Skip to main content
ARS Home » Pacific West Area » Pendleton, Oregon » Columbia Plateau Conservation Research Center » Research » Publications at this Location » Publication #231576
Title: Sensitivity of Ground-Based Remote Sensing Estimates of Wheat Chlorophyll Content to Variation in Soil Reflectance

Author
item EITEL, JAN - UNIVERSITY OF IDAHO
item Long, Daniel
item GESSLER, P - UNIVERSITY OF IDAHO
item HUNT, EARLE - 1265-06-00
item BROWN, DAVID - WASHINGTON STATE UNIV

Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/1/2009
Publication Date: 9/1/2009
Citation: Eitel, J.U., Long, D.S., Gessler, P.E., Hunt, E.R., Brown, D.J. 2009. Sensitivity of Ground-Based Remote Sensing Estimates of Wheat Chlorophyll Content to Variation in Soil Reflectance. Soil Science Society of America Journal. 73:1715-1723

Interpretive Summary: Ground-based, optical sensing is available for sensing crop canopy spectral reflectance in real-time and determining chlorophyll content and nitrogen (N) status. This information is then applied into precision N fertilization, which promises to improve N use efficiency, increase profitability, and minimize N losses to the environment. In dryland environments, crop cover (ground area covered by the crop) is rarely 100%- a certain amount of crop spectral reflectance is comprised of background reflectance from the soil. The influence of soil background reflectance on ground-based remote sensing of crop chlorophyll content was investigated with use of a crop canopy reflectance model and the known spectral reflectance from 121 soils in wheat production areas in the United States. The simulation results showed that variation in crop cover was a larger source of error than soil reflectance (97% for crop cover vs. <6% for soil). Good relations were found for spectral indices that are formed from the combination of one index that is more sensitive to variation in crop cover with another that is more sensitive to variation in chlorophyll. The results demonstrate that ground-based sensing can be used to predict crop chlorophyll over a wide range of soil types within a farm field provided combined indices are used that accommodate variation due to soil reflectance and crop cover.

Technical Abstract: Most ground-based optical sensors utilize spectral indices (SI) to estimate wheat (Triticum aestivum L.) chlorophyll a and b content (Cab) critical for advising in-season nitrogen fertilizer needs. These indices are sensitive to Cab, leaf area index (LAI), and soil background variation, but relative sensitivities to these three factors vary for a given index. Combining a SI primarily sensitive to Cab with an index primarily sensitive to LAI has been shown to improve chlorophyll estimates. However, relatively little is known about the sensitivity of these combined indices to soil background effects. The objective of this study was to evaluate the sensitivity of combined spectral indices to variation in soil reflectance and how this may affect overall index performance for ground-based sensing of Cab in wheat. Selected spectral indices were extracted from spectra simulated by the PROSPECT+SAIL radiative-transfer model for various LAI and a total of 121 dry soil surface reflectance spectra. These spectra were selected to represent the diversity of soils across the major wheat growing areas in the United States. Soil properties and reflectance varied widely among the diverse soil types indicated by the high index variation for LAI values < 1.5. The Normalized Difference Red Edge Index/Normalized Difference Vegetation Index (NDRE/NDVI) was least affected by soil background variation. Overall, soil background variation contributed considerably less to the observed index variability (<6%) than LAI (<97%). The combined indices NDRE/NDVI and the Modified Chlorophyll Absorption Ratio Index/Second Modified Triangular Vegetation Index (MCARI/MTVI2) accounted for most of the variance attributable to both soil background and LAI and showed high sensitivity to chlorophyll variation. This suggests that ground sensing of chlorophyll may be improved through the use of combined indices to accommodate extraneous variation due to soil reflectance and LAI. Further research is needed to evaluate the effect of soil moisture, surface roughness, residue, growth stage and shadow on the studied indices.