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Special Issue "Advances in Remote Sensors for Earth Observation and Modeling of Earth Processes"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 September 2020).

Special Issue Editors

Prof. Dr. Assefa M. Melesse
E-Mail Website
Guest Editor
Dr. Juan-Carlos Jiménez-Muñoz
E-Mail Website
Guest Editor
Image Processing Laboratory, University of Valencia, Valencia, Spain
Interests: thermal remote sensing; land surface temperature/emissivity retrieval; temperature trends over tropical forests
Special Issues and Collections in MDPI journals
Dr. Robert Knuteson
E-Mail Website
Guest Editor
Senior Scientist, Space Science and Engineering Center, University of Wisconsin-Madison, 1225 W. Dayton St., Madison, WI, USA
Interests: calibration; climate change; hyperspectral infrared remote sensing; Fourier transform spectrometers; infrared land surface emissivity

Special Issue Information

Dear Colleagues,

Decades of remote sensing technology has transformed the frontiers of our understanding of the universe. We now better observe, map, and model earth processes to understand not only the dynamics of these processes but also the earth–atmosphere interactions. Natural resources mapping, simulation of water, energy and carbon fluxes, groundwater dynamics, soil moisture and precipitation prediction, natural hazards modeling, and many other areas of applications have been easier because of the available remote sensing tools. The technology of remote sensing has benefited from the advancement of new engineering hardware, sensor technologies, and the invention of superfast computers. We can now acquire images that have much better spatiotemporal resolutions, use new electromagnetic wave bands, processes, and interpret images faster and more efficiently, and have new areas of applications.

In this invitation-only Special Issue, we would like to publish manuscripts of high quality both on scientific and review contributions that demonstrate the advancement of remote sensing technology and successfully present new application areas. Areas of interest include evaluating new sensors, new applications, improving the spatial resolution of existing imageries through downscaling techniques, parametrization, model enhancement using the addition of remotely sensed data, and similar areas. Topics of interest also include hydrological modeling, atmospheric research, natural hazards mapping and modeling, value-added data generation, and related topics.

Prof. Dr. Assefa M. Melesse
Dr. Juan-Carlos Jimenez-Munoz
Dr. Robert Knuteson
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 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. Sensors 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 2200 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

  • multispectral, hyperspectral, and active radar and LiDAR sensors
  • space-borne, air-borne, and UAV platforms
  • proximal sensing and robotic manned and unmanned systems
  • accuracy of sensor measurements and parameters estimates
  • data processing techniques and related big data problem and solution
  • decision support systems and making (AI, machine learning)
  • sensor fusion
  • fusing model
  • downscaling model
  • upscaling techniques
  • water resources
  • extreme events
  • land cover dynamics
  • ecosystem functioning

Published Papers (7 papers)

