Rice Yield Estimation Using Landsat ETM+ Data and Field Observation
TLDR
This study used the Normalized Difference Vegetation Index (NDVI) of Landsat Enhanced Thematic Mapper plus (ETM+) images of rice plants to estimate rice yield based on field observation and showed that the rice yield could be estimated using the exponential equation of y = 0.3419e 4.1587x.Abstract:
Forecasting rice yield before harvest time is important to supporting planners and decision makers to predict the amount of rice that should be imported or exported and to enable governments to put in place strategic contingency plans for the redistribution of food during times of famine. This study used the Normalized Difference Vegetation Index (NDVI) of Landsat Enhanced Thematic Mapper plus (ETM+) images of rice plants to estimate rice yield based on field observation. The result showed that the rice yield could be estimated using the exponential equation of y = 0.3419e 4.1587x , where y and x are rice yield and NDVI, respectively. The R 2 and SE of the estimation were 0.852 and 0.077 ton/ha, respectively. An accuracy assessment of rice yield estimation using Landsat images was performed by comparing the rice yields from the estimation result and the reference data. The results show that the linear relationship with the Rread more
Citations
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Journal ArticleDOI
Application of Remote Sensors in Mapping Rice Area and Forecasting Its Production: A Review
TL;DR: The overview of the studies to date indicated that remote sensing-based methods using optical and microwave imagery found to be encouraging, however, there were having some limitations, such as: optical remote sensing imagery had relatively low spatial resolution led to inaccurate estimation of rice areas; and radar imagery would suffer from speckles, which potentially would degrade the quality of the images.
Journal ArticleDOI
Rice yield estimation based on K-means clustering with graph-cut segmentation using low-altitude UAV images
TL;DR: In this paper, an image processing method that combines K-means clustering with a graph-cut (KCG) algorithm was proposed to segment the rice grain areas using low altitude RGB images collected using a rotary-wing type UAV.
Journal ArticleDOI
Rice yield estimation using Landsat ETM+ Data
TL;DR: In this paper, a regression model based on Landsat-derived normalized difference vegetation index and ratio vegetation index (RVI) values against historic, reported yield values was used to estimate rice yield in Larkana district in Sindh province, Pakistan.
Journal ArticleDOI
Evaluation of sum-NDVI values to estimate wheat grain yields using multi-temporal Landsat OLI data
TL;DR: In this paper, normalized difference vegetation index (NDVI)-based models have been developed to derive wheat grain yields with multispectral images using field measurements and Landsat 8 images.
Journal ArticleDOI
Paddy acreage mapping and yield prediction using sentinel-based optical and SAR data in Sahibganj district, Jharkhand (India)
TL;DR: In this article, a robust machine learning Random Forest (RF) classification technique was deployed for the paddy acreage mapping in Sahibganj district, Jharkhand during the monsoon season in 2017.
References
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Frédéric Baret,G. Guyot +1 more
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