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Atmosphere
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Agrometeorology and Remote Sensing of Land–Atmosphere
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Agrometeorology and Remote Sensing of Land–Atmosphere

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biosphere/Hydrosphere/Land–Atmosphere Interactions".

Deadline for manuscript submissions: closed (1 July 2024) | Viewed by 2909

Special Issue Editors

Dr. Jhon Lennon Bezerra Da Silva

E-Mail Website
Guest Editor
Cerrado Irrigation Graduate Program, Goiano Federal Institute, Campus Ceres, GO-154, km 218—Zona Rural, Ceres 76300-000, Brazil
Interests: remote sensing; satellite images; climate change; radiation and energy balance; vegetation and water indices; evapotranspiration; rainfall and drought
Special Issues, Collections and Topics in MDPI journals
Dr. Marcos Vinícius Da Silva

E-Mail Website
Guest Editor
Department of Forestry Sciences, Federal University of Campina Grande, Av. Universitária, s/n, Santa Cecília, Patos 58708-110, Brazil
Interests: remote sensing; satellite images; vegetation and water indices; climate change; statistical modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Márcio Mesquita

E-Mail Website
Guest Editor
Department of Agronomy at the Federal University of Goiás (UFG), Goiânia 74690-900, Brazil
Interests: agrometeorology

Special Issue Information

Dear Colleagues,

The main objective of this Special Issue, based on the suggested topic of "Agrometeorology and Remote Sensing of Land–Atmosphere", is the environmental monitoring and recovery of agricultural soils and degraded areas. The aim is to determine agrometeorological and spectral parameters between the surface and atmosphere through surface meteorological data and from the use of geoprocessing and its geotechnologies, such as the sets of remote sensing techniques and satellite images, with a special focus on the multispectral monitoring of the spatio-temporal dynamics of land cover and use, especially of the arid and semiarid regions, which suffer from, among other things, the severe drought events and effects of desertification. Scientific advances in this sense will mainly subsidize studies of climate and environmental forecasts, agricultural monitoring, as well as studies of climate change and of land cover and use.

Dr. Jhon Lennon Bezerra Da Silva
Dr. Marcos Vinícius Da Silva
Dr. Márcio Mesquita
Guest Editors

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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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

  • agrometeorology and climatology
  • geoprocessing
  • remote sensing of the atmosphere
  • climate variability
  • hydrological modelling
  • land degradation

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Published Papers (2 papers)

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Research

19 pages, 5422 KiB  
Article
Assessing the Potential for Photochemical Reflectance Index to Improve the Relationship between Solar-Induced Chlorophyll Fluorescence and Gross Primary Productivity in Crop and Soybean
by Jidai Chen, Lizhou Huang, Qinwen Zuo and Jiasong Shi
Atmosphere 2024, 15(4), 463; https://doi.org/10.3390/atmos15040463 - 9 Apr 2024
Viewed by 994
Abstract
Photosynthesis is influenced by dynamic energy allocation under various environmental conditions. Solar-induced chlorophyll fluorescence (SIF), an important pathway for dissipating absorbed energy, has been extensively used to evaluate gross primary productivity (GPP). However, the potential for photochemical reflectance index (PRI), as an indicator [...] Read more.
Photosynthesis is influenced by dynamic energy allocation under various environmental conditions. Solar-induced chlorophyll fluorescence (SIF), an important pathway for dissipating absorbed energy, has been extensively used to evaluate gross primary productivity (GPP). However, the potential for photochemical reflectance index (PRI), as an indicator of non-photochemical quenching (NPQ), to improve the SIF-based GPP estimation, has not been thoroughly investigated. In this study, using continually tower-based observations, we examined how PRI affected the link between SIF and GPP for corn and soybean at half-hourly and daily timescales. The relationship of GPP to SIF and PRI is impacted by stress indicated by vapor pressure deficit (VPD) and crop water stress index (CWSI). Moreover, the ratio of GPP to SIF of corn was more sensitive to PRI compared to soybean. Whether in Pearson or Partial correlation analysis, the relationships of PRI to the ratio of GPP to SIF were almost all significant, regardless of controlling structural-physiological (stomatal conductance, vegetation indices) and environmental variables (light intensity, etc.). Therefore, PRI significantly affects the SIF–GPP relationship for corn (r > 0.31, p < 0.01) and soybean (r > 0.22, p < 0.05). After combining SIF and PRI using the multi-variable linear model, the GPP estimation has been largely improved (the coefficient of determination, abbreviated as R2, increased from 0.48 to 0.49 to 0.78 to 0.84 and the Root Mean Square Error, abbreviated as RMSE, decreased from 6.38 to 10.22 to 3.56 to 6.60 μmol CO2·m−2·s−1 for corn, R2 increased from 0.54 to 0.62 to 0.78 to 0.82 and RMSE decreased from 6.25 to 9.59 to 4.34 to 6.60 μmol CO2·m−2·s−1 for soybean). It suggests that better GPP estimations for corn and soybean can be obtained when SIF is combined with PRI. Full article
(This article belongs to the Special Issue Agrometeorology and Remote Sensing of Land–Atmosphere)
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17 pages, 5901 KiB  
Article
Seasonality of Biophysical Parameters in Extreme Years of Precipitation in Pernambuco: Relations, Regionalities, and Variability
by Alan Cézar Bezerra, Jhon Lennon Bezerra da Silva, Douglas Alberto de Oliveira Silva, Cristina Rodrigues Nascimento, Eberson Pessoa Ribeiro, Josiclêda Domiciano Galvincio, Marcos Vinícius da Silva, Henrique Fonseca Elias de Oliveira, Márcio Mesquita, José Francisco de Oliveira-Júnior, Alexsandro Claudio dos Santos Almeida, Pabrício Marcos Oliveira Lopes and Geber Barbosa de Albuquerque Moura
Atmosphere 2023, 14(12), 1712; https://doi.org/10.3390/atmos14121712 - 21 Nov 2023
Cited by 3 | Viewed by 1336
Abstract
This study analyzed the seasonality of biophysical parameters in the extreme years of precipitation and the relationship with the monthly precipitation of the state of Pernambuco at the regional level (Pernambuco) and homogeneous precipitation zones: zone 1—semiarid, zone 2—transition and zone 3—coastal. For [...] Read more.
This study analyzed the seasonality of biophysical parameters in the extreme years of precipitation and the relationship with the monthly precipitation of the state of Pernambuco at the regional level (Pernambuco) and homogeneous precipitation zones: zone 1—semiarid, zone 2—transition and zone 3—coastal. For this, the biophysical parameters at the monthly level in the extreme years, 2004 (wet) and 2012 (dry) were related to precipitation data of 45 rainfall stations. Using the Google Earth Engine platform, we calculate the biophysical parameters with MODIS products: Albedo, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI) and surface temperature (ST). Considering the most critical period, between September and December, of a wet year (2004) with a dry year (2012), there is an average reduction of 14% of vegetation indices (NDVI, EVI and SAVI), a 60% reduction in NDWI, an increase of 4% in albedo and 3% in surface temperature. For monitoring the water conditions of the state of Pernambuco, the most appropriate biophysical parameter is the NDWI index and surface temperature. In addition to NDWI, it is recommended to use EVI for semiarid areas (zone 1) and ST for coastal areas (Zones 2 and 3). Full article
(This article belongs to the Special Issue Agrometeorology and Remote Sensing of Land–Atmosphere)
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