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Research

Article
Automotive Radar in a UAV to Assess Earth Surface Processes and Land Responses
Sensors 2020, 20(16), 4463; https://doi.org/10.3390/s20164463 - 10 Aug 2020
Cited by 1 | Viewed by 772
Abstract
The use of unmanned aerial vehicles (UAVs) in earth science research has drastically increased during the last decade. The reason being innumerable advantages to detecting and monitoring various environmental processes before and after certain events such as rain, wind, flood, etc. or to [...] Read more.
The use of unmanned aerial vehicles (UAVs) in earth science research has drastically increased during the last decade. The reason being innumerable advantages to detecting and monitoring various environmental processes before and after certain events such as rain, wind, flood, etc. or to assess the current status of specific landforms such as gullies, rills, or ravines. The UAV equipped sensors are a key part to success. Besides commonly used sensors such as cameras, radar sensors are another possibility. They are less known for this application, but already well established in research. A vast number of research projects use professional radars, but they are expensive and difficult to handle. Therefore, the use of low-cost radar sensors is becoming more relevant. In this article, to make the usage of radar simpler and more efficient, we developed with automotive radar technology. We introduce basic radar techniques and present two radar sensors with their specifications. To record the radar data, we developed a system with an integrated camera and sensors. The weight of the whole system is about 315 g for the small radar and 450 g for the large one. The whole system was integrated into a UAV and test flights were performed. After that, several flights were carried out, to verify the system with both radar sensors. Thereby, the records provide an insight into the radar data. We demonstrated that the recording system works and the radar sensors are suitable for the usage in a UAV and future earth science research because of its autonomy, precision, and lightweight. Full article
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Article
Combined Use of Sentinel-1 SAR and Landsat Sensors Products for Residual Soil Moisture Retrieval over Agricultural Fields in the Upper Blue Nile Basin, Ethiopia
Sensors 2020, 20(11), 3282; https://doi.org/10.3390/s20113282 - 09 Jun 2020
Cited by 2 | Viewed by 1009
Abstract
The objective of this paper is to investigate the potential of sentinel-1 SAR sensor products and the contribution of soil roughness parameters to estimate volumetric residual soil moisture (RSM) in the Upper Blue Nile (UBN) basin, Ethiopia. The backscatter contribution of crop residue [...] Read more.
The objective of this paper is to investigate the potential of sentinel-1 SAR sensor products and the contribution of soil roughness parameters to estimate volumetric residual soil moisture (RSM) in the Upper Blue Nile (UBN) basin, Ethiopia. The backscatter contribution of crop residue water content was estimated using Landsat sensor product and the water cloud model (WCM). The surface roughness parameters were estimated from the Oh and Baghdadi models. A feed-forward artificial neural network (ANN) method was tested for its potential to translate SAR backscattering and surface roughness input variables to RSM values. The model was trained for three inversion configurations: (i) SAR backscattering from vertical transmit and vertical receive (SAR VV) polarization only; (ii) using SAR VV and the standard deviation of surface heights ( h r m s ), and (iii) SAR VV, h r m s , and optimal surface correlation length ( l e f f ). Field-measured volumetric RSM data were used to train and validate the method. The results showed that the ANN soil moisture estimation model performed reasonably well for the estimation of RSM using the single input variable of SAR VV data only. The ANN prediction accuracy was slightly improved when SAR VV and the surface roughness parameters ( h r m s and l e f f ) were incorporated into the prediction model. Consequently, the ANN’s prediction accuracy with root mean square error (RMSE) = 0.035 cm3/cm3, mean absolute error (MAE) = 0.026 cm3/cm3, and r = 0.73 was achieved using the third inversion configuration. The result implies the potential of Sentinel-1 SAR data to accurately retrieve RSM content over an agricultural site covered by stubbles. The soil roughness parameters are also potentially an important variable to soil moisture estimation using SAR data although their contribution to the accuracy of RSM prediction is slight in this study. In addition, the result highlights the importance of combining Sentinel-1 SAR and Landsat images based on an ANN approach for improving RSM content estimations over crop residue areas. Full article
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Article
Operational Global Actual Evapotranspiration: Development, Evaluation, and Dissemination
Sensors 2020, 20(7), 1915; https://doi.org/10.3390/s20071915 - 30 Mar 2020
Cited by 5 | Viewed by 2638
Abstract
Satellite-based actual evapotranspiration (ETa) is becoming increasingly reliable and available for various water management and agricultural applications from water budget studies to crop performance monitoring. The Operational Simplified Surface Energy Balance (SSEBop) model is currently used by the US Geological Survey (USGS) Famine [...] Read more.
Satellite-based actual evapotranspiration (ETa) is becoming increasingly reliable and available for various water management and agricultural applications from water budget studies to crop performance monitoring. The Operational Simplified Surface Energy Balance (SSEBop) model is currently used by the US Geological Survey (USGS) Famine Early Warning System Network (FEWS NET) to routinely produce and post multitemporal ETa and ETa anomalies online for drought monitoring and early warning purposes. Implementation of the global SSEBop using the Aqua satellite’s Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and global gridded weather datasets is presented. Evaluation of the SSEBop ETa data using 12 eddy covariance (EC) flux tower sites over six continents indicated reasonable performance in capturing seasonality with a correlation coefficient up to 0.87. However, the modeled ETa seemed to show regional biases whose natures and magnitudes require a comprehensive investigation using complete water budgets and more quality-controlled EC station datasets. While the absolute magnitude of SSEBop ETa would require a one-time bias correction for use in water budget studies to address local or regional conditions, the ETa anomalies can be used without further modifications for drought monitoring. All ETa products are freely available for download from the USGS FEWS NET website. Full article
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Article
Evaluation of Remotely Sensed and Interpolated Environmental Datasets for Vector-Borne Disease Monitoring Using In Situ Observations over the Amhara Region, Ethiopia
Sensors 2020, 20(5), 1316; https://doi.org/10.3390/s20051316 - 28 Feb 2020
Viewed by 1067
Abstract
