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Remote Sensing in Agriculture: State-of-the-Art

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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 2020) | Viewed by 64917

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Special Issue Editors

Prof. Dr. Enrico Corrado Borgogno Mondino

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Department of Agricultural, Forest and Food Sciences, University of Turin, L.go Braccini 2, 10095 Grugliasco, Italy
Interests: remote sensing; spatial analysis and landscape planning; GIS; digital photogrammetry; precision farming; lidar
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Eufemia Tarantino

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Guest Editor

Department of Civil, Environmental, Land, Construction and Chemistry (DICATECh), Politecnico di Bari, Via Orabona 4, 70125 Bari, Italy
Interests: geomatics; optical remote sensing; pixel-based and geographic object-based image analysis (GEOBIA); UAV applications; digital photogrammetry and spatial analysis; methodologies for multi-temporal analysis (change detection) and classification of optical satellite sensor data aimed at environmental, agricultural and cultural heritage monitoring and documentation
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Dr. Alessandra Capolupo

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Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055 Portici, Italy
Interests: remote sensing, digital photogrammetry and spatial analysis for heavy metals detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue Remote Sensing in Agriculture: State-of-the-Art is intended to assemble high-level contributions giving an exhaustive overlook of the ongoing remote sensing technology transfer into the agricultural (crops and forests) sector. Contributions from authors should not report simple case studies but more properly highlight opportunities, limitations, and criticalities still persisting in this context.

The following themes are warmly encouraged:

Design and implementation of institutional services for agriculture (controls and management) based on satellite/aerial/UAV data with special concerns about image time series and monitoring instances;
Technical criticalities/limitations/potentialities and possible solutions of new low-cost sensors with special concerns about UAV (unmanned aerial vehicle) and UGV (unmanned ground vehicle)-based systems;
Remote sensing data processing for agronomic information retrieval (both qualitative and quantitative). Works presenting procedures to validate prescription maps and criteria definition to derive reliable intensity rates of agronomic interventions from RS data are encouraged;
Crop water requirement estimation from RS data;
Crop yield estimation from RS data;
Phytopathological alerts for crops, based on RS data;
Economical evaluations concerning costs of RS, actual, and forecasted income improvements in the agriculture sector, potential market;
Relationship between ground and RS data with special concerns about future scenarios were BIG DATA from distributed ground sources (networks of distributed sensors, smartphone-based ground observations, etc.), will enter the RS workflow;
Standardization of processes and outputs

All other issues related to the adoption of RS in the agriculture sector will be evaluated, as well.

Prof. Dr. Enrico Borgogno-Mondino
Prof. Dr. Eufemia Tarantino
Dr. Alessandra Capolupo
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

Free satellite Data
RPAS/UAV
Copernicus
Prescription maps
Crop Management
Crop Monitoring
Crop Water Requirements
Services in Agriculture
Crop Productivity
Data Process Standardization

Published Papers (10 papers)

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16 pages, 4027 KiB

Open AccessArticle

Sentinel-1 Polarimetry to Map Apple Orchard Damage after a Storm

by Samuele De Petris, Filippo Sarvia, Michele Gullino, Eufemia Tarantino and Enrico Borgogno-Mondino

Remote Sens. 2021, 13(5), 1030; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13051030 - 09 Mar 2021

Cited by 16 | Viewed by 3370

Abstract

Climate change increases extreme whether events such as floods, hailstorms, or storms, which can affect agriculture, causing damages and economic loss within the agro-food sector. Optical remote sensing data have been successfully used in damage detections. Cloud conditions limit their potential, especially while monitoring floods or storms that are usually related to cloudy situations. Conversely, data from the Polarimetric Synthetic Aperture Radar (PolSAR) are operational in all-weather conditions and are sensitive to the geometrical properties of crops. Apple orchards play a key role in the Italian agriculture sector, presenting a cultivation system that is very sensitive to high-wind events. In this work, the H-α-A polarimetric decomposition technique was adopted to map damaged apple orchards with reference to a stormy event that had occurred in the study area (NW Italy) on 12 August 2020. The results showed that damaged orchards have higher H (entropy) and α (alpha angle) values compared with undamaged ones taken as reference (Mann–Whitney one-tailed test U = 14,514, p < 0.001; U = 16604, p < 0.001 for H and α, respectively). By contrast, A (anisotropy) values were significantly lower for damaged orchards (Mann–Whitney one-tailed test U = 8616, p < 0.001). Based on this evidence, the authors generated a map of potentially storm-damaged orchards, assigning a probability value to each of them. This map is intended to support local funding restoration policies by insurance companies and local administrations. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture: State-of-the-Art)

