A Simple Model for Yield Prediction of Rice Based on Vegetation Index Derived from Satellite and AMeDAS Data during Ripening Period

2003 ◽  
Vol 59 (4) ◽  
pp. 277-286 ◽  
Author(s):  
Yasuyuki WAKIYAMA ◽  
Kimio INOUE ◽  
Kou NAKAZONO
Keyword(s):  
Simple Model ◽  
Vegetation Index ◽  
Yield Prediction ◽  
Ripening Period

scholarly journals Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 340
Author(s):  
Ewa Panek ◽  
Dariusz Gozdowski
Keyword(s):  
Linear Regression ◽  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arable Land ◽  
Early Spring ◽  
European Countries ◽  
Yield Prediction ◽  
Country Level ◽  
Using Data

In this study, the relationships between normalized difference vegetation index (NDVI) obtained based on MODIS satellite data and grain yield of all cereals, wheat and barley at a country level were analyzed. The analysis was performed by using data from 2010–2018 for 20 European countries, where percentage of cereals is high (at least 35% of the arable land). The analysis was performed for each country separately and for all of the collected data together. The relationships between NDVI and cumulative NDVI (cNDVI) were analyzed by using linear regression. Relationships between NDVI in early spring and grain yield of cereals were very strong for Croatia, Czechia, Germany, Hungary, Latvia, Lithuania, Poland and Slovakia. This means that the yield prediction for these countries can be as far back as 4 months before the harvest. The increase of NDVI in early spring was related to the increase of grain yield by about 0.5–1.6 t/ha. The cumulative of averaged NDVI gives more stable prediction of grain yield per season. For France and Belgium, the relationships between NDVI and grain yield were very weak.

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.

scholarly journals Prediction of Maize Yields from In-Season GreenSeeker Normalized Difference Vegetation Index and Dry Biomass as Influenced by Different Nutrient Combinations

2020 ◽  
Vol 13 (1) ◽  
pp. 165
Author(s):  
Hillary M. O. Otieno ◽  
George N. Chemining’wa ◽  
Shamie Zingore
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Nutrient Management ◽  
Fertilizer Application ◽  
Growth And Yield ◽  
Yield Prediction ◽  
Maize Productivity ◽  
Dry Biomass ◽  
The Right

To mitigate low maize productivity, improve on-farm planning and policy implementation, the right fertilizer combinations and yield forecasting should be prioritized. Therefore, this research aimed at assessing the effect of applying different nutrient combinations on maize growth and yield and in-season grain yield prediction from biomass and normalized difference vegetation index (NDVI) readings. The research was done in Embu and Kirinyaga counties, in Central Kenya. Nutrient combinations tested were P+K, N+K, N+P, N+P+K, and N+P+K+Ca+Mg+Zn+B+S. The results showed consistently lowest and highest NDVI reading, dry biomass, and grain yields due to P+K and N+P+K+Ca+Mg+Zn+B+S treatments, respectively. Positive NDVI responses of 56%, 14%, 15%, and 15% were recorded with N, P, K, and combined Ca+Mg+Zn+B+S, respectively. These nutrients, in the same order, recorded 54%, 20%, 8%, and 18% positive responses with biomass. The GreenSeeker NDVI reading with grain yield and aboveground dry biomass with grain yield recorded R2 ranging from 0.23-0.53 and 0.30-0.61 (in Embu), and 0.31-0.64 and 0.30-0.50 (in Kirinyaga), respectively. When data were pooled, the prediction strength increased, reaching a maximum of 67% and 58% with NDVI and biomass, respectively. Yield prediction was even more robust when the independent variables were combined through multiple linear model at both 85 and 105 days after emergence. From this research, it is evident that the effects of balanced fertilizer application are detectable from NDVI readings—providing a tool for tracking and monitoring nutrient management effects—not just from the nitrogen perspective as commonly studied but from the combined effects of multiple nutrients. Also, grain yield could be accurately predicted early before harvesting by combining NDVI and biomass yields.

scholarly journals Macadamia Orchard Planting Year and Area Estimation at a National Scale

2020 ◽  
Vol 12 (14) ◽  
pp. 2245
Author(s):  
James Brinkhoff ◽  
Andrew J. Robson
Keyword(s):  
Vegetation Index ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Tree Age ◽  
Prediction Algorithm ◽  
Yield Prediction ◽  
National Scale ◽  
Planting Dates ◽  
Crop Area ◽  
Improving Accuracy

Accurate estimates of tree crop orchard age and historical crop area are important to develop yield prediction algorithms, and facilitate improving accuracy in ongoing crop forecasts. This is particularly relevant for the increasingly productive macadamia industry in Australia, where knowledge of tree age, as well as total planted area, are important predictors of productivity, and the area devoted to macadamia orchards is rapidly increasing. We developed a technique to aggregate more than 30 years of historical imagery, generate summary tables from the data, and search multiple combinations of parameters to find the most accurate planting year prediction algorithm. This made use of known planting dates of more than 90 macadamia blocks spread across multiple growing regions. The selected algorithm achieved a planting year mean absolute error of 1.7 years. The algorithm was then applied to all macadamia features in east Australia, as defined in an recent Australian tree crops map, to determine the area planted per year and the total cumulative area of macadamia orchards in Australia. The area estimates were refined by improving the resolution of the mapped macadamia features, by removing non-productive areas based on an optimal vegetation index threshold.

