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Remote Sensing for Agrometeorology II
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Remote Sensing for Agrometeorology II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 2373

Special Issue Editor

Prof. Dr. Nicolas R. Dalezios

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Thessaly, Pedion Areos, 38334 Volos, Greece
Interests: agrometeorological and hydrological modeling; stochastic and systems hydrology; environmental remote sensing; environmental hazards risk management and climate variability/change; impacts-mitigation-adaptation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agrometeorology is a multi-disciplinary scientific field which is currently receiving significant attention mainly due to climate change, among other reasons. Agrometeorology deals with meteorological, hydrological, edaphological, and biological factors and parameters which affect agricultural production, and examines the inter-relationship between agriculture, forestry, and the environment. Agrometeorology is a horizontal science, which applies atmospheric and soil physics to agriculture and combines physical and biological sciences. Similarly, at present, remote sensing is a very fast-evolving scientific and technological field with steadily increasing reliability, and there are more new systems every year with continuously improving temporal and spatial resolution.

The previous Special Issue, ‘Remote Sensing for Agrometeorology’, was a great success. The aim of this Special Issue is to foster advances in remote sensing science and technology for a range of practical applications and research investigations in agrometeorology. I would like to encourage both theoretical and applied research contributions, furthering knowledge on the use of this remote sensing science and technology in all disciplines of contemporary agrometeorology. Such contributions can be focused on various aspects, including, but not limited to: active and passive remote sensing data and methods (e.g., satellites, weather radar, SAR, UAV, and sensors); applications in environmental hazards affecting agriculture; agrometeorological simulation and modeling; decision support systems in agrometeorology; climate change: impact–mitigation–adaptation; precision agriculture; agroclimatic classification; software tool development for data collection and processing; as well as their applications.

Prof. Dr. Nicolas R. Dalezios
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing for agrometeorological simulation and modeling
  • remote sensing for monitoring and forecasting of agricultural production
  • remote sensing for agrohydrological simulation and modeling
  • weather radar applications in agrometeorology, agrohydrology, and agriculture
  • weather modification (hail suppression and precipitation enhancement) for agriculture
  • remote sensing for hydrometeorological hazards in agriculture (floods and excess rain, hail, storms, droughts, and desertification)
  • remote sensing for biophysical hazards in agriculture (frost, heatwaves, wildfires, and biohazards)
  • remote sensing for micrometeorology (e.g., canopy radiation transfer, turbulence near the ground, evapotranspiration, energy balance, and fluxes of trace gases)
  • remote sensing for biometeorology (e.g., the effect of weather and climate on plant distribution, crop yield, water-use efficiency, and plant phenology)
  • remote sensing for agrometeorological plant protection
  • remote sensing for aerobiology (e.g., pollen dispersion, spores, insects, and pesticides)
  • remote sensing for climate change and agriculture: impact–mitigation–adaptation
  • remote sensing for agroclimatic and hydroclimatic classification of plant cultivation zones
  • renewable energy aspects referring to meteorological and remote sensing analysis
  • remote sensing for precision agriculture
  • remote sensing for decision support systems (DSS) in agrometeorology
  • remotely sensed management of agrometeorological information
  • sensoring systems (active–passive sensors, e.g., satellites, weather radar, SAR, and UAV)
  • software tool development for data collection and processing in agrometeorology

Published Papers (2 papers)

