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Remote Sensing for Agriculture in the Developing World

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 August 2020) | Viewed by 7274

Special Issue Editor


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Guest Editor
Climate Hazards Center, Department of Geography, University of California, Santa Barbara, CA 93106, USA
Interests: precipitation; food security; statistical analysis

Special Issue Information

Dear Colleagues,

We would like to invite you to submit a manuscript to this Special Issue of Remote Sensing highlighting new techniques, applications and/or results of implementing Earth Observations in monitoring and analyzing agriculture in the developing world.  More specifically, we are seeking publishable research in using satellite data along with satellite-derived and modeled products to identify or estimate cropped area, crop yields, and/or production in the developing world.  Along with the direct analysis of these products, we also look forward to submissions highlighting the impacts of agricultural shortfall in the developing world. 

We think this special issue will highlight the value of remote sensing in these locations, and push the state-of-the-practice for agricultural monitoring in these vulnerable areas.

Dr. Gregory J. Husak
Guest Editor

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.

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Published Papers (1 paper)

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Research

15 pages, 4297 KiB  
Article
Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
by Meghan Hegarty-Craver, Jason Polly, Margaret O’Neil, Noel Ujeneza, James Rineer, Robert H. Beach, Daniel Lapidus and Dorota S. Temple
Remote Sens. 2020, 12(12), 1984; https://doi.org/10.3390/rs12121984 - 20 Jun 2020
Cited by 46 | Viewed by 6885
Abstract
Timely and accurate agricultural information is needed to inform resource allocation and sustainable practices to improve food security in the developing world. Obtaining this information through traditional surveys is time consuming and labor intensive, making it difficult to collect data at the frequency [...] Read more.
Timely and accurate agricultural information is needed to inform resource allocation and sustainable practices to improve food security in the developing world. Obtaining this information through traditional surveys is time consuming and labor intensive, making it difficult to collect data at the frequency and resolution needed to accurately estimate the planted areas of key crops and their distribution during the growing season. Remote sensing technologies can be leveraged to provide consistent, cost-effective, and spatially disaggregated data at high temporal frequency. In this study, we used imagery acquired from unmanned aerial vehicles to create a high-fidelity ground-truth dataset that included examples of large mono-cropped fields, small intercropped fields, and natural vegetation. The imagery was acquired in three rounds of flights at six sites in different agro-ecological zones to capture growing conditions. This dataset was used to train and test a random forest model that was implemented in Google Earth Engine for classifying cropped land using freely available Sentinel-1 and -2 data. This model achieved an overall accuracy of 83%, and a 91% accuracy for maize specifically. The model results were compared with Rwanda’s Seasonal Agricultural Survey, which highlighted biases in the dataset including a lack of examples of mixed land cover. Full article
(This article belongs to the Special Issue Remote Sensing for Agriculture in the Developing World)
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