remotesensing-logo

Journal Browser

Journal Browser

Remote Sensing in Geomatics

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: 15 April 2024 | Viewed by 18928

Special Issue Editors

Department of Engineering, Università degli Studi di Palermo, 90100 Palermo, Italy
Interests: Galileo; GLONASS; GPS; GNSS; CORS; remote sensing; geomatics; dam displacements
Special Issues, Collections and Topics in MDPI journals
Department of Engineering, University of Palermo, Viale delle Scienze, Building 8, 90128 Palermo, Italy
Interests: photogrammetry; laser scanning; 3D modeling; HBIM; cultural heritage documentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Allowing the observation and quantification of spatiotemporal processes of the Earth’s surface, remote sensing is one of the most widely used disciplines in geomatics. Moreover, remote sensing has an intrinsic interdisciplinary connotation, being interrelated with most disciplines in geomatics, including global satellite positioning techniques, photogrammetry, laser scanning, geostatistics, geographic information systems (GIS), decision support systems, WebGIS, and geomatics applications of artificial intelligence (AI).

Since these specialized fields are intimately interconnected, innovative research on complex contexts often relies on the integration of remote sensing with other geomatics disciplines.

The key question, therefore, is: To what extent does the study of Earth surface processes benefit from the synergy of remote sensing with and among geomatics disciplines?

We are seeking novel, hypothesis-driven, high-impact research on geomatics that interfaces remote sensing with GNSS, photogrammetry, LIDAR, GIS, geostatistics, and more.

Dr. Gino Dardanelli
Dr. Mauro Lo Brutto
Dr. Antonino Maltese
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
  • radar, thermal, optical, interferometry, hyperspectral
  • geostatistics
  • geodesy
  • cartography
  • GIS, WebGIS, DSS
  • GNSS
  • LIDAR
  • geometric and radiometric accuracy
  • photogrammetry
  • UAV

Published Papers (10 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

25 pages, 11855 KiB  
Article
DInSAR Multi-Temporal Analysis for the Characterization of Ground Deformations Related to Tectonic Processes in the Region of Bucaramanga, Colombia
Remote Sens. 2024, 16(3), 449; https://doi.org/10.3390/rs16030449 - 24 Jan 2024
Viewed by 436
Abstract
The analysis of the degree of surface deformation can be a relevant aspect in the study of surface stability conditions, as it provides added value in the construction of risk management plans. This analysis provides the opportunity to establish the behaviors of the [...] Read more.
The analysis of the degree of surface deformation can be a relevant aspect in the study of surface stability conditions, as it provides added value in the construction of risk management plans. This analysis provides the opportunity to establish the behaviors of the internal dynamics of the earth and its effects on the surface as a prediction tool for possible future effects. To this end, this study was approached through the analysis of Synthetic Aperture Radar (SAR) images using the Differential Interferometry (DInSAR) technique, which, in turn, is supported by the Small Baseline Subset (SBAS) technique to take advantage of the orbital separation of the Sentinel-1 satellite images in ascending and descending trajectory between the years 2014 and 2021. As a result, a time series was obtained in which there is a maximum uplift of 117.5 mm (LOS-ascending) or 49.3 mm (LOS-descending) and a maximum subsidence of −86.2 mm (LOS-ascending) or −71.5 mm (LOS-descending), with an oscillating behavior. These deformation conditions are largely associated with the kinematics of the Bucaramanga Fault, but a recurrent action of deep seismic activity from the Bucaramanga Seismic Nest was also observed, generating a surface deformation of ±20 mm for the period evaluated. These deformations have a certain degree of impact on the generation of mass movements, evaluated by the correlation with the LOS-descending images. However, their action is more focused as an inherent factor of great weight, which makes it possible to respond to early care and allows real-time follow-up, giving positive feedback to the system. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics)
Show Figures

