New Insights in Crop Monitoring and Management Using Remote Sensing Data
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: 30 July 2024 | Viewed by 10405
Special Issue Editors
Interests: crop modelling; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: grassland agro-ecosystem carbon–water–gas exchange; crop growth observation; model simulation
Special Issue Information
Dear Colleagues,
(1) Remote sensing is promising for the monitoring and evaluation of crop growth, soil water and nutritional conditions and other associated agricultural indicators. This technology, combined with various analytical tools, such as artificial intelligence and process-based crop models, can be utilized to collect within-season and spatial data to derive information on crop growth and eco-physiological conditions. Remote sensing with multiple sensors on diverse platforms can generate big data, posing severe challenges in data processing, analysis and assimilation for the practical application of such data in agricultural production. On the other hand, technological developments in data fusion, machine learning and artificial intelligence provide opportunities to generate big data and derive new information, allowing for the optimization of crop production at unprecedented spatial and temporal scales.
(2) This Special Issue aims to compile the latest research in remote sensing technologies applied for monitoring and retrieving crop and soil biophysical variables and genetic and phenotypic parameters; it also welcomes remote-sensing-based solutions supporting field management decisions for improved resource use efficiency and sustainable production. These research topics look to resolve food security issues in developing and developed nations. We believe this issue will be of particular interest for stakeholders in agricultural policy areas, including climate change adaptation, digital agriculture and modern farming techniques.
(3) We welcome original research contributions, exhaustive reviews, new remote sensing methodologies and relevant applications in diverse agricultural environments using the latest agricultural technologies. Specifically, we invite papers on the following research topics: progress in scientific methodologies for crop monitoring and management using remote sensing data; innovative remote sensing and image analysis tools or methods for enhanced quantification of biophysical and biochemical variables of crops and soils; and application of a holistic system encompassing these approaches.
Prof. Dr. Jonghan Ko
Prof. Dr. Wei Xue
Dr. Xinwei Li
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
- agricultural production
- agroecosystems
- crop management
- crop monitoring
- ground, UAV, airborne and satellite platforms
- image processing and data-fusion technology
- remote sensing
- spatial data
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Testing the Performance of LSTM and ARIMA Models for In-Season Forecasting of Canopy Cover (CC) in Cotton Crops
Authors: Sambandh Bhusan Dhal; Stavros Kalafatis; Krishna Chaitanya Gadepally; Jose Luis Landivar-Scott; Lei Zhao; Kevin Nowka; Ulisses Braga-Neto; Juan Landivar; Pankaj Pal; Mahendra Bhandari
Affiliation: Texas A&M AgriLife Research and Extension Center Corpus Christi Texas
Abstract: Cotton (Gossypium spp.) is a significant cash crop in the United States, requiring continuous monitoring of growth parameters for informed decision-making from planting to harvest. Recently, forecasting models have gained attention for predicting canopy indicators, aiding in-season planning and management decisions to enhance cotton production and profitability. This study utilized Unmanned Aerial System (UAS) technology to collect canopy cover (CC) data from a 40-hectare cotton field in Driscoll, Texas, in 2020 and 2021. The 2020 data were used to train Long Short-Term Memory (LSTM) models, which were then employed to predict CC values for 2021. These LSTM models were compared with real-time ARIMA models, evaluating their efficacy in predicting CC values up to 14 days in advance starting from the 28th day after crop emergence. Results indicated that multiple-input multi-step output LSTM models showed higher accuracy in predicting in-season CC values during early growth stages (up to the 56th day). For later stages, stacked-LSTM models exhibited precision in CC prediction. This underscores the potential of LSTM models for in-season CC forecasting, facilitating effective management strategies in cotton production.