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State-of-the-Art Remote Sensing Technologies for Environmental Monitoring

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

Deadline for manuscript submissions: closed (15 April 2025) | Viewed by 32632

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Special Issue Editors

Dr. Monica Rivas Casado

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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

Dr. Marco Palma

E-Mail Website
Co-Guest Editor

1. Dipartimento di Scienze della Vita e dell’Ambiente, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy
2. Habitats Edge Ltd, 39 High Street, Bedford MK416AG, UK
Interests: underwater photogrammetry; marine habitat monitoring and restoration; environmental accounting; taxonomy; innovative technologies

Prof. Dr. Paul Leinster

E-Mail Website
Co-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,

Current technological advances in remote sensing are proving to be key engineering tools for environmental surveying tasks. The range of available technologies is wide and varied, and includes unmanned aerial systems, semi-autonomous and autonomous boats, autonomous underwater vehicles and remotely operated vehicles, amongst others. Similarly, their applications have expanded across different environmental domains, from atmospheric measurements to coral reef characterization. The uptake of these technologies has enabled increased data quality (accuracy) and quantity (coverage), which necessitates the use and development of advanced mathematical and statistical methods for data analysis and interpretation. This Special Issue aims to collate manuscripts showcasing recent applications of novel remote sensing technological advances within the context of environmental monitoring. Manuscripts can be related to any aspects of remote sensing techniques used for environmental assessment, characterization, and protection. Of special interest are those manuscripts covering the integrated use of state-of-the art remote sensing technology for environmental data capture and advanced statistical methods for data analysis and interpretation. The following topics will be considered for this Special Issue:

Subtopics:

Robots and autonomous systems for environmental remote sensing;
Emerging technologies for environmental remote sensing;
Holistic and integrated approaches for remote sensing data collection;
Novel advances in remote sensing for the collection of collocated spatio-temporal data;
Technological solutions for high-resolution wide-area data collection;
Industrial- and regulatory-based applications of monitoring environmental processes
Remote sensing solutions to unbiased environmental monitoring;
Uncertainty and accuracy of remote sensing techniques for environmental assessment;
Comparison of novel and traditional remote sensing methods for environmental monitoring;
Data fusion solutions for enhanced environmental characterization;
Optimization of monitoring/sampling programs for environmental mapping, assessment, and characterization;
Technological tools and solutions to map extreme environmental events and their impact;
Increased environmental change detection through novel remote sensing technologies;
Identification of advantages and limitations of novel remote sensing methods via applied environmental examples.

Dr. Monica Rivas Casado
Dr. Marco Palma
Professor Paul Leinster CBE
Guest Editor

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

emerging technologies
robots
autonomous systems
environmental assessment
advanced statistics
data analysis
unmanned aerial systems
autonomous underwater vehicles
remotely operated vehicles

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Published Papers (11 papers)

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17 pages, 4800 KiB

Open AccessArticle

A Study on Remote Monitoring of NO_x Emissions from Inland Vessels

by Mengtao Deng, Jianbo Hu, Zhaoyu Qi and Shitao Peng

Remote Sens. 2025, 17(1), 168; https://doi.org/10.3390/rs17010168 - 6 Jan 2025

Viewed by 639

Abstract

In order to demonstrate the feasibility of the tunable diode laser absorption spectroscopy (TDLAS) technology for monitoring NO_x emissions from inland vessels, an equipment is designed to monitor emissions for inland vessels. The equipment was installed at the Jianbi locks, where experimental measurements were conducted on vessels passing through the locks, with a total of 330 vessels being measured. The detection rate for vessels was 50.3%, with a detection rate of 72.4% for fully loaded vessels and 24.7% for unloaded vessels. In addition, the exhaust emission patterns of inland vessels, the NO_x emission patterns and detection rate of fully loaded and unloaded vessels, and the key parameter of the NO_x emission factor of inland vessels were comprehensively analyzed. The experimental results show that CO₂ and NO_x in the exhaust gas of inland vessels have high signal intensity and good synchronization and can be applied to the regulatory monitoring of NO_x emissions from inland vessels. Furthermore, the ratios of NO/CO₂ and NO₂/CO₂ from fully loaded and unloaded vessels were significantly different. indicating that the NO₂ indicator must be included in the remote monitoring indicators for inland vessel exhaust gases. Otherwise, the remote monitoring results for NO_x may be significantly underestimated. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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30 pages, 60239 KiB

