A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data

Becker‐Reshef, Inbal; Vermote, Éric; Lindeman, M.; Justice, Christopher O.

doi:10.1016/j.rse.2010.01.010

Cited by 460 publications

(319 citation statements)

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“…A range of techniques have been developed to estimate crop production with varying degrees of success (Becker-Reshef et al, 2010). They include visual field techniques, crop simulation models and remote sensing (Wall et al, 2007in Becker-Reshef et al, 2010.…”

Section: Technologies Tools and Methodsologies For Vegetation Monitoringmentioning

confidence: 99%

See 1 more Smart Citation

Remote sensing in food production and #8211; a review

Calvão

Pessoa

2015

Emir. J. Food Agric

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FAO's most recent assessments indicate that, globally, in 2011-13, about one in eight people in the world are likely to have suffered from chronic hunger, not having adequate food supplies for an active and healthy life. Food security crises are now caused, almost exclusively, by problems in access to food, not absolute food availability, but, monitoring agricultural production remains fundamental. Traditional ground-based systems of production estimation have many limitations which have restricted their use. However, remotely sensed satellite data offer timely, objective, economical, and synoptic information for crop monitoring. The objective of this paper is to review the contribution of remote sensing techniques in the classification, monitoring of crop phenology and condition and estimation of production.

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Section: Technologies Tools and Methodsologies For Vegetation Monitoringmentioning

confidence: 99%

“…Thus, food production is a key determinant of food availability and consequently of food security. In fact, in order to develop robust policies and strategies for food management that may guarantee food security, timely and accurate evaluations of global crop production are vital (Becker-Reshef et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Remote sensing in food production and #8211; a review

Calvão

Pessoa

2015

Emir. J. Food Agric

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“…There is a need for improvements in crop prediction models, both at high (field level) (Becker-Reshef et al 2010) and moderate (district level) resolution (Deryng et al 2011). The satellite-derived wetness index provides data at a moderate spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Use of Satellite Information on Wetness and Temperature for Crop Yield Prediction and River Resource Planning

Basist¹,

Dinar

Blankespoor

et al. 2017

Climate Smart Agriculture

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Satellite derived measurements are essential inputs to monitor water management and agricultural production for improving regional food security. Near real-time satellites observations can be used to mitigate the adverse impacts of extreme events and promote climate resilience. Population growth and demand of resources in developing countries will increase vulnerability in agriculture production and are likely to be exacerbated by the effects of climate change. This paper introduces wetness and temperature products as important factors in decision and policy making, especially in regions with sparse surface observations. These objective satellite data serve as: (1) an early detector of growing conditions and thus food supply; (2) an index for insurance programs (i.e. risk management) that can more quickly trigger release of catastrophic bonds to farmers to mitigate crop failure impact; (3) an important educational and informational tool in crop selection, resource management, and other adaptation or mitigation strategies; (4) an important tool in food aid and transport; (5) and management of water resource allocation. The two new indices (surface wetness and temperature) are meant to complement currently available datasets, such as the greenness index, soil moisture measurements, and river guages.

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“…Several techniques have been developed to achieve accurate yield estimates, namely the linear regression analysis based on remote sensing data (Wall et al, 2008). This approach is based on estimating photosynthetic capacity from vegetation indices related to yield (Becker-Reshef et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…The authors found that indicators based on CNDVI were more closely related to crop yield than those based on NDVI. Becker-Reshef et al (2010) used a regression model to estimate wheat yield in Kansas, the United States, based on a percentage map using the pure pixels that allowed reliable yield estimates prior to harvesting. Maselli and Rembold (2001) found that improvement in estimates of yield capacity depends on the crop and the values of vegetation index considered in the area.…”

Section: Introductionmentioning

confidence: 99%

Correlation maps to assess soybean yield from EVI data in Paraná State, Brazil

Figueiredo

Brunsell

Higa

et al. 2016

Sci. agric. (Piracicaba, Braz.)

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Vegetation indices are widely used to monitor crop development and generally used as input data in models to forecast yield. The first step of this study consisted of using monthly . We compared the ability of forecasting crop yield based on correlation maps and crop specific masks. We ran a preliminary regression model to test its ability on yield estimation for four municipalities during the soybean growing season. A regression model was developed for both methodologies to forecast soybean crop yield using leave-one-out cross validation.The Root Mean Squared Error (RMSE) values in the implementation of the model ranged from 0.037 t ha −1 to 0.19 t ha −1 using correlation maps, while for crop specific masks, it varied from 0.21 t ha −1 to 0.35 t ha −1 . The model was able to explain 96 % to 98 % of the variance in estimated yield from correlation maps, while it was able to explain only 2 % to 67 % for crop specific mask approach. The results showed that the correlation maps could be used to predict crop yield more effectively than crop specific masks. In addition, this method can provide an indication of soybean yield prior to harvesting.

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A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data

Cited by 460 publications

References 50 publications

Remote sensing in food production and #8211; a review

Remote sensing in food production and #8211; a review

Use of Satellite Information on Wetness and Temperature for Crop Yield Prediction and River Resource Planning

Correlation maps to assess soybean yield from EVI data in Paraná State, Brazil

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