Integration of Radiometric Ground-Based Data and High-Resolution QuickBird Imagery with Multivariate Modeling to Estimate Maize Traits in the Nile Delta of Egypt.

Adel H Elmetwalli, Andrew N Tyler, Farahat S Moghanm, Saad A M Alamri, Ebrahem M Eid, Salah Elsayed

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Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Adel H ElmetwalliAgricultural Engineering Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt.

Andrew N TylerSchool of Biological and Environmental Sciences, University of Stirling, Stirling, Scotland FK9 4LA, UK.

Farahat S MoghanmSoil and Water Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Sheikh 33516, Egypt.ORCID 0000-0003-3318-5358

Saad A M AlamriBiology Department, College of Science, King Khalid University, Abha 61321, Saudi Arabia.

Ebrahem M EidBiology Department, College of Science, King Khalid University, Abha 61321, Saudi Arabia.ORCID 0000-0003-2452-4469

Salah ElsayedAgricultural Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Minufiya 32897, Egypt.

Funding

King Khalid University IFP-KKU-2020/3

6 · The paper itself

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 R

Indexed as

Satellite ImageryZea maysChlorophyllEgyptLeast-Squares AnalysisChlorophyllmaizePLSR and MLRQuickBird imagerysalinity stressspectral indiceswater stress

Identifiers

PMID34204099

DOI10.3390/s21113915

PMCPMC8200994

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.