Open AccessArticle

Retrieval and Evaluation of Global Surface Albedo Based on AVHRR GAC Data of the Last 40 Years

by Shaopeng Li, Xiongxin Xiao, Christoph Neuhaus and Stefan Wunderle

Remote Sens. 2025, 17(1), 117; https://doi.org/10.3390/rs17010117 - 1 Jan 2025

Viewed by 1137

Abstract

In this study, the global land surface albedo namely GAC43 was retrieved for the years 1979 to 2020 using Advanced Very High Resolution Radiometer (AVHRR) global area coverage (GAC) data onboard National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational (MetOp) satellites. We provide a comprehensive retrieval process of the GAC43 albedo, followed by a comprehensive assessment against in situ measurements and three widely used satellite-based albedo products, the third edition of the CM SAF cLoud, Albedo and surface RAdiation (CLARA-A3), the Copernicus Climate Change Service (C3S) albedo product, and MODIS BRDF/albedo product (MCD43). Our quantitative evaluations indicate that GAC43 demonstrates the best stability, with a linear trend of ±0.002 per decade at nearly all pseudo invariant calibration sites (PICS) from 1982 to 2020. In contrast, CLARA-A3 exhibits significant noise before the 2000s due to the limited availability of observations, while C3S shows substantial biases during the same period due to imperfect sensors intercalibrations. Extensive validation at globally distributed homogeneous sites shows that GAC43 has comparable accuracy to C3S, with an overall RMSE of approximately 0.03, but a smaller positive bias of 0.012. Comparatively, MCD43C3 shows the lowest RMSE (~0.023) and minimal bias, while CLARA-A3 displays the highest RMSE (~0.042) and bias (0.02). Furthermore, GAC43, CLARA-A3, and C3S exhibit overestimation in forests, with positive biases exceeding 0.023 and RMSEs of at least 0.028. In contrast, MCD43C3 shows negligible bias and a smaller RMSE of 0.015. For grasslands and shrublands, GAC43 and MCD43C3 demonstrate comparable estimation uncertainties of approximately 0.023, with close positive biases near 0.09, whereas C3S and CLARA-A3 exhibit higher RMSEs and biases exceeding 0.032 and 0.022, respectively. All four albedo products show significant RMSEs around 0.035 over croplands but achieve the highest estimation accuracy better than 0.020 over deserts. It is worth noting that significant biases are typically attributed to insufficient spatial representativeness of the measurement sites. Globally, GAC43 and C3S exhibit similar spatial distribution patterns across most land surface conditions, including an overestimation compared to MCD43C3 and an underestimation compared to CLARA-A3 in forested areas. In addition, GAC43, C3S, and CLARA-A3 estimate higher albedo values than MCD43C3 in low-vegetation regions, such as croplands, grasslands, savannas, and woody savannas. Besides the fact that the new GAC43 product shows the best stability covering the last 40 years, one has to consider the higher proportion of backup inversions before 2000. Overall, GAC43 offers a promising long-term and consistent albedo with good accuracy for future studies such as global climate change, energy balance, and land management policy. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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19 pages, 24334 KiB

