Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAA's-AVHRR

Mkhabela, M.S.; Mkhabela, Milton S.; Mashinini, Nkosazana N.

doi:10.1016/j.agrformet.2004.12.006

Cited by 138 publications

(79 citation statements)

References 22 publications

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“…However, Esquerdo et al [44] found that a full-season averaging window was optimal for estimating soybean yields in Brazil using NDVI. Mkhabela et al [45] found the best time for making an accurate maize yield forecast was from the late January through late March depending on the agro-ecological region. Anderson et al [43] analyzed the relationship between remotely sensed indices and crop yield in Brazil and also found that in some regions, some degree of additional time-averaging (moving upwards in the plots) helps to improve correlations with yields.…”

Section: Optimal Temporal Window For Yield Prediction Using Ndvi and mentioning

confidence: 99%

Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards

et al. 2017

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Wine grape quality and quantity are affected by vine growing conditions during critical phenological stages. Field observations of vine growth stages are too sparse to fully capture the spatial variability of vine conditions. In addition, traditional grape yield prediction methods are time consuming and require large amount grape samples. Remote sensing data provide detailed spatial and temporal information regarding vine development that is useful for vineyard management. In this study, Landsat surface reflectance products from 2013 and 2014 were used to map satellite-based Normalized Difference Vegetation Index (NDVI) and leaf area index (LAI) over two Vitis vinifera L. cv. Pinot Noir vineyards in California, USA. The spatial correlation between grape yield maps and the interpolated daily time series (LAI and NDVI) was quantified. NDVI and LAI were found to have similar performance as a predictor of spatial yield variability, providing peak correlations of 0.8 at specific times during the growing season, and the timing of this peak correlation differed for the two years of study. In addition, correlations with maximum and seasonal-cumulative vegetation indices were also evaluated, and showed slightly lower correlations with the observed yield maps. Finally, the within-season grape yield predictability was examined using a simple strategy in which the relationship between grape yield and vegetation indices were calibrated with limited ground measurements. This strategy has a strong potential to improve the accuracy and efficiency of yield estimation in comparison with traditional approaches used in the wine grape growing industry.

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Section: Optimal Temporal Window For Yield Prediction Using Ndvi and mentioning

confidence: 99%

Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards

et al. 2017

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“…Satellite spectra derived vegetation indexes have been useful in relating satellite imagery to yield in corn (Zea may. L.) (Mkhabela and Mashinini, 2005;Prasad et al, 2006;Bognár et al, 2011), winter wheat (Triticum aestivum, L.) (Salazar et al, 2007;Ren et al, 2008;Becker-Reshef et al, 2010;Bognár et al, 2011;), potato (Solanum tuberosum, L.) (Bala and Islam, 2009), barley (Hordeum vulgare, L.), canola (Brassica napus, L.), field pea (Pisum sativum, L.), spring wheat (Triticum aestivum. L.) (Mkhabela et al, 2011), and sorghum (Sorghum bicolor, L.) (Shamseddin and Adeeb, 2012).…”

Section: Comparison Of Satellite Imagery and Ground-based Active Opticalmentioning

confidence: 99%

Comparison of Satellite Imagery and Ground‐Based Active Optical Sensors as Yield Predictors in Sugar Beet, Spring Wheat, Corn, and Sunflower

Sharma

Denton

et al. 2017

Agronomy Journal

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Algorithms using active-optical (AO) sensors have been developed to direct in-season N application to crops. Many farmers in the United States have a large number of farm fi elds to manage. Farmers using AO technology must visit each fi eld and operate the sensor across the entire fi eld in order to conduct in-season N application. A fi eld might be driven over with an on-the-go N fertilizer applicator, but the application might not be required. Th e objective of this study was to determine whether satellite imagery might be used to predict yield in sugar beet, spring wheat, corn and sunfl ower similar to the yield prediction possible using AO sensors. If so, the algorithms produced could be used to select fi elds that would benefi t from in-season N application. Two N-rate studies in sugar beet, spring wheat, corn and sunfl ower, were conducted with experimental unit size of 9 by 9 m large enough to fi t a satellite pixel of 5 by 5 m size within each unit. Th e AO sensor and satellite imagery data were related to yield of sugar beet, spring wheat, corn and sunfl ower in some site-years. Th e problem is the ability to acquire the satellite imagery early enough in the season to be useful as a screening tool. Th ese results indicate that even though satellite imagery could be used as a fi eld screening tool, a better option may be to mount an AO sensor on a farm implement for an early season activity, or to explore the use of unmanned aerial vehicles (UAVs).

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“…Este aná-lisis coincide en términos de anticipo con los resultados obtenidos por Mkhabela et al (2005).…”

Section: Modelo De Estimaciónunclassified

Integración del Índice de Vegetación de la Diferencia Normalizada (NDVI) y del Ciclo Fenológico de Maíz para Estimar el Rendimiento a Escala Departamental en Córdoba, Argentina

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Ovando²

2007

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Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAA's-AVHRR

Cited by 138 publications

References 22 publications

Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards

Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards

Comparison of Satellite Imagery and Ground‐Based Active Optical Sensors as Yield Predictors in Sugar Beet, Spring Wheat, Corn, and Sunflower

Integración del Índice de Vegetación de la Diferencia Normalizada (NDVI) y del Ciclo Fenológico de Maíz para Estimar el Rendimiento a Escala Departamental en Córdoba, Argentina

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