model

2016 ◽  
Vol 14 (3) ◽  
pp. e0907 ◽  
Author(s):  
Mostafa K. Mosleh ◽  
Quazi K. Hassan ◽  
Ehsan H. Chowdhury
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rice Yield ◽  
Decision Makers ◽  
Coefficient Of Determination ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Boro Rice

This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh.

scholarly journals Spearman Correlation between the NDVI and Quercus Airborne Pollen in the SW of the Iberian Peninsula

Proceedings ◽  
2018 ◽  
Vol 2 (20) ◽  
pp. 1519
Author(s):  
Rocío González-Naharro ◽  
Elia Quirós ◽  
Santiago Fernández-Rodríguez ◽  
Inmaculada Silva-Palacios ◽  
Rafael Tormo-Molina ◽  
...  
Keyword(s):  
Iberian Peninsula ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Airborne Pollen ◽  
Training Data ◽  
Oak Trees ◽  
Pollen Concentrations ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Spearman Test

New space technologies as Advanced Very High Resolution Radiometer (AVHRR) and the MODerate resolution Imaging Spectroradiometer (MODIS) have been used to display several phenological cycles of ecosystems around the world. The aim of this study was to establish the relationship between Normalized Difference Vegetation Index, NDVI, associated to oak trees within three training data polygons (15, 25 and 50 km-distance to the volumetric sampler Hirst), and the daily average Quercus airborne pollen concentrations in 20 years. The study was developed in Badajoz (SW Iberian Peninsula) with a continuous pollen recording in the period from 1994 to 2013. The main novelty of this study has been the analysis of the correlation between the two-time series, using Spearman test. Within the 20 studied years, 12 years obtained significant values in the Spearman test in the whole studied area.

scholarly journals Savannah Phenological Dynamics Reveal Spatio-Temporal Landscape Heterogeneity in Karamoja Sub-region, Uganda

2020 ◽  
Vol 4 ◽  
Author(s):  
Anthony Egeru ◽  
John Paul Magaya ◽  
Derick Ansyijar Kuule ◽  
Aggrey Siya ◽  
Anthony Gidudu ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landscape Heterogeneity ◽  
Global Environmental Change ◽  
Arid Environment ◽  
Modis Ndvi ◽  
Moderate Resolution ◽  
Spatio Temporal ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Phenological Metrics

Phenological properties are critical in understanding global environmental change patterns. This study analyzed phenological dynamics in a savannah dominated semi-arid environment of Uganda. We used moderate-resolution imaging spectroradiometer normalized difference vegetation index (MODIS NDVI) imagery. TIMESAT program was used to analyse the imagery to determine key phenological metrics; onset of greenness (OGT), onset of greenness value, end of greenness time (EGT), end of greenness value, maximum NDVI, time of maximum NDVI, duration of greenup (DOG) and range of normalized difference vegetation index (RNDVI). Results showed that thicket and shrubs had the earliest OGT on day 85 ± 14, EGT on day 244 ± 32 and a DOG of 158 ± 25 days. Woodland had the highest NDVI value for maximum NDVI, OGT, EGT, and RNDVI. In the bushland, OGT occurs on average around day 90 ± 11, EGT on day 255 ± 33 with a DOG of 163 ± 36 days. The grassland showed that OGT occurs on day 96 ± 13, EGT on day 252 ± 36 with a total DOG of 156 ± 33 days. Early photosynthesis activity was observed in central to eastern Karamoja in the districts of Moroto and Kotido. There was a positive relationship between rainfall and NDVI across all vegetation cover types as well as between phenological parameters and season dynamics. Vegetation senescence in the sub-region occurs around August to mid-September (day 244–253). The varied phenophases observed in the sub-region reveal an inherent landscape heterogeneity that is beneficial to extensive pastoral livestock production. Continuous monitoring of savannah phenological patterns in the sub-region is required to decipher landscape ecosystem processes and functioning.

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