Open AccessArticle

A 40-Year Time Series of Land Surface Emissivity Derived from AVHRR Sensors: A Fennoscandian Perspective

by Mira Barben, Stefan Wunderle and Sonia Dupuis

Remote Sens. 2024, 16(19), 3686; https://doi.org/10.3390/rs16193686 - 2 Oct 2024

Cited by 2 | Viewed by 1184

Abstract

Accurate land surface temperature (LST) retrieval depends on precise knowledge of the land surface emissivity (LSE). Neglecting or inaccurately estimating the emissivity introduces substantial errors and uncertainty in LST measurements. The emissivity, which varies across different surfaces and land uses, reflects material composition and surface roughness. Satellite data offer a robust means to determine LSE at large scales. This study utilises the Normalised Difference Vegetation Index Threshold Method (NDVI^THM) to produce a novel emissivity dataset spanning the last 40 years, specifically tailored for the Fennoscandian region, including Norway, Sweden, and Finland. Leveraging the long and continuous data series from the Advanced Very High Resolution Radiometer (AVHRR) sensors aboard the NOAA and MetOp satellites, an emissivity dataset is generated for 1981–2022. This dataset incorporates snow-cover information, enabling the creation of annual emissivity time series that account for winter conditions. LSE time series were extracted for six 15 × 15 km study sites and compared against the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11A2 LSE product. The intercomparison reveals that, while both datasets generally align, significant seasonal differences exist. These disparities are attributable to differences in spectral response functions and temporal resolutions, as well as the method considering fixed values employed to calculate the emissivity. This study presents, for the first time, a 40-year time series of the emissivity for AVHRR channels 4 and 5 in Fennoscandia, highlighting the seasonal variability, land-cover influences, and wavelength-dependent emissivity differences. This dataset provides a valuable resource for future research on long-term land surface temperature and emissivity (LST&E) trends, as well as land-cover changes in the region, particularly with the use of Sentinel-3 data and upcoming missions such as EUMETSAT’s MetOp Second Generation, scheduled for launch in 2025. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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29 pages, 6358 KiB

Open AccessArticle

A Distributed Deadlock-Free Task Offloading Algorithm for Integrated Communication–Sensing–Computing Satellites with Data-Dependent Constraints

by Ruipeng Zhang, Yikang Yang and Hengnian Li

Remote Sens. 2024, 16(18), 3459; https://doi.org/10.3390/rs16183459 - 18 Sep 2024

Viewed by 1018

Abstract

Integrated communication–sensing–computing (ICSC) satellites, which integrate edge computing servers on Earth observation satellites to process collected data directly in orbit, are attracting growing attention. Nevertheless, some monitoring tasks involve sequential sub-tasks like target observation and movement prediction, leading to data dependencies. Moreover, the limited energy supply on satellites requires the sequential execution of sub-tasks. Therefore, inappropriate assignments can cause circular waiting among satellites, resulting in deadlocks. This paper formulates task offloading in ICSC satellites with data-dependent constraints as a mixed-integer linear programming (MILP) problem, aiming to minimize service latency and energy consumption simultaneously. Given the non-centrality of ICSC satellites, we propose a distributed deadlock-free task offloading (DDFTO) algorithm. DDFTO operates in parallel on each satellite, alternating between sub-task inclusion and consensus and sub-task removal until a common offloading assignment is reached. To avoid deadlocks arising from sub-task inclusion, we introduce the deadlock-free insertion mechanism (DFIM), which strategically restricts the insertion positions of sub-tasks based on interval relationships, ensuring deadlock-free assignments. Extensive experiments demonstrate the effectiveness of DFIM in avoiding deadlocks and show that the DDFTO algorithm outperforms benchmark algorithms in achieving deadlock-free offloading assignments. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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29 pages, 8398 KiB

Open AccessArticle

Evolution of Coastal Environments under Inundation Scenarios Using an Oceanographic Model and Remote Sensing Data

by Sergio Cappucci, Adriana Carillo, Roberto Iacono, Lorenzo Moretti, Massimiliano Palma, Gaia Righini, Fabrizio Antonioli and Gianmaria Sannino

Remote Sens. 2024, 16(14), 2599; https://doi.org/10.3390/rs16142599 - 16 Jul 2024

