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- W1524277586 abstract "Crop yield estimation by remote sensing is an important task in food security. Despite numerous studies in this field, there is a lack of a systematic analysis on the past studies to enable researchers to link individual case studies together. In this article, meta-analysis is adopted to pool various studies around the world and reveal the influences of the factors (such as crop types, soil orders, and climate systems) on the correlations between crop yield and remote-sensing data. The meta-analysis synthesizes the data on the effectiveness of NDVI (normalized difference vegetation index) on crop-yield estimation and it has been found that the correlation coefficient (r) varies significantly. Different correlation values are found in different crop types, with the highest 0.88 in cotton and the lowest 0.79 in sugar cane. They are found to be related to the single-leaf blades of the crops except for sugar cane. In addition, the meta-analysis results show that the correlations are also affected by the soil ..." @default.
- W1524277586 created "2016-06-24" @default.
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- W1524277586 date "2014-03-05" @default.
- W1524277586 modified "2023-09-27" @default.
- W1524277586 title "Meta-analysis of influential factors on crop yield estimation by remote sensing" @default.
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- W1524277586 doi "https://doi.org/10.1080/01431161.2014.890761" @default.
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