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Application of MODIS Data for Environmental Research

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 22048

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Special Issue Editors

Dr. Shaktiman Singh

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Guest Editor

School of Geosciences, University of Aberdeen, Aberdeen AB24 3FX, UK
Interests: glaciology; hydrology; remote sensing; climate change
Special Issues, Collections and Topics in MDPI journals

Dr. Anshuman Bhardwaj

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Guest Editor

School of Geosciences, University of Aberdeen, King’s College, Aberdeen AB24 3UE, UK
Interests: remote sensing applications in land dynamics; landforms and surface processes on Mars; glacial and periglacial geomorphology; glacial hazards; Mars analogue research; high-resolution terrain modelling and interpretation; UAVs for environmental remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Lydia Sam

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Guest Editor

School of Geosciences, University of Aberdeen, Aberdeen AB24 3FX, UK
Interests: remote sensing; glaciology; cryosphere; physical geography; terrain modelling; land cover changes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The open access Moderate Resolution Imaging Spectroradiometer (MODIS) on-board the Terra (available from February 2020) and Aqua (available from July 2002) satellites provide environmental observations like land-surface temperature (both day and night), snow cover, aerosol, total precipitable water, vegetation index, evapotranspiration, and water masks at a high temporal scale. The science team of MODIS has been continuously working to update the algorithms in order to provide improved estimates for each product.

The unavailability of reliable observations with spatial continuity in remote areas with extreme weather conditions and difficult terrains hampers our understanding of the environmental changes. The data products derived from remote observations of MODIS are critical for environmental research in inaccessible areas because of their spatiotemporal continuity. In addition, as a result of computational easiness, there has been significant development in terms of different methods regarding the use of MODIS datasets in environmental analysis. Parallel to that, various algorithms for the estimation of the accuracy and for filling in data gaps caused by cloud cover have also been developed. Regardless of the extensive use of the MODIS datasets, there are still several research areas in which they can be applied for better results and understanding in several environmental science disciplines, such as climatology, glaciology, hydrology, geology, and palaeosciences.

Through this Special Issue, we invite contributions from researchers working in the field of environmental sciences using any MODIS dataset or product. The contribution can be related to (but not restricted to) the following:

advancement in the use of MODIS data in environmental science;
data fusion with other satellite products;
development of algorithms for accuracy assessment and filling in data gaps;
comparison with station-based observations;
reconstruction using geophysical model;
assessment of long-term environmental changes;
prediction of future changes.

Dr. Shaktiman Singh
Dr. Anshuman Bhardwaj
Dr. Lydia Sam
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

MODIS
environment
land cover changes
snow
glacier
permafrost
modelling
remote sensing

Published Papers (6 papers)

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Research

24 pages, 19008 KiB

Open AccessArticle

Modelling Permafrost Distribution in Western Himalaya Using Remote Sensing and Field Observations

by Md Ataullah Raza Khan, Shaktiman Singh, Pratima Pandey, Anshuman Bhardwaj, Sheikh Nawaz Ali, Vasudha Chaturvedi and Prashant Kumar Champati Ray

Remote Sens. 2021, 13(21), 4403; https://doi.org/10.3390/rs13214403 - 1 Nov 2021

Cited by 12 | Viewed by 4281

Abstract

The presence and extent of permafrost in the Himalaya, which is a vital component of the cryosphere, remains severely under-researched with its future climatic-driven trajectory only partly understood and the future consequences on high-altitude ecosystem tentatively sketched out. Previous studies and available permafrost maps for the Himalaya relied primarily upon the modelled meteorological inputs to further model the likelihood of permafrost. Here, as a maiden attempt, we have quantified the distribution of permafrost at 30 m grid-resolution in the Western Himalaya using observations from multisource satellite datasets for estimating input parameters, namely temperature, potential incoming solar radiation (PISR), slope, aspect and land use, and cover. The results have been compared to previous studies and have been validated through field investigations and geomorphological proxies associated with permafrost presence. A large part of the study area is barren land (~69%) due to its extremely resistive climate condition with ~62% of the total area having a mean annual air temperature of (MAAT) <1 °C. There is a high inter-annual variability indicated by varying standard deviation (1–3 °C) associated with MAAT with low standard deviation in southern part of the study area indicating low variations in areas with high temperatures and vice-versa. The majority of the study area is northerly (~36%) and southerly (~38%) oriented, receiving PISR between 1 and 2.5 MW/m². The analysis of permafrost distribution using biennial mean air temperature (BMAT) for 2002-04 to 2018-20 suggests that the ~25% of the total study area has continuous permafrost, ~35% has discontinuous permafrost, ~1.5% has sporadic permafrost, and ~39% has no permafrost presence. The temporal analysis of permafrost distribution indicates a significant decrease in the permafrost cover in general and discontinuous permafrost in particular, from 2002-04 to 2018-20, with a loss of around 3% for the total area (~8340.48 km²). The present study will serve as an analogue for future permafrost studies to help understand the permafrost dynamics associated with the effects of the recent abrupt rise in temperature and change in precipitation pattern in the region. Full article