Cited by 5 | Viewed by 1731

Abstract

A new methodology to map Italian coastal areas at risk of flooding is presented. This approach relies on detailed projections of the future sea level from a high-resolution, three-dimensional model of the Mediterranean Sea circulation, on the best available digital terrain model of the Italian coasts, and on the most advanced satellite-derived data of ground motion, provided by the European Ground Motion Service of Copernicus. To obtain a reliable understanding of coastal evolution, future sea level projections and estimates of the future vertical ground motion based on the currently available data were combined and spread over the digital terrain model, using a GIS-based approach specifically developed for this work. The coastal plains of Piombino-Follonica and Marina di Campo (Tuscany Region), Alghero-Fertilia (Sardinia), and Rome and Latina-Sabaudia (Lazio Region) were selected as test cases for the new approach. These coastal stretches are important for the ecosystems and the economic activities they host and are relatively stable areas from a geological point of view. Flood maps were constructed for these areas, for the reference periods 2010–2040, 2040–2070, and 2040–2099. Where possible, the new maps were compared with previous results, highlighting differences that are mainly due to the more refined and resolved sea-level projection and to the detailed Copernicus ground motion data. Coastal flooding was simulated by using the “bathtub” approach without considering the morphodynamic processes induced by waves and currents during the inundation process. The inundation zone was represented by the water level raised on a coastal DTM, selecting all vulnerable areas that were below the predicted new water level. Consequent risk was related to the exposed asset. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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28 pages, 19785 KiB

Open AccessArticle

Development of a UAV Based Framework for CH₄ Monitoring in Sludge Treatment Centres

by Hiniduma Gamage Kavindi Abeywickrama, Yadira Bajón-Fernández, Bharanitharan Srinamasivayam, Duncan Turner and Mónica Rivas Casado

Remote Sens. 2023, 15(15), 3704; https://doi.org/10.3390/rs15153704 - 25 Jul 2023

Cited by 2 | Viewed by 2930

Abstract

With the increasing trend in the global average temperature, the UK’s water industry has committed to achieve Net Zero by 2030 and part of this includes cutting CH₄ emissions from sludge treatment facilities. Currently, emissions are estimated following the carbon accounting workbook guidelines and using default emission factors. However, this method might not be a true representation of emissions as these vary depending on many factors. The use of unmanned aerial vehicles (UAVs) has proved cost effective for environmental monitoring tasks requiring high spatial resolution information. Within the context of CH₄ emissions and in the last decade, the technology has been curtailed by sensor weight and size. Recent advances in sensor technology have enabled the development of a fit-for purpose UAV CH₄ sensor (U10) which uses Tuneable Diode Laser Absorption Spectroscopy. This study intends to develop a framework for CH₄ data collection strategies from sludge treatment centres using UAV-U10 technology and asset level CH₄ enhancement estimations based on geostatistical interpolation techniques and the mass balance approach. The framework presented here enables the characterization of spatial and temporal variations in CH₄ concentrations. It promotes asset level CH₄ enhancement estimation based on on-site measurements. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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20 pages, 4886 KiB

Open AccessEditor’s ChoiceArticle

Accuracy Assessment of Surveying Strategies for the Characterization of Microtopographic Features That Influence Surface Water Flooding

by Rakhee Ramachandran, Yadira Bajón Fernández, Ian Truckell, Carlos Constantino, Richard Casselden, Paul Leinster and Mónica Rivas Casado

