Models for wheat yield prediction using remotely sensed canopy temperature based indices

1989 ◽  
Vol 17 (4) ◽  
pp. 9-18 ◽  
Author(s):  
S. K. Saha ◽  
Ajai
Keyword(s):  
Wheat Yield ◽  
Canopy Temperature ◽  
Remotely Sensed ◽  
Yield Prediction

Wheat Yield Prediction Using Remotely Sensed Agromet Trend-Based Models for Hoshiarpur District of Punjab, India

2008 ◽  
Vol 8 (3) ◽  
pp. 510-515 ◽  
Author(s):  
S. Bazgeer ◽  
R.K. Mahey ◽  
S.S. Sidhu ◽  
P.K. Sharma ◽  
A. Sood ◽  
...  
Keyword(s):  
Wheat Yield ◽  
Remotely Sensed ◽  
Yield Prediction

Comparing the Hedging Effectiveness of Weather Derivatives Based on Remotely Sensed Vegetation Health Indices and Meteorological Indices

2018 ◽  
Vol 11 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Johannes Möllmann ◽  
Matthias Buchholz ◽  
Oliver Musshoff
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
Condition Index ◽  
Remotely Sensed ◽  
Weather Derivatives ◽  
Basis Risk ◽  
Hedging Effectiveness ◽  
Winter Wheat Yield ◽  
Vegetation Health ◽  
Northeastern Germany

Abstract Weather derivatives are considered a promising agricultural risk management tool. Station-based meteorological indices typically provide the data underlying these instruments. However, the main shortcoming of these weather derivatives is an imperfect correlation between the weather index and the yield of the insured crop, called basis risk. This paper considers three available remotely sensed vegetation health (VH) indices, namely, the vegetation condition index (VCI), the temperature condition index (TCI), and the vegetation health index (VHI), as indices for weather derivatives in a German case study. We investigated the correlation and period of highest correlation with winter wheat yield. Moreover, we analyzed whether the use of remotely sensed VH indices for weather derivatives can reduce basis risk and thus improve the performance of weather derivatives. The two commonly used meteorological indices, precipitation and temperature sums, were employed as benchmarks. Quantile regression and index value simulation were used for the design and pricing of the weather derivatives. The analysis for the selected farms and corresponding counties in northeastern Germany revealed that, on average, the VHI resulted in the highest correlation with winter wheat yield, and VHI-based weather derivatives were also superior in terms of the hedging effectiveness. The total periods of the highest correlations ranged from the beginning of April to the end of July. VHI- and VCI-based weather derivatives led to statistically significant reductions of basis risk, compared to the benchmarks. Our results indicate that the VHI-based weather derivatives can be useful alternatives to meteorological indices, especially in regions with sparser weather station networks.

Wheat Yield Prediction Using Feedforward Neural Networks

2021 ◽  
Author(s):  
Vikas Lamba ◽  
Susheela Hooda ◽  
Rakesh Ahuja ◽  
Amandeep Kaur
Keyword(s):  
Neural Networks ◽  
Wheat Yield ◽  
Feedforward Neural Networks ◽  
Yield Prediction

scholarly journals Predicting Wheat Yield at the Field Scale by Combining High-Resolution Sentinel-2 Satellite Imagery and Crop Modelling

2020 ◽  
Vol 12 (6) ◽  
pp. 1024 ◽  
Author(s):  
Yan Zhao ◽  
Andries B Potgieter ◽  
Miao Zhang ◽  
Bingfang Wu ◽  
Graeme L Hammer
Keyword(s):  
High Resolution ◽  
Crop Production ◽  
Vegetation Index ◽  
Wheat Yield ◽  
Crop Model ◽  
Stress Index ◽  
Yield Prediction ◽  
Field Scale ◽  
Crop Water Stress Index ◽  
Sentinel 2

Accurate prediction of crop yield at the field scale is critical to addressing crop production challenges and reducing the impacts of climate variability and change. Recently released Sentinel-2 (S2) satellite data with a return cycle of five days and a high resolution at 13 spectral bands allows close observation of crop phenology and crop physiological attributes at field scale during crop growth. Here, we test the potential for indices derived from S2 data to estimate dryland wheat yields at the field scale and the potential for enhanced predictability by incorporating a modelled crop water stress index (SI). Observations from 103 study fields over the 2016 and 2017 cropping seasons across Northeastern Australia were used. Vegetation indices derived from S2 showed moderately high accuracy in yield prediction and explained over 70% of the yield variability. Specifically, the red edge chlorophyll index (CI; chlorophyll) (R2 = 0.76, RMSE = 0.88 t/ha) and the optimized soil-adjusted vegetation index (OSAVI; structural) (R2 = 0.74, RMSE = 0.91 t/ha) showed the best correlation with field yields. Furthermore, combining the crop model-derived SI with both structural and chlorophyll indices significantly enhanced predictability. The best model with combined OSAVI, CI and SI generated a much higher correlation, with R2 = 0.91 and RMSE = 0.54 t/ha. When validating the models on an independent set of fields, this model also showed high correlation (R2 = 0.93, RMSE = 0.64 t/ha). This study demonstrates the potential of combining S2-derived indices and crop model-derived indices to construct an enhanced yield prediction model suitable for fields in diversified climate conditions.

Combined TBATS and SVM model of minimum and maximum air temperatures applied to wheat yield prediction at different locations in Europe

2020 ◽  
Vol 281 ◽  
pp. 107827 ◽  
Author(s):  
Magdalena Gos ◽  
Jaromir Krzyszczak ◽  
Piotr Baranowski ◽  
Małgorzata Murat ◽  
Iwona Malinowska
Keyword(s):  
Wheat Yield ◽  
Yield Prediction ◽  
Air Temperatures ◽  
Svm Model

Accounting for phenology in maize yield prediction using remotely sensed dry dekads

2017 ◽  
Vol 33 (7) ◽  
pp. 723-736 ◽  
Author(s):  
Farai Kuri ◽  
Amon Murwira ◽  
Karin S. Murwira ◽  
Mhosisi Masocha
Keyword(s):  
Maize Yield ◽  
Remotely Sensed ◽  
Yield Prediction
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