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Emerging Technologies in Earth Observations for Agricultural Monitoring

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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 15003

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Special Issue Editors

Dr. Luís Pádua

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Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
Interests: digital image processing; unmanned aerial vehicles; precision viticulture; precision agriculture; photogrammetric processing; multi-temporal analysis; spectral imaging; machine learning
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Dr. Joaquim João Sousa

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Engineering Department, School of Science and Technology, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: UAV; image processing algorithms (RGB, NIR, multi- and hyperspectral, thermal and LiDAR sensors); InSAR; precision agriculture; precision forestry
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Dr. Salvatore Filippo Di Gennaro

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Institute of BioEconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy
Interests: precision agriculture; remote sensing; UAS applied to agriculture and forestry; wireless sensors network; UAS in field high throughput phenotyping; viticulture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade Earth Observation systems shown significant advances, allowing an unprecedent improvement of data quality and availability with high spatial, spectral and temporal resolutions from different types of sensors. Among the various application areas, agriculture is one of the socio-economic sectors that has the most to gain from remote sensing data. This is even more notable in a context of population growth combined with climate change impacts, forcing the development of more effective and optimized agricultural practices. For this purpose, the data obtained using remote sensing platforms will play a crucial role. However, the large volume of data generated/obtained poses challenges that can only be solved using Big Data and artificial intelligence approaches. As such, the development of new technologies for data processing and interpretation from remote sensed data can be considered. When combined with application of machine learning and image processing techniques a range of applications can be explored for, which can serve as a source of information to improve the decision support to assess multiple agronomical challenges. This Special Issue aims to promote the publication of case studies proposing the development of new methodologies, algorithms, and applications in remote sensing for agricultural monitoring. Submissions are not limited to a given platform (satellite or airborne) or sensor type, as long as innovative methods, approaches or applications are proposed.

Mr. Luís Pádua
Dr. Joaquim João Sousa
Dr. Salvatore Filippo Di Gennaro
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

multi-temporal analysis
multi-sensor systems
data fusion
decision support systems
machine learning
synthetic aperture radar
multispectral and hyperspectral imagery
thermal infrared imagery
laser scanning
crop modelling
yield mapping
abiotic and biotic issues
crop detection

Published Papers (5 papers)

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Research

18 pages, 7828 KiB

Open AccessArticle

The Efficiency of Foliar Kaolin Spray Assessed through UAV-Based Thermal Infrared Imagery

by Luís Pádua, Sara Bernardo, Lia-Tânia Dinis, Carlos Correia, José Moutinho-Pereira and Joaquim J. Sousa

Remote Sens. 2022, 14(16), 4019; https://doi.org/10.3390/rs14164019 - 18 Aug 2022

Cited by 3 | Viewed by 2027

Abstract

The water content in an agricultural crop is of crucial importance and can either be estimated through proximal or remote sensing techniques, allowing better irrigation scheduling and avoiding extreme water stress periods. However, the current climate change context is increasing the use of eco-friendly practices to reconcile water management and thermal protection from sunburn. These approaches aim to mitigate summer stress factors (high temperature, high radiation, and water shortage) and improve the plants’ thermal efficiency. In this study, data from unmanned aerial vehicles (UAVs) were used to monitor the efficiency of foliar kaolin application (5%) in a commercial vineyard. Thermal infrared imagery (TIR) was used to compare the canopy temperature of grapevines with and without kaolin and to compute crop water stress and stomatal conductance indices. The gas exchange parameters of single leaves were also analysed to ascertain the physiological performance of vines and validate the UAV-based TIR data. Generally, plants sprayed with kaolin presented a lower temperature compared to untreated plants. Moreover, UAV-based data also showed a lower water stress index and higher stomatal conductance, which relate to eco-physiological measurements carried out in the field. Thus, the suitability of UAV-based TIR data proved to be a good approach to monitor entire vineyards in regions affected by periods of heatwaves, as is the case of the analysed study area. Full article

(This article belongs to the Special Issue Emerging Technologies in Earth Observations for Agricultural Monitoring)