Figure 1

22 pages, 14880 KiB  
Article
NO2 Concentration Estimation at Urban Ground Level by Integrating Sentinel 5P Data and ERA5 Using Machine Learning: The Milan (Italy) Case Study
Remote Sens. 2023, 15(22), 5400; https://doi.org/10.3390/rs15225400 - 17 Nov 2023
Viewed by 729
Abstract
The measurement of atmospheric NO2 pollution concentrations has become a critical topic due to its impact on human health. Ground sensors are the most popular method for measuring atmospheric pollution, but they can be expensive to purchase, install, and maintain. In contrast, [...] Read more.
The measurement of atmospheric NO2 pollution concentrations has become a critical topic due to its impact on human health. Ground sensors are the most popular method for measuring atmospheric pollution, but they can be expensive to purchase, install, and maintain. In contrast, satellite technology offers global coverage but typically provides concentration estimates at the tropospheric level, not at the ground level where most human activities take place. This work presents a model that can be used to estimate NO2 ground-level concentrations in metropolitan areas using Sentinel-5P satellite images and ERA5 meteorological data. The primary goal is to offer a cost-effective solution for Low- and Medium-Income Countries (LMICs) to assess air quality, thereby addressing the air quality measurement constraints. To validate the model’s accuracy, study points were selected in alignment with the Regional Agency for the Environment Protection (ARPA) NO2 sensor network in the Metropolitan City of Milan. The results showed that the RMSE of the model estimations was significantly lower than the standard deviation of the real measurements. This work fills the gaps in the literature by providing an accurate estimation model of NO2 in the Metropolitan City of Milan using both satellite data and ERA5 meteorological data. This work presents as an alternative to ground sensors by enabling more regions to assess their air quality effectively. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics)
Show Figures

Figure 1

24 pages, 3694 KiB  
Article
Improving the Spatial Prediction of Sand Content in Forest Soils Using a Multivariate Geostatistical Analysis of LiDAR and Hyperspectral Data
Remote Sens. 2023, 15(18), 4416; https://doi.org/10.3390/rs15184416 - 07 Sep 2023
Viewed by 874
Abstract
Soil sand particles play a crucial role in soil erosion because they are more susceptible to being detached and transported by erosive forces than silt and clay particles. Therefore, in soil erosion assessment and mitigation, it is crucial to model and predict soil [...] Read more.
Soil sand particles play a crucial role in soil erosion because they are more susceptible to being detached and transported by erosive forces than silt and clay particles. Therefore, in soil erosion assessment and mitigation, it is crucial to model and predict soil sand particles at unsampled locations using appropriate methods. The study was aimed to evaluate the ability of a multivariate approach based on non-stationary geostatistics to merge LiDAR and visible-near infrared (Vis-NIR) diffuse reflectance data with laboratory analyses to produce high-resolution maps of soil sand content. Remotely sensed, high-resolution LiDAR-derived topographic attributes can be used as auxiliary variables to estimate soil textural particle-size fractions. The proposed approach was compared with the commonly used univariate approach of ordinary kriging to evaluate the contribution of auxiliary variables. Soil samples (0–0.20 m depth) were collected at 135 locations within a 139 ha forest catchment with granitic parent material and subordinately alluvial deposits, where soils classified as Typic Xerumbrepts and Ultic Haploxeralf crop out. A number of linear trend models coupled with different auxiliary variables were compared. The best model for predicting sand content was the one with elevation derived from LIDAR data as the only auxiliary variable. Although the improvement in estimation over the univariate model was rather marginal, the proposed approach proved very flexible and scalable to include any type of auxiliary variable. The application of LiDAR data is expected to expand as it allows the high-resolution prediction of soil properties, generally insufficiently sampled, at different spatial scales. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics)
Show Figures

