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• W1966347846 abstract "If farmers could predict yield on a spatially variable basis, they could better understand risks and returns in applying costly inputs such as fertilizers, etc. To this end, several remotely sensed images of a cotton field were collected during the 2002 growing season, along with daily high and low temperatures. Image data were converted to normalized-difference vegetation index (NDVI), and temperature data were used to normalize NDVI changes over periods between image collections. Remote-sensing and weather data were overlaid in a geographic information system (GIS) with data from the field: topography, soil texture, and historical cotton yield. All these data were used to develop relationships with yield data collected at the end of the 2002 season. Stepwise regression was conducted at grid-cell sizes from 10 m square (100 m²) to 100 m square (10,000 m²) in 10-m increments. Relationships at each cell size were calculated with data available at the beginning of the season, at the first image date, at the second image date, and so on. Stepwise linear regression was used to select variables at each date that would constitute an appropriate model to predict yield. Results indicated that, at most dates, model accuracy was highest at the 100-m cell size. Remotely sensed data combined with weather data contributed much information to the models, particularly with data collected within 2.5 months of planting. The most appropriate model had an R² value of 0.63, and its average prediction error was about 0.5 bale/ha (0.2 bale/ac, or roughly 100 lb/ac)." @default.
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• W1966347846 date "2004-01-05" @default.
• W1966347846 modified "2023-09-27" @default.
• W1966347846 title "Remote sensing and weather information in cotton yield prediction" @default.
• W1966347846 doi "https://doi.org/10.1117/12.506984" @default.
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