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Remote Sensing in Environmental Modelling

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

Deadline for manuscript submissions: closed (1 December 2024) | Viewed by 24224

Special Issue Editors


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Guest Editor
School of Water, Energy and Environment, Cranfield University, College Road, Cranfield, Bedfordshire MK430AL, UK
Interests: surface water flooding; standardised monitoring approaches; systems engineering; disruptive technologies; climate change; extreme events
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Water, Energy and Environment, Cranfield University, College Road, Cranfield, Bedfordshire MK430AL, UK
Interests: environmental policy; environmental regulation; sustainability; governance; monitoring; natural capital; ecosystem services; risk assessment; emergency response; systems-based approaches; operationalizing research findings
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Environmental models are used for a wide range of applications, including natural resource management and flood emergency planning. Input data for such models increasingly rely on remote sensing technologies, methodologies and derived geomatic products. Remote sensing has become an integral part of accurate and detailed, and fit for purpose, environmental modelling. This Special Issue looks at compiling examples of timely applications of remote sensing for environmental modelling. We are interested in manuscripts around the creation, collection, storage, processing, interpretation, visualisation, assessment and dissemination of data and modelled outputs. We are particularly interested in country-specific case studies demonstrating the use and benefit of remote sensing for environmental modelling. The following topics will be of particular interest:

  • Forest ecology
  • Biodiversity and wildlife
  • Environmental informatics
  • Ecoinformatics
  • Biodiversity and environmental net gain assessments
  • Nature based intervention assessments
  • Natural resource management and planning
  • Climate change and extreme events
  • Atmospheric processes
  • Carbon sequestration
  • Sustainability and resilience
  • Hydrological and flood modelling
  • Country specific case studies

Dr. Monica Rivas Casado
Prof. Dr. Paul Leinster
Guest Editors

Manuscript Submission Information

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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

  • process modelling
  • environmental informatics
  • climate change
  • ecology
  • forest
  • sustainability
  • resilience
  • ecoinformatics

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Published Papers (10 papers)

