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

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.

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An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products

Remote Sensing ◽

10.3390/rs13040719 ◽

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.

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

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2016000700009 ◽

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.

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

International Journal of Wildland Fire ◽

10.1071/wf16131 ◽

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.

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Development of a remote sensing-based rice yield forecasting model

Spanish Journal of Agricultural Research ◽

10.5424/sjar/2016143-8347 ◽

2016 ◽

Vol 14 (3) ◽

pp. e0907 ◽

Cited By ~ 14

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.

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

Remote Sensing ◽

10.3390/rs11151823 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1823 ◽

Cited By ~ 9

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.

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Comparison of MODIS 250 m products for early corn yield predictions: a case study in Vojvodina, Serbia

Open Geosciences ◽

10.1515/geo-2016-0070 ◽

2016 ◽

Vol 8 (1) ◽

Cited By ~ 3

Author(s):

Miro Govedarica ◽

Dušan Jovanović ◽

Filip Sabo ◽

Mirko Borisov ◽

Milan Vrtunski ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Corn Yield ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

AbstractThe aim of the paper is to compare Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (

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Development and Demonstration of a Method for GEO-to-LEO NDVI Transformation

Remote Sensing ◽

10.3390/rs13204085 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4085

Author(s):

Kenta Obata ◽

Kenta Taniguchi ◽

Masayuki Matsuoka ◽

Hiroki Yoshioka

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Middle Latitude ◽

Transformation Method ◽

Low Earth Orbit ◽

Linear Mixture Model ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Index Time Series

This study presents a new method that mitigates biases between the normalized difference vegetation index (NDVI) from geostationary (GEO) and low Earth orbit (LEO) satellites for Earth observation. The method geometrically and spectrally transforms GEO NDVI into LEO-compatible GEO NDVI, in which GEO’s off-nadir view is adjusted to a near-nadir view. First, a GEO-to-LEO NDVI transformation equation is derived using a linear mixture model of anisotropic vegetation and nonvegetation endmember spectra. The coefficients of the derived equation are a function of the endmember spectra of two sensors. The resultant equation is used to develop an NDVI transformation method in which endmember spectra are automatically computed from each sensor’s data independently and are combined to compute the coefficients. Importantly, this method does not require regression analysis using two-sensor NDVI data. The method is demonstrated using Himawari 8 Advanced Himawari Imager (AHI) data at off-nadir view and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at near-nadir view in middle latitude. The results show that the magnitudes of the averaged NDVI biases between AHI and MODIS for five test sites (0.016–0.026) were reduced after the transformation (<0.01). These findings indicate that the proposed method facilitates the combination of GEO and LEO NDVIs to provide NDVIs with smaller differences, except for cases in which the fraction of vegetation cover (FVC) depends on the view angle. Further investigations should be conducted to reduce the remaining errors in the transformation and to explore the feasibility of using the proposed method to predict near-real-time and near-nadir LEO vegetation index time series using GEO data.

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ANALISIS TUTUPAN LAHAN DENGAN MENGGUNAKAN METODE BREAK FOR ADDITIVE SEASONAL AND TREND (BFAST) BERDASARKAN INDEKS VEGETASI

10.31227/osf.io/yeba6 ◽

2017 ◽

Author(s):

Seminar Nasional Multidisiplin Ilmu 2017 ◽

Ramos Lumban Tobing

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Trend Data ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

Metode penginderaan jarak jauh (Remote Sensing) telah banyak digunakan dalam berbagai bidang termasuk diantaranya bidang tutupan lahan/vegetasi termasuk perkebunan. Produk dari penginderaan jauh tersebut banyak tersedia diantaranya NDVI (Normalized Difference Vegetation Index) dan EVI (Enhanced Vegetation Indeks) yang merupakan indikator proxy dari suatu lokasi atau kondisi tutupan lahan lokasi tersebut. Dari beberapa penilitian, NDVI telah banyak digunakan namun EVI masih belum banyak digunakan. Kami membandingkan pengaruh dari penggunaan NDVI dan EVI pada jumlah dan waktu perubahan yang terekam dengan menggunakan metode BFAST (Breaks For Additive Seasonal and Trend). Data yang digunakan adalah MODIS (Moderate Resolution Imaging Spectroradiometer)16 harian NDVI dan EVI berupa gambar komposit (06 April 2000 s.d. 16 November 2014) dari empat piksel (pixel 293,294,295 dan 296) disekitar menara fluks Aek Loba.Hasil penelitian menunjukkan bahwa EVI untuk pemantauan tutupan lahan di kawasan perkebunan tropis yang ditutupi oleh awan intens lebih baik dari NDVI itu. Meskipun demikian, penelitian lebih lanjut dengan meningkatkan resolusi spasial dari citra satelit untuk aplikasi NDVI sangat dianjurkan

