Special Issue "Remote Sensing of Environmental Changes in Cold Regions"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 July 2019).

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Jinyang Du
E-Mail Website
Guest Editor
NTSG, University of Montana, Missoula, MT 59812, USA
Interests: quantitative remote sensing of land surface parameters, including landscape freeze/thaw state; vegetation water content; soil moisture and snow water equivalent for global environmental change studies
Dr. Jennifer D. Watts
E-Mail Website
Guest Editor
Woods Hole Research Center, Falmouth, MA 02540-1644, USA
The Spatial Science Center, Montana State University. LJH 202. Bozeman, Montana 59717, USA
Interests: global wetlands; arctic-boreal regions; remote sensing
Prof. Hui Lu
E-Mail Website
Guest Editor
Department of Earth System Science, Tsinghua University, Beijing100084, China
Tel. +86-10-62772565
Interests: microwave remote sensing, data assimilation, land surface model, distributed hydrological model
Special Issues and Collections in MDPI journals
Dr. Lingmei Jiang
E-Mail Website
Guest Editor
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: microwave emission model; snow and soil parameters retrieval; climatic and environmental change; land surface modelling
Prof. Dr. Paolo Tarolli
E-Mail Website
Guest Editor
Department of Land, Environment, Agriculture and Forestry, University of Padova, viale dell’Università 16, 35020 Legnaro (PD), Italy
Tel. +39 (049) 8272677; Fax: +39 (049) 8272750
Interests: digital terrain analysis; earth surface processes analysis; natural hazards; geomorphology; hydro-geomorphology; lidar; structure-from-motion photogrammetry; new research directions include the analysis of topographic signatures and using remote sensing of human activities from local to regional scale
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Cold regions including the northern high latitudes, polar regions, and Tibetan Plateau are highly sensitive to global warming and are undergoing dramatic changes in ecological, hydrologic, and climatic processes. Yet, studies of these regions are restricted by their limited in-situ measurements, and remote sensing provides key supports for monitoring and interpreting the on-going environmental changes.

We are pleased to announce a Special Issue of the journal Remote Sensing on “Remote Sensing of Environmental Changes in Cold Regions”. We solicit manuscripts focusing on, but not limited to, the following topics:

  • Long-term monitoring of the dynamic changes of air temperature, glacier, snow cover, permafrost, lake bodies and ponds, river systems, and vegetation. Integration of multi-year and multi-source remote sensing data is highly encouraged;
  • Applying emerging remote sensing techniques to the mapping of land surface parameters. We are interested in studies related to SmallSats and CubeSats, Unmanned Aerial Vehicle (UAV), GNSS, and near-nadir SAR and InSAR imaging;
  • Investigating the use of current and future satellite missions such as SMAP, SMOS, and SWOT in monitoring hydrological and cryospheric parameters;
  • Interpreting massive remote sensing data based on cloud computation and machine learning techniques for cold region studies.
Related References
  • Pan, C.G.; Kirchner, P.B.; Kimball, J.S.; Kim, Y.; Du, J. Rain-on-snow events in Alaska, and their frequency and distribution from satellite observations. Environmental Research Letters, 2018.
  • Kim, Y.; Kimball, J.S.; Glassy, J.; Du, J. An extended global earth system data record on daily landscape freeze-thaw status determined from satellite passive microwave remote sensing.  Earth System Science Data, 2017
  • Du, J.; Kimball, J.S.; Duguay, C.; Kim, Y.; Watts, J.D. Satellite microwave assessment of Northern Hemisphere lake ice phenology from 2002 to 2015. The Cryosphere, 2017.
  • Du, J.; Kimball, J.S.; Jones, L.A.; Watts, J.D. Implementation of satellite based fractional water cover indices in the pan-Arctic region using AMSR-E and MODIS. Remote Sensing of Environment, 2016.
  • Du, J.; Kimball, J.S.; Azarderakhsh, M.; Dunbar, R.S.; Moghaddam, M.; McDonald, K.C.; Classification of Alaska spring thaw characteristics using satellite L-band radar remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 2015.
  • Liu, Q.; Du, J.; Shi, J.; Jiang, L.; Analysis of spatial distribution and multi-year trend of the remotely sensed soil moisture on the Tibetan Plateau. Science China Earth Sciences, 2013.

