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Utility of Remote Sensing in Predicting Crop and Soil Characteristics

Utility of Remote Sensing in Predicting Crop and Soil Characteristics Remote sensing during the production season can provide visual indications of crop growth along with the geographic locations of those areas. A grid coordinate system was used to sample cotton and soybean fields to determine the relationship between spectral radiance, soil parameters, and cotton and soybean yield. During the 2 years of this study, mid- to late-season correlation coefficients between spectral radiance and yield generally ranged from 0.52 to 0.87. These correlation coefficients were obtained using the green–red ratio and a vegetation index similar to the normalized difference vegetation index (NDVI) using the green and red bands. After 102 days after planting (DAP), the ratio vegetation index (RVI), difference vegetation index (DVI), NDVI, and soil-adjusted vegetation index (SAVI) generally provided correlation coefficients from 0.54 to 0.87. Correlation coefficients for cotton plant height measurements taken 57 and 66 DAP during 2000 ranged from 0.51 to 0.76 for all bands, ratios, and indices examined, with the exception of Band 4 (720 nm). The most consistent correlation coefficients for soybean yield were obtained 89–93 DAP, corresponding to peak vegetative production and early pod set, using RVI, DVI, NDVI, and SAVI. Correlation coefficients generally ranged from 0.52 to 0.86. When the topographic features and soil nutrient data were analyzed using principal component analysis (PCA), the interaction between the crop canopy, topographic features, and soil parameters captured in the imagery allowed the formation of predictive models, indicating soil factors were influencing crop growth and could be observed by the imagery. The optimum time during 1999 and 2000 for explaining the largest amount of variability for cotton growth occurred during the period from first bloom to first open boll, with R values ranging from 0.28 to 0.70. When the PCA-stepwise regression analysis was performed on the soybean fields, R 2 values were obtained ranging from 0.43 to 0.82, 15 DAP, and ranged from 0.27 to 0.78, 55–130 DAP. The use of individual bands located in the green, red, and NIR, ratios such as RVI and DVI, indices such as NDVI, and stepwise regression procedures performed on the cotton and soybean fields performed well during the cotton and soybean production season, though none of these single bands, ratios, or indices was consistent in the ability to correlate well with crop and soil characteristics over multiple dates within a production season. More research needs to be conducted to determine whether a certain image analysis method will be needed on a field-by-field basis, or whether multiple analysis procedures will need to be performed for each imagery date in order to provide reliable estimates of crop and soil characteristics. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Utility of Remote Sensing in Predicting Crop and Soil Characteristics

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References (55)

Publisher
Springer Journals
Copyright
Copyright © 2003 by Kluwer Academic Publishers
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
DOI
10.1023/A:1026387830942
Publisher site
See Article on Publisher Site

Abstract

Remote sensing during the production season can provide visual indications of crop growth along with the geographic locations of those areas. A grid coordinate system was used to sample cotton and soybean fields to determine the relationship between spectral radiance, soil parameters, and cotton and soybean yield. During the 2 years of this study, mid- to late-season correlation coefficients between spectral radiance and yield generally ranged from 0.52 to 0.87. These correlation coefficients were obtained using the green–red ratio and a vegetation index similar to the normalized difference vegetation index (NDVI) using the green and red bands. After 102 days after planting (DAP), the ratio vegetation index (RVI), difference vegetation index (DVI), NDVI, and soil-adjusted vegetation index (SAVI) generally provided correlation coefficients from 0.54 to 0.87. Correlation coefficients for cotton plant height measurements taken 57 and 66 DAP during 2000 ranged from 0.51 to 0.76 for all bands, ratios, and indices examined, with the exception of Band 4 (720 nm). The most consistent correlation coefficients for soybean yield were obtained 89–93 DAP, corresponding to peak vegetative production and early pod set, using RVI, DVI, NDVI, and SAVI. Correlation coefficients generally ranged from 0.52 to 0.86. When the topographic features and soil nutrient data were analyzed using principal component analysis (PCA), the interaction between the crop canopy, topographic features, and soil parameters captured in the imagery allowed the formation of predictive models, indicating soil factors were influencing crop growth and could be observed by the imagery. The optimum time during 1999 and 2000 for explaining the largest amount of variability for cotton growth occurred during the period from first bloom to first open boll, with R values ranging from 0.28 to 0.70. When the PCA-stepwise regression analysis was performed on the soybean fields, R 2 values were obtained ranging from 0.43 to 0.82, 15 DAP, and ranged from 0.27 to 0.78, 55–130 DAP. The use of individual bands located in the green, red, and NIR, ratios such as RVI and DVI, indices such as NDVI, and stepwise regression procedures performed on the cotton and soybean fields performed well during the cotton and soybean production season, though none of these single bands, ratios, or indices was consistent in the ability to correlate well with crop and soil characteristics over multiple dates within a production season. More research needs to be conducted to determine whether a certain image analysis method will be needed on a field-by-field basis, or whether multiple analysis procedures will need to be performed for each imagery date in order to provide reliable estimates of crop and soil characteristics.

Journal

Precision Agriculture – Springer Journals

Published: Oct 3, 2004

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