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TVDI Obtido de Imagens OLI/TIRS e MODIS

Revista Brasileira de Meteorologia ◽

10.1590/0102-7786344070 ◽

2019 ◽

Vol 34 (4) ◽

pp. 573-583

Author(s):

Lucimara Wolfarth Schirmbeck ◽

Denise Cybis Fontana ◽

Juliano Schirmbeck ◽

Carolina Bremm

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Thermal Infrared ◽

Landsat 8 ◽

Infrared Sensor ◽

Moderate Resolution ◽

Dryness Index ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

Resumo O objetivo do estudo foi analisar a variabilidade no TVDI (Temperature-Vegetation Dryness Index) obtido de sensores orbitais com resoluções distintas, em região agrícola no sul do Brasil. Utilizou-se três imagens OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) do satélite Landsat 8, e 12 imagens MODIS (Moderate Resolution Imaging Spectroradiometer) do satélite Terra. Dados coletados em campo serviram como base para classificação de imagem OLI/TIRS e mapeamento de áreas de arroz, soja, campos naturais, mata ciliar e solo exposto. O TVDI foi obtido por duas parametrizações em períodos distintos, utilizando as dispersões entre Temperatura de Superfície (TS) e NDVI (Normalized Difference Vegetation Index). O TVDI obtido para ambos sensores apresentou padrão similar possibilitando diferenciar os alvos. Na média de todas as datas e classes, o TVDI obtido das imagens MODIS foi superior em 0,128 unidades ao TVDI obtido com o OLI/TIRS. Quando utilizado OLI/TIRS há um melhor detalhamento espacial das condições hídricas, mas com menor repetição ao longo da safra; já utilizando o TVDI-MODIS é possível monitorar as condições hídricas em escala regional, com menor detalhamento espacial, mas com maior repetitividade no tempo. O TVDI estimado pelos sensores OLI/TIRS e MODIS, pode ser utilizado de forma conjunta, trazendo informações complementares.

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Implicaciones del filtrado de calidad del índice de vegetación EVI para el seguimiento funcional de ecosistemas

Revista de Teledetección ◽

10.4995/raet.2015.3316 ◽

2015 ◽

pp. 11 ◽

Cited By ~ 4

Author(s):

A. Reyes Díez ◽

D. Alcaraz-Segura ◽

J. Cabello Piñar

Keyword(s):

Quality Assessment ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Enhanced Vegetation Index ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

El seguimiento de los ecosistemas con imágenes procedentes del sensor MODIS (Moderate Resolution Imaging Spectroradiometer, espectroradiómetro de imágenes de resolución media) está actualmente muy extendido tanto en tareas de investigación como de gestión. Los índices de vegetación NDVI (Normalized Difference Vegetation Index, índice de vegetación de la diferencia normalizada) y EVI (Enhanced Vegetation Index, índice de vegetación mejorado) son ampliamente usados para la caracterización del funcionamiento ecosistémico. Ambos índices se emplean como estimadores lineales de la fracción de radiación fotosintéticamente activa interceptada por la vegetación (fAPAR), el principal control de la producción primaria. A pesar de sus ventajas, las imágenes de índices de vegetación no están libres de errores. El producto índices de vegetación MOD13Q1 proporciona una capa QA (Quality assessment,evaluación de la calidad) que informa sobre la calidad asociada a cada píxel. Esta información representa una gran ventaja para el usuario, al permitir filtrar aquellos datos que puedan inducir a errores al verse alterados por la presencia de aerosoles, nubes, nieve o sombras. Sin embargo, la realización de un filtrado homogéneo a lo largo de una gran región puede ocasionar la pérdida sistemática de información en determinadas zonas o épocas del año, introduciendo así un sesgo espacial o en la serie temporal. Esta situación puede ser especialmente crítica en regiones con alta heterogeneidad ambiental, como el Sureste Ibérico. En este trabajo evaluamos el efecto que el filtrado de calidad tiene sobre la información espacial y temporal de la base de datos del EVI en el periodo 2001-2010. Los esultados, expresados en porcentaje de información perdida (filtrada) y como efecto de estas pérdidas sobre los valores del EVI, indican que mientras que las áreas de menor altitud no se ven afectadas por el filtrado, las regiones de alta montaña muestran variaciones significativas en sus valores del EVI cuando son filtrados por aerosoles, sombras o la presencia de hielo o nieve. Esto pone de manifiesto la importancia del establecimiento de un protocolo para el procesamiento de la información que considere las características espaciales y temporales de los datos a filtrar.

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