Dr. Jinyang Du
Dr. Jennifer D. Watts
Prof. Hui Lu
Dr. Lingmei Jiang
Prof. Dr. Paolo Tarolli
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 papers will be 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 1800 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

  • environmental changes
  • cold region
  • remote sensing
  • land surface parameters
  • machine learning

Published Papers (11 papers)

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Editorial

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Open AccessEditorial
Editorial for Special Issue: “Remote Sensing of Environmental Changes in Cold Regions”
Remote Sens. 2019, 11(18), 2165; https://doi.org/10.3390/rs11182165 - 18 Sep 2019
Abstract
Cold regions, characterized by the presence of permafrost and extensive snow and ice cover, are significantly affected by changing climate [...] Full article

Research

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Open AccessArticle
Development of a Parameterized Model to Estimate Microwave Radiation Response Depth of Frozen Soil
Remote Sens. 2019, 11(17), 2028; https://doi.org/10.3390/rs11172028 - 28 Aug 2019
Cited by 1
Abstract
The sensing depth of passive microwave remote sensing is a significant factor in quantitative frozen soil studies. In this paper, a microwave radiation response depth (MRRD) was proposed to describe the source of the main signals of passive microwave remote sensing. The main [...] Read more.
The sensing depth of passive microwave remote sensing is a significant factor in quantitative frozen soil studies. In this paper, a microwave radiation response depth (MRRD) was proposed to describe the source of the main signals of passive microwave remote sensing. The main goal of this research was to develop a simple and accurate parameterized model for estimating the MRRD of frozen soil. A theoretical model was introduced first to describe the emission characteristics of a three-layer case, which incorporates multiple reflections at the two boundaries. Based on radiative transfer theory, the total emission of the three layers was calculated. A sensitivity analysis was then performed to demonstrate the effects of soil properties and frequency on the MRRD based on a simulation database comprising a wide range of soil characteristics and frequencies. Sensitivity analysis indicated that soil temperature, soil texture, and frequencies are three of the primary variables affecting MRRD, and a definite empirical relationship existed between the three parameters and the MRRD. Thus, a parameterized model for estimating MRRD was developed based on the sensitivity analysis results. A controlled field experiment using a truck-mounted multi-frequency microwave radiometer (TMMR) was designed and performed to validate the emission model of the soil freeze–thaw cycle and the parameterized model of MRRD developed in this work. The results indicated that the developed parameterized model offers a relatively accurate and simple way of estimating the MRRD. The total root mean square error (RMSE) between the calculated and measured MRRD of frozen loam soil was approximately 0.5 cm for the TMMR’s four frequencies. Full article
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Open AccessArticle
Comparison of Passive Microwave Data with Shipborne Photographic Observations of Summer Sea Ice Concentration along an Arctic Cruise Path
Remote Sens. 2019, 11(17), 2009; https://doi.org/10.3390/rs11172009 - 26 Aug 2019
Cited by 1
Abstract
Arctic sea ice concentration (SIC) has been studied extensively using passive microwave (PM) remote sensing. This technology could be used to improve navigation along vessel cruise paths; however, investigations on this topic have been limited. In this study, shipborne photographic observation (P-OBS) of [...] Read more.