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

2019 ◽  
Vol 162 ◽  
pp. 1011-1027 ◽  
Author(s):  
Tiecheng Bai ◽  
Nannan Zhang ◽  
Benoit Mercatoris ◽  
Youqi Chen
Keyword(s):  
Vegetation Index ◽  
Prediction Method ◽  
Landsat 8 ◽  
Yield Prediction

scholarly journals Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn

2020 ◽  
Vol 12 (15) ◽  
pp. 2392
Author(s):  
Razieh Barzin ◽  
Rohit Pathak ◽  
Hossein Lotfi ◽  
Jac Varco ◽  
Ganesh C. Bora
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Prediction Models ◽  
Feature Selection Method ◽  
Spectral Information ◽  
Gradient Boosting ◽  
Spatial And Temporal Variability ◽  
Yield Prediction ◽  
Phenological Stages ◽  
Red Edge

Changes in spatial and temporal variability in yield estimation are detectable through plant biophysical characteristics observed at different phenological development stages of corn. A multispectral red-edge sensor mounted on an Unmanned Aerial Systems (UAS) can provide spatial and temporal information with high resolution. Spectral analysis of UAS acquired spatiotemporal images can be used to develop a statistical model to predict yield based on different phenological stages. Identifying critical vegetation indices (VIs) and significant spectral information could lead to increased yield prediction accuracy. The objective of this study was to develop a yield prediction model at specific phenological stages using spectral data obtained from a corn field. The available spectral bands (red, blue, green, near infrared (NIR), and red-edge) were used to analyze 26 different VIs. The spectral information was collected from a cornfield at Mississippi State University using a MicaSense multispectral red-edge sensor, mounted on a UAS. In this research, a new empirical method used to reduce the effects of bare soil pixels in acquired images was introduced. The experimental design was a randomized complete block that consisted of 16 blocks with 12 rows of corn planted in each block. Four treatments of nitrogen (N) including 0, 90, 180, and 270 kg/ha were applied randomly. Random forest was utilized as a feature selection method to choose the best combination of variables for different stages. Multiple linear regression and gradient boosting decision trees were used to develop yield prediction models for each specific phenological stage by utilizing the most effective variables at each stage. At the V3 (3 leaves with visible leaf collar) and V4-5 (4-5 leaves with visible leaf collar) stages, the Optimized Soil Adjusted Vegetation Index (OSAVI) and Simplified Canopy Chlorophyll Content Index (SCCCI) were the single dominant variables in the yield predicting models, respectively. A combination of the Green Atmospherically Resistant Index (GARI), Normalized Difference Red-Edge (NDRE), and green Normalized Difference Vegetation Index (GNDVI) at V6-7, SCCCI, and Soil-Adjusted Vegetation Index (SAVI) at V10,11, and SCCCI, Green Leaf Index (GLI), and Visible Atmospherically Resistant Index (VARIgreen) at tasseling stage (VT) were the best indices for predicting grain yield of corn. The prediction models at V10 and VT had the greatest accuracy with a coefficient of determination of 0.90 and 0.93, respectively. Moreover, the SCCCI as a combined index seemed to be the most proper index for predicting yield at most of the phenological stages. As corn development progressed, the models predicted final grain yield more accurately.

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

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1406
Author(s):  
Zhonglin Ji ◽  
Yaozhong Pan ◽  
Xiufang Zhu ◽  
Jinyun Wang ◽  
Qiannan Li
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Arid Regions ◽  
Vegetation Index ◽  
Corn Yield ◽  
Yield Prediction ◽  
Multivariate Models ◽  
Ndvi Time Series ◽  
Phenological Metrics ◽  
Semi Arid

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.

scholarly journals Establishment of Plot-Yield Prediction Models in Soybean Breeding Programs Using UAV-Based Hyperspectral Remote Sensing

2019 ◽  
Vol 11 (23) ◽  
pp. 2752 ◽  
Author(s):  
Xiaoyan Zhang ◽  
Jinming Zhao ◽  
Guijun Yang ◽  
Jiangang Liu ◽  
Jiqiu Cao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Growth Stage ◽  
Vegetation Index ◽  
Prediction Models ◽  
Hyperspectral Remote Sensing ◽  
Yield Prediction ◽  
Breeding Programs ◽  
Soybean Breeding ◽  
Breeding Lines ◽  
Plot Yield

Yield evaluation of breeding lines is the key to successful release of cultivars, which is becoming a serious issue due to soil heterogeneity in enlarged field tests. This study aimed at establishing plot-yield prediction models using unmanned aerial vehicle (UAV)-based hyperspectral remote sensing for yield-selection in large-scale soybean breeding programs. Three sets of soybean breeding lines (1103 in total) were tested in blocks-in-replication experiments for plot yield and canopy spectral reflectance on 454~950 nm bands at different growth stages using a UAV-based hyperspectral spectrometer (Cubert UHD185 Firefly). The four elements for plot-yield prediction model construction were studied respectively and concluded as: the suitable reflectance-sampling unit-size in a plot was its 20%–80% central part; normalized difference vegetation index (NDVI) and ration vegetation index (RVI) were the best combination of vegetation indices; the initial seed-filling stage (R5) was the best for a single stage prediction, while another was the best combination for a two growth-stage prediction; and multi-variate linear regression was suitable for plot-yield prediction. In model establishment for each material-set, a random half was used for modelling and another half for verification. Twenty-one two growth-stage two vegetation-index prediction models were established and compared for their modelling coefficient of determination (RM2) and root mean square error of the model (RMSEM), verification RV2 and RMSEV, and their sum RS2 and RMSES. Integrated with the coincidence rate between the model predicted and the practical yield-selection results, the models, MA1-2, MA4-2 and MA6-2, with coincidence rates of 56.8%, 58.5% and 52.4%, respectively, were chosen for yield-prediction in yield-test nurseries. The established model construction elements and methods can be used as local models for pre-harvest yield-selection and post-harvest integrated yield-selection in advanced breeding nurseries as well as yield potential prediction in plant-derived-line nurseries. Furthermore, multiple models can be used jointly for plot-yield prediction in soybean breeding programs.

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