Wheat yield estimates using multi-temporal NDVI satellite imagery

Labus, Mari; Nielsen, G. A.; Lawrence, Rick L.; Engel, R. E.; Long, Dan S.

doi:10.1080/01431160110107653

Cited by 209 publications

(128 citation statements)

References 0 publications

Supporting

Mentioning

113

Contrasting

Unclassified

Order By: Relevance

“…É, portanto, importante a proposição de métodos objetivos nas estimativas de safras agrícolas. O uso de técnicas de sensoriamento remoto orbital tem-se mostrado uma ferramenta valiosa, pois possibilita o monitoramento agrícola do vigor e do tipo de cobertura vegetal em escala regional (REES, 1990;LABUS et al, 2002), resultados com maior antecedência e precisão, e menor custo operacional, em relação às técnicas tradicionais (FAO, 1998). A possibilidade de identificação e de quantificação das áreas agrícolas, de forma objetiva e rápida, é apresentada por SOUZA et al (2015) como um aspecto relevante no contexto agrícola brasileiro, dada a extensão territorial das áreas cultivadas.…”

Section: Introductionunclassified

Uso de imagens do sensor orbital modis na estimação de datas do ciclo de desenvolvimento da cultura da soja para o estado do Paraná – Brasil

et al. 2016

View full text Add to dashboard Cite

RESUMO O Estado do Paraná caracteriza-se por uma grande variabilidade de épocas de semeadura (DS) e, consequentemente, pelo desenvolvimento máximo vegetativo (DMDV), colheita (DC) e ciclo (CI) para a cultura da soja. O objetivo deste trabalho foi estimar essas datas para o período de primavera-verão do ano-safra de 2011/2012, por meio de séries temporais de imagens do Índice de Vegetação Realçado (do inglês Enhanced Vegetation Index - EVI) do sensor Modis (Moderate Resolution Imaging Spectroradiometer). Gerou-se um perfil espectrotemporal médio de EVI, considerando todos os pixels mapeados como soja dentro de cada município. Estes dados serviram de entrada no software Timesat para estimar os decêndios do ciclo da cultura (DS, DMDV, DC e CI) por municípios. Os resultados mostraram que existe grande variabilidade de datas de plantio em diferentes mesorregiões do Estado. Verificaram-se também divergências entre os resultados encontrados e os dados oficiais de DS e DC. A maior parte da semeadura (65,16%) esteve entre o terceiro decêndio de outubro e o primeiro decêndio de novembro. A maior parte da área de soja do Estado do Paraná (65,46%) teve seu DMDV em janeiro e colheita em março (53,92%).

show abstract

Section: Introductionunclassified

Uso de imagens do sensor orbital modis na estimação de datas do ciclo de desenvolvimento da cultura da soja para o estado do Paraná – Brasil

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Tucker et al [29] explained 79% of the variation in total wheat dry-matter accumulation by integrating NDVI over the growing season. Labus et al [30] calculated NDVI from an AVHRR time series for the U.S. state of Montana and found strong correlations between wheat yield and integrated NDVI, as well as late-season NDVI parameters. Mkhabela et al [31] forecasted the yield of major crops in the Canadian Prairies, including spring wheat, using 10-day MODIS NDVI composites.…”