Arctic sea ice concentration (SIC) has been studied extensively using passive microwave (PM) remote sensing. This technology could be used to improve navigation along vessel cruise paths; however, investigations on this topic have been limited. In this study, shipborne photographic observation (P-OBS) of sea ice was conducted using oblique-oriented cameras during the Chinese National Arctic Research Expedition in the summer of 2016. SIC and the areal fractions of open water, melt ponds, and sea ice (Aw, Ap, and Ai, respectively) were determined along the cruise path. The distribution of SIC along the cruise path was U-shaped, and open water accounted for a large proportion of the path. The SIC derived from the commonly used PM algorithms was compared with the moving average (MA) P-OBS SIC, including Bootstrap and NASA Team (NT) algorithms based on Special Sensor Microwave Imager/Sounder (SSMIS) data; and ARTIST sea ice, Bootstrap, Sea Ice Climate Change Initiative, and NASA Team 2 (NT2) algorithms based on Advanced Microwave Scanning Radiometer 2 (AMSR2) data. P-OBS performed better than PM remote sensing at detecting low SIC (< 10%). Our results indicate that PM SIC overestimates MA P-OBS SIC at low SIC, but underestimates it when SIC exceeds a turnover point (TP). The presence of melt ponds affected the accuracy of the PM SIC; the PM SIC shifted from an overestimate to an underestimate with increasing Ap, compared with MA P-OBS SIC below the TP, while the underestimation increased above the TP. The PM algorithms were then ranked; SSMIS-NT and AMSR2-NT2 are the best and worst choices for Arctic navigation, respectively. Full article
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Open AccessArticle
Development of Supraglacial Ponds in the Everest Region, Nepal, between 1989 and 2018
Remote Sens. 2019, 11(9), 1058; https://doi.org/10.3390/rs11091058 - 05 May 2019
Cited by 4
Abstract
Several supraglacial ponds are developing and increasing in size and number in the Himalayan region. They are the precursors of large glacial lakes and may become potential for glacial lake outburst floods (GLOFs). Recently, GLOF events originating from supraglacial ponds were recorded; however, [...] Read more.
Several supraglacial ponds are developing and increasing in size and number in the Himalayan region. They are the precursors of large glacial lakes and may become potential for glacial lake outburst floods (GLOFs). Recently, GLOF events originating from supraglacial ponds were recorded; however, the spatial, temporal, and seasonal distributions of these ponds are not well documented. We chose 23 debris-covered glaciers in the Everest region, Nepal, to study the development of supraglacial ponds. We used historical Landsat images (30-m resolution) from 1989 to 2017, and Sentinel-2 (10-m resolution) images from 2016 to 2018 to understand the long-term development and seasonal variations of these ponds. We also used fine-resolution (0.5–2 m) WorldView and GeoEye imageries to reveal the high-resolution inventory of these features and these images were also used as references for accuracy assessments. We observed a continuous increase in the area and number of ponds from 1989–2017, with minor fluctuations. Similarly, seasonal variations were observed at the highest ponded area in the pre- and postmonsoon seasons, and lowest ponded area in the winter season. Substantial variations of the ponds were also observed among glaciers corresponding to their size, slope, width, moraine height, and elevation. The persistency and densities of the ponds with sizes >0.005 km2 were found near the glacier terminuses. Furthermore, spillway lakes on the Ngozompa, Bhote Koshi, Khumbu, and Lumsamba glaciers were expanding at a faster rate, indicating a trajectory towards large lake development. Our analysis also found that Sentinel-2 (10-m resolution) has good potential to study the seasonal changes of supraglacial ponds, while fine-resolution (<2 m) imagery is able to map the supraglacial ponds with high accuracy and can help in understanding the surrounding morphology of the glacier. Full article
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Open AccessArticle
Development of a Snow Depth Estimation Algorithm over China for the FY-3D/MWRI
Remote Sens. 2019, 11(8), 977; https://doi.org/10.3390/rs11080977 - 24 Apr 2019
Cited by 3
Abstract