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Research

27 pages, 22290 KiB  
Article
Real-Time Environmental Contour Construction Using 3D LiDAR and Image Recognition with Object Removal
by Tzu-Jung Wu, Rong He and Chao-Chung Peng
Remote Sens. 2024, 16(23), 4513; https://doi.org/10.3390/rs16234513 - 1 Dec 2024
Viewed by 675
Abstract
In recent years, due to the significant advancements in hardware sensors and software technologies, 3D environmental point cloud modeling has gradually been applied in the automation industry, autonomous vehicles, and construction engineering. With the high-precision measurements of 3D LiDAR, its point clouds can [...] Read more.
In recent years, due to the significant advancements in hardware sensors and software technologies, 3D environmental point cloud modeling has gradually been applied in the automation industry, autonomous vehicles, and construction engineering. With the high-precision measurements of 3D LiDAR, its point clouds can clearly reflect the geometric structure and features of the environment, thus enabling the creation of high-density 3D environmental point cloud models. However, due to the enormous quantity of high-density 3D point clouds, storing and processing these 3D data requires a considerable amount of memory and computing time. In light of this, this paper proposes a real-time 3D point cloud environmental contour modeling technique. The study uses the point cloud distribution from the 3D LiDAR body frame point cloud to establish structured edge features, thereby creating a 3D environmental contour point cloud map. Additionally, unstable objects such as vehicles will appear during the mapping process; these specific objects will be regarded as not part of the stable environmental model in this study. To address this issue, the study will further remove these objects from the 3D point cloud through image recognition and LiDAR heterogeneous matching, resulting in a higher quality 3D environmental contour point cloud map. This 3D environmental contour point cloud not only retains the recognizability of the environmental structure but also solves the problems of massive data storage and processing. Moreover, the method proposed in this study can achieve real-time realization without requiring the 3D point cloud to be organized in a structured order, making it applicable to unorganized 3D point cloud LiDAR sensors. Finally, the feasibility of the proposed method in practical applications is also verified through actual experimental data. Full article
(This article belongs to the Special Issue Remote Sensing in Environmental Modelling)
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31 pages, 3112 KiB  
Article
Fusing Multispectral and LiDAR Data for CNN-Based Semantic Segmentation in Semi-Arid Mediterranean Environments: Land Cover Classification and Analysis
by Athanasia Chroni, Christos Vasilakos, Marianna Christaki and Nikolaos Soulakellis
Remote Sens. 2024, 16(15), 2729; https://doi.org/10.3390/rs16152729 - 25 Jul 2024
Cited by 1 | Viewed by 818
Abstract
Spectral confusion among land cover classes is quite common, let alone in a complex and heterogenous system like the semi-arid Mediterranean environment; thus, employing new developments in remote sensing, such as multispectral imagery (MSI) captured by unmanned aerial vehicles (UAVs) and airborne light [...] Read more.
Spectral confusion among land cover classes is quite common, let alone in a complex and heterogenous system like the semi-arid Mediterranean environment; thus, employing new developments in remote sensing, such as multispectral imagery (MSI) captured by unmanned aerial vehicles (UAVs) and airborne light detection and ranging (LiDAR) techniques, with deep learning (DL) algorithms for land cover classification can help to address this problem. Therefore, we propose an image-based land cover classification methodology based on fusing multispectral and airborne LiDAR data by adopting CNN-based semantic segmentation in a semi-arid Mediterranean area of northeastern Aegean, Greece. The methodology consists of three stages: (i) data pre-processing, (ii) semantic segmentation, and (iii) accuracy assessment. The multispectral bands were stacked with the calculated Normalized Difference Vegetation Index (NDVI) and the LiDAR-based attributes height, intensity, and number of returns converted into two-dimensional (2D) images. Then, a hyper-parameter analysis was performed to investigate the impact on the classification accuracy and training time of the U-Net architecture by varying the input tile size and the patch size for prediction, including the learning rate and algorithm optimizer. Finally, comparative experiments were conducted by altering the input data type to test our hypothesis, and the CNN model performance was analyzed by using accuracy assessment metrics and visually comparing the segmentation maps. The findings of this investigation showed that fusing multispectral and LiDAR data improves the classification accuracy of the U-Net, as it yielded the highest overall accuracy of 79.34% and a kappa coefficient of 0.6966, compared to using multispectral (OA: 76.03%; K: 0.6538) or LiDAR (OA: 37.79%; K: 0.0840) data separately. Although some confusion still exists among the seven land cover classes observed, the U-Net delivered a detailed and quite accurate segmentation map. Full article
(This article belongs to the Special Issue Remote Sensing in Environmental Modelling)
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29 pages, 26734 KiB  
Article
Variational-Based Spatial–Temporal Approximation of Images in Remote Sensing
by Majid Amirfakhrian and Faramarz F. Samavati
Remote Sens. 2024, 16(13), 2349; https://doi.org/10.3390/rs16132349 - 27 Jun 2024
Viewed by 930
Abstract
Cloud cover and shadows often hinder the accurate analysis of satellite images, impacting various applications, such as digital farming, land monitoring, environmental assessment, and urban planning. This paper presents a new approach to enhancing cloud-contaminated satellite images using a novel variational model for [...] Read more.