Remote Sens. 2023, 15(7), 1912; https://doi.org/10.3390/rs15071912 - 2 Apr 2023

Cited by 6 | Viewed by 3172

Abstract

With the increase in rainfall intensity, population, and urbanised areas, surface water flooding (SWF) is an increasing concern impacting properties, businesses, and human lives. Previous studies have shown that microtopography significantly influences flow paths, flow direction, and velocity, impacting flood extent and depth, particularly for the shallow flow associated with urban SWF. This study compares two survey strategies commonly used by flood practitioners, S1 (using Unmanned Aerial Systems-based RGB data) and S2 (using manned aircraft with LiDAR scanners), to develop guidelines on where to use each strategy to better characterise microtopography for a range of flood features. The difference between S1 and S2 in elevation and their accuracies were assessed using both traditional and robust statistical measures. The results showed that the difference in elevation between S1 and S2 varies between 11 cm and 37 cm on different land use and microtopographic flood features. Similarly, the accuracy of S1 ranges between 3 cm and 70 cm, and the accuracy of S2 ranges between 3.8 cm and 30.3 cm on different microtopographic flood features. Thus, this study suggests that the flood features of interest in any given flood study would be key to select the most suitable survey strategy. A decision framework was developed to inform data collection and integration of the two surveying strategies to better characterise microtopographic features. The findings from this study will help improve the microtopographic representation of flood features in flood models and, thus, increase the ability to identify high flood-risk prompt areas accurately. It would also help manage and maintain drainage assets, spatial planning of sustainable drainage systems, and property level flood resilience and insurance to better adapt to the effects of climate change. This study is another step towards standardising flood extent and impact surveying strategies. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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19 pages, 7174 KiB

Open AccessArticle

Achieving Higher Resolution Lake Area from Remote Sensing Images Through an Unsupervised Deep Learning Super-Resolution Method

by Mengjiao Qin, Linshu Hu, Zhenhong Du, Yi Gao, Lianjie Qin, Feng Zhang and Renyi Liu

Remote Sens. 2020, 12(12), 1937; https://doi.org/10.3390/rs12121937 - 15 Jun 2020

Cited by 17 | Viewed by 4117

Abstract

Lakes have been identified as an important indicator of climate change and a finer lake area can better reflect the changes. In this paper, we propose an effective unsupervised deep gradient network (UDGN) to generate a higher resolution lake area from remote sensing images. By exploiting the power of deep learning, UDGN models the internal recurrence of information inside the single image and its corresponding gradient map to generate images with higher spatial resolution. The gradient map is derived from the input image to provide important geographical information. Since the training samples are only extracted from the input image, UDGN can adapt to different settings per image. Based on the superior adaptability of the UDGN model, two strategies are proposed for super-resolution (SR) mapping of lakes from multispectral remote sensing images. Finally, Landsat 8 and MODIS (moderate-resolution imaging spectroradiometer) images from two study areas on the Tibetan Plateau in China were used to evaluate the performance of UDGN. Compared with four unsupervised SR methods, UDGN obtained the best SR results as well as lake extraction results in terms of both quantitative and visual aspects. The experiments prove that our approach provides a promising way to break through the limitations of median-low resolution remote sensing images in lake change monitoring, and ultimately support finer lake applications. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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14 pages, 2707 KiB

Open AccessFeature PaperArticle

Combining Unmanned Aircraft Systems and Image Processing for Wastewater Treatment Plant Asset Inspection

by Jorge Sancho Martínez, Yadira Bajón Fernández, Paul Leinster and Mónica Rivas Casado

Remote Sens. 2020, 12(9), 1461; https://doi.org/10.3390/rs12091461 - 5 May 2020

Cited by 5 | Viewed by 4846

Abstract

Wastewater treatment plants are essential for preserving the water quality of freshwater and marine ecosystems. It is estimated that, in the UK, as much as 11 billion liters of wastewater are treated on a daily basis. Effective and efficient treatment of wastewater requires treatment plants to be maintained in good condition. Recent studies have highlighted the potential of unmanned aircraft systems (UASs) and image processing to be used in autonomous and automated monitoring systems. However, the combined use of UASs and image processing for wastewater treatment plant inspections has not yet been tested. This paper presents a novel image processing-UAS framework for the identification of failures in trickling filters and activated sludge facilities. The results show that the proposed framework has an accuracy of 95% in the detection of failures in activated sludge assets, with the accuracy ranging between 55% and 81% for trickling filters. These results are promising and they highlight the potential use of the technology for the inspection of wastewater treatment plants. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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21 pages, 3193 KiB