Launched on 15 November 2017, China’s FengYun-3D (FY-3D) has taken over prime operational weather service from the aging FengYun-3B (FY-3B). Rather than directly implementing an FY-3B operational snow depth retrieval algorithm on FY-3D, we investigated this and four other well-known snow depth algorithms [...] Read more.
Launched on 15 November 2017, China’s FengYun-3D (FY-3D) has taken over prime operational weather service from the aging FengYun-3B (FY-3B). Rather than directly implementing an FY-3B operational snow depth retrieval algorithm on FY-3D, we investigated this and four other well-known snow depth algorithms with respect to regional uncertainties in China. Applicable to various passive microwave sensors, these four snow depth algorithms are the Environmental and Ecological Science Data Centre of Western China (WESTDC) algorithm, the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) algorithm, the Chang algorithm, and the Foster algorithm. Among these algorithms, validation results indicate that FY-3B and WESTDC perform better than the others. However, these two algorithms often result in considerable underestimation for deep snowpack (greater than 20 cm), while the other three persistently overestimate snow depth, probably because of their poor representation of snowpack characteristics in China. To overcome the retrieval errors that occur under deep snowpack conditions without sacrificing performance under relatively thin snowpack conditions, we developed an empirical snow depth retrieval algorithm suite for the FY-3D satellite. Independent evaluation using weather station observations in 2014 and 2015 demonstrates that the FY-3D snow depth algorithm’s root mean square error (RMSE) and bias are 6.6 cm and 0.2 cm, respectively, and it has advantages over other similar algorithms. Full article
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Open AccessArticle
Impacts of Climate Change and Intensive Lesser Snow Goose (Chen caerulescens caerulescens) Activity on Surface Water in High Arctic Pond Complexes
Remote Sens. 2018, 10(12), 1892; https://doi.org/10.3390/rs10121892 - 27 Nov 2018
Cited by 1
Abstract
Rapid increases in air temperature in Arctic and subarctic regions are driving significant changes to surface waters. These changes and their impacts are not well understood in sensitive high-Arctic ecosystems. This study explores changes in surface water in the high Arctic pond complexes [...] Read more.
Rapid increases in air temperature in Arctic and subarctic regions are driving significant changes to surface waters. These changes and their impacts are not well understood in sensitive high-Arctic ecosystems. This study explores changes in surface water in the high Arctic pond complexes of western Banks Island, Northwest Territories. Landsat imagery (1985–2015) was used to detect sub-pixel trends in surface water. Comparison of higher resolution aerial photographs (1958) and satellite imagery (2014) quantified changes in the size and distribution of waterbodies. Field sampling investigated factors contributing to the observed changes. The impact of expanding lesser snow goose populations and other biotic or abiotic factors on observed changes in surface water were also investigated using an information theoretic model selection approach. Our analyses show that the pond complexes of western Banks Island lost 7.9% of the surface water that existed in 1985. Drying disproportionately impacted smaller sized waterbodies, indicating that climate is the main driver. Model selection showed that intensive occupation by lesser snow geese was associated with more extensive drying and draining of waterbodies and suggests this intensive habitat use may reduce the resilience of pond complexes to climate warming. Changes in surface water are likely altering permafrost, vegetation, and the utility of these areas for animals and local land-users, and should be investigated further. Full article
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Open AccessArticle
Recovery Rates of Wetland Vegetation Greenness in Severely Burned Ecosystems of Alaska Derived from Satellite Image Analysis
