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Proceedings Paper

Remote sensing in dryland cotton: relation to yield potential and soil properties
Author(s): John J. Read; Javed Iqbal; John A. Thomasson; Jeffrey L. Willers; Johnie N. Jenkins
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Paper Abstract

The use of soil and topography information to explain crop yield variation across fields is often applied for crop management purposes. Remote sensed data is a potential source of information for site-specific crop management, providing both spatial and temporal information about soil and crop condition. Studies were conducted in a 104-acre (42-hectare) dryland cotton field in 2001 and 2002 in order to (1) qualitatively assess the spatial variability of soil physical properties from kriged estimates, (2) compare actual yields with normalized difference vegetation reflectance indices (NDVI) obtained from multispectral imagery and from in situ radiometer data, and (3) predict site-specific cotton yields using a crop simulation model, GOSSYM. An NDVI map of soybean in 2000 obtained from a multispectral image was used to establish four sites in each low, medium and high NDVI class. These 12 sites were studied in 2001 and 12 more sites selected at random were studied in 2002 (n = 24). Site-specific measurements included leaf area index (LAI), canopy hyperspectral reflectance, and three-band multispectral image data for green, red, and near-infrared reflectance wavebands at spatial resolutions of 2 m in 2001 and 0.5 m in 2002. Imagery was imported into the image analysis software Imagine (ERDAS, v. 8.5) for georegistration and image analysis. A 6x6 pixels (144 m2) area of interest was established on top of each field plot site and digital numbers (DN) from reflectance imagery were extracted from each band for derivation of NDVI maps for each of four sampling dates. Lint yield from each plot site was collected by hand and also by a cotton picker equipped with AgLeader yield monitor and OmniStar differential global positioning system. We found plant height, leaf area index, and lint yield were closely associated with NDVI maps and with NIR band values acquired from either an aircraft or handheld (GER-1500) sensor during peak bloom in mid July. Results indicate NDVI and NIR bands could be used to produce estimated field maps of plant height, leaf area index and yield, which offer a potentially attractive mid-season management tool for site specific farming in dryland cotton.

Paper Details

Date Published: 22 December 2003
PDF: 12 pages
Proc. SPIE 5153, Ecosystems' Dynamics, Agricultural Remote Sensing and Modeling, and Site-Specific Agriculture, (22 December 2003); doi: 10.1117/12.505875
Show Author Affiliations
John J. Read, U.S. Dept. of Agriculture (United States)
Javed Iqbal, Mississippi State Univ. (United States)
John A. Thomasson, Mississippi State Univ. (United States)
Jeffrey L. Willers, USDA Agricultural Research Service (United States)
Johnie N. Jenkins, USDA Agricultural Research Service (United States)


Published in SPIE Proceedings Vol. 5153:
Ecosystems' Dynamics, Agricultural Remote Sensing and Modeling, and Site-Specific Agriculture
Wei Gao; David R. Shaw, Editor(s)

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