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23 pages, 42852 KiB

Open AccessArticle

Projections of Climate Change Impacts on Flowering-Veraison Water Deficits for Riesling and Müller-Thurgau in Germany

by Chenyao Yang, Christoph Menz, Maxim Simões De Abreu Jaffe, Sergi Costafreda-Aumedes, Marco Moriondo, Luisa Leolini, Arturo Torres-Matallana, Daniel Molitor, Jürgen Junk, Helder Fraga, Cornelis van Leeuwen and João A. Santos

Remote Sens. 2022, 14(6), 1519; https://doi.org/10.3390/rs14061519 - 21 Mar 2022

Cited by 7 | Viewed by 3238

Abstract

With global warming, grapevine is expected to be increasingly exposed to water deficits occurring at various development stages. In this study, we aimed to investigate the potential impacts of projected climate change on water deficits from the flowering to veraison period for two main white wine cultivars (Riesling and Müller-Thurgau) in Germany. A process-based soil-crop model adapted for grapevine was utilized to simulate the flowering-veraison crop water stress indicator (CWSI) of these two varieties between 1976–2005 (baseline) and 2041–2070 (future period) based on a suite of bias-adjusted regional climate model (RCM) simulations under RCP4.5 and RCP8.5. Our evaluation indicates that the model can capture the early-ripening (Müller-Thurgau) and late-ripening (Riesling) traits, with a mean bias of prediction of ≤2 days and a well-reproduced inter-annual variability for more than 60 years. Under climate projections, the flowering stage is advanced by 10–20 days (higher in RCP8.5) between the two varieties, whereas a slightly stronger advancement is found for Müller-Thurgau than for Riesling for the veraison stage. As a result, the flowering-veraison phenophase is mostly shortened for Müller-Thurgau, whereas it is extended by up to two weeks for Riesling in cool and high-elevation areas. The length of phenophase plays an important role in projected changes of flowering-veraison mean temperature and precipitation. The late-ripening trait of Riesling makes it more exposed to increased summer temperature (mainly in August), resulting in a higher mean temperature increase for Riesling (1.5–2.5 °C) than for Müller-Thurgau (1–2 °C). As a result, an overall increased CWSI by up to 15% (ensemble median) is obtained for both varieties, whereas the upper (95th) percentile of simulations shows a strong signal of increased water deficit by up to 30%, mostly in the current winegrowing regions. Intensified water deficit stress can represent a major threat for high-quality white wine production, as only mild water deficits are acceptable. Nevertheless, considerable variabilities of CWSI were discovered among RCMs, highlighting the importance of efforts towards reducing uncertainties in climate change impact assessment. Full article

(This article belongs to the Special Issue Emerging Technologies in Earth Observations for Agricultural Monitoring)

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25 pages, 9491 KiB

Open AccessArticle

Development of a Combined Orchard Harvesting Robot Navigation System

by Wenju Mao, Heng Liu, Wei Hao, Fuzeng Yang and Zhijie Liu

Remote Sens. 2022, 14(3), 675; https://doi.org/10.3390/rs14030675 - 31 Jan 2022

Cited by 21 | Viewed by 4213

Abstract

Our research concerned the development of an autonomous robotic navigation system for orchard harvesting with a dual master-slave mode, the autonomous navigation tractor orchard transport robot being the master followed by a navigation orchard picking robot as the slave. This addresses the problem that in single master-slave navigation mode, agricultural combined harvesting equipment cannot stop repeatedly between rows of apple trees and drive continuously when turning. According to distances obtained from a global positioning system (GNSS), ground points were used to switch the navigation mode of the transport and picking robot. A cloth simulation filter (CSF) and random sample consensus (RANSAC) algorithm was used to obtain inter-row waypoints. The GNSS point was manually selected as the turn waypoint of the master and a kinematic model was used to compute the turn waypoints of the slave. Finally, we used a pure pursuit algorithm to track these waypoints sequentially to achieve master-slave navigation and ground head master-slave command navigation. The experimental results show that the data packet loss rate was less than 1.2% when the robot communicated in the orchard row within 50 m which meets the robot orchard communication requirements. The master-slave robot can achieve repeated stops in the row using follow navigation, which meets the demands of joint orchard harvesting. The maximum, minimum, mean and standard deviation of position deviation of the master robot were 5.3 cm, 0.8 cm, 2.4 cm, and 0.9 cm, respectively. The position deviations of the slave robot were larger than those of the master robot, with maximum, minimum, mean and standard deviation of 39.7 cm, 1.1 cm, 4.1 cm, and 5.6 cm, respectively. The maximum, minimum, mean and standard deviation of the following error between the master-slave robot were 4.4 cm, 0 cm, 1.3 cm, and 1 cm respectively. Concerning the ground head turn, the command navigation method allowed continuous turning, but the lateral deviation between robots was more than 0.3 m and less than 1 m, and the heading deviation was more than 10° and less than 90°. Full article

