Abstract
In site-specific management, rapid and accurate identification of crop stress at a large scale is critical. Radiometric ground-based data and satellite imaging with advanced spatial and spectral resolution allow for a deeper understanding of crop stress and the level of stress in a given area. This research aimed to assess the potential of radiometric ground-based data and high-resolution QuickBird satellite imagery to determine the leaf area index (LAI), biomass fresh weight (BFW) and chlorophyll meter (Chlm) of maize across well-irrigated, water stress and salinity stress areas in the Nile Delta of Egypt. Partial least squares regression (PLSR) and multiple linear regression (MLR) were evaluated to estimate the three measured traits based on vegetation spectral indices (vegetation-SRIs) derived from these methods and their combination. Maize field visits were conducted during the summer seasons from 28 to 30 July 2007 to collect ground reference data concurrent with the acquisition of radiometric ground-based measurements and QuickBird satellite imagery. The results showed that the majority of vegetation-SRIs extracted from radiometric ground-based data and high-resolution satellite images were more effective in estimating LAI, BFW, and Chlm. In general, the vegetation-SRIs of radiometric ground-based data showed higher R2 with measured traits compared to the vegetation-SRIs extracted from high-resolution satellite imagery. The coefficient of determination (R2) of the significant relationships between vegetation-SRIs of both methods and three measured traits varied from 0.64 to 0.89. For example, with QuickBird high-resolution satellite images, the relationships of the green normalized difference vegetation index (GNDVI) with LAI and BFW showed the highest R2 of 0.80 and 0.84, respectively. Overall, the ground-based vegetation-SRIs and the satellite-based indices were found to be in good agreement to assess the measured traits of maize. Both the calibration (Cal.) and validation (Val.) models of PLSR and MLR showed the highest performance in predicting the three measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery. For example, validation (Val.) models of PLSR and MLR showed the highest performance in predicting the measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery with R2 (0.91) of both methods for LAI, R2 (0.91–0.93) for BFW respectively, and R2 (0.82) of both methods for Chlm. The models of PLSR and MLR showed approximately the same performance in predicting the three measured traits and no clear difference was found between them and their combinations. In conclusion, the results obtained from this study showed that radiometric ground-based measurements and high spectral resolution remote-sensing imagery have the potential to offer necessary crop monitoring information across well-irrigated, water stress and salinity stress in regions suffering lack of freshwater resources.
Highlights
Moisture and salinity stress are two of the most limiting factors in crop production.Both are significant factors influencing crop growth and final productivity in arid and semi-arid climates
Fifteen fields in this study, which were exposed to full irrigation and different stress conditions such as water and salinity stress, were chosen to present the variation in the values of leaf area index (LAI), biomass fresh weight (BFW) and chlorophyll meter (Chlm) of maize
The results showed that the majority of SRIs extracted from radiometric ground-based data and highresolution QuickBird satellite imagery were more effective in estimating LAI, BFW, and
Summary
Moisture and salinity stress are two of the most limiting factors in crop production.Both are significant factors influencing crop growth and final productivity in arid and semi-arid climates. Estimates of crop biophysical and biochemical traits, as well as other related properties, from remotely sensed data are critical for avoiding crop yield losses [7,8]. Satellite platforms of high spatial and spectral resolution and radiometric ground-based data capabilities are suitable for estimating crop traits and many studies have demonstrated the potential of remotely sensed data in mapping vegetation [3,7,8,9]. As monitoring plant status using the sample-point technique at a regional scale is tedious, laborious, and expensive, several studies have demonstrated the potential of remotely sensed data to assess plant traits
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