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Integration of Radiometric Ground-Based Data and High-Resolution QuickBird Imagery with Multivariate Modeling to Estimate Maize Traits in the Nile Delta of Egypt.
Elmetwalli, Adel H; Tyler, Andrew N; Moghanm, Farahat S; Alamri, Saad A M; Eid, Ebrahem M; Elsayed, Salah.
Afiliação
  • Elmetwalli AH; Agricultural Engineering Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt.
  • Tyler AN; School of Biological and Environmental Sciences, University of Stirling, Stirling, Scotland FK9 4LA, UK.
  • Moghanm FS; Soil and Water Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Sheikh 33516, Egypt.
  • Alamri SAM; Biology Department, College of Science, King Khalid University, Abha 61321, Saudi Arabia.
  • Eid EM; Biology Department, College of Science, King Khalid University, Abha 61321, Saudi Arabia.
  • Elsayed S; Botany Department, Faculty of Science, Kafrelsheikh University, Kafr El-Sheikh 33516, Egypt.
Sensors (Basel) ; 21(11)2021 Jun 06.
Article em En | MEDLINE | ID: mdl-34204099
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Zea mays / Imagens de Satélites Tipo de estudo: Prognostic_studies País/Região como assunto: Africa Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Zea mays / Imagens de Satélites Tipo de estudo: Prognostic_studies País/Região como assunto: Africa Idioma: En Ano de publicação: 2021 Tipo de documento: Article