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19 pages, 13558 KiB

Open AccessArticle

Experimental Evaluation and Consistency Comparison of UAV Multispectral Minisensors

by Han Lu, Tianxing Fan, Prakash Ghimire and Lei Deng

Remote Sens. 2020, 12(16), 2542; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12162542 - 07 Aug 2020

Cited by 38 | Viewed by 6969

Abstract

In recent years, the use of unmanned aerial vehicles (UAVs) has received increasing attention in remote sensing, vegetation monitoring, vegetation index (VI) mapping, precision agriculture, etc. It has many advantages, such as high spatial resolution, instant information acquisition, convenient operation, high maneuverability, freedom from cloud interference, and low cost. Nowadays, different types of UAV-based multispectral minisensors are used to obtain either surface reflectance or digital number (DN) values. Both the reflectance and DN values can be used to calculate VIs. The consistency and accuracy of spectral data and VIs obtained from these sensors have important application value. In this research, we analyzed the earth observation capabilities of the Parrot Sequoia (Sequoia) and DJI Phantom 4 Multispectral (P4M) sensors using different combinations of correlation coefficients and accuracy assessments. The research method was mainly focused on three aspects: (1) consistency of spectral values, (2) consistency of VI products, and (3) accuracy of normalized difference vegetation index (NDVI). UAV images in different resolutions were collected using these sensors, and ground points with reflectance values were recorded using an Analytical Spectral Devices handheld spectroradiometer (ASD). The average spectral values and VIs of those sensors were compared using different regions of interest (ROIs). Similarly, the NDVI products of those sensors were compared with ground point NDVI (ASD-NDVI). The results show that Sequoia and P4M are highly correlated in the green, red, red edge, and near-infrared bands (correlation coefficient (R²) > 0.90). The results also show that Sequoia and P4M are highly correlated in different VIs; among them, NDVI has the highest correlation (R² > 0.98). In comparison with ground point NDVI (ASD-NDVI), the NDVI products obtained by both of these sensors have good accuracy (Sequoia: root-mean-square error (RMSE) < 0.07; P4M: RMSE < 0.09). This shows that the performance of different sensors can be evaluated from the consistency of spectral values, consistency of VI products, and accuracy of VIs. It is also shown that different UAV multispectral minisensors can have similar performances even though they have different spectral response functions. The findings of this study could be a good framework for analyzing the interoperability of different sensors for vegetation change analysis. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture: State-of-the-Art)

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20 pages, 9981 KiB

Open AccessArticle

Accuracies of Soil Moisture Estimations Using a Semi-Empirical Model over Bare Soil Agricultural Croplands from Sentinel-1 SAR Data

by Anil Kumar Hoskera, Giovanni Nico, Mohammed Irshad Ahmed and Anthony Whitbread

Remote Sens. 2020, 12(10), 1664; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12101664 - 22 May 2020

Cited by 14 | Viewed by 4579

Abstract

This study describes a semi-empirical model developed to estimate volumetric soil moisture (

ϑ_{v}

) in bare soils during the dry season (March–May) using C-band (5.42 GHz) synthetic aperture radar (SAR) imagery acquired from the Sentinel-1 European satellite platform at a 20 [...] Read more.

This study describes a semi-empirical model developed to estimate volumetric soil moisture (

ϑ_{v}

) in bare soils during the dry season (March–May) using C-band (5.42 GHz) synthetic aperture radar (SAR) imagery acquired from the Sentinel-1 European satellite platform at a 20 m spatial resolution. The semi-empirical model was developed using backscatter coefficient (

σ ° dB

) and in situ soil moisture collected from Siruguppa taluk (sub-district) in the Karnataka state of India. The backscatter coefficients

σ_{V V}^{0}

and

σ_{V H}^{0}

were extracted from SAR images at 62 geo-referenced locations where ground sampling and volumetric soil moisture were measured at a 10 cm (0–10 cm) depth using a soil core sampler and a standard gravimetric method during the dry months (March–May) of 2017 and 2018. A linear equation was proposed by combining

σ_{V V}^{0}

and

σ_{V H}^{0}

to estimate soil moisture. Both localized and generalized linear models were derived. Thirty-nine localized linear models were obtained using the 13 Sentinel-1 images used in this study, considering each polarimetric channel Co-Polarization (VV) and Cross-Polarization (VH) separately, and also their linear combination of VV + VH. Furthermore, nine generalized linear models were derived using all the Sentinel-1 images acquired in 2017 and 2018; three generalized models were derived by combining the two years (2017 and 2018) for each polarimetric channel; and three more models were derived for the linear combination of