Graphical abstract

19 pages, 5440 KiB  
Article
Sentinel-1 Response to Canopy Moisture in Mediterranean Forests before and after Fire Events
Remote Sens. 2023, 15(3), 823; https://doi.org/10.3390/rs15030823 - 01 Feb 2023
Cited by 1 | Viewed by 1647
Abstract
This study investigates the sensibility of Sentinel-1 C-band backscatter to the moisture content of tree canopies over an area of about 500 km2 in north-western Portugal, with specific analysis over burnt areas. Sentinel-1 C-VV and C-VH backscatter values from 276 images acquired [...] Read more.
This study investigates the sensibility of Sentinel-1 C-band backscatter to the moisture content of tree canopies over an area of about 500 km2 in north-western Portugal, with specific analysis over burnt areas. Sentinel-1 C-VV and C-VH backscatter values from 276 images acquired between January 2018 and December 2020 were assigned to five classes depending on the Drought Code (DC) scenario over several unburned and burned sites with total (>90%) forest canopy cover. Confounding variables such as tree cover and incidence angle were accounted for by masking using specific thresholds. The following results are discussed: (a) C-VV and C-VH backscatter values are inversely correlated (R2 = 0.324 to 0.438 −p < 0.001) with local incidence angle over canopies; (b) correlation is significantly stronger over very wet scenarios (DC class = 0 to 1); (c) C-VV and C-VH backscatter values can discriminate wet to dry forest environments, but they are less sensitive to the transition between dry (DC classes = 1 to 10, 10 to 100) and extremely dry environments (DC classes = 100 to 1000); (d) C-VH is more sensible than C-VV to capture burnt canopy; and (e) the C-VH polarization captures post-fire recovery after an average minimum period of 360 days after the fire event, although with less distinction for extremely wet soils. We conclude that C-band VH backscatter intensity decreases from wet to dry canopy conditions, that this behavior of the backscatter signal with respect to canopy dryness is lost after a fire event, and that after one year it is recovered. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics)
Show Figures

Graphical abstract

21 pages, 6292 KiB  
Article
Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
Remote Sens. 2023, 15(3), 584; https://doi.org/10.3390/rs15030584 - 18 Jan 2023
Cited by 2 | Viewed by 1457
Abstract
The Southern Patagonian Icefield (SPI) is the largest continuous ice mass in the Southern Hemisphere outside Antarctica. It has been shrinking since the Little Ice Age (LIA) period, with increasing rates in recent years. An uplift of crustal deformation in response to this [...] Read more.
The Southern Patagonian Icefield (SPI) is the largest continuous ice mass in the Southern Hemisphere outside Antarctica. It has been shrinking since the Little Ice Age (LIA) period, with increasing rates in recent years. An uplift of crustal deformation in response to this deglaciation process has been expected. The goal of this investigation is to analyze the crustal deformation caused by ice retreat using time-series data from continuous GPS stations (2015–2020) in the northern area of the SPI. For this purpose, we installed two continuous GPS stations on rocky nunataks of the SPI (the GRCS near Greve glacier and the GBCS close by Cerro Gorra Blanca). In addition, ice elevation changes (2000–2019) were analyzed by the co-registration of the SRTM digital elevation model and ICESat elevation data points. The results of the vertical components are positive (36.55 ± 2.58 mm a−1), with a maximum at GBCS, indicating the highest rate of crustal uplift ever continuously recorded in Patagonia; in addition, the mean horizontal velocities reached 11.7 mm a−1 with an azimuth of 43°. The negative ice elevation changes detected in the region have also accelerated in the recent two decades, with a median Δh (elevation change) of −3.36 ± 0.01 m a−1 in the ablation zone. The seasonality of the GPS signals was contrasted with the water levels of the main Patagonian lakes around the SPI, detecting a complex interplay between them. Hence, the study sheds light on the knowledge of the crustal uplift as evidence of the wastage experienced by the SPI glaciers. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics)
Show Figures

