Special Issue "Remote Sensing for Crop Stress Monitoring and Yield Prediction"

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: 28 February 2022.

Special Issue Editors

Dr. Jianxiu Qiu
E-Mail Website
Guest Editor
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Interests: drought detection; microwave-based soil moisture retrieval; land data assimilation
Dr. Xiaohu Zhang
E-Mail Website
Guest Editor
National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Interests: crop monitoring; machine learning; remote sensing; detection and mapping; spatial analysis
Dr. Zhenzhong Zeng
E-Mail Website
Guest Editor
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Interests: water and agriculture resources; biosphere-atmosphere interactions; global change and the Earth system
Dr. Gabriel de Oliveira
E-Mail Website
Guest Editor
Department of Earth Sciences, University of South Alabama, Mobile, AL 36688, USA
Interests: vegetation dynamics; water and carbon balances; ecophysiological consequences of climate change on terrestrial ecosystems
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Understanding and predicting crop stress and yield with changing climate is critical for designing effective adaptation and mitigation strategies. Recently, numerous studies have been conducted for multi-scale crop stress monitoring and climate impact assessment, using various data sources and novel algorithms. This Special Issue is designed to synthesize recent advances in utilizing remote sensing for cropland and irrigation mapping, crop growth assessment, crop water and heat stress monitoring, as well as yield prediction. Studies combing remote sensing and process-based/statistical models for better yield prediction under extreme weather (e.g., droughts, floods, heatwaves, heavy winds) and quantifying the associated uncertainties through inter-method and inter-model comparisons are especially welcomed.

Dr. Jianxiu Qiu
Dr. Xiaohu Zhang
Dr. Zhenzhong Zeng
Dr. Gabriel de Oliveira
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 papers will be 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 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

  • crop monitoring
  • crop yield prediction
  • multi-scale modeling
  • multi-source data fusion
  • climate impact assessment

Published Papers (1 paper)

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Research

Article
A Practical Remote Sensing Monitoring Framework for Late Frost Damage in Wine Grapes Using Multi-Source Satellite Data
Remote Sens. 2021, 13(16), 3231; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163231 - 14 Aug 2021
Viewed by 523
Abstract
Late frost damage is one of the main meteorological disasters that affect the growth of wine grapes in spring, causing a decline in wine grapes quality and a reduction in yield in Northwest China. At present, remote sensing technology has been widely used [...] Read more.
Late frost damage is one of the main meteorological disasters that affect the growth of wine grapes in spring, causing a decline in wine grapes quality and a reduction in yield in Northwest China. At present, remote sensing technology has been widely used in the field of crop meteorological disasters monitoring and loss assessments, but little research has been carried out on late frost damage in wine grapes. To monitor the impact of late frost in wine grapes accurately and quickly, in this research, we selected the Ningxia planting area as the study area. A practical framework of late frost damage on wine grapes by integrating visible, near-infrared, and thermal infrared satellite data is proposed. This framework includes: (1) Wine grape planting area extraction using Gaofen-1 (GF-1), Landsat-8, and Sentinel-2 based on optimal feature selection and Random Forest (RF) algorithm; (2) retrieval of the land surface temperature (LST) using Landsat-8 thermal infrared data; (3) data fusion using Landsat-8 LST and MODIS LST for a high spatiotemporal resolution of LST with the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM); (4) the estimation of daily minimum air temperature (Tmin) using downscaled LST and meteorological station data; (5) monitoring and evaluation of the degree of late frost damage in wine grapes in April 2020 by combining satellite-derived data and late frost indicators. The results show that the total area of wine grapes extracted in Ningxia was about 39,837 ha. The overall accuracy was 90.47%, the producer’s accuracy was 91.09%, and the user’s accuracy was 90.22%. The root mean square (RMSE) and the coefficient of determination (R2) of the Tmin estimation model were 1.67 ℃ and 0.91, respectively. About 41.12% of the vineyards suffered severe late frost damage, and the total affected area was about 16,381 ha during April 20–25, 2020. This suggests the satellite data can accurately monitor late frost damage in wine grapes by mapping the wine grape area and estimating Tmin. The results can help farmers to take remedial measures to reduce late frost damage in wine grapes, and provide an objective evaluation of late frost damage insurance claims for wine grapes. With the increasing weather extremes, this study has an important reference value for standardized global wine grape management and food security planning. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Stress Monitoring and Yield Prediction)
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