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Big Earth Data for Climate Studies
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Big Earth Data for Climate Studies

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 9254

Special Issue Editors

Prof. Dr. Chaowei Yang

E-Mail Website
Guest Editor
NSF Spatiotemporal Innovation Center, Department of Geography & GeoInformation Science, George Mason University, Fairfax, VA 22030-4444, USA
Interests: spatiotemporal intelligence; big earth data; spatial cloud computing; ML & DL for geosciences; knowledge base and applications; spatiotemporal computing
Special Issues, Collections and Topics in MDPI journals
Dr. Daniel Q. Duffy

E-Mail Website
Guest Editor
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Interests: BIG DATA; data container; geospatial raster data management; GIS; GeoAI; precipitation detection; convective; stratiform; ABI; deep learning; natural disaster; flooding; global water cycle; spatiotemporal data analytics
Sudhir Shrestha

E-Mail Website
Guest Editor Assistant
NOAA National Weather Service, Office of Water Prediction, Silver Spring, MD, USA
Interests: geospatial information system; cloud computing; hydro informatics; data management; data engineering; geospatial machine learning; internet of things (IoT); water prediction

Special Issue Information

Dear Colleagues,

Climate change is increasing the severity and frequency of natural disasters and other impacts on our home planet and living environment. Various observation platforms, raging from satellite to drone, in situ and citizen science as well as long-term records, provide valuable information to understand, for example, a) how climate change has led to the severe drought in the Southwest US as well as the historical hurricane activity and flooding in the middle Atlantic region; b) how climate change may lead to the extreme wildfire in Australia and U.S.; c) how the polar climate anomaly impacts the cold winter weather in Texas and the lower continents of US and Asia; d) why we need to convert to electronic vehicles and how that may reduce climate change; e) what environmental policies should be put in place to mitigate impact; and e) how climate change may impact food security, coastal living, urban sustainability, and public health in the next decades and century.

Many of these questions can only be investigated with large amounts of data collected through observation platforms. Utilizing such big data through preprocessing, correlation analyses, simulation, and forecasting is critical to address these regional-to-global climate change challenges. The recent advancement of big data analytics on new observing systems for collecting data, machine learning and new computing architecture for enabling analytics and transfer/interpretive learning for bridging the traditional geophysical modelling and machine learning.

This Special Issue invites research, review, vision and case study papers on the use of advanced computing techniques, cutting-edge big data analytics, machine learning methods, and any new tools to understand various dimensions of climate change from regional to global scale. Topics include, but are not limited to, the following:

  • Big Earth data collection for climate change;
  • Preprocessing for analytical-ready data;
  • Big Earth data management in a FAIR fashion (find, access, interoperability, and replicable);
  • Geospatial data processing;
  • Geophysical simulation based on big data;
  • Big data visualization and presentation for decision support;
  • Building digital twins with big Earth data;
  • Open source for climate change;
  • New computing methods for climate change;
  • Climate change use cases, such as sea level rise, sea ice change, global warming, flooding, wildfire, hurricane, drought, etc.;
  • Climate justice – impacts of climate change due to rising sea levels, sunken islands, climate refugees, urban heat island, air quality, health effects, fires, etc.

Prof. Dr. Chaowei Yang
Dr. Daniel Q. Duffy
Guest Editors

Sudhir Shrestha
Guest Editor Assistant

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

  • big earth data
  • climate change
  • geospatial data
  • geo-computation
  • GPU computing
  • natural disaster
  • spatiotemporal data analytics
  • resilience
  • rapid response
  • community inequality

Published Papers (5 papers)