σ_{V V}^{0}

and

σ_{V H}^{0}

. The above set of equations were validated and the Root Mean Square Error (RMSE) was 0.030 and 0.030 for 2017 and 2018, respectively, and 0.02 for the combined years of 2017 and 2018. Both localized and generalized models were compared with in situ data. Both kind of models revealed that the linear combination of

σ_{V V}^{0}

σ_{V H}^{0}

showed a significantly higher R² than the individual polarimetric channels. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture: State-of-the-Art)

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20 pages, 4008 KiB

Open AccessArticle

To Blend or Not to Blend? A Framework for Nationwide Landsat–MODIS Data Selection for Crop Yield Prediction

by Yang Chen, Tim R. McVicar, Randall J. Donohue, Nikhil Garg, François Waldner, Noboru Ota, Lingtao Li and Roger Lawes

Remote Sens. 2020, 12(10), 1653; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12101653 - 21 May 2020

Cited by 8 | Viewed by 3271

Abstract

The onus for monitoring crop growth from space is its ability to be applied anytime and anywhere, to produce crop yield estimates that are consistent at both the subfield scale for farming management strategies and the country level for national crop yield assessment. Historically, the requirements for satellites to successfully monitor crop growth and yield differed depending on the extent of the area being monitored. Diverging imaging capabilities can be reconciled by blending images from high-temporal-frequency (HTF) and high-spatial-resolution (HSR) sensors to produce images that possess both HTF and HSR characteristics across large areas. We evaluated the relative performance of Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat, and blended imagery for crop yield estimates (2009–2015) using a carbon-turnover yield model deployed across the Australian cropping area. Based on the fraction of missing Landsat observations, we further developed a parsimonious framework to inform when and where blending is beneficial for nationwide crop yield prediction at a finer scale (i.e., the 25-m pixel resolution). Landsat provided the best yield predictions when no observations were missing, which occurred in 17% of the cropping area of Australia. Blending was preferred when <42% of Landsat observations were missing, which occurred in 33% of the cropping area of Australia. MODIS produced a lower prediction error when ≥42% of the Landsat images were missing (~50% of the cropping area). By identifying when and where blending outperforms predictions from either Landsat or MODIS, the proposed framework enables more accurate monitoring of biophysical processes and yields, while keeping computational costs low. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture: State-of-the-Art)

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27 pages, 14092 KiB

Open AccessArticle

Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

by Luyi Sun, Jinsong Chen, Shanxin Guo, Xinping Deng and Yu Han

Remote Sens. 2020, 12(1), 158; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12010158 - 02 Jan 2020

Cited by 27 | Viewed by 4240

Abstract

Timely and accurate crop type mapping is a critical prerequisite for the estimation of water availability and environmental carrying capacity. This research proposed a method to integrate time series Sentinel-1 (S1) and Sentinel-2 (S2) data for crop type mapping over oasis agricultural areas through a case study in Northwest China. Previous studies using synthetic aperture radar (SAR) data alone often yield quite limited accuracy in crop type identification due to speckles. To improve the quality of SAR features, we adopted a statistically homogeneous pixel (SHP) distributed scatterer interferometry (DSI) algorithm, originally proposed in the interferometric SAR (InSAR) community for distributed scatters (DSs) extraction, to identify statistically homogeneous pixel subsets (SHPs). On the basis of this algorithm, the SAR backscatter intensity was de-speckled, and the bias of coherence was mitigated. In addition to backscatter intensity, several InSAR products were extracted for crop type classification, including the interferometric coherence, master versus slave intensity ratio, and amplitude dispersion derived from SAR data. To explore the role of red-edge wavelengths in oasis crop type discrimination, we derived 11 red-edge indices and three red-edge bands from Sentinel-2 images, together with the conventional optical features, to serve as input features for classification. To deal with the high dimension of combined SAR and optical features, an automated feature selection method, i.e., recursive feature increment, was developed to obtain the optimal combination of S1 and S2 features to achieve the highest mapping accuracy. Using a random forest classifier, a distribution map of five major crop types was produced with an overall accuracy of 83.22% and kappa coefficient of 0.77. The contribution of SAR and optical features were investigated. SAR intensity in VH polarization was proved to be most important for crop type identification among all the microwave and optical features employed in this study. Some of the InSAR products, i.e., the amplitude dispersion, master versus slave intensity ratio, and coherence, were found to be beneficial for oasis crop type mapping. It was proved the inclusion of red-edge wavelengths improved the overall accuracy (OA) of crop type mapping by 1.84% compared with only using conventional optical features. In comparison, it was demonstrated that the synergistic use of time series Sentinel-1 and Sentinel-2 data achieved the best performance in the oasis crop type discrimination. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture: State-of-the-Art)