Remote Sens. 2018, 10(9), 1456; https://doi.org/10.3390/rs10091456 - 12 Sep 2018
Cited by 5
Abstract
The analysis of wildfire impacts at the scale of less than a square kilometer can reveal important patterns of vegetation recovery and regrowth in freshwater Arctic and boreal regions. For this study, NASA Landsat burned area products since the year 2000, and a [...] Read more.
The analysis of wildfire impacts at the scale of less than a square kilometer can reveal important patterns of vegetation recovery and regrowth in freshwater Arctic and boreal regions. For this study, NASA Landsat burned area products since the year 2000, and a near 20-year record of vegetation green cover from the MODIS (Moderate Resolution Imaging Spectroradiometer) satellite sensor were combined to reconstruct the recovery rates and seasonal profiles of burned wetland ecosystems in Alaska. Region-wide breakpoint analysis results showed that significant structural change could be detected in the 250-m normalized difference vegetation index (NDVI) time series for the vast majority of wetland locations in the major Yukon river drainages of interior Alaska that had burned at high severity since the year 2001. Additional comparisons showed that wetland cover locations across Alaska that have burned at high severity subsequently recovered their green cover seasonal profiles to relatively stable pre-fire levels in less than 10 years. Negative changes in the MODIS NDVI, namely lower greenness in 2017 than pre-fire and incomplete greenness recovery, were more commonly detected in burned wetland areas after 2013. In the years prior to 2013, the NDVI change tended to be positive (higher greenness in 2017 than pre-fire) at burned wetland elevations lower than 400 m, whereas burned wetland locations at higher elevation showed relatively few positive greenness recovery changes by 2017. Full article
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Open AccessArticle
Modelling the L-Band Snow-Covered Surface Emission in a Winter Canadian Prairie Environment
Remote Sens. 2018, 10(9), 1451; https://doi.org/10.3390/rs10091451 - 11 Sep 2018
Cited by 3
Abstract
Detailed angular ground-based L-band brightness temperature (TB) measurements over snow covered frozen soil in a prairie environment were used to parameterize and evaluate an electromagnetic model, the Wave Approach for LOw-frequency MIcrowave emission in Snow (WALOMIS), for seasonal snow. WALOMIS, [...] Read more.
Detailed angular ground-based L-band brightness temperature (TB) measurements over snow covered frozen soil in a prairie environment were used to parameterize and evaluate an electromagnetic model, the Wave Approach for LOw-frequency MIcrowave emission in Snow (WALOMIS), for seasonal snow. WALOMIS, initially developed for Antarctic applications, was extended with a soil interface model. A Gaussian noise on snow layer thickness was implemented to account for natural variability and thus improve the TB simulations compared to observations. The model performance was compared with two radiative transfer models, the Dense Media Radiative Transfer-Multi Layer incoherent model (DMRT-ML) and a version of the Microwave Emission Model for Layered Snowpacks (MEMLS) adapted specifically for use at L-band in the original one-layer configuration (LS-MEMLS-1L). Angular radiometer measurements (30°, 40°, 50°, and 60°) were acquired at six snow pits. The root-mean-square error (RMSE) between simulated and measured TB at vertical and horizontal polarizations were similar for the three models, with overall RMSE between 7.2 and 10.5 K. However, WALOMIS and DMRT-ML were able to better reproduce the observed TB at higher incidence angles (50° and 60°) and at horizontal polarization. The similar results obtained between WALOMIS and DMRT-ML suggests that the interference phenomena are weak in the case of shallow seasonal snow despite the presence of visible layers with thicknesses smaller than the wavelength, and the radiative transfer model can thus be used to compute L-band brightness temperature. Full article
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Review