Despite the sparse distribution of meteorological stations and issues with missing data, vector-borne disease studies in Ethiopia have been commonly conducted based on the relationships between these diseases and ground-based in situ measurements of climate variation. High temporal and spatial resolution satellite-based remote-sensing [...] Read more.
Despite the sparse distribution of meteorological stations and issues with missing data, vector-borne disease studies in Ethiopia have been commonly conducted based on the relationships between these diseases and ground-based in situ measurements of climate variation. High temporal and spatial resolution satellite-based remote-sensing data is a potential alternative to address this problem. In this study, we evaluated the accuracy of daily gridded temperature and rainfall datasets obtained from satellite remote sensing or spatial interpolation of ground-based observations in relation to data from 22 meteorological stations in Amhara Region, Ethiopia, for 2003–2016. Famine Early Warning Systems Network (FEWS-Net) Land Data Assimilation System (FLDAS) interpolated temperature showed the lowest bias (mean error (ME) ≈ 1–3 °C), and error (mean absolute error (MAE) ≈ 1–3 °C), and the highest correlation with day-to-day variability of station temperature (COR ≈ 0.7–0.8). In contrast, temperature retrievals from the blended Advanced Microwave Scanning Radiometer on Earth Observing Satellite (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) passive microwave and Moderate-resolution Imaging Spectroradiometer (MODIS) land-surface temperature data had higher bias and error. Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) rainfall showed the least bias and error (ME ≈ −0.2–0.2 mm, MAE ≈ 0.5–2 mm), and the best agreement (COR ≈ 0.8), with station rainfall data. In contrast FLDAS had the higher bias and error and the lowest agreement and Global Precipitation Mission/Tropical Rainfall Measurement Mission (GPM/TRMM) data were intermediate. This information can inform the selection of geospatial data products for use in climate and disease research and applications. Full article
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Article
Retrieving Decadal Climate Change from Satellite Radiance Observations—A 100-year CO2 Doubling OSSE Demonstration
Sensors 2020, 20(5), 1247; https://doi.org/10.3390/s20051247 - 25 Feb 2020
Cited by 1 | Viewed by 731
Abstract
Preparing for climate change depends on the observation and prediction of decadal trends of the environmental variables, which have a direct impact on the sustainability of resources affecting the quality of life on our planet. The NASA Climate Absolute Radiance and Refractivity Observatory [...] Read more.
Preparing for climate change depends on the observation and prediction of decadal trends of the environmental variables, which have a direct impact on the sustainability of resources affecting the quality of life on our planet. The NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission is proposed to provide climate quality benchmark spectral radiance observations for the purpose of determining the decadal trends of climate variables, and validating and improving the long-range climate model forecasts needed to prepare for the changing climate of the Earth. The CLARREO will serve as an in-orbit, absolute, radiometric standard for the cross-calibration of hyperspectral radiance spectra observed by the international system of polar operational satellite sounding sensors. Here, we demonstrate that the resulting accurately cross-calibrated polar satellite global infrared spectral radiance trends (e.g., from the Metop IASI instrument considered here) can be interpreted in terms of temperature and water vapor profile trends. This demonstration is performed using atmospheric state data generated for a 100-year period from 2000–2099, produced by a numerical climate model prediction that was forced by the doubling of the global average atmospheric CO2 over the 100-year period. The vertical profiles and spatial distribution of temperature decadal trends were successfully diagnosed by applying a linear regression profile retrieval algorithm to the simulated hyperspectral radiance spectra for the 100-year period. These results indicate that it is possible to detect decadal trends in atmospheric climate variables from high accuracy all-sky satellite infrared radiance spectra using the linear regression retrieval technique. Full article
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Article
The Impact of Low Latency Satellite Sounder Observations on Local Severe Storm Forecasts in Regional NWP
Sensors 2020, 20(3), 650; https://doi.org/10.3390/s20030650 - 24 Jan 2020
Cited by 2 | Viewed by 842
Abstract
The forecasts of local severe storms (LSS) are highly dependent on how well the pre-convection environment is characterized in the numerical weather prediction (NWP) model analysis. The usefulness of the forecasts is highly dependent on how frequently the forecast is updated. Therefore, the [...] Read more.
The forecasts of local severe storms (LSS) are highly dependent on how well the pre-convection environment is characterized in the numerical weather prediction (NWP) model analysis. The usefulness of the forecasts is highly dependent on how frequently the forecast is updated. Therefore, the data latency is critical for assimilation into regional NWP models for it to be able to assimilate more data within the data cut-off window. These low latency data can be obtained through direct broadcast sites and direct receiving systems. Observing system experiments (OSE) were performed to study the impact of data latency on the LSS forecasts. The experiments assimilated all existing observations including conventional data (from the global telecommunication system, GTS) and satellite sounder radiance data (AMSU-A (The Advanced Microwave Sounding Unit-A), ATMS (Advanced Technology Microwave Sounder), CrIS (Cross-track Infrared Sounder), and IASI (Infrared Atmospheric Sounding Interferometer)). They were carried out in a nested domain with a horizontal resolution of 9 km and 3 km in the weather research and forecasting (WRF) model. The forecast quality scores of the LSS precipitation forecasts were calculated and compared with different data cut-off widows to evaluate the impact of data latency. The results showed that low latency can lead to an improved and positive impact on precipitation and other forecasts, which indicates the potential application of LEO direct broadcast (DB) data in a high-resolution regional NWP for LSS forecasts. Full article
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Article
Surface Deformation Analysis of the Wider Zagreb Area (Croatia) with Focus on the Kašina Fault, Investigated with Small Baseline InSAR Observations
Sensors 2019, 19(22), 4857; https://doi.org/10.3390/s19224857 - 07 Nov 2019
Cited by 1 | Viewed by 1303
Abstract
The wider Zagreb area is considered one of the few seismically active areas in the Republic of Croatia. During the period 1880–1906, moderate to strong seismic activity with three earthquakes magnitude ML ≥ 6 occurred on the NW-SE striking Kašina Fault and [...] Read more.