Section: Vegetation Indicesmentioning

confidence: 99%

Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics

et al. 2014

View full text Add to dashboard Cite

Policy makers, government planners and agricultural market participants in Pakistan require accurate and timely information about wheat yield and production. Punjab Province is by far the most important wheat producing region in the country. The manual collection of field data and data processing for crop forecasting by the provincial government requires significant amounts of time before official reports can be released. Several studies have shown that wheat yield can be effectively forecast using satellite remote sensing data. In this study, we developed a methodology for estimating wheat yield and area for Punjab Province from freely available Landsat and MODIS satellite imagery approximately six weeks before harvest. Wheat yield was derived by regressing reported yield values against time series of four different peak-season MODIS-derived vegetation indices. We also tested deriving wheat area from the same MODIS time series using a regression-tree approach. Among the four evaluated indices, WDRVI provided more consistent and accurate yield forecasts compared to NDVI, EVI2 and saturation-adjusted normalized difference vegetation index (SANDVI). The lowest RMSE values at the district level for forecast versus reported yield were found when using six or more years of training data. 9654 reported values. Absolute deviations of wheat area and production forecasts from reported values were slightly greater compared to using the previous year's or the three-or six-year moving average values, implying that 250-m MODIS data does not provide sufficient spatial resolution for providing improved wheat area and production forecasts.

show abstract

“…There is a strong relationship between NDVI and agricultural yield [27,28]. Vegetation properties, such as length of growing season, onset date of greenness, and date of maximum photosynthetic activity are often derived from NDVI time series for monitoring changes in agricultural systems [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

How Normalized Difference Vegetation Index (NDVI) Trendsfrom Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d’Observation de la Terre VEGETATION (SPOT VGT) Time Series Differ in Agricultural Areas: An Inner Mongolian Case Study

et al. 2012

View full text Add to dashboard Cite

Detailed information from global remote sensing has greatly advanced our understanding of Earth as a system in general and of agricultural processes in particular. Vegetation monitoring with global remote sensing systems over long time periods is critical to gain a better understanding of processes related to agricultural change over long time periods. This specifically relates to sub-humid to semi-arid ecosystems, where agricultural change in grazing lands can only be detected based on long time series. By integrating data from different sensors it is theoretically possible to construct NDVI time series back to the early 1980s. However, such integration is hampered by uncertainties in the comparability between different sensor products. To be able to rely on vegetation trends derived from integrated time series it is therefore crucial to investigate whether vegetation trends derived from NDVI and phenological parameters are consistent across products. In this paper we analyzed several indicators of vegetation change for a range of agricultural systems in Inner Mongolia, China, and compared the results across different satellite archives. Specifically, we compared two of the prime NDVI archives-AVHRR OPEN ACCESSRemote Sens. 2012, 4 3365Global Inventory Modeling and Mapping Studies (GIMMS) and SPOT Vegetation (VGT) NDVI. Because a true accuracy assessment of long time series is not possible, we further compared SPOT VGT NDVI with NDVI from MODIS Terra as a benchmark. We found high similarities in interannual trends, and also in trends of the seasonal amplitude and integral between SPOT VGT and MODIS Terra (r > 0.9). However, we observed considerable disagreements in NDVI-derived trends between AVHRR GIMMS and SPOT VGT. We detected similar discrepancies for trends based on phenological parameters, such as amplitude and integral of NDVI curves corresponding to seasonal vegetation cycles. Inconsistencies were partially related to land cover and vegetation density. Different pre-processing schemes and the coarser spatial resolution of AVHRR GIMMS introduced further uncertainties. Our results corroborate findings from other studies that vegetation trends derived from AVHRR GIMMS data not always reflect true vegetation changes. A more thorough understanding of the factors introducing uncertainties in AVHRR GIMMS time series is needed, and we caution against using AVHRR GIMMS data in regional studies without applying regional sensitivity analyses.

show abstract

Wheat yield estimates using multi-temporal NDVI satellite imagery

Cited by 209 publications

References 0 publications

Uso de imagens do sensor orbital modis na estimação de datas do ciclo de desenvolvimento da cultura da soja para o estado do Paraná – Brasil

Uso de imagens do sensor orbital modis na estimação de datas do ciclo de desenvolvimento da cultura da soja para o estado do Paraná – Brasil

Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics

How Normalized Difference Vegetation Index (NDVI) Trendsfrom Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d’Observation de la Terre VEGETATION (SPOT VGT) Time Series Differ in Agricultural Areas: An Inner Mongolian Case Study

Contact Info

Product

Resources

About