Figure 1

22 pages, 5998 KiB  
Article
On the Choice of the Most Suitable Period to Map Hill Lakes via Spectral Separability and Object-Based Image Analyses
Remote Sens. 2023, 15(1), 262; https://doi.org/10.3390/rs15010262 - 02 Jan 2023
Viewed by 1397
Abstract
Technological advances in Earth observation made images characterized by high spatial and temporal resolutions available, nevertheless bringing with them the radiometric heterogeneity of small geographical entities, often also changing in time. Among small geographical entities, hill lakes exhibit a widespread distribution, and their [...] Read more.
Technological advances in Earth observation made images characterized by high spatial and temporal resolutions available, nevertheless bringing with them the radiometric heterogeneity of small geographical entities, often also changing in time. Among small geographical entities, hill lakes exhibit a widespread distribution, and their census is sometimes partial or shows unreliable data. High resolution and heterogeneity have boosted the development of geographic object-based image analysis algorithms. This research analyzes which is the most suitable period for acquiring satellite images to identify and delimitate hill lakes. This is achieved by analyzing the spectral separability of the surface reflectance of hill lakes from surrounding bare or vegetated soils and by implementing a semiautomatic procedure to enhance the segmentation phase of a GEOBIA algorithm. The proposed procedure was applied to high spatial resolution satellite images acquired in two different climate periods (arid and temperate), corresponding to dry and vegetative seasons. The segmentation parameters were tuned by minimizing an under- and oversegmentation metric on surfaces and perimeters of hill lakes selected as the reference. The separability of hill lakes from their surrounding was evaluated using Euclidean and divergence metrics both in the arid and temperate periods. The classification accuracy was evaluated by calculating the error matrix and normalized error matrix. Classes’ reflectances in the image acquired in the arid period show the highest average separability (3–4 higher than in the temperate one). The segmentation based on the reference areas performs more than that based on the reference perimeters (metric ≈ 20% lower). Both separability metrics and classification accuracies indicate that images acquired in the arid period are more suitable than temperate ones to map hill lakes. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics)
Show Figures

Figure 1

19 pages, 9807 KiB  
Article
Two-Dimensional InSAR Monitoring of the Co- and Post-Seismic Ground Deformation of the 2021 Mw 5.9 Arkalochori (Greece) Earthquake and Its Impact on the Deformations of the Heraklion City Wall Relic
Remote Sens. 2022, 14(20), 5212; https://doi.org/10.3390/rs14205212 - 18 Oct 2022
Cited by 6 | Viewed by 2404
Abstract
Contributing to the United Nations 2030 Sustainable Development Goals (SDGs) within Target 11.4 “Strengthen efforts to protect and safeguard the world’s cultural and natural heritage”, it is critical to monitor the spatial and temporal stabilities of cultural heritages. The study of the interactive [...] Read more.
Contributing to the United Nations 2030 Sustainable Development Goals (SDGs) within Target 11.4 “Strengthen efforts to protect and safeguard the world’s cultural and natural heritage”, it is critical to monitor the spatial and temporal stabilities of cultural heritages. The study of the interactive relationship between earthquakes and the protection of cultural heritages needs to be strengthened. On 27 September 2021, the destructive Mw 5.9 Arkalochori earthquake occurred ~25 km away from the city of Heraklion (Greece) where the Heraklion City Wall (HCW), a representative cultural heritage of Greece and Europe, was located. This offered a proper case to investigate the shortcomings aforementioned. Here, we intend to set up and answer the following three questions (Whether, Where and What, 3Ws): Whether there were impacts on the HCW caused by the Arkalochori earthquake? Where did the maximum deformation occur? What was the relationship between seismic deformation between the epicenter and the HCW over time? We performed two-dimensional (2D) InSAR measurements for both co-seismic and post-seismic deformations using the ascending and descending Sentinel-1A SAR images. The spatial-temporal characteristics of Up–Down (UD) and East–West (EW) were revealed. The 2D co-seismic deformation field showed that the near-filed deformations were dominating compared with the deformations at the HCW, the UD deformation was mainly featured with subsidence with a maximum value of ~21 cm, the EW deformation was ~9 cm westward and ~10 cm eastward. The time-series measurements showed that: (1) temporally, the HCW responded quickly to the Arkalochori earthquake, and the accumulative deformations at the seven different bastions of the HCW showed the same trend as the near-field area over time. (2) Spatially, the closer to the Mw 5.9 epicenter, the larger the deformations that occurred. (3) The EW and UD deformation trends of the HCW that were consistent with the Mw 5.9 epicenter were interrupted at the middle time spot (22 January 2022), indicating the influence of another earthquake sequence consisting of eight earthquakes with magnitudes larger than 3.5 that happened on 16–18 January 2022. Respectively, to summarize and address the aforementioned 3Ws based on the post-seismic analysis accomplished by the MSBAS method, the Arkalochori earthquake did affect the HCW; besides, the influences of the ~13 km earthquake sequence were also detected; the nearest part to the epicenter suffered the most; the deformation trends of the HCW were approximately the same with the epicenter area of the Arkalochori earthquake both in the UD and EW directions. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics)
Show Figures