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Open AccessEditor’s ChoiceReview
Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
Remote Sens. 2019, 11(16), 1952; https://doi.org/10.3390/rs11161952 - 20 Aug 2019
Cited by 1
Abstract
Cold regions, including high-latitude and high-altitude landscapes, are experiencing profound environmental changes driven by global warming. With the advance of earth observation technology, remote sensing has become increasingly important for detecting, monitoring, and understanding environmental changes over vast and remote regions. This paper [...] Read more.
Cold regions, including high-latitude and high-altitude landscapes, are experiencing profound environmental changes driven by global warming. With the advance of earth observation technology, remote sensing has become increasingly important for detecting, monitoring, and understanding environmental changes over vast and remote regions. This paper provides an overview of recent achievements, challenges, and opportunities for land remote sensing of cold regions by (a) summarizing the physical principles and methods in remote sensing of selected key variables related to ice, snow, permafrost, water bodies, and vegetation; (b) highlighting recent environmental nonstationarity occurring in the Arctic, Tibetan Plateau, and Antarctica as detected from satellite observations; (c) discussing the limits of available remote sensing data and approaches for regional monitoring; and (d) exploring new opportunities from next-generation satellite missions and emerging methods for accurate, timely, and multi-scale mapping of cold regions. Full article
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Other

Open AccessTechnical Note
Radar Scatter Decomposition to Differentiate between Running Ice Accumulations and Intact Ice Covers along Rivers
Remote Sens. 2019, 11(3), 307; https://doi.org/10.3390/rs11030307 - 03 Feb 2019
Cited by 3
Abstract
For ice-jam flood forecasting it is important to differentiate between intact ice covers and ice runs. Ice runs consist of long accumulations of rubble ice that stem from broken up ice covers or ice-jams that have released. A water wave generally travels ahead [...] Read more.
For ice-jam flood forecasting it is important to differentiate between intact ice covers and ice runs. Ice runs consist of long accumulations of rubble ice that stem from broken up ice covers or ice-jams that have released. A water wave generally travels ahead of the ice run at a faster celerity, arriving at the potentially high flood–risk area much sooner than the ice accumulation. Hence, a rapid detection of the ice run is necessary to lengthen response times for flood mitigation. Intact ice covers are stationary and hence are not an immediate threat to a downstream flood situation, allowing more time for flood preparedness. However, once ice accumulations are moving and potentially pose imminent impacts to flooding, flood response may have to switch from a mitigation to an evacuation mode of the flood management plan. Ice runs are generally observed, often by chance, through ground observations or airborne surveys. In this technical note, we introduce a novel method of differentiating ice runs from intact ice covers using imagery acquired from space-borne radar backscatter signals. The signals are decomposed into different scatter components—surface scattering, volume scattering and double-bounce—the ratios of one to another allow differentiation between intact and running ice. The method is demonstrated for the breakup season of spring 2018 along the Athabasca River, when an ice run shoved into an intact ice cover which led to some flooding in Fort McMurray, Alberta, Canada. Full article
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Open AccessLetter
Mapping High Mountain Lakes Using Space-Borne Near-Nadir SAR Observations
Remote Sens. 2018, 10(9), 1418; https://doi.org/10.3390/rs10091418 - 06 Sep 2018
Cited by 3
Abstract
Near-nadir interferometric imaging SAR (Synthetic Aperture Radar) techniques are promising in measuring global water extent and surface height at fine spatial and temporal resolutions. The concept of near-nadir interferometric measurements was implemented in the experimental Interferometric Imaging Radar Altimeters (InIRA) mounted on Chinese [...] Read more.
Near-nadir interferometric imaging SAR (Synthetic Aperture Radar) techniques are promising in measuring global water extent and surface height at fine spatial and temporal resolutions. The concept of near-nadir interferometric measurements was implemented in the experimental Interferometric Imaging Radar Altimeters (InIRA) mounted on Chinese Tian Gong 2 (TG-2) space laboratory. This study is focused on mapping the extent of high mountain lakes in the remote Qinghai–Tibet Plateau (QTP) areas using the InIRA observations. Theoretical simulations were first conducted to understand the scattering mechanisms under near-nadir observation geometry. It was found that water and surrounding land pixels are generally distinguishable depending on the degree of their difference in dielectric properties and surface roughness. The observed radar backscatter is also greatly influenced by incidence angles. A dynamic threshold method was then developed to detect water pixels based on the theoretical analysis and ancillary data. As assessed by the LandSat results, the overall classification accuracy is higher than 90%, though the classifications are affected by low backscatter possibly from very smooth water surface. The algorithms developed from this study can be extended to all InIRA land measurements and provide support for the similar space missions in the future. Full article
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