Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

Remote Sensing for Future Food Security and Sustainable Agriculture

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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 December 2021) | Viewed by 28305

Share This Special Issue

Special Issue Editors

Dr. Karel Charvat

E-Mail Website
Guest Editor

Czech Centre for Science and Society, WirelessInfo, Plan4all z.s., K Rybníčku 557, 33012 Horní Bříza, Czech Republic
Interests: remote sensing; ICT; IoT; open data; big data; agriculture; rural development; semantic data; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Gregory Giuliani

E-Mail Website
Guest Editor

Institute for Environmental Sciences & UNEP/GRID-Geneva, University of Geneva, 66 Boulevard Carl-Vogt, CH 1205 Geneva, Switzerland
Interests: earth observations; data cube; sustainable development; GEO/GEOSS; environmental sciences
Special Issues, Collections and Topics in MDPI journals

Dr. Tomas Mildorf

E-Mail Website
Guest Editor

Department of GeoInformatics, University of West Bohemia, Technická 8, 30100 Pilsen, Czech Republic
Interests: terminology; open data; spatial data infrastructures
Special Issues, Collections and Topics in MDPI journals

Dr. Hana Kubickova

E-Mail Website
Guest Editor

Plan4all z.s., K Rybníčku 557, 33012 Horní Bříza, Czech Republic
Interests: remote sensing; agriculture; artificial intelligence

Dr. Sarka Horakova

E-Mail Website
Guest Editor

WirelessInfo, Cholinská 1048/19, 78401 Litovel, Czech Republic
Interests: rural development; forest planning; landscape management; ecollaboration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to invite you to contribute to this Special Issue of the Remote Sensing journal, “Remote Sensing for Future Food Security and Sustainable Agriculture”. There are several reasons behind this special issue. Agriculture comprises 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. A key part of such knowledge management systems is data, 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, but also 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. Hana Kubickova
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

Published Papers (8 papers)

Download All Papers

Research

Jump to: Review

17 pages, 5587 KiB

Open AccessArticle

Upscaling Remote Sensing Inversion Model of Wheat Field Cultivated Land Quality in the Huang-Huai-Hai Agricultural Region, China

by Yinshuai Li, Chunyan Chang, Zhuoran Wang, Guanghui Qi, Chao Dong and Gengxing Zhao

Remote Sens. 2021, 13(24), 5095; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245095 - 15 Dec 2021

Cited by 7 | Viewed by 2236

Abstract

It is an objective demand for sustainable agricultural development to realize fast and accurate cultivated land quality assessment. In this paper, Tengzhou city (county-scale hilly area: scale A), Shanghe county (county-scale plain area: scale B), and Huang-Huai-Hai region (including large-scale hilly and plain area: scale C and D) were taken as research areas. Through the conversion of evaluation systems, the inversion models at the county-scale were constructed. Then, the image scale conversion was carried out based on the numerical regression method, and the upscaling inversion was realized. The results showed that: (1) the conversion models of evaluation systems (CMES) are Y = 1.021x − 4.989 (CMES_A−B), Y = 0.801x + 16.925 (CMES_A−C), and Y = 0.959x + 3.458 (CMES_C−D); (2) the booting stage is the best inversion phase; (3) the back propagation neural network model based on the combination index group (CI-BPNN) is the best inversion model, with the R² are 0.723 (modeling set) and 0.722 (verification set). CI-BPNN and CI-BPNN-CMES_A−B models are suitable for the hilly and plain areas at the county-scale, and the level area ratio difference is less than 4.87%. Furthermore, (4) the reflectance conversion model of short-wave infrared 2 is cubic, and the rest are quadratic. CI-BPNN-CMES_A−C and CI-BPNN-CMES_A−C-CMES_C−D models realized upscaling inversion in the hilly and plain areas, with the maximum level area ratio difference being 1.60%. Additionally, (5) the wheat field quality has improved steadily since 2001 in the Huang-Huai-Hai region. This study proposes an upscaling inversion method of wheat field quality, which provides a scientific basis for cultivated land management and agricultural production in large areas. Full article

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

► Show Figures

Graphical abstract

22 pages, 91942 KiB

Open AccessArticle

Landsat-Derived Annual Maps of Agricultural Greenhouse in Shandong Province, China from 1989 to 2018

by Cong Ou, Jianyu Yang, Zhenrong Du, Tingting Zhang, Bowen Niu, Quanlong Feng, Yiming Liu and Dehai Zhu