Graphical abstract

24 pages, 11634 KiB  
Article
Ground Penetrating Radar in Coastal Hazard Mitigation Studies Using Deep Convolutional Neural Networks
Remote Sens. 2022, 14(19), 4899; https://doi.org/10.3390/rs14194899 - 30 Sep 2022
Cited by 3 | Viewed by 1871
Abstract
There is a long history of coastal erosion caused by frequent storm surges in the coastal regions of Australia, which imposes great threats to communities and infrastructures alongside the beach. Old Bar Beach, New South Wales, Australia, is one such hotspot famous for [...] Read more.
There is a long history of coastal erosion caused by frequent storm surges in the coastal regions of Australia, which imposes great threats to communities and infrastructures alongside the beach. Old Bar Beach, New South Wales, Australia, is one such hotspot famous for its extreme coastal erosion. To apply remedial measures such as beach nourishment effectively and economically, estimating/reconstructing the subsurface hydrogeology over the coastal areas is essential. A geophysical tool such as a ground-penetrating radar (GPR) which works on the principle of reflecting electromagnetic (EM) waves, can be conveniently deployed to delineate the soil and rock profiling, water-table depth, bedrock depth, and the subsurface structural features. Here, DeepLabv3+ architecture based newly developed deep convolutional neural networks (DCNNs) were used to establish an inherent non-linear relationship between the GPR data and the EM wave velocity. The presented DCNNs have a lesser number of layers, a lesser number of trainable (learnable) parameters, a high convergence rate and, at the same time, achieve prediction accuracy comparable to that of well-established DeepLabv3+ networks, having high trainable parameters and a relatively low convergence rate. Here, firstly the DCNNs were trained and validated on small 1D datasets. Each dataset contains a 1D GPR trace and a corresponding EM velocity model. The DCNNs turned out to be quite promising in the 1D case, with training, validation, and testing accuracy of approximately 95%, 94%, and 95%, respectively. Secondly, 1D trained weights were applied to 2D synthetic GPR data for EM velocity prediction, and the accuracy of prediction achieved was approximately 95%. Seeing the excellent performance of the DCNNs in the 2D prediction case using 1D trained weights, a large amount of 1D synthetic datasets (approximately 1.2 million) were generated and gaussian noise was added to it to replicate the real field scenario. Thirdly, topographically corrected GPR data acquired over the Old Bar Beach were inverted using the DCNNs trained on 1.2 million 1D synthetic datasets to obtain the subsurface high-resolution, high-precision EM velocity, and εr distribution information to understand the hydrogeology over the beach. The findings presented in this paper agree well with the previous hydrogeological studies carried out using GPR. Our findings show that DCNNs, along with GPR, can be successfully used in coastal environments for the quick and accurate hydrogeological investigation required for the implementation of coastal erosion mitigation methods such as beach nourishment. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics)
Show Figures

