Jujube yield prediction method combining Landsat 8 Vegetation Index and the phenological length

Bai, Tiecheng; Zhang, Nannan; Mercatoris, Benoît; Chen, Youqi

doi:10.1016/j.compag.2019.05.035

Cited by 31 publications

(15 citation statements)

References 51 publications

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“…In this study, the Landsat derived vegetation indices during the flowering stage of the wheat development was found to be siginificantly correlated to the wheat yield in all study site. In agreement with previous studies, the current study has demostrated correlations between the VIs during the flowering/fruiting spike period and crop yields (Marti et al, 2007, Labus et al, 2002, Mkhabela et al, 2011, Tiecheng et al, 2019, which has been admited as the most critical period for most crops yield forecasting, however, SAVI demostrated higher correlations during the flowering/fruiting spike period and crop yield. This also is in agreement with Liaqat et al,…”

Section: Discussionsupporting

confidence: 91%

Wheat Yield Forecasting Based on Landsat NDVI and SAVI Time Series

Adeniyi¹,

Szabó²,

Tamás³

et al. 2020

Preprint

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Due to increase demand of food grain in the world, assessment of yield before actual production is important in making policies and decisions in agricultural production system. For a large area, forecast models developed from vegetation indices derived from remote sensing satellite data possesses the potential to give quantitative and timely information on crops over large areas. Different vegetation indices are being made used for this purpose, however, their efficiency in estimating crop yield is needed to be certainly tested. In this study, wheat yield forecast was derived by regressing ground truthing yield data against time series of spatial vegetation indices for the 2013 to 2019 growing seasons. These spatial vegetation indices derived from Landsat 8 image data: Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were compared to evaluate the most appropriate index that performs better in forecasting wheat production at Karcag, Kunhegyes and Ecsegfalva settlements in Jász-Nagykun-Szolnok county, in the Northern Great Plain region of central Hungary. The best time for making wheat yield prediction with Landsat 8- SAVI and NDVI was found to be the beginning of ripening period (160th day of the year) with higher correlation between the vegetation indices and the wheat yield. The validation results revealed that the model from SAVI provides more consistent and accurate forecasts yield compared to NDVI. The SAVI model forecast yield for the validation years, 2018 and 2019 were within 6.00% and 4.41% of the final reported values while that of NDVI model were within 8.31% and 6.27%. Nash-Sutcliffe efficiency index is positive with E1= 0.99 for the model from SAVI and for NDVI, E1=0.57, which connote that the forecasting method developed and evaluated performs acceptable forecast efficiency.

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Section: Discussionsupporting

confidence: 91%

Wheat Yield Forecasting Based on Landsat NDVI and SAVI Time Series

Adeniyi¹,

Szabó²,

Tamás³

et al. 2020

Preprint

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“…In addition to the phenological date, one piece of essential phenological information is GP duration [ 30 ]. Bai et al [ 31 ] noted that the phase duration could be combined with remote-sensing-based parameters to improve crop yield prediction. The GPs in their study were divided by the effective accumulated temperature.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Pan

Zhu

et al. 2021

Sensors

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Phenology is an indicator of crop growth conditions, and is correlated with crop yields. In this study, a phenological approach based on a remote sensing vegetation index was explored to predict the yield in 314 counties within the US Corn Belt, divided into semi-arid and non-semi-arid regions. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product MOD09Q1 was used to calculate the normalized difference vegetation index (NDVI) time series. According to the NDVI time series, we divided the corn growing season into four growth phases, calculated phenological information metrics (duration and rate) for each growth phase, and obtained the maximum correlation NDVI (Max-R2). Duration and rate represent crop growth days and rate, respectively. Max-R2 is the NDVI value with the most significant correlation with corn yield in the NDVI time series. We built three groups of yield regression models, including univariate models using phenological metrics and Max-R2, and multivariate models using phenological metrics, and multivariate models using phenological metrics combined with Max-R2 in the whole, semi-arid, and non-semi-arid regions, respectively, and compared the performance of these models. The results show that most phenological metrics had a statistically significant (p < 0.05) relationship with corn yield (maximum R2 = 0.44). Models established with phenological metrics realized yield prediction before harvest in the three regions with R2 = 0.64, 0.67, and 0.72. Compared with the univariate Max-R2 models, the accuracy of models built with Max-R2 and phenology metrics improved. Thus, the phenology metrics obtained from MODIS-NDVI accurately reflect the corn characteristics and can be used for large-scale yield prediction. Overall, this study showed that phenology metrics derived from remote sensing vegetation indexes could be used as crop yield prediction variables and provide a reference for data organization and yield prediction with physical crop significance.