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Research

26 pages, 11888 KiB  
Article
Remotely Sensed Agroclimatic Classification and Zoning in Water-Limited Mediterranean Areas towards Sustainable Agriculture
by Ioannis Faraslis, Nicolas R. Dalezios, Nicolas Alpanakis, Georgios A. Tziatzios, Marios Spiliotopoulos, Stavros Sakellariou, Pantelis Sidiropoulos, Nicholas Dercas, Alfonso Domínguez, José Antonio Martínez-López, Ramón López-Urrea, Fadi Karam, Hacib Amami and Radhouan Nciri
Remote Sens. 2023, 15(24), 5720; https://doi.org/10.3390/rs15245720 - 13 Dec 2023
Cited by 2 | Viewed by 926
Abstract
Agroclimatic classification identifies zones for efficient use of natural resources leading to optimal and non-optimal crop production. The aim of this paper is the development of a methodology to determine sustainable agricultural zones in three Mediterranean study areas, namely, “La Mancha Oriental” in [...] Read more.
Agroclimatic classification identifies zones for efficient use of natural resources leading to optimal and non-optimal crop production. The aim of this paper is the development of a methodology to determine sustainable agricultural zones in three Mediterranean study areas, namely, “La Mancha Oriental” in Spain, “Sidi Bouzid” in Tunisia, and “Bekaa” valley in Lebanon. To achieve this, time series analysis with advanced geoinformatic techniques is applied. The agroclimatic classification methodology is based on three-stages: first, the microclimate features of the region are considered using aridity and vegetation health indices leading to water-limited growth environment (WLGE) zones based on water availability; second, landform features and soil types are associated with WLGE zones to identify non-crop-specific agroclimatic zones (NCSAZ); finally, specific restricted crop parameters are combined with NCSAZ to create the suitability zones. The results are promising as compared with the current crop production systems of the three areas under investigation. Due to climate change, the results indicate that these arid or semi-arid regions are also faced with insufficient amounts of precipitation for supporting rainfed annual crops. Finally, the proposed methodology reveals that the employment and use of remote sensing data and methods could be a significant tool for quickly creating detailed, and up to date agroclimatic zones. Full article
(This article belongs to the Special Issue Remote Sensing for Agrometeorology II)
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20 pages, 8325 KiB  
Article
Evaluation of the Monitoring Capability of 20 Vegetation Indices and 5 Mainstream Satellite Band Settings for Drought in Spring Wheat Using a Simulation Method
by Chang Xiao, Yinan Wu and Xiufang Zhu
Remote Sens. 2023, 15(19), 4838; https://doi.org/10.3390/rs15194838 - 06 Oct 2023
Viewed by 934
Abstract
This study simulated the canopy reflectance of spring wheat at five distinct growth stages (jointing, booting, heading, flowering, and pustulation) and under four drought scenarios (no drought, mild drought, moderate drought, and severe drought) using the PROSAIL radiative transfer model, and it identified [...] Read more.
This study simulated the canopy reflectance of spring wheat at five distinct growth stages (jointing, booting, heading, flowering, and pustulation) and under four drought scenarios (no drought, mild drought, moderate drought, and severe drought) using the PROSAIL radiative transfer model, and it identified the wavelength range most sensitive to drought. Additionally, the efficacy of 5 mainstream satellites (Sentinel-2, Landsat 8, Worldview-2, MODIS, and GF-2) and 20 commonly utilized remote sensing vegetation indicators (NDVI, SAVI, EVI, ARVI, GVMI, LSWI, VSDI, NDGI, SWIRR, NDWI, PRI, NDII, MSI, WI, SRWI, DSWI, NDREI1, NDREI2, ZMI, and MTCI) in drought monitoring was evaluated. The results indicated that the spectral response characteristics of spring wheat canopy reflectance vary significantly across the growth stages. Notably, the wavelength ranges of 1405–1505 nm and 2140–2190 nm were identified as optimal for drought monitoring throughout the growth period. Considering only the spectral bands, MODIS band 7 was determined to be the most suitable satellite band for monitoring drought in spring wheat at different growth stages. Among the 20 indices examined, WI, MSI, and SRWI, followed by LSWI and GVMI calculated using MODIS bands 2 and 6 as well as bands 8 and 11 of Sentinel-2, demonstrated superior capabilities in differentiating drought scenarios. These conclusions have important implications because they provide valuable guidance for selecting remote sensing drought monitoring data and vegetation indices, and they present insights for future research on the design of new remote sensing indices for assisting drought monitoring and the configuration of remote sensing satellite sensors. Full article
(This article belongs to the Special Issue Remote Sensing for Agrometeorology II)
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