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17 pages, 5769 KiB  
Article
Downscaling Land Surface Temperature Derived from Microwave Observations with the Super-Resolution Reconstruction Method: A Case Study in the CONUS
by Yu Li, Donglian Sun, Xiwu Zhan, Paul Houser, Chaowei Yang and John J. Qu
Remote Sens. 2024, 16(5), 739; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16050739 - 20 Feb 2024
Viewed by 654
Abstract
Optical sensors cannot penetrate clouds and can cause serious missing data problems in optical-based Land Surface Temperature (LST) products. Under cloudy conditions, microwave observations are usually utilized to derive the land surface temperature. However, microwave sensors usually have coarse spatial resolutions. High-Resolution (HR) [...] Read more.
Optical sensors cannot penetrate clouds and can cause serious missing data problems in optical-based Land Surface Temperature (LST) products. Under cloudy conditions, microwave observations are usually utilized to derive the land surface temperature. However, microwave sensors usually have coarse spatial resolutions. High-Resolution (HR) LST data products are usually desired for many applications. Instead of developing and launching new high-resolution satellite sensors for LST observations, a more economical and practical way is to develop proper methodologies to derive high-resolution LSTs from available Low-Resolution (LR) datasets. This study explores different algorithms to downscale low-resolution LST data to a high resolution. The existing regression-based downscaling methods usually require simultaneous observations and ancillary data. The Super-Resolution Reconstruction (SRR) method developed for traditional image enhancement can be applicable to high-resolution LST generation. For the first time, we adapted the SRR method for LST data. We specifically built a unique database of LSTs for the example-based SRR method. After deriving the LST data from the coarse-resolution passive microwave observations, the AMSR-E at 25 km and/or AMSR-2 at 10 km, we developed an algorithm to downscale them to a 1 km spatial resolution with the SRR method. The SRR downscaling algorithm can be implemented to obtain high-resolution LSTs without auxiliary data or any concurrent observations. The high-resolution LSTs are validated and evaluated with the ground measurements from the Surface Radiation (SURFRAD) Budget Network. The results demonstrate that the downscaled microwave LSTs have a high correlation coefficient of over 0.92, a small bias of less than 0.5 K, but a large Root Mean Square Error (RMSE) of about 4 K, which is similar to the original microwave LST, so the errors in the downscaled LST could have been inherited from the original microwave LSTs. The validation results also indicate that the example-based method shows a better performance than the self-similarity-based algorithm. Full article
(This article belongs to the Special Issue Big Earth Data for Climate Studies)
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21 pages, 10409 KiB  
Article
An Evaluation of CRA40 and ERA5 Precipitation Products over China
by Zelan Zhou, Sheng Chen, Zhi Li and Yongming Luo
Remote Sens. 2023, 15(22), 5300; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15225300 - 09 Nov 2023
Cited by 1 | Viewed by 1020
Abstract
Precipitation datasets derived from reanalysis products play a crucial role in weather forecasting and hydrological applications. This paper aims to evaluate the performance of two distinct reanalysis precipitation products, i.e., the first-generation Chinese global land-surface reanalysis precipitation product (CRA40) and the fifth-generation European [...] Read more.
Precipitation datasets derived from reanalysis products play a crucial role in weather forecasting and hydrological applications. This paper aims to evaluate the performance of two distinct reanalysis precipitation products, i.e., the first-generation Chinese global land-surface reanalysis precipitation product (CRA40) and the fifth-generation European reanalysis precipitation product (ERA5), over mainland China. The evaluation is based on continuous and categorical statistical indicators with daily-scale gridded-point rain gauge data obtained from Chinese surface meteorological stations. The findings of this study can be summarized as follows: (1) CRA40 demonstrates a clear superiority over ERA5 in terms of the 13-year daily mean precipitation and seasonal daily precipitation. CRA40 exhibits better correlation coefficients (0.97), relative biases (5.25%), root mean square errors (0.34 mm), and fractional standard errors (0.05). (2) Both reanalyzed precipitation products generally exhibit an overestimation of precipitation in mainland China. The degree of overestimation is particularly pronounced in dry climatic regions (e.g., QZ, XJ), while wet regions (e.g., CJ, HN) demonstrate relatively less overestimation. (3) ERA5 shows better performance in the detection of daily precipitation. Neither CRA40 nor ERA5 can capture heavy precipitation events well. These findings are expected to advance our understanding of the strengths and limitations of the reanalysis precipitation products, CRA40 and ERA5, over China. Full article
(This article belongs to the Special Issue Big Earth Data for Climate Studies)
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25 pages, 146585 KiB  
Article
Near Real-Time Flood Mapping with Weakly Supervised Machine Learning
by Jirapa Vongkusolkit, Bo Peng, Meiliu Wu, Qunying Huang and Christian G. Andresen
Remote Sens. 2023, 15(13), 3263; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15133263 - 25 Jun 2023
Cited by 3 | Viewed by 1480
Abstract
Advances in deep learning and computer vision are making significant contributions to flood mapping, particularly when integrated with remotely sensed data. Although existing supervised methods, especially deep convolutional neural networks, have proved to be effective, they require intensive manual labeling of flooded pixels [...] Read more.
