Yield Estimation Model and Water Productivity of Wheat Crop (Triticum aestivum) in an Irrigation Command Using Remote Sensing and GIS

Gontia, Narendra Kumar; Tiwari, K. N.

doi:10.1007/s12524-011-0065-7

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…This problem could be solved by using the EVI, which was proposed to reduce atmospheric influences and decouple the canopy background signal, according to Deng et al [32] and Son et al [31]. Currently, studies have been conducted using the SAVI by taking the influence of the soil background in the early period and the interference of high vegetation coverage in the late period into consideration [33][34][35]. Guo et al [34] incorporated temporal remote sensing vegetation indices (VIs) and the wheat grow-PROSAIL model-simulated VIs to forecast regional crop yields in Yanhu Farm and Baimahu Farm.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Maize Yield at the City Level in China Using Multi-Source Data

et al. 2021

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Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices from all stages, respectively. R increased by 0.003, 0.032, and 0.042 when adding meteorological indices from the early, peak, and late stages to climate and satellite data from all stages, respectively. The analysis found that the spatial divergences were large and the R value in Northwest region reached 0.942, 0.904, 0.934, and 0.850 for the Cubist, RF, SVM, and Xgboost, respectively. This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms.

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Section: Introductionmentioning

confidence: 99%

Prediction of Maize Yield at the City Level in China Using Multi-Source Data

et al. 2021

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“…MODIS data have been successfully used for yield estimation and prediction (Bala and Islam, 2009;Doraiswamy et al, 2004;Huang et al, 2012;Mkhabela et al, 2011;Ren et al, 2008;Shunlin et al, 2004;Son et al, 2013c). In particular, studies have used remotely sensed vegetation indices, such as the normalized difference vegetation index (NDVI) (Kastens et al, 2005;Mkhabela et al, 2011;Mkhabela et al, 2005;Moriondo et al, 2007;Quarmby et al, 1993;Ren et al, 2008), the enhanced vegetation index (EVI) (Bolton and Friedl, 2013;Kyoungdo et al, 2013;Shunlin et al, 2004), and the soil-adjusted vegetation index (SAVI) (Gontia and Tiwari, 2011;Mandal et al, 2007;Noureldin et al, 2013;Panda et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation

Son

Chen

et al. 2014

Agricultural and Forest Meteorology

200

104

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“…In Skakun's study, yield estimation was made using only Landsat-8, only Sentinel-2 and NDVI value obtained from the combined model and R 2 values were found to be 0.64, 0.88, and 0.90, respectively. [27] compared the NDVI and SAVI results and they indicated that SAVI performed better than NDVI.…”

Section: Introductionmentioning

confidence: 99%

A linear approach for wheat yield prediction by using different spectral vegetation indices

Kaya

Polat

2023

International Journal of Engineering and Geosciences

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Yield prediction before harvest is one of the important issues in terms of managing agricultural policies and making the right decisions for the future. Using remote sensing techniques in yield estimation studies is one of the important steps for many countries to reach their 21st-century agricultural targets. The aim of this study is to develop a wheat yield model using Landsat-8 and Sentinel-2 satellite data. In this study, the development stages of winter wheat were examined with the help of satellite images obtained between the years 2015-2018 of a selected region in Sanliurfa, Turkey, and it was aimed to predict the yields for other years by establishing a yield estimation model. The yield estimation model was established with the help of Normalized Difference Vegetation Index (NDVI), Soil-adjusted Vegetation Index (SAVI), Green Normalized Difference Vegetation Index (GNDVI) and Modified Soil-adjusted Vegetation Index (MSAVI) obtained from remote sensing satellite images. Linear regression analysis was established between calculated NDVI, SAVI, GNDVI, MSAVI indices, and actual yield values on the pre-flowering, flowering stage, and postflowering stage. As a result of the study, the highest correlation coefficient was found in the flowering stage between the vegetation indices values and the actual yield values. The values of NDVI, SAVI, GNDVI, and MSAVI and correlation coefficients are obtained in the flowering stage were 0.82, 0.80, 0.86, and 0.87, respectively. With the established model, yield values in 2019 were tried to be estimated for three different fields. The highest correlations were seen in the flowering stage for MSAVI and GNDVI, pre-flowering stage for NDVI and post-flowering stage for SAVI. This clearly shows that the satellite images can be used in yield estimation studies with a remarkable correlation between vegetation indices and actual yield values.

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Yield Estimation Model and Water Productivity of Wheat Crop (Triticum aestivum) in an Irrigation Command Using Remote Sensing and GIS

Cited by 7 publications

References 24 publications

Prediction of Maize Yield at the City Level in China Using Multi-Source Data

Prediction of Maize Yield at the City Level in China Using Multi-Source Data

A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation

A linear approach for wheat yield prediction by using different spectral vegetation indices

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