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Dear Colleagues,

We would like to invite you to contribute to this Special Issue of Remote Sensing, titled “Remote Sensing for Future Food Security and Sustainable Agriculture”. There are several reasons behind this Special Issue. Agriculture is a vital economic sector producing food, agro-industrial feedstock, and energy and providing environmental services through managing soil, water, air, and biodiversity holistically. The agri-food chain involves multiple actors and stakeholders that produce and provide food and agricultural commodities to consumers. In addition to farmers, there are farm suppliers, processors, transporters and market intermediaries. These actors make the agri-food chain efficient. Current agriculture is under pressure to produce high-quality products with fewer inputs and in smaller areas.

In order to provide solutions to all complex problems related to the agri-food chain, we need to better understand all processes and build an interoperable knowledge management system for each agriculture sector. Data are a key part of such knowledge management systems, including remote sensing data. The intention of this Special Issue is to collect ideas on how remote sensing and data derived from remote sensing can help future knowledge management for global food security and better sustainability of agriculture production in varying climatic conditions, and how remote sensing can support the UN Sustainable Development Goals and the European Green Deal.

There are new systems for Earth monitoring; a number of delivery platforms have been developed, and new technologies such as artificial intelligence are now starting to be used. Remote sensing can bring data and knowledge from the global scale to provide global monitoring, monitor production on a country or regional level, and monitor field variability. In order to optimally use remote sensing for agriculture, capacity building and training people will become a key part of the entire process.

As the Special Issue looks for innovative methods of applying remote sensing in agriculture at all scales, many different aspects have to be addressed. We hope you find the topic of this Special Issue interesting, and we look forward to your research contributions.

Dr. Karel Charvat
Dr. Gregory Giuliani
Dr. Tomas Mildorf
Dr. Sarka Horakova
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

remote sensing
satellites
knowledge management
agriculture
food security
sustainability
biodiversity
artificial intelligence
cloud computing

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30 pages, 32630 KiB

Open AccessArticle

Spatiotemporal Evolution and Hysteresis Analysis of Drought Based on Rainfed-Irrigated Arable Land

by Enyu Du, Fang Chen, Huicong Jia, Lei Wang and Aqiang Yang

Remote Sens. 2023, 15(6), 1689; https://doi.org/10.3390/rs15061689 - 21 Mar 2023

Cited by 4 | Viewed by 1762

Abstract

Drought poses a serious threat to agricultural production and food security in the context of global climate change. Few studies have explored the response mechanism and lag time of agricultural drought to meteorological drought from the perspective of cultivated land types. This paper analyzes the spatiotemporal evolution patterns and hysteresis relationship of meteorological and agricultural droughts in the middle and lower reaches of the Yangtze River in China. Here, the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index products and surface temperature products were selected to calculate the Temperature Vegetation Dryness Index (TVDI) from 2010 to 2015. Furthermore, we obtained the Standardized Precipitation Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI) for the same period. Based on these indices, we analyzed the correlation and the hysteresis relationship between agricultural and meteorological drought in rainfed and irrigated arable land. The results showed that, (1) compared with SPEI, the high spatial resolution PDSI data were deemed more suitable for the subsequent accurate and scientific analysis of the relationship between meteorological and agricultural droughts. (2) When meteorological drought occurs, irrigated arable land is the first to experience agricultural drought, and then alleviates when the drought is most severe in rainfed arable land, indicating that irrigated arable land is more sensitive to drought events when exposed to the same degree of drought risk. However, rainfed arable land is actually more susceptible to agricultural drought due to the intervention of irrigation measures. (3) According to the cross-wavelet transform analysis, agricultural droughts significantly lag behind meteorological droughts by about 33 days during the development process of drought events. (4) The spatial distribution of the correlation coefficient between the PDSI and TVDI shows that the area with negative correlations of rainfed croplands and the area with positive correlations of irrigated croplands account for 77.55% and 68.04% of cropland areas, respectively. This study clarifies and distinguishes the details of the meteorological-to-agricultural drought relationship in rainfed and irrigated arable land, noting that an accurate lag time can provide useful guidance for drought monitoring management and irrigation project planning in the middle and lower reaches of the Yangtze River. Full article

(This article belongs to the Special Issue Remote Sensing for Future Food Security and Sustainable Agriculture: Part II)

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