Cloud cover and shadows often hinder the accurate analysis of satellite images, impacting various applications, such as digital farming, land monitoring, environmental assessment, and urban planning. This paper presents a new approach to enhancing cloud-contaminated satellite images using a novel variational model for approximating the combination of the temporal and spatial components of satellite imagery. Leveraging this model, we derive two spatial-temporal methods containing an algorithm that computes the missing or contaminated data in cloudy images using the seamless Poisson blending method. In the first method, we extend the Poisson blending method to compute the spatial-temporal approximation. The pixel-wise temporal approximation is used as a guiding vector field for Poisson blending. In the second method, we use the rate of change in the temporal domain to divide the missing region into low-variation and high-variation sub-regions to better guide Poisson blending. In our second method, we provide a more general case by introducing a variation-based method that considers the temporal variation in specific regions to further refine the spatial–temporal approximation. The proposed methods have the same complexity as conventional methods, which is linear in the number of pixels in the region of interest. Our comprehensive evaluation demonstrates the effectiveness of the proposed methods through quantitative metrics, including the Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Metric (SSIM), revealing significant improvements over existing approaches. Additionally, the evaluations offer insights into how to choose between our first and second methods for specific scenarios. This consideration takes into account the temporal and spatial resolutions, as well as the scale and extent of the missing data. Full article
(This article belongs to the Special Issue Remote Sensing in Environmental Modelling)
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24 pages, 5600 KiB  
Article
Assessing Tree Water Balance after Forest Thinning Treatments Using Thermal and Multispectral Imaging
by Charlie Schrader-Patton, Nancy E. Grulke, Paul D. Anderson, Jamieson Chaitman and Jeremy Webb
Remote Sens. 2024, 16(6), 1005; https://doi.org/10.3390/rs16061005 - 13 Mar 2024
Cited by 1 | Viewed by 1333
Abstract
The health of coniferous forests in the western U.S. is under threat from mega-drought events, increasing vulnerability to insects, disease, and mortality. Forest densification resulting from fire exclusion increases these susceptibilities. Silvicultural treatments to reduce stand density and promote resilience to both fire [...] Read more.
The health of coniferous forests in the western U.S. is under threat from mega-drought events, increasing vulnerability to insects, disease, and mortality. Forest densification resulting from fire exclusion increases these susceptibilities. Silvicultural treatments to reduce stand density and promote resilience to both fire and drought have been used to reduce these threats but there are few quantitative evaluations of treatment effectiveness. This proof-of-concept study focused on such an evaluation, using field and remote sensing metrics of mature ponderosa pine (Pinus ponderosa Doug. Laws) in central Oregon. Ground metrics included direct measures of transpiration (sapflow), branch and needle measures and chlorosis; drone imagery included thermal (TIR) and five-band spectra (R, G, B, Re, NIR). Thermal satellite imagery was derived from ECOSTRESS, a space-borne thermal sensor that is on-board the International Space Station (ISS). All metrics were compared over 2 days at a time of maximum seasonal drought stress (August). Tree water status in unthinned, light, and heavy thinning from below density reduction treatments was evaluated. Tree crowns in the heavy thin site had greater transpiration and were cooler than those in the unthinned site, while the light thin site was not significantly cooler than either unthinned or the heavy thin site. There was a poor correlation (Adj. R2 0.10–0.13) between remotely sensed stand temperature and stand-averaged transpiration, and tree level temperature and transpiration (Adj. R2 0.04–0.19). Morphological attributes such as greater needle chlorosis and reduced elongation growth supported transpirational indicators of tree drought stress. The multispectral indices CCI and NDRE, along with the NIR and B bands, show promise as proxies for crown temperature and transpiration, and may serve as a proof of concept for an approach to evaluate forest treatment effectiveness in reducing tree drought stress. Full article
(This article belongs to the Special Issue Remote Sensing in Environmental Modelling)
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18 pages, 13428 KiB  
Article
Structural and Geomechanical Analysis of Natural Caves and Rock Shelters: Comparison between Manual and Remote Sensing Discontinuity Data Gathering
by Abdelmadjid Benrabah, Salvador Senent Domínguez, Fernando Carrera-Ramírez, David Álvarez-Alonso, María de Andrés-Herrero and Luis Jorda Bordehore
Remote Sens. 2024, 16(1), 72; https://doi.org/10.3390/rs16010072 - 23 Dec 2023
Cited by 3 | Viewed by 2006
Abstract
The stability of many shallow caves and rock shelters relies heavily on understanding rock discontinuities, such as stratification, faults, and joints. Analyzing these discontinuities and determining their orientations and dispersion are crucial for assessing the overall stability of the cave or shelter. Traditionally, [...] Read more.