(This article belongs to the Special Issue Application of MODIS Data for Environmental Research)

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34 pages, 10118 KiB

Open AccessArticle

Evapotranspiration Changes over the European Alps: Consistency of Trends and Their Drivers between the MOD16 and SSEBop Algorithms

by Mariapina Castelli

Remote Sens. 2021, 13(21), 4316; https://doi.org/10.3390/rs13214316 - 27 Oct 2021

Cited by 5 | Viewed by 2207

Abstract

In the Alps, understanding how climate change is affecting evapotranspiration (ET) is relevant due to possible implications on water availability for large lowland areas of Europe. Here, changes in ET were studied based on 20 years of MODIS data. MOD16 and operational Simplified Surface Energy Balance (SSEBop) products were compared with eddy-covariance data and analyzed for trend detection. The two products showed a similar relationship with ground observations, with RMSE between 0.69 and 2 mm day⁻¹, and a correlation coefficient between 0.6 and 0.83. A regression with the potential drivers of ET showed that, for climate variables, ground data were coherent with MOD16 at grassland sites, where r² was 0.12 for potential ET, 0.17 for precipitation, and 0.57 for air temperature, whereas ground data agreed with SSEBop at forest sites, with an r² of 0.46 for precipitation, no correlation with temperature, and negative correlation with potential ET. Interestingly, ground-based correlation corresponded to SSEBop for leaf area index (LAI), while it matched with MOD16 for land surface temperature (LST). Through the trend analysis, both MOD16 and SSEBop revealed positive trends in the south-west, and negative trends in the south and north-east. Moreover, in summer, positive trends prevailed at high elevations for grasslands and forests, while negative trends dominated at low elevations for croplands and grasslands. However, the Alpine area share with positive ET trends was 16.6% for MOD16 and 3.9% for SSEBop, while the share with negative trends was 1.2% for MOD16 and 15.3% for SSEBop. A regression between trends in ET and in climate variables, LST, and LAI indicated consistency, especially between ET, temperature, and LAI increase, but low correlation. Overall, the discrepancies in the trends, and the fact that none of the two products outperformed the other when compared to ground data, suggest that, in the Alps, SSEBop and MOD16 might not be accurate enough to be a robust basis to study ET changes. Full article

(This article belongs to the Special Issue Application of MODIS Data for Environmental Research)

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27 pages, 6923 KiB

Open AccessArticle

Identifying Metocean Drivers of Turbidity Using 18 Years of MODIS Satellite Data: Implications for Marine Ecosystems under Climate Change

by Paula J. Cartwright, Peter R. C. S. Fearns, Paul Branson, Michael V. W. Cuttler, Michael O’Leary, Nicola K. Browne and Ryan J. Lowe

Remote Sens. 2021, 13(18), 3616; https://doi.org/10.3390/rs13183616 - 10 Sep 2021

Cited by 12 | Viewed by 3798

Abstract

Turbidity impacts the growth and productivity of marine benthic habitats due to light limitation. Daily/monthly synoptic and tidal influences often drive turbidity fluctuations, however, our understanding of what drives turbidity across seasonal/interannual timescales is often limited, thus impeding our ability to forecast climate change impacts to ecologically significant habitats. Here, we analysed long term (18-year) MODIS-aqua data to derive turbidity and the associated meteorological and oceanographic (metocean) processes in an arid tropical embayment (Exmouth Gulf in Western Australia) within the eastern Indian Ocean. We found turbidity was associated with El Niño Southern Oscillation (ENSO) cycles as well as Indian Ocean Dipole (IOD) events. Winds from the adjacent terrestrial region were also associated with turbidity and an upward trend in turbidity was evident in the body of the gulf over the 18 years. Our results identify hydrological processes that could be affected by global climate cycles undergoing change and reveal opportunities for managers to reduce impacts to ecologically important ecosystems. Full article

(This article belongs to the Special Issue Application of MODIS Data for Environmental Research)

