Dr. Javed Iqbal Profile

Dr. Javed Iqbal

Research Assistant at Mississippi State Univ

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Publications (3)

Proceedings Article | 9 November 2004 Paper

Remote sensing input to GIS-integrated cotton growth model: preliminary results

Swapna Gogineni, J. Alex Thomasson, Javed Iqbal, James Wooten, Bulli Kolla, Ruixiu Sui

Proceedings Volume 5544, (2004) https://doi.org/10.1117/12.562692

KEYWORDS: Data modeling, Remote sensing, Geographic information systems, Soil science, Satellite imaging, Satellites, Earth observing sensors, Agriculture, Human-machine interfaces, Systems modeling

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Proceedings Article | 22 December 2003 Paper

Remote sensing in dryland cotton: relation to yield potential and soil properties

John Read, Javed Iqbal, John Thomasson, Jeffrey Willers, Johnie Jenkins

Proceedings Volume 5153, (2003) https://doi.org/10.1117/12.505875

KEYWORDS: Reflectivity, Soil science, Near infrared, Remote sensing, Multispectral imaging, Radiometry, Data modeling, Spatial resolution, Image analysis, Digital imaging

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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.

Proceedings Article | 22 December 2003 Paper

Spatial validation of cotton simulation model in relation to soils and multispectral imagery

Javed Iqbal, Frank Whisler, John Read, John Thomasson, Ishtiaq Ali, Johnie Jenkins, Dorgelis Villarroel

Proceedings Volume 5153, (2003) https://doi.org/10.1117/12.505913

KEYWORDS: Soil science, Multispectral imaging, Data modeling, Solar radiation models, Vegetation, Hydrology, Data acquisition, Image acquisition, Biological research, Geographic information systems

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Field studies were conducted in 1998 and 1999 in Livingston Field at Perthshire Farm, Bolivar County which is located in west-central Mississippi along the Mississippi River. It is a 162 ha field and has a 2-m elevation range. The dominant soil series of the field are Commerce silt loam, Robinsonville fine sandy loam and Souva silty clay loam. The objectives of the study were to (1) compare GOSSYM simulated yield with actual yield, (2) study spatial and temporal pattern of cotton crop across two growing seasons using multispectral imagery, 3) predict field based lint yield from remote sensed data, and determine age of the crop most suitable for aerial image acquisition in predicting yield and/or discriminating differences in cotton growth. Two transects were selected for GOSSYM study, each containing twelve sites. A 1-m length of single row plot was established at each profile. Plant mapping was done five times in 1998 and seven times in 1999 growing seasons. GOSSYM simulation runs were made for each profile and compared with actual crop parameters using root mean square error (RMSE). Results based on averaging common soil mapping units indicate that GOSSYM accuracy in predicting yield varied from 0.45 bales acre^-1 to 0.61 bales acre^-1. To monitor and estimate the biophysical condition of the cotton crop, airborne multispectral images were acquired on 10 dates in 1998 and 17 dates in 1999 from April to September. In both years site-specific yield and normalized difference vegetation index (NDVI) were significantly (p < 0.0001) correlated in July. Changes in NDVI in 1999 across sampling dates for the different sites showed the least distinctiveness due to somewhat wetter weather conditions, as compared to drier weather in 1998. Crop growing in or near the drainage areas were low in NDVI and lint yield. Multispectral images acquired between ~ 300 - 600 growing degree days above 60°C (GDD60) may be useful decision tools for replanting certain areas of the field, especially in dry weather conditions when variability in crop growth pattern is enhanced due to spatial variability in soil texture, which influences the capacity of a soil to hold moisture and to release it to plants for growth. Results suggest that 2-3 multispectral images acquired between 800 and 1500 GDD60 can be used to monitor crop health and predict yield in a normal weather condition.

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