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18 pages, 4948 KiB

Open AccessArticle

Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar

by Jelle ten Harkel, Harm Bartholomeus and Lammert Kooistra

Remote Sens. 2020, 12(1), 17; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12010017 - 18 Dec 2019

Cited by 111 | Viewed by 8745

Abstract

Phenotyping of crops is important due to increasing pressure on food production. Therefore, an accurate estimation of biomass during the growing season can be important to optimize the yield. The potential of data acquisition by UAV-LiDAR to estimate fresh biomass and crop height was investigated for three different crops (potato, sugar beet, and winter wheat) grown in Wageningen (The Netherlands) from June to August 2018. Biomass was estimated using the 3DPI algorithm, while crop height was estimated using the mean height of a variable number of highest points for each m². The 3DPI algorithm proved to estimate biomass well for sugar beet (R² = 0.68, RMSE = 17.47 g/m²) and winter wheat (R² = 0.82, RMSE = 13.94 g/m²). Also, the height estimates worked well for sugar beet (R² = 0.70, RMSE = 7.4 cm) and wheat (R² = 0.78, RMSE = 3.4 cm). However, for potato both plant height (R² = 0.50, RMSE = 12 cm) and biomass estimation (R² = 0.24, RMSE = 22.09 g/m²), it proved to be less reliable due to the complex canopy structure and the ridges on which potatoes are grown. In general, for accurate biomass and crop height estimates using those algorithms, the flight conditions (altitude, speed, location of flight lines) should be comparable to the settings for which the models are calibrated since changing conditions do influence the estimated biomass and crop height strongly. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture: State-of-the-Art)

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21 pages, 4668 KiB

Open AccessArticle

Determination of Appropriate Remote Sensing Indices for Spring Wheat Yield Estimation in Mongolia

by Battsetseg Tuvdendorj, Bingfang Wu, Hongwei Zeng, Gantsetseg Batdelger and Lkhagvadorj Nanzad

Remote Sens. 2019, 11(21), 2568; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11212568 - 01 Nov 2019

Cited by 42 | Viewed by 6464

Abstract

In Mongolia, the monitoring and estimation of spring wheat yield at the regional and national levels are key issues for the agricultural policy and food management as well as for the economy and society as a whole. The remote sensing data and technique have been widely used for the estimation of crop yield and production in the world. For the current research, nine remote sensing indices were tested that include normalized difference drought index (NDDI), normalized difference water index (NDWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), normalized multi-band drought index (NMDI), visible and shortwave infrared drought index (VSDI), and vegetation supply water index (VSWI). These nine indices derived from MODIS/Terra satellite have so far not been used for crop yield prediction in Mongolia. The primary objective of this study was to determine the best remote sensing indices in order to develop an estimation model for spring wheat yield using correlation and regression method. The spring wheat yield data from the ground measurements of eight meteorological stations in Darkhan and Selenge provinces from 2000 to 2017 have been used. The data were collected during the period of the growing season (June–August). Based on the analysis, we constructed six models for spring wheat yield estimation. The results showed that the range of the root-mean-square error (RMSE) values of estimated spring wheat yield was between 4.1 (100 kg ha⁻¹) to 4.8 (100 kg ha⁻¹), respectively. The range of the mean absolute error (MAE) values was between 3.3 to 3.8 and the index of agreement (d) values was between 0.74 to 0.84, respectively. The conclusion was that the best model would be (R² = 0.55) based on NDWI, VSDI, and NDVI out of the nine indices and could serve as the most effective predictor and reliable remote sensing indices for monitoring the spring wheat yield in the northern part of Mongolia. Our results showed that the best timing of yield prediction for spring wheat was around the end of June and the beginning of July, which is the flowering stage of spring wheat in this study area. This means an accurate yield prediction for spring wheat can be achieved two months before the harvest time using the regression model. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture: State-of-the-Art)

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20 pages, 5912 KiB

Open AccessArticle

In-Field Detection of Yellow Rust in Wheat on the Ground Canopy and UAV Scale

by David Bohnenkamp, Jan Behmann and Anne-Katrin Mahlein

Remote Sens. 2019, 11(21), 2495; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11212495 - 25 Oct 2019

