Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco
TLDR
This study proposes empirical ordinary least squares regression models to forecast the yields at provincial and national levels of wheat in Morocco based on dekadal (10-daily) NDVI/AVHRR, deKadal rainfall sums and average monthly air temperatures.About:
This article is published in International Journal of Applied Earth Observation and Geoinformation.The article was published on 2008-12-01 and is currently open access. It has received 175 citations till now.read more
Citations
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Crop yield forecasting on the Canadian Prairies using MODIS NDVI data
TL;DR: In this paper, the authors evaluated the possibility of using MODIS-NDVI data derived from the advanced very high resolution radiometer (AVHRR) sensor to forecast crop yield on the Canadian Prairies and also to identify the best time for making a reliable crop yield forecast.
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Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches
Yaping Cai,Kaiyu Guan,David B. Lobell,Andries Potgieter,Shaowen Wang,Jian Peng,Tianfang Xu,Senthold Asseng,Yongguang Zhang,Yongguang Zhang,Liangzhi You,Bin Peng +11 more
TL;DR: The results confirm that combining climate and satellite data can achieve high performance of yield prediction at the SD level and find that using EVI as an input can achieve better performance in yield prediction than SIF, primarily due to the large noise in the satellite-based SIF data.
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Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection
TL;DR: The limitations created by the mixed nature of low resolution pixels are being progressively reduced by the higher resolution offered by new sensors, while the continuity of existing systems remains crucial for ensuring the stability of these systems.
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Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index
TL;DR: Results showed that TVDI data can be used effectively to predict crop yield on the Argentine Pampas and a generalized model of crop yield and dryness index relationship which could be applicable in other regions and crops at regional scale was developed.
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A review of remote sensing applications in agriculture for food security: Crop growth and yield, irrigation, and crop losses
TL;DR: In this article, the authors review how satellite remote sensing information is utilized to assess and manage agriculture, an important component of eco-hydrology, and conclude the review with an outlook of challenges and recommendations.
References
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Book
Applied Linear Statistical Models
TL;DR: Applied Linear Statistical Models 5e as discussed by the authors is the leading authoritative text and reference on statistical modeling, which includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half.
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Applied Linear Statistical Models
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A new land-cover map of Africa for the year 2000
TL;DR: This first version of the map should provide an important input for regional stratification and planning purposes for natural resources, biodiversity and climate studies and is the most spatially detailed view yet published at this scale.
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Crop yield estimation model for Iowa using remote sensing and surface parameters
TL;DR: In this article, a non-linear Quasi-Newton multi-variate optimization method is utilized, which reasonably minimizes inconsistency and errors in yield prediction, and minimization of least square loss function has been carried out through iterative convergence using pre-defined empirical equation that provided acceptable lower residual values with predicted values very close to observed ones.
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Drought Monitoring and Corn Yield Estimation in Southern Africa from AVHRR Data
Leonard S Unganai,Felix Kogan +1 more
TL;DR: In this article, the authors used the Advanced Very High Resolution Radiometer (AVHRR) sensor on board the NOAA polar-orbiting satellites to study the temporal and spatial characteristics of drought in southern Africa.
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