Remote Sens. 2021, 13(23), 4830; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234830 - 28 Nov 2021

Cited by 7 | Viewed by 2472

Abstract

Agricultural greenhouse (AG), one of the fastest-growing technology-based approaches worldwide in terms of controlling the environmental conditions of crops, plays an essential role in food production, resource conservation and the rural economy, but has also caused environmental and socio-economic problems due to policy promotion and market demand. Therefore, long-term monitoring of AG is of utmost importance for the sustainable management of protected agriculture, and previous efforts have verified the effectiveness of remote sensing-based techniques for mono-temporal AG mapping in a relatively small area. However, currently, a continuous annual AG remote sensing-based dataset at large-scale is generally unavailable. In this study, an annual AG mapping method oriented to the provincial area and long-term period was developed to produce the first Landsat-derived annual AG dataset in Shandong province, China from 1989 to 2018 on the Google Earth Engine (GEE) platform. The mapping window for each year was selected based on the vegetation growth and the phenological information, which was critical in distinguishing AG from other misclassified categories. Classification for each year was carried out initially based on the random forest classifier after the feature optimization. A temporal consistency correction algorithm based on classification probability was then proposed to the classified AG maps for further improvement. Finally, the average User’s Accuracy, Producer’s Accuracy and F1-score of AG based on visually-interpreted samples over 30 years reached 96.56%, 86.64% and 0.911, respectively. Furthermore, we also found that the ranked features via calculating the importance of each tested feature resulted in the highest accuracy and the strongest stability in the initial classification stage, and the proposed temporal consistency correction algorithm improved the final products by approximately five percent on average. In general, the resultant AG sequence dataset from our study has revealed the expansion of this typical object of “Human–Nature” interaction in agriculture and has a potential application in use of greenhouse-related technology and the scientific planning of protected agriculture. Full article

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

► Show Figures

Graphical abstract

17 pages, 4405 KiB

Open AccessArticle

Mapping the Dynamics of Winter Wheat in the North China Plain from Dense Landsat Time Series (1999 to 2019)

by Wenmin Zhang, Martin Brandt, Alexander V. Prishchepov, Zhaofu Li, Chunguang Lyu and Rasmus Fensholt

Remote Sens. 2021, 13(6), 1170; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061170 - 19 Mar 2021

Cited by 10 | Viewed by 3382

Abstract

Monitoring spatio-temporal changes in winter wheat planting areas is of high importance for the evaluation of food security. This is particularly the case in China, having the world’s largest population and experiencing rapid urban expansion, concurrently, it puts high pressure on food demands and the availability of arable land. The relatively high spatial resolution of Landsat is required to resolve the historical mapping of smallholder wheat fields in China. However, accurate Landsat-based mapping of winter wheat planting dynamics over recent decades have not been conducted for China, or anywhere else globally. Based on all available Landsat TM/ETM+/OLI images (~28,826 tiles) using Google Earth Engine (GEE) cloud computing and a Random Forest machine-learning classifier, we analyzed spatio-temporal dynamics in winter wheat planting areas during 1999–2019 in the North China Plain (NCP). We applied a median value of 30-day sliding windows to fill in potential data gaps in the available Landsat images, and six EVI-based phenological features were then extracted to discriminate winter wheat from other land cover types. Reference data for training and validation were extracted from high-resolution imagery available via Google Earth™ online mapping service, Sentinel-2 and Landsat imagery. We ran a sensitivity analysis to derive the optimal training sample class ratio (β = 1.8) accounting for the unbalanced distribution of land-cover types. We mapped winter wheat planting areas for 1999–2019 with overall accuracies ranging from 82% to 99% and the user’s/producer’s accuracies of winter wheat range between 90% and 99%. We observed an overall increase in winter wheat planting areas of 1.42 × 10⁶ ha in the NCP as compared to the year 2000, with a significant increase in the Shandong and Hebei provinces (p < 0.05). This result contrasts the general discourse suggesting a decline in croplands (e.g., rapid urbanization) and climate change-induced unfavorable cropping conditions in the NCP. This suggests adjustments of the winter wheat planting area over time to satisfy wheat supply in relation to food security. This study highlights the application of Landsat images through GEE in documenting spatio-temporal dynamics of winter wheat planting areas for adequate management of cropping systems and assessing food security in China. Full article