Cited by 55 | Viewed by 6009

Abstract

The application of hyperspectral imaging technology for plant disease detection in the field is still challenging. Existing equipment and analysis algorithms are adapted to highly controlled environmental conditions in the laboratory. However, only real time information from the field scale is able to guide plant protection measures and to optimize the use of resources. At the field scale, many parameters such as the optimal measurement distance, informative feature sets, and suitable algorithms have not been investigated. In this study, the hyperspectral detection and quantification of yellow rust in wheat was evaluated using two measurement platforms: a ground-based vehicle and an unmanned aerial vehicle (UAV). Different disease development stages and disease severities were provided in a plot-based field experiment. Measurements were performed weekly during the vegetation period. Data analysis was performed by three prediction algorithms with a focus on the selection of optimal feature sets. In this context, the across-scale application of optimized feature sets, an approach of information transfer between scales, was also evaluated. Relevant aspects for an on-line disease assessment in the field integrating affordable sensor technology, sensor spatial resolution, compact analysis models, and fast evaluation have been outlined and reflected upon. For the first time, a hyperspectral imaging observation experiment of a plant disease was comparatively performed at two scales, ground canopy and UAV. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture: State-of-the-Art)

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Review

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26 pages, 8982 KiB

Open AccessReview

Applications of UAV Thermal Imagery in Precision Agriculture: State of the Art and Future Research Outlook

by Gaetano Messina and Giuseppe Modica

Remote Sens. 2020, 12(9), 1491; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091491 - 08 May 2020

Cited by 143 | Viewed by 17419

Abstract

Low-altitude remote sensing (RS) using unmanned aerial vehicles (UAVs) is a powerful tool in precision agriculture (PA). In that context, thermal RS has many potential uses. The surface temperature of plants changes rapidly under stress conditions, which makes thermal RS a useful tool for real-time detection of plant stress conditions. Current applications of UAV thermal RS include monitoring plant water stress, detecting plant diseases, assessing crop yield estimation, and plant phenotyping. However, the correct use and interpretation of thermal data are based on basic knowledge of the nature of thermal radiation. Therefore, aspects that are related to calibration and ground data collection, in which the use of reference panels is highly recommended, as well as data processing, must be carefully considered. This paper aims to review the state of the art of UAV thermal RS in agriculture, outlining an overview of the latest applications and providing a future research outlook. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture: State-of-the-Art)

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Other

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15 pages, 21296 KiB

Open AccessTechnical Note

Landscape-Scale Crop Lodging Assessment across Iowa and Illinois Using Synthetic Aperture Radar (SAR) Images

by Olaniyi A. Ajadi, Heming Liao, Jason Jaacks, Alfredo Delos Santos, Siva P. Kumpatla, Rinkal Patel and Anu Swatantran

Remote Sens. 2020, 12(23), 3885; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233885 - 27 Nov 2020

Cited by 10 | Viewed by 2539

Abstract

Crop lodging, the tilting of stems from their natural upright position, usually occurs after a heavy storm event. Since lodging of a crop seriously affects its yield, rapid assessment of crop lodging is valuable for farmers, policymakers, agronomists, insurance companies, and relief workers. Synthetic Aperture Radar (SAR) sensors have been recognized as valuable data sources for mapping lodging extent because of their good penetrating power and high-resolution remote sensing ability. Compared to other sources, SAR’s weather and illumination independence and large area coverage at fine spatial resolution (3 m to 20 m) support frequent and detailed observations. Because of these advantages, SAR has the potential in supporting near real-time monitoring of lodging in fields when combined with automated image processing. In this study, a method based on change detection using modified Hidden Markov Random Field (HMRF) and Sentinel-1A data were utilized to identify lodging and map its extent. Results obtained have shown that when lodging occurs, the VH polarization’s backscatter (σVH) increases between the pre-lodging event image and the post-lodging event image. The increase in σVH is due to the increase in volume scattering and vegetation-soil double bounce scattering resulting from the structural changes in the crop canopy. Using Sentinel-1A images and applying our proposed approach across several fields in Iowa and Illinois, we mapped the extent of the 2020 Derecho (wind storm) lodging disaster. In addition, we separated lodged regions into severely and moderately lodged areas. We estimated that approximately 2.56 million acres of corn and 1.27 million acres of soybean were lodged. Further analysis also showed the separation between un-lodged (healthy) fields and lodged fields. The observations in this study can guide future use of SAR-based information for operational crop lodging assessment. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture: State-of-the-Art)

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