Open AccessArticle

Quantifying Coral Reef Composition of Recreational Diving Sites: A Structure from Motion Approach at Seascape Scale

by Marco Palma, Chiara Magliozzi, Monica Rivas Casado, Ubaldo Pantaleo, João Fernandes, Gianpaolo Coro, Carlo Cerrano and Paul Leinster

Remote Sens. 2019, 11(24), 3027; https://doi.org/10.3390/rs11243027 - 16 Dec 2019

Cited by 14 | Viewed by 5584

Abstract

Recreational diving is known to have both direct and indirect impacts on coral habitats. Direct impacts include increasing sedimentation, breaks and diseases that lead to a decrease in the richness and abundances of hard corals. Indirect impacts include urban development, land management and sewage disposal. The ecological effects of scuba diving on the spatial composition metrics of reef benthic communities are less well studied, and they have not been investigated at seascape scale. In this study, we combine orthomosaics derived from Structure from Motion (SfM) photogrammetry and data-mining techniques to study the spatial composition of reef benthic communities of recreational diving sites at seascape scale (>25 m

^{2}

). The study focuses on the case study area of Ponta do Ouro Partial Marine Reserve (Mozambique). Results showed that scuba-diving resistant taxa (i.e., sponges and algae) were abundant at small (>850 m

^{2}

) and highly dived sites (>3000 dives yr

^{- 1}

), characterized by low diversity and density, and big organisms with complex shapes. Fragile taxa (i.e., Acropora spp.) were abundant at low (365 dives yr

^{- 1}

) and moderately dived sites (1000–3000 dives yr

^{- 1}

) where the greater depth and wider coral reef surfaces attenuate the abrasive effect of waves and re-suspended sediments. Highest taxa diversity and density, and lowest abundance of resistant taxa were recorded at large (>2000 m

^{2}

) and rarely dived sites. This study highlights the potential applications for a photogrammetric approach to support monitoring programs at Ponta do Ouro Partial Marine Reserve (Mozambique), and provides some insight to understand the influence of scuba diving on benthic communities. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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12 pages, 2925 KiB

Open AccessLetter

Estimating Meltwater Drainage Onset Timing and Duration of Landfast Ice in the Canadian Arctic Archipelago Using AMSR-E Passive Microwave Data

by Yasuhiro Tanaka

Remote Sens. 2020, 12(6), 1033; https://doi.org/10.3390/rs12061033 - 23 Mar 2020

Cited by 2 | Viewed by 3014

Abstract

Meltwater drainage onset (DO) timing and drainage duration (DD) related to snowmelt-water redistribution are both important for understanding not only the Arctic energy and heat budgets but also the salt/heat balance of the mixed layer in the ocean and sea-ice ecosystem. We present DO and DD as determined from the time series of Advanced Microwave Scanning Radiometer-Earth observing system (AMSR-E) melt pond fraction (MPF) estimates in an area with Canadian landfast ice. To address the lack of evaluation on a day-by-day basis for the AMSR-E MPF estimate, we first compared AMSR-E MPF with the daily Medium Resolution Imaging Spectrometer (MERIS) MPF. The AMSR-E MPF estimate correlates significantly with the MERIS MPF (r = 0.73–0.83). The estimate has a product quality similar to the MERIS MPF only when the albedo is around 0.5–0.7 and a positive bias of up to 10% in areas with an albedo of 0.7–0.9, including melting snow. The DO/DD estimates are determined by using a polynomial regression curve fitted on the time series of the AMSR-E MPF. The DOs/DDs from time series of the AMSR-E and MERIS MPFs are compared, revealing consistency in both DD and DO. The DO timing from 2006 to 2011 is correlated with melt onset timing. To the best of our knowledge, our study provides the first large-scale information on both DO timing and DD. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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