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

► Show Figures

Graphical abstract

24 pages, 3092 KiB

Open AccessArticle

Complex Analysis of the Efficiency of Difference Reflectance Indices on the Basis of 400–700 nm Wavelengths for Revealing the Influences of Water Shortage and Heating on Plant Seedlings

by Ekaterina Sukhova, Lyubov Yudina, Ekaterina Gromova, Anastasiia Ryabkova, Dmitry Kior and Vladimir Sukhov

Remote Sens. 2021, 13(5), 962; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050962 - 04 Mar 2021

Cited by 9 | Viewed by 1986

Abstract

A drought, which can be often accompanied by increased temperature, is a key adverse factor for agricultural plants. Remote sensing of early plant changes under water shortage is a prospective way to improve plant cultivation; in particular, the sensing can be based on measurement of difference reflectance indices (RIs). We complexly analyzed the efficiency of RIs based on 400–700 nm wavelengths for revealing the influences of water shortage and short-term heating on plant seedlings. We measured spectra of reflected light in leaves of pea, wheat, and pumpkin under control and stress conditions. All possible RIs in the 400–700 nm range were calculated, significances of differences between experimental and control indices were estimated, and heatmaps of the significances were constructed. It was shown that the water shortage (pea seedlings) changed absolute values of large quantity of calculated RIs. Absolute values of some RIs were significantly changed for 1–5 or 2–5 days of the water shortage; they were strongly correlated to the potential quantum yield of photosystem II and relative water content in leaves. In contrast, the short-term heating (pea, wheat, and pumpkin seedlings) mainly influenced light-induced changes in RIs. Our results show new RIs, which are potentially sensitive to the action of stressors. Full article

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

► Show Figures

Figure 1

17 pages, 11154 KiB

Open AccessArticle

Evaluation of Crop Type Classification with Different High Resolution Satellite Data Sources

by Jinlong Fan, Xiaoyu Zhang, Chunliang Zhao, Zhihao Qin, Mathilde De Vroey and Pierre Defourny

Remote Sens. 2021, 13(5), 911; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050911 - 28 Feb 2021

Cited by 14 | Viewed by 4654

Abstract

Crop type classification with satellite imageries is widely applied in support of crop production management and food security strategy. The abundant supply of these satellite data is accelerating and blooming the application of crop classification as satellite data at 10 m to 30 m spatial resolution have been made accessible easily, widely and free of charge, including optical sensors, the wide field of viewer (WFV) onboard the GaoFen (GF, high resolution in English) series from China, the MultiSpectral Instrument (MSI) onboard Sentinel 2 (S2) from Europe and the Operational Land Imager (OLI) onboard Landsat 8 (L8) from USA, thanks to the implementation of the open data policy. There are more options in using the satellite data as these three data sources are available. This paper explored the different capability of these three data sources for the crop type mapping in the same area and within the same growing season. The study was executed in a flat and irrigated area in Northwest China. Nine types of crop were classified using these three kinds of time series of data sources in 2017 and 2018, respectively. The same suites of the training samples and validation samples were applied for each of the data sources. Random Forest (RF) was used as the classifier for the crop type classification. The confusion error matrix with the OA, Kappa and F1-score was used to evaluate the accuracy of the classifications. The result shows that GF-1 relatively has the lowest accuracy as a consequence of the limited spectral bands, but the accuracy is at 93–94%, which is still excellent and acceptable for crop type classification. S2 achieved the highest accuracy of 96–98%, with 10 available bands for the crop type classification at either 10 m or 20 m. The accuracy of 97–98% for L8 is in the middle but the difference is small in comparison with S2. Any of these satellite data may be used for the crop type classification within the growing season, with a very good accuracy if the training datasets were well tuned. Full article

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

► Show Figures

Figure 1

19 pages, 18519 KiB

Open AccessArticle

Using Long-Term Earth Observation Data to Reveal the Factors Contributing to the Early 2020 Desert Locust Upsurge and the Resulting Vegetation Loss

by Lei Wang, Wen Zhuo, Zhifang Pei, Xingyuan Tong, Wei Han and Shibo Fang

Remote Sens. 2021, 13(4), 680; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040680 - 13 Feb 2021

