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Remote Sensing of Plant-Climate Interactions

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 11929

Special Issue Editors


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Guest Editor
Integrative Crop Ecophysiology Group, Department B.E.E.C.A. Plant Physiology Section, Faculty of Biology, University of Barcelona, Av Diagonal 643, 08028 Barcelona, Spain
Interests: remote sensing; plant ecophysiology; agriculture, forestry; plant phenotyping; spectroscopy and imaging spectroscopy; UAVs; machine learning; data fusion; data processing
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Guest Editor
University of Georgia, Department of Crop and Soil Sciences, 3105 Miller Plant Sciences Building, Athens, GA 30602, USA
Interests: evapotranspiration; remote sensing; agriculture; soil physics; crop science

Special Issue Information

Dear Colleagues,

The remote sensing dynamic processes related to the plant–climate interactions, such as evapotranspiration (ET) and studies on the interface of plants and the atmosphere, including climate change impacts on plant physiological processes (heat, water stress, plant biogenic volatile emissions), are key to understanding the water balance, energy budget, feedback mechanisms, and productivity of the vegetation land surface moving forward. Improving accuracy in assessing these dynamic processes has implications for water management in precision agriculture practices, impact mitigation of extreme events, agricultural management practices, as well as larger-scale modeling of the Earth’s climate and weather owing to its impact on land surface–atmosphere interactions and soil water content. For example, remote sensing has enabled estimation of ET on a global scale at high space–time resolution. Various ET estimation techniques have also been developed for remote sensing products, but the accuracy of these algorithms varies with land cover, hydroclimate, terrain, seasonality, and the space and time scale of remote sensing data. Additionally, calibration and validation of these algorithms remains a challenge because of (1) the limited availability of ground-based calibration and validation data, (2) space–time scale discrepancy between footprints of available ground-based measurements techniques like eddy covariance towers, lysimeter methods, scintillometer methods, and remote sensing data, and (3) scale discrepancy between various inputs of ET and water and heat stress estimation algorithms and remote sensing data.

This Special Issue invites studies focused on improving estimates of all plant–climate interface interactions, including but not limited to evapotranspiration and other assessments of water and heat stress from remote sensing platforms (satellite, UAV, or proximal sensing) under varying hydroclimates and land-cover conditions. We especially encourage studies that (1) provide guidelines for improved parameterizations of different existing algorithms for remote sensing data under different hydroclimates, seasons, and land cover conditions, (2) use advanced techniques like machine learning, data assimilation, data fusion for utilizing various sources of ground-based data or multiple remote sensing platforms for estimating, calibrating, and validating remote sensing data and (3) assess the accuracy of different plant–climate interface algorithms with varying space–time scale remote sensing inputs. Multimodel comparisons and feedback mechanisms under different land-use land cover, seasons, and hydroclimates are also encouraged.

  • Studies exploring the efficacy of different remote sensing-based products to compute water use efficiency and their comparison with water use efficiency products from platforms like ECOSTRESS are particularly invited;
  • Monitoring and modeling agroclimatic interactions with cultivars at different scales, including applications in high throughput plant phenotyping and precision agriculture;
  • Novel developments in software, sensors, platforms or methods towards assessing climate change impacts using different remote sensing approaches;
  • Advances in image segmentation and classification for the study of specific traits related to plant–climate interactions and the soil–plant–atmosphere continuum;
  • Integration of different scales of remote sensing measurements (e.g. satellite, UAV, ground-based measurements) towards further understanding scaling dynamics related to plant–climate interactions;
  • Comparisons of long-term vegetation activity based on remote sensing data and field-based measurements (permanent plots, eddy covariance, tree-ring parameters);
  • Remote sensing based smart apps for irrigation scheduling, phenotyping, and plant stress monitoring and their evolving role in a scenario of increased frequency of extreme events like flash droughts and floods.

Dr. Shawn C. Kefauver
Dr. Nandita Gaur
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. 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

  • Plant ecophysiology
  • Evapotranspiration
  • Crop and soil science
  • Agroforestry productivity
  • Climate change
  • Biogenic volatile organic compounds (BVOCs)
  • High throughput plant phenotyping
  • Precision agriculture
  • Unmanned aerial vehicles (UAVs)
  • Satellite remote sensing
  • Proximal imaging
  • Image data fusion
  • Smart apps

Published Papers (3 papers)