The stability of many shallow caves and rock shelters relies heavily on understanding rock discontinuities, such as stratification, faults, and joints. Analyzing these discontinuities and determining their orientations and dispersion are crucial for assessing the overall stability of the cave or shelter. Traditionally, this analysis has been conducted manually using a compass with a clinometer, but it has certain limitations, as only fractures located in accessible areas like the lower part of cave walls and entrances are visible and can be assessed. Over the past decade, remote sensing techniques like LiDAR and photogrammetry have gained popularity in characterizing rocky massifs. These techniques provide 3D point clouds and high-resolution images of the cave or shelter walls and ceilings. With these data, it becomes possible to perform a three-dimensional reconstruction of the cavity and obtain important parameters of the discontinuities, such as orientation, spacing, persistence, or roughness. This paper presents a comparison between the geomechanical data obtained using the traditional manual procedures (compass readings in accessible zones) and a photogrammetric technique called Structure from Motion (SfM). The study was conducted in two caves, namely, the Reguerillo Cave (Madrid) and the Cova dos Mouros (Lugo), along with two rock shelters named Abrigo de San Lázaro and Abrigo del Molino (Segovia). The results of the study demonstrate an excellent correlation between the geomechanical parameters obtained from both methods. Indeed, the combination of traditional manual techniques and photogrammetry (SfM) offers significant advantages in developing a more comprehensive and realistic discontinuity census. Full article
(This article belongs to the Special Issue Remote Sensing in Environmental Modelling)
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21 pages, 8929 KiB  
Article
Towards a Digital Twin Prototype of Alpine Glaciers: Proposal for a Possible Theoretical Framework
by Vanina Fissore, Lorenza Bovio, Luigi Perotti, Piero Boccardo and Enrico Borgogno-Mondino
Remote Sens. 2023, 15(11), 2844; https://doi.org/10.3390/rs15112844 - 30 May 2023
Cited by 4 | Viewed by 2239
Abstract
The Destination Earth (DestinE) European initiative has recently brought into the scientific community the concept of the Digital Twin (DT) applied to Earth Sciences. Within 2030, a very high precision digital model of the Earth, continuously fed and powered by Earth Observation (EO) [...] Read more.
The Destination Earth (DestinE) European initiative has recently brought into the scientific community the concept of the Digital Twin (DT) applied to Earth Sciences. Within 2030, a very high precision digital model of the Earth, continuously fed and powered by Earth Observation (EO) data, will provide as many digital replicas (DTs) as the different domains of the earth sciences are. Considering that a DT is driven by use cases, depending on the selected application, the provided information has to change. It follows that, to achieve a reliable representation of the selected use case, a reasonable and complete a priori definition of the needed elements that DT must contain is mandatory. In this work, we define a possible theoretical framework for a future DT of the Italian Alpine glaciers, trying to define and describe all those information (both EO and in situ data) and relationships that necessarily have to enter the process as building blocks of the DT itself. Two main aspects of glaciers were considered and investigated: (i) the “metric quantification” of their spatial dynamics (achieved through measures) and (ii) the “qualitative (semantic) description” of their health status as definable through observations from domain experts. After the first identification of the building blocks, the work proceeds focusing on existing EO data sources providing their essential elements, with specific focus on open access high-resolution (HR) and very-high-resolution (VHR) images. This last issue considered two scales of analysis: local (single glacier) and regional (Italian Alps). Some considerations were furtherly reported about the expected glaciers-related applications enabled by the availability of a DT at regional level. Applications involving both metric and semantic information were considered and grouped in three main clusters: Glaciers Evolution Modelling (GEM), 4D Multi Reality, and Virtual Reality. Limitations were additionally explored, mainly related to the technical characteristics of available EO VHR open data and some conclusions provided. Full article
(This article belongs to the Special Issue Remote Sensing in Environmental Modelling)
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22 pages, 4544 KiB  
Article
Enhancing Solar Energy Forecast Using Multi-Column Convolutional Neural Network and Multipoint Time Series Approach
by Anil Kumar, Yashwant Kashyap and Panagiotis Kosmopoulos
Remote Sens. 2023, 15(1), 107; https://doi.org/10.3390/rs15010107 - 25 Dec 2022
Cited by 7 | Viewed by 2969
Abstract
The rapid expansion of solar industries presents unknown technological challenges. A dedicated and suitable energy forecast is an effective solution for the daily dispatching and production of the electricity grid. The traditional forecast technique uses weather and plant parameters as the model information. [...] Read more.
The rapid expansion of solar industries presents unknown technological challenges. A dedicated and suitable energy forecast is an effective solution for the daily dispatching and production of the electricity grid. The traditional forecast technique uses weather and plant parameters as the model information. Nevertheless, these are insufficient to consider problematic weather variability and the various plant characteristics in the actual field. Considering the above facts and inspired by the excellent implementation of the multi-column convolutional neural network (MCNN) in image processing, we developed a novel approach for forecasting solar energy by transforming multipoint time series (MT) into images for the MCNN to examine. We first processed the data to convert the time series solar energy into image matrices. We observed that the MCNN showed a preeminent response under a ground-based high-resolution spatial–temporal image matrix with a 0.2826% and 0.5826% RMSE for 15 min-ahead forecast under clear (CR) and cloudy (CD) conditions, respectively. Our process was performed on the MATLAB deep learning platform and tested on CR and CD solar energy conditions. The excellent execution of the suggested technique was compared with state-of-the-art deep neural network solar forecasting techniques. Full article
(This article belongs to the Special Issue Remote Sensing in Environmental Modelling)