Advances in deep learning and computer vision are making significant contributions to flood mapping, particularly when integrated with remotely sensed data. Although existing supervised methods, especially deep convolutional neural networks, have proved to be effective, they require intensive manual labeling of flooded pixels to train a multi-layer deep neural network that learns abstract semantic features of the input data. This research introduces a novel weakly supervised approach for pixel-wise flood mapping by leveraging multi-temporal remote sensing imagery and image processing techniques (e.g., Normalized Difference Water Index and edge detection) to create weakly labeled data. Using these weakly labeled data, a bi-temporal U-Net model is then proposed and trained for flood detection without the need for time-consuming and labor-intensive human annotations. Using floods from Hurricanes Florence and Harvey as case studies, we evaluated the performance of the proposed bi-temporal U-Net model and baseline models, such as decision tree, random forest, gradient boost, and adaptive boosting classifiers. To assess the effectiveness of our approach, we conducted a comprehensive assessment that (1) covered multiple test sites with varying degrees of urbanization, and (2) utilized both bi-temporal (i.e., pre- and post-flood) and uni-temporal (i.e., only post-flood) input. The experimental results showed that the proposed framework of weakly labeled data generation and the bi-temporal U-Net could produce near real-time urban flood maps with consistently high precision, recall, f1 score, IoU score, and overall accuracy compared with baseline machine learning algorithms. Full article
(This article belongs to the Special Issue Big Earth Data for Climate Studies)
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17 pages, 19497 KiB  
Article
TSRC: A Deep Learning Model for Precipitation Short-Term Forecasting over China Using Radar Echo Data
by Qiqiao Huang, Sheng Chen and Jinkai Tan
Remote Sens. 2023, 15(1), 142; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15010142 - 27 Dec 2022
Cited by 9 | Viewed by 3527
Abstract
Currently, most deep learning (DL)-based models for precipitation forecasting face two conspicuous issues: the smoothing effect in the precipitation field and the degenerate effect of forecasting precipitation intensity. Therefore, this study proposes “time series residual convolution (TSRC)”, a DL-based convolutional neural network for [...] Read more.
Currently, most deep learning (DL)-based models for precipitation forecasting face two conspicuous issues: the smoothing effect in the precipitation field and the degenerate effect of forecasting precipitation intensity. Therefore, this study proposes “time series residual convolution (TSRC)”, a DL-based convolutional neural network for precipitation nowcasting over China with a lead time of 3 h. The core idea of TSRC is it compensates the current local cues with previous local cues during convolution processes, so more contextual information and less uncertain features would remain in deep networks. We use four years’ radar echo reflectivity data from 2017 to 2020 for model training and one year’s data from 2021 for model testing and compare it with two commonly used nowcasting models: optical flow model (OF) and UNet. Results show that TSRC obtains better forecasting performances than OF and UNet with a relatively high probability of detection (POD), low false alarm rate (FAR), small mean absolute error (MAE) and high structural similarity index (SSIM), especially at longer lead times. Meanwhile, the results of two case studies suggest that TSRC still introduces smoothing effects and slightly outperforms UNet at longer lead times. The most considerable result is that our model can forecast high-intensity radar echoes even for typhoon rainfall systems, suggesting that the degenerate effect of forecasting precipitation intensity can be improved by our model. Future works will focus on the combination of multi-source data and the design of the model’s architecture to gain further improvements in precipitation short-term forecasting. Full article
(This article belongs to the Special Issue Big Earth Data for Climate Studies)
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15 pages, 3103 KiB  
Technical Note
Evaluation of Arctic Sea Ice Thickness from a Parameter-Optimized Arctic Sea Ice–Ocean Model
by Qiaoqiao Zhang, Hao Luo, Chao Min, Yongwu Xiu, Qian Shi and Qinghua Yang
Remote Sens. 2023, 15(10), 2537; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15102537 - 12 May 2023
Viewed by 1551
Abstract
Sea ice thickness (SIT) presents comprehensive information on Arctic sea ice changes and their role in the climate system. However, our understanding of SIT is limited by a scarcity of observations and inaccurate model simulations. Based on simultaneous parameter optimization with a micro [...] Read more.
Sea ice thickness (SIT) presents comprehensive information on Arctic sea ice changes and their role in the climate system. However, our understanding of SIT is limited by a scarcity of observations and inaccurate model simulations. Based on simultaneous parameter optimization with a micro genetic algorithm, the North Atlantic/Arctic Ocean–Sea Ice Model (NAOSIM) has already demonstrated advantages in Arctic sea ice simulations. However, its performance in simulating pan-Arctic SITs remains unclear. In this study, a further evaluation of Arctic SITs from NAOSIM was conducted based on a comparison with satellite and in situ observations. Generally, NAOSIM can reproduce the annual cycle and downward trend in the sea ice volume. However, deficiencies can still be found in the simulation of SIT spatial patterns. NAOSIM overestimates the SIT of thinner ice (<1.5 m) in the Beaufort Sea, underestimates the SIT of thick ice (>1.5 m) in the central Arctic and is unable to capture the upward trend in the SIT in the north of the Canadian Archipelago as well as to reproduce the intensity of the observed SIT variability. In terms of SIT simulation, NAOSIM performs better as the time approaches the optimization window (2000–2012). Therefore, in the context of rapid changes in Arctic sea ice, how to optimize this model based on limited observations still remains a challenge. Full article
(This article belongs to the Special Issue Big Earth Data for Climate Studies)
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