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Research

21 pages, 11518 KiB  
Article
Impacts of Heat and Drought on Gross Primary Productivity in China
by Xiufang Zhu, Shizhe Zhang, Tingting Liu and Ying Liu
Remote Sens. 2021, 13(3), 378; https://doi.org/10.3390/rs13030378 - 22 Jan 2021
Cited by 33 | Viewed by 3966
Abstract
Heat and drought stress, which often occur together, are the main environmental factors limiting the survival and growth of vegetation. Studies on the response of gross primary production (GPP) to extreme climate events such as heat and drought are highly significant for the [...] Read more.
Heat and drought stress, which often occur together, are the main environmental factors limiting the survival and growth of vegetation. Studies on the response of gross primary production (GPP) to extreme climate events such as heat and drought are highly significant for the identification of ecologically vulnerable regions, ecological risk assessments, and ecological environmental protection. We got 1982–2017 climatic data from the University of East Anglia Climatic Research Unit, Norwich, England, and GPP data from National Earth System Science Data Sharing Service Platform, Beijing, China. Using Theil–Sen median trend analysis and the Mann–Kendall test, we analyzed trends in temperature and the standardized precipitation/standardized precipitation evapotranspiration indices in the eight vegetation regions of China. Additionally, the response of GPP to the single and combined impacts of heat and drought were analyzed using multidimensional copula functions, and GPP reduction probabilities were estimated under different drought levels and heat intensities. The results showed that the probability of a drastic GPP reduction increases with increasing drought levels and heat intensities. The combined impacts of heat and drought on vegetation productivity is greater than the impacts of either drought or heat alone and presents a nonlinear superposition of the two extremes. The impact of heat on GPP is not evident when the drought level is high. The temperate grassland and warm temperate deciduous broad-leaved forest regions are the most sensitive regions to drought and heat in China. This study provides a scientific basis for the comprehensive evaluation of the risk of GPP reduction under the single and combined impacts of heat stress and drought stress. Full article
(This article belongs to the Special Issue Remote Sensing of Plant-Climate Interactions)
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15 pages, 7339 KiB  
Article
Seasonal Cropland Trends and Their Nexus with Agrometeorological Parameters in the Indus River Plain
by Qiming Zhou and Ali Ismaeel
Remote Sens. 2021, 13(1), 41; https://doi.org/10.3390/rs13010041 - 24 Dec 2020
Cited by 5 | Viewed by 2318
Abstract
The fine-scale insights of existing cropland trends and their nexus with agrometeorological parameters are of paramount importance in assessing future food security risks and analyzing adaptation options under climate change. This study has analyzed the seasonal cropland trends in the Indus River Plain [...] Read more.
The fine-scale insights of existing cropland trends and their nexus with agrometeorological parameters are of paramount importance in assessing future food security risks and analyzing adaptation options under climate change. This study has analyzed the seasonal cropland trends in the Indus River Plain (IRP), using multi-year remote sensing data. A combination of Sen’s slope estimator and Mann–Kendall test was used to quantify the existing cropland trends. A correlation analysis between enhanced vegetation index (EVI) and 9 agrometeorological parameters, derived from reanalysis and remote sensing data, was conducted to study the region’s cropland-climate nexus. The seasonal trend analysis revealed that more than 50% of cropland in IRP improved significantly from the year 2003 to 2018. The lower reaches of the IRP had the highest fraction of cropland, showing a significant decreasing trend during the study period. The nexus analysis showed a strong correlation of EVI with the evaporative stress index (ESI) during the water-stressed crop season. Simultaneously, it exhibited substantial nexus of EVI with actual evapotranspiration (AET) during high soil moisture crop season. Temperature and solar radiation had a negative linkage with EVI response. In contrast, a positive correlation of rainfall with EVI trends was spatially limited to the IRP’s upstream areas. The relative humidity had a spatially broad positive correlation with EVI compare to other direct climatic parameters. The study concluded that positive and sustainable growth in IRP croplands could be achieved through effective agriculture policies to address spatiotemporal AET anomalies. Full article
(This article belongs to the Special Issue Remote Sensing of Plant-Climate Interactions)
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24 pages, 3964 KiB  
Article
Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain
by Joel Segarra, Jon González-Torralba, Íker Aranjuelo, Jose Luis Araus and Shawn C. Kefauver
Remote Sens. 2020, 12(14), 2278; https://doi.org/10.3390/rs12142278 - 15 Jul 2020
Cited by 15 | Viewed by 4922
Abstract
Reliable methods for estimating wheat grain yield before harvest could help improve farm management and, if applied on a regional level, also help identify spatial factors that influence yield. Regional grain yield can be estimated using conventional methods, but the typical process is [...] Read more.
Reliable methods for estimating wheat grain yield before harvest could help improve farm management and, if applied on a regional level, also help identify spatial factors that influence yield. Regional grain yield can be estimated using conventional methods, but the typical process is complex and labor-intensive. Here we describe the development of a streamlined approach using publicly accessible agricultural data, field-level yield, and remote sensing data from Sentinel-2 satellite to estimate regional wheat grain yield. We validated our method on wheat croplands in Navarre in northern Spain, which features heterogeneous topography and rainfall. First, this study developed stepwise multilinear equations to estimate grain yield based on various vegetation indices, which were measured at various phenological stages in order to determine the optimal timings. Second, the most suitable model was used to estimate grain yield in wheat parcels mapped from Sentinel-2 satellite images. We used a supervised pixel-based random forest classification and the estimates were compared to government-published post-harvest yield statistics. When tested, the model achieved an R2 of 0.83 in predicting grain yield at field level. The wheat parcels were mapped with an accuracy close to 86% for both overall accuracy and compared to official statistics. Third, the validated model was used to explore potential relationships of the mapped per-parcel grain yield estimation with topographic features and rainfall by using geographically weighted regressions. Topographic features and rainfall together accounted for an average for 11 to 20% of the observed spatial variation in grain yield in Navarre. These results highlight the ability of our method for estimating wheat grain yield before harvest and determining spatial factors that influence yield at the regional scale. Full article
(This article belongs to the Special Issue Remote Sensing of Plant-Climate Interactions)
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