Remote Sensing

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Special Issue Information

Dear Colleagues,

Remote sensing involves the collection and processing of information about an object from a distance. It deals with data collected using electromagnetic radiation emitted by and/or reflected from an object. Since it identifies and collects information without having physical contact with the object, remote sensing refers to data curation through the use of sensors that use different types of electromagnetic radiation. Precision agriculture researchers use remote sensing for acquiring useful data about soil water content, soil salinity, plant evapotranspiration, soil temperature, and crop characteristics. Farmers can observe, measure, and respond to inter- and intra-field variations of soil and crops. Remote sensing helps farmers to estimate their crop yields and make informed decisions for optimal use of crop inputs to maximize the returns from their inputs while conserving resources. In modern-day agriculture, remote sensing is used to reap the benefits of sensors, computers, and decision support systems. Remote sensing uses sensors to receive electromagnetic radiation to be converted into signals that can be recorded and displayed as images and/or digits (numerical data). Computers are then used to process images and numerical data for informed decision-making.

This Special Issue of Remote Sensing will publish content related to i) types of remote sensing systems, ii) elements involved in remote sensing, iii) advanced and basic processes of remote sensing, iv) applications of remote sensing specific to natural resource management, and v) the use of remote sensing in food security, sustainability, or precision agriculture.

Prof. Dr. Aitazaz A. Farooque
Dr. Farhat Abbas
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

Satellite ,GIS and GPS
drones and UAV
image processing
advanced ground-truthing
sensors and thermal sensors
remote sensing and proximal sensing
land use and land cover classification
soil water sensors and soil sensing
vegetation indices and crop sensing
remote sensing data analytics with artificial intelligence
remote data acquisition, undisturbed sampling, distance data curation
spatial resolution, spectral resolution, radiometric resolution, and temporal resolution

Published Papers (2 papers)

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Research

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18 pages, 2844 KiB

Open AccessArticle

Evaluation of Food Security Based on Remote Sensing Data—Taking Egypt as an Example

by Shuzhu Shi, Yu Ye and Rui Xiao

Remote Sens. 2022, 14(12), 2876; https://doi.org/10.3390/rs14122876 - 16 Jun 2022

Cited by 8 | Viewed by 2868

Abstract

Egypt, a country with a harsh natural environment and rapid population growth, is facing difficulty in ensuring its national food security. A novel model developed for assessing food security in Egypt, which applies remote sensing techniques, is presented. By extracting the gray-level co-occurrence matrix (GLCM) mean texture features from Sentinel-1 and Landsat-7 images, the arable land used to grow grain crops was first classified and extracted using a support vector machine. In terms of the classified results, meteorological data, and normalized difference vegetation index (NDVI) data, the Carnegie–Ames–Stanford approach (CASA) model was adopted to compute the annual net primary production (NPP). Then, the NPP yield conversion formula was used to forecast the annual grain yield. Finally, a method for evaluating food security, which involves four dimensions, i.e., quantity security, economic security, quality security, and resource security, was established to evaluate food security in Egypt in 2010, 2015, and 2020. Based on the proposed model, a classification accuracy of the crop distribution map, which is above 82%, can be achieved. Moreover, the reliability of yield estimation is verified compared to the result estimated using statistics data provided by Food and Agriculture Organization (FAO). Our evaluation results show that food security in Egypt is declining, the quantity and quality security show large fluctuations, and economic and resource security are relatively stable. This model can satisfy the requirements for estimating grain yield at a wide scale and evaluating food security on a national level. It can be used to provide useful suggestions for governments regarding improving food security. Full article

(This article belongs to the Special Issue Remote Sensing for Food Security, Sustainability, and Precision Agriculture)

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Other

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27 pages, 6151 KiB

Open AccessTechnical Note

Crop Detection Using Time Series of Sentinel-2 and Sentinel-1 and Existing Land Parcel Information Systems

by Herman Snevajs, Karel Charvat, Vincent Onckelet, Jiri Kvapil, Frantisek Zadrazil, Hana Kubickova, Jana Seidlova and Iva Batrlova

Remote Sens. 2022, 14(5), 1095; https://doi.org/10.3390/rs14051095 - 23 Feb 2022

Cited by 8 | Viewed by 3738

Abstract

Satellite crop detection technologies are focused on the detection of different types of crops in fields. The information of crop-type area is more useful for food security than the earlier phenology stage is. Currently, data obtained from remote sensing (RS) are used to solve tasks related to the identification of the type of agricultural crops; additionally, modern technologies using AI methods are desired in the postprocessing stage. In this paper, we develop a methodology for the supervised classification of time series of Sentinel-2 and Sentinel-1 data, compare the accuracies based on different input datasets and find how the accuracy of classification develops during the season. In the EU, a unified Land Parcel Identification System (LPIS) is available to provide essential field borders. To increase usability, we also provide a classification of the entire field. This field classification also improves overall accuracy. Full article

(This article belongs to the Special Issue Remote Sensing for Food Security, Sustainability, and Precision Agriculture)

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