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Biophysical and yield information for precision farming from near-real-time and historical Landsat TM images

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Abstract:

The main goal of this study was to quantify within and between field variability in mapping agricultural crop types, their biophysical characteristics, and yield for precision-farming applications using near-real-time and historical (archival) Landsat Thematic Mapper (TM) images. Data for six crops (wheat, barley, chickpea, lentil, vetch and cumin) were gathered from a representative benchmark study area in the semi-arid environment of the world. Spectro-biophysical and yield models were established for each crop using a near-real-time TM image of 6 April 1998 acquired to coincide with an extensive ground data collection campaign. The models developed using this near-real-time acquisition were then used to extrapolate and quantify characteristics in the historical Landsat TM images of 5 April 1986 and 4 May 1988 acquired for the same area with limited ground data, thus adding scientific and commercial value to archival TM images. A farm-by-farm (or pixel-by-pixel) within and between field variability in agricultural land cover, biophysical quantities [e.g. biomass and Leaf Area Index (LAI)] and yield was established and illustrated. For the near-real-time image of 1998: (a) quantitative biophysical characteristics such as LAI and biomass were mapped at 81% overall accuracy (Khat=0.76) or higher; (b) within field variability (commission errors) was mapped with an accuracy between 74-100%; and (c) between field variability (omission errors) was mapped with an accuracy between 76-100%. Temporal variability in biomass and LAI were mapped for the study area and highlighted for individual farms. Significant relationships existed between grain yields measured using field-based combine-mounted sensors and Landsat TM derived indices. The results demonstrate the ability of using near-real-time and historical Landsat TM images for obtaining quantitative biophysical and yield information that highlight within and between field variability, which is of critical importance in precision-farming applications.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/01431160710155974

Affiliations: Center for Earth Observation (CEO), Department of Geology and Geophysics, Kline Geology Laboratory, PO Box 208109, 210 Whitney Avenue, Yale University, New Haven, CT 06520-8109, USA;, Email: prasad.thenkabail@yale.edu

Publication date: July 1, 2003

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