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27 pages, 12365 KiB

Open AccessArticle

Reconstruction of the Daily MODIS Land Surface Temperature Product Using the Two-Step Improved Similar Pixels Method

by Junlei Tan, Tao Che, Jian Wang, Ji Liang, Yang Zhang and Zhiguo Ren

Remote Sens. 2021, 13(9), 1671; https://doi.org/10.3390/rs13091671 - 26 Apr 2021

Cited by 24 | Viewed by 3386

Abstract

The MODIS land surface temperature (LST) product is one of the most widely used data sources to study the climate and energy-water cycle at a global scale. However, the large number of invalid values caused by cloud cover limits the wide application of the MODIS LST. In this study, a two-step improved similar pixels (TISP) method was proposed for cloudy sky LST reconstruction. The TISP method was validated using a temperature-based method over various land cover types. The ground measurements were collected at fifteen stations from 2013 to 2018 during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) field campaign in China. The estimated theoretical clear-sky temperature indicates that clouds cool the land surface during the daytime and warm it at nighttime. For bare land, the surface temperature shows a clear seasonal trend and very similar across stations, with a cooling amplitude of 4.14 K in the daytime and a warming amplitude of 3.99 K at nighttime, as a yearly average. The validation result showed that the reconstructed LST is highly consistent with in situ measurements and comparable with MODIS LST validation accuracy, with a mean bias of 0.15 K at night (−0.43 K in the day), mean RMSE of 2.91 K at night (4.41 K in the day), and mean R² of 0.93 at night (0.90 in the day). The developed method maximizes the potential of obtaining quality MODIS LST retrievals, ancillary data, and in situ observations, and the results show high accuracy for most land cover types. Full article

(This article belongs to the Special Issue Application of MODIS Data for Environmental Research)

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19 pages, 4847 KiB

Open AccessArticle

Automatic High-Accuracy Sea Ice Mapping in the Arctic Using MODIS Data

by Liyuan Jiang, Yong Ma, Fu Chen, Jianbo Liu, Wutao Yao and Erping Shang

Remote Sens. 2021, 13(4), 550; https://doi.org/10.3390/rs13040550 - 4 Feb 2021

Cited by 5 | Viewed by 2893

Abstract

The sea ice cover is changing rapidly in polar regions, and sea ice products with high temporal and spatial resolution are of great importance in studying global climate change and navigation. In this paper, an ice map generation model based on Moderate-Resolution Imaging Spectroradiometer (MODIS) reflectance bands is constructed to obtain sea ice data with a high temporal and spatial resolution. By constructing a training sample library and using a multi-feature fusion machine learning algorithm for model classification, the high-accuracy recognition of ice and cloud regions is achieved. The first product provided by this algorithm is a near real-time single-scene sea ice presence map. Compared with the photo-interpreted ground truth, the verification shows that the algorithm can obtain a higher recognition accuracy for ice, clouds, and water, and the accuracy exceeds 98%. The second product is a daily and weekly clear sky map, which provides synthetic ice presence maps for one day or seven consecutive days. A filtering method based on cloud motion is used to make the product more accurate. The third product is a weekly fusion of clear sky optical images. In a comparison with the Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration products performed in August 2019 and September 2020, these composite images showed spatial consistency over time, suggesting that they can be used in many scientific and practical applications in the future. Full article

(This article belongs to the Special Issue Application of MODIS Data for Environmental Research)

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19 pages, 3086 KiB

Open AccessArticle

River Flow Monitoring by Sentinel-3 OLCI and MODIS: Comparison and Combination

by Angelica Tarpanelli, Filippo Iodice, Luca Brocca, Marco Restano and Jérôme Benveniste

Remote Sens. 2020, 12(23), 3867; https://doi.org/10.3390/rs12233867 - 25 Nov 2020

Cited by 20 | Viewed by 4012

Abstract

The monitoring of rivers by satellite is an up-to-date subject in hydrological studies as confirmed by the interest of space agencies to finance specific missions that respond to the quantification of surface water flows. We address the problem by using multi-spectral sensors, in the near-infrared (NIR) band, correlating the reflectance ratio between a dry and a wet pixel extracted from a time series of images, the C/M ratio, with five river flow-related variables: water level, river discharge, flow area, mean flow velocity and surface width. The innovative aspect of this study is the use of the Ocean and Land Colour Instrument (OLCI) on board Sentinel-3 satellites, compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) used in previous studies. Our results show that the C/M ratio from OLCI and MODIS is more correlated with the mean flow velocity than with other variables. To improve the number of observations, OLCI and MODIS products are combined into multi-mission time series. The integration provides good quality data at around daily resolution, appropriate for the analysis of the Po River investigated in this study. Finally, the combination of only MODIS products outperforms the other configurations with a frequency slightly lower (~1.8 days). Full article

(This article belongs to the Special Issue Application of MODIS Data for Environmental Research)

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