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26 pages, 11465 KiB  
Article
Identification of Potential Natural Aquifer Recharge Sites in Islamabad, Pakistan, by Integrating GIS and RS Techniques
by Farooq Alam, Muhammad Azmat, Riaz Zarin, Shakil Ahmad, Abdur Raziq, Hsu-Wen Vincent Young, Kim-Anh Nguyen and Yuei-An Liou
Remote Sens. 2022, 14(23), 6051; https://doi.org/10.3390/rs14236051 - 29 Nov 2022
Cited by 8 | Viewed by 3488
Abstract
Islamabad is essentially the only well-planned city in Pakistan, but groundwater depletion has become a serious issue there because of the rapid increase in population, poor water management, and deforestation. The current water demand of the city is about 220 million gallons per [...] Read more.
Islamabad is essentially the only well-planned city in Pakistan, but groundwater depletion has become a serious issue there because of the rapid increase in population, poor water management, and deforestation. The current water demand of the city is about 220 million gallons per day, with the Capital Development Authority (CDA) providing up to 70 million gallons per day. The need for water is mostly fulfilled through groundwater sources, such as water bores and commercial tube wells. Hence, identifying recharge sites for natural aquifers is a significant component of groundwater required to overcome the water crisis. Therefore, this study aims to identify potential sites for natural aquifer recharge by using analytical hierarchy process (AHP), weighted linear combination (WLC), and fuzzy logic methods. To achieve the stated objective, seven local influencing factors including soil, slope, water table, population density, land use land cover (LULC), drainage density, and elevation have been utilized in this study. AHP was utilized for the evaluation of the relative importance of the above-mentioned factors, while fuzzy logic was applied for the standardization of these factors. Finally, the AHP-WLC and fuzzy logic approaches were used to merge factor maps in order to identify suitable sites for natural aquifer recharge in Islamabad City. Two different suitability maps were constructed from both techniques, and on each of the resulting maps, the subregions were categorized into five classes: not suitable, less suitable, moderate, suitable, and most suitable. Based on the AHP-WLC results, 5% of the whole study area is deemed most suitable for natural aquifer recharge (NAR), whereas from the fuzzy logic results, 10% of the study area is marked as most suitable. In contrast, 37% and 32% of the whole study area were identified as suitable by the AHP-WLC and fuzzy logic methods, respectively. While both techniques can obtain satisfactory outcomes, the suitability map from fuzzy logic has produced more precise results. Hence, we propose to CDA-Islamabad here different sites for recharge wells based on the results of fuzzy logic. As recommended by this study, to date CDA has constructed twelve recharge wells. Full article
(This article belongs to the Special Issue Remote Sensing in Environmental Modelling)
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16 pages, 3206 KiB  
Article
Geospatial Technology-Based Analysis of Air Quality in India during the COVID-19 Pandemic
by Ajay Kumar Taloor, Anil Kumar Singh, Pankaj Kumar, Amit Kumar, Jayant Nath Tripathi, Maya Kumari, Bahadur Singh Kotlia, Girish Ch Kothyari, Surya Prakash Tiwari and Brian Alan Johnson
Remote Sens. 2022, 14(18), 4650; https://doi.org/10.3390/rs14184650 - 17 Sep 2022
Cited by 4 | Viewed by 3145
Abstract
The study evaluates the impacts of India’s COVID-19 lockdown and unlocking periods on the country’s ambient air quality. India experienced three strictly enforced lockdowns followed by unlocking periods where economic and social restrictions were gradually lifted. We have examined the in situ and [...] Read more.
The study evaluates the impacts of India’s COVID-19 lockdown and unlocking periods on the country’s ambient air quality. India experienced three strictly enforced lockdowns followed by unlocking periods where economic and social restrictions were gradually lifted. We have examined the in situ and satellite data of NO2 emissions for several Indian cities to assess the impacts of the lockdowns in India. Additionally, we analyzed NO2 data acquired from the Sentinel-5P TROPOMI sensor over a few districts of the Punjab state, as well as the National Capital Region. The comparisons between the in situ and satellite NO2 emissions were performed for the years 2019, 2020 and up to July 2021. Further analysis was conducted on the satellite data to map the NO2 emissions over India during March to July for the years of 2019, 2020 and 2021. Based on the in situ and satellite observations, we observed that the NO2 emissions significantly decreased by 45–55% in the first wave and 30% in the second wave, especially over the Northern Indian cities during the lockdown periods. The improved air quality over India is indicative of reduced pollution in the atmosphere due to the lockdown process, which slowed down the industrial and commercial activities, including the migration of humans from one place to another. Overall, the present study contributes to the understanding of the trends of the ambient air quality over large geographical areas using the Sentinel-5P satellite data and provides valuable information for regulatory bodies to design a better decision support system to improve air quality. Full article
(This article belongs to the Special Issue Remote Sensing in Environmental Modelling)
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31 pages, 23991 KiB  
Article
Global Evaluation of SMAP/Sentinel-1 Soil Moisture Products
by Farzane Mohseni, S. Mohammad Mirmazloumi, Mehdi Mokhtarzade, Sadegh Jamali and Saeid Homayouni
Remote Sens. 2022, 14(18), 4624; https://doi.org/10.3390/rs14184624 - 16 Sep 2022
Cited by 10 | Viewed by 4592
Abstract
SMAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km [...] Read more.
SMAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types. Full article
(This article belongs to the Special Issue Remote Sensing in Environmental Modelling)
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