(This article belongs to the Special Issue Emerging Technologies in Earth Observations for Agricultural Monitoring)

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18 pages, 72373 KiB

Open AccessArticle

Crop Mapping in the Sanjiang Plain Using an Improved Object-Oriented Method Based on Google Earth Engine and Combined Growth Period Attributes

by Mengyao Li, Rui Zhang, Hongxia Luo, Songwei Gu and Zili Qin

Remote Sens. 2022, 14(2), 273; https://doi.org/10.3390/rs14020273 - 7 Jan 2022

Cited by 9 | Viewed by 2607

Abstract

In recent years, the scale of rural land transfer has gradually expanded, and the phenomenon of non-grain-oriented cultivated land has emerged. Obtaining crop planting information is of the utmost importance to guaranteeing national food security; however, the acquisition of the spatial distribution of crops in large-scale areas often has the disadvantages of excessive calculation and low accuracy. Therefore, the IO-Growth method, which takes the growth stage every 10 days as the index and combines the spectral features of crops to refine the effective interval of conventional wavebands for object-oriented classification, was proposed. The results were as follows: (1) the IO-Growth method obtained classification results with an overall accuracy and F1 score of 0.92, and both values increased by 6.98% compared to the method applied without growth stages; (2) the IO-Growth method reduced 288 features to only 5 features, namely Sentinel-2: Red Edge1, normalized difference vegetation index, Red, short-wave infrared2, and Aerosols, on the 261st to 270th days, which greatly improved the utilization rate of the wavebands; (3) the rise of geographic data processing platforms makes it simple to complete computations with massive data in a short time. The results showed that the IO-Growth method is suitable for large-scale vegetation mapping. Full article

(This article belongs to the Special Issue Emerging Technologies in Earth Observations for Agricultural Monitoring)

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16 pages, 1750 KiB

Open AccessArticle

An Attenuation Model of Node Signals in Wireless Underground Sensor Networks

by Meng Han, Zenglin Zhang, Jie Yang, Jiayun Zheng and Wenting Han

Remote Sens. 2021, 13(22), 4642; https://doi.org/10.3390/rs13224642 - 18 Nov 2021

Cited by 3 | Viewed by 1850

Abstract

Wireless underground sensor networks (WUSN) consist of sensor nodes that are operated in the soil medium. To evaluate the signal attenuation law of WUSN nodes, in this study, a WUSN node signal transmission test platform was built in the laboratory. The signal intensity data of WUSN nodes under different experimental conditions were obtained by orthogonal test. The WUSN node signal attenuation model was established. The test results show that the transmission of WUSN node signals in the soil medium is seriously affected by soil moisture content, node burial depth, soil compactness, and horizontal distance between nodes. The R² of the models was between 0.790 and 0.893, and the RMSE of the models was between 2.489 and 4.192 dbm. Then, the WUSN node signal attenuation model involving the four factors was established. The R² and RMSE of the model were, respectively, 0.822 and 4.87 dbm. The WUSN node signal attenuation model established in this paper can facilitate WUSN node deployment. Full article

(This article belongs to the Special Issue Emerging Technologies in Earth Observations for Agricultural Monitoring)

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