Cited by 15 | Viewed by 3456

Abstract

Massive desert locust swarms have been threatening and devouring natural vegetation and agricultural crops in East Africa and West Asia since 2019, and the event developed into a rare and globally concerning locust upsurge in early 2020. The breeding, maturation, concentration and migration of locusts rely on appropriate environmental factors, mainly precipitation, temperature, vegetation coverage and land-surface soil moisture. Remotely sensed images and long-term meteorological observations across the desert locust invasion area were analyzed to explore the complex drivers, vegetation losses and growing trends during the locust upsurge in this study. The results revealed that (1) the intense precipitation events in the Arabian Peninsula during 2018 provided suitable soil moisture and lush vegetation, thus promoting locust breeding, multiplication and gregarization; (2) the regions affected by the heavy rainfall in 2019 shifted from the Arabian Peninsula to West Asia and Northeast Africa, thus driving the vast locust swarms migrating into those regions and causing enormous vegetation loss; (3) the soil moisture and NDVI anomalies corresponded well with the locust swarm movements; and (4) there was a low chance the eastwardly migrating locust swarms would fly into the Indochina Peninsula and Southwest China. Full article

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

► Show Figures

Graphical abstract

21 pages, 6214 KiB

Open AccessArticle

UAV Data as an Alternative to Field Sampling to Monitor Vineyards Using Machine Learning Based on UAV/Sentinel-2 Data Fusion

by Xixuan Zhou, Liao Yang, Weisheng Wang and Baili Chen

Remote Sens. 2021, 13(3), 457; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030457 - 28 Jan 2021

Cited by 15 | Viewed by 3878

Abstract

Pests and diseases affect the yield and quality of grapes directly and engender noteworthy economic losses. Diagnosing “lesions” on vines as soon as possible and dynamically monitoring symptoms caused by pests and diseases at a larger scale are essential to pest control. This study has appraised the capabilities of high-resolution unmanned aerial vehicle (UAV) data as an alternative to manual field sampling to obtain sampling canopy sets and to supplement satellite-based monitoring using machine learning models including partial least squared regression (PLSR), support vector regression (SVR), random forest regression (RFR), and extreme learning regression (ELR) with a new activation function. UAV data were acquired from two flights in Turpan to determine disease severity (DS) and disease incidence (DI) and compared with field visual assessments. The UAV-derived canopy structure including canopy height (CH) and vegetation fraction cover (VFC), as well as satellite-based spectral features calculated from Sentinel-2A/B data were analyzed to evaluate the potential of UAV data to replace manual sampling data and predict DI. It was found that SVR slightly outperformed the other methods with a root mean square error (RMSE) of 1.89%. Moreover, the combination of canopy structure (CS) and vegetation index (VIs) improved prediction accuracy compared with single-type features (RMSEcs of 2.86% and RMSE_VIs of 1.93%). This study tested the ability of UAV sampling to replace manual sampling on a large scale and introduced opportunities and challenges of fusing different features to monitor vineyards using machine learning. Within this framework, disease incidence can be estimated efficiently and accurately for larger area monitoring operation. Full article

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

► Show Figures

Graphical abstract

Review

Jump to: Research

27 pages, 3520 KiB

Open AccessReview

The Role of Earth Observation in Achieving Sustainable Agricultural Production in Arid and Semi-Arid Regions of the World

by Sarchil Hama Qader, Jadu Dash, Victor A. Alegana, Nabaz R. Khwarahm, Andrew J. Tatem and Peter M. Atkinson

Remote Sens. 2021, 13(17), 3382; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173382 - 26 Aug 2021

Cited by 10 | Viewed by 4563

Abstract

Crop production is a major source of food and livelihood for many people in arid and semi-arid (ASA) regions across the world. However, due to irregular climatic events, ASA regions are affected commonly by frequent droughts that can impact food production. In addition, ASA regions in the Middle East and Africa are often characterised by political instability, which can increase population vulnerability to hunger and ill health. Remote sensing (RS) provides a platform to improve the spatial prediction of crop production and food availability, with the potential to positively impact populations. This paper, firstly, describes some of the important characteristics of agriculture in ASA regions that require monitoring to improve their management. Secondly, it demonstrates how freely available RS data can support decision-making through a cost-effective monitoring system that complements traditional approaches for collecting agricultural data. Thirdly, it illustrates the challenges of employing freely available RS data for mapping and monitoring crop area, crop status and forecasting crop yield in these regions. Finally, existing approaches used in these applications are evaluated, and the challenges associated with their use and possible future improvements are discussed. We demonstrate that agricultural activities can be monitored effectively and both crop area and crop yield can be predicted in advance using RS data. We also discuss the future challenges associated with maintaining food security in ASA regions and explore some recent advances in RS that can be used to monitor cropland and forecast crop production and yield. Full article

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

► Show Figures