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“…Many researchers have used MODIS and Landsat image to estimate crop yields and achieved high yield estimation accuracy [ 45 , 46 , 47 , 48 , 49 ]. Becker et al [ 45 ] used MODIS data and applied a generalized regression-based model to predict winter wheat yields in Kansas and Ukraine, with an error of 7 to 10%, compared with statistical yields.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Winter Wheat Yield in Arid and Semiarid Regions Based on Assimilated Multi-Source Sentinel Data and the CERES-Wheat Model

Liu

Zhou

et al. 2021

Sensors

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The farmland area in arid and semiarid regions accounts for about 40% of the total area of farmland in the world, and it continues to increase. It is critical for global food security to predict the crop yield in arid and semiarid regions. To improve the prediction of crop yields in arid and semiarid regions, we explored data assimilation-crop modeling strategies for estimating the yield of winter wheat under different water stress conditions across different growing areas. We incorporated leaf area index (LAI) and soil moisture derived from multi-source Sentinel data with the CERES-Wheat model using ensemble Kalman filter data assimilation. According to different water stress conditions, different data assimilation strategies were applied to estimate winter wheat yields in arid and semiarid areas. Sentinel data provided LAI and soil moisture data with higher frequency (<14 d) and higher precision, with root mean square errors (RMSE) of 0.9955 m2 m−2 and 0.0305 cm3 cm−3, respectively, for data assimilation-crop modeling. The temporal continuity of the CERES-Wheat model and the spatial continuity of the remote sensing images obtained from the Sentinel data were integrated using the assimilation method. The RMSE of LAI and soil water obtained by the assimilation method were lower than those simulated by the CERES-Wheat model, which were reduced by 0.4458 m2 m−2 and 0.0244 cm3 cm−3, respectively. Assimilation of LAI independently estimated yield with high precision and efficiency in irrigated areas for winter wheat, with RMSE and absolute relative error (ARE) of 427.57 kg ha−1 and 6.07%, respectively. However, in rain-fed areas of winter wheat under water stress, assimilating both LAI and soil moisture achieved the highest accuracy in estimating yield (RMSE = 424.75 kg ha−1, ARE = 9.55%) by modifying the growth and development of the canopy simultaneously and by promoting soil water balance. Sentinel data can provide high temporal and spatial resolution data for deriving LAI and soil moisture in the study area, thereby improving the estimation accuracy of the assimilation model at a regional scale. In the arid and semiarid region of the southeastern Loess Plateau, assimilation of LAI independently can obtain high-precision yield estimation of winter wheat in irrigated area, while it requires assimilating both LAI and soil moisture to achieve high-precision yield estimation in the rain-fed area.

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Jujube yield prediction method combining Landsat 8 Vegetation Index and the phenological length

Cited by 31 publications

References 51 publications

Wheat Yield Forecasting Based on Landsat NDVI and SAVI Time Series

Wheat Yield Forecasting Based on Landsat NDVI and SAVI Time Series

Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Estimation of Winter Wheat Yield in Arid and Semiarid Regions Based on Assimilated Multi-Source Sentinel Data and the CERES-Wheat Model

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