Figure 1

16 pages, 7104 KiB  
Article
Evaluation of Simulated AVIRIS-NG Imagery Using a Spectral Reconstruction Method for the Retrieval of Leaf Chlorophyll Content
Remote Sens. 2022, 14(15), 3560; https://doi.org/10.3390/rs14153560 - 25 Jul 2022
Cited by 5 | Viewed by 1574
Abstract
The leaf chlorophyll content (LCC) is a vital parameter that indicates plant production, stress, and nutrient availability. It is critically needed for precision farming. There are several multispectral images available freely, but their applicability is restricted due to their low spectral resolution, whereas [...] Read more.
The leaf chlorophyll content (LCC) is a vital parameter that indicates plant production, stress, and nutrient availability. It is critically needed for precision farming. There are several multispectral images available freely, but their applicability is restricted due to their low spectral resolution, whereas hyperspectral images which have high spectral resolution are very limited in availability. In this work, hyperspectral imagery (AVIRIS-NG) is simulated using a multispectral image (Sentinel-2) and a spectral reconstruction method, namely, the universal pattern decomposition method (UPDM). UPDM is a linear unmixing technique, which assumes that every pixel of an image can be decomposed as a linear composition of different classes present in that pixel. The simulated AVIRIS-NG was very similar to the original image, and its applicability in estimating LCC was further verified by using the ground based measurements, which showed a good correlation value (R = 0.65). The simulated image was further classified using a spectral angle mapper (SAM), and an accuracy of 87.4% was obtained, moreover a receiver operating characteristic (ROC) curve for the classifier was also plotted, and the area under the curve (AUC) was calculated with values greater than 0.9. The obtained results suggest that simulated AVIRIS-NG is quite useful and could be used for vegetation parameter retrieval. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics)
Show Figures

Graphical abstract

18 pages, 4687 KiB  
Article
Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling
Remote Sens. 2022, 14(13), 3005; https://doi.org/10.3390/rs14133005 - 23 Jun 2022
Cited by 23 | Viewed by 4193
Abstract
Globally, estimating crop acreage and yield is one of the most critical issues that policy and decision makers need for assessing annual crop productivity and food supply. Nowadays, satellite remote sensing and geographic information system (GIS) can enable the estimation of these crop [...] Read more.
Globally, estimating crop acreage and yield is one of the most critical issues that policy and decision makers need for assessing annual crop productivity and food supply. Nowadays, satellite remote sensing and geographic information system (GIS) can enable the estimation of these crop production parameters over large geographic areas. The present work aims to estimate the wheat (Triticum aestivum) acreage and yield of Maharajganj, Uttar Pradesh, India, using satellite-based data products and the Carnegie-Ames-Stanford Approach (CASA) model. Uttar Pradesh is the largest wheat-producing state in India, and this district is well known for its quality organic wheat. India is the leader in wheat grain export, and, hence, its monitoring of growth and yield is one of the top economic priorities of the country. For the calculation of wheat acreage, we performed supervised classification using the Random Forest (RF) and Support Vector Machine classifiers and compared their classification accuracy based on ground-truthing. We found that RF performed a significantly accurate acreage assessment (kappa coefficient 0.84) compared to SVM (0.68). The CASA model was then used to calculate the winter crop (Rabi, winter-sown, and summer harvested) wheat net primary productivity (NPP) in the study area for the 2020–2021 growth season using the RF-based acreage product. The model used for wheat NPP-yield conversion (CASA) showed 3100.27 to 5000.44 kg/ha over 148,866 ha of the total wheat area. The results showed that in the 2020–2021 growing season, all the districts of Uttar Pradesh had similar wheat growth trends. A total of 30 observational data points were used to verify the CASA model-based estimates of wheat yield. Field-based verification shows that the estimated yield correlates well with the observed yield (R2 = 0.554, RMSE = 3.36 Q/ha, MAE −0.56 t ha−1, and MRE = −4.61%). Such an accuracy for assessing regional wheat yield can prove to be one of the promising methods for calculating the whole region’s agricultural yield. The study concludes that RF classifier-based yield estimation has shown more accurate results and can meet the requirements of a regional-scale wheat grain yield estimation and, thus, can prove highly beneficial in policy and decision making. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics)
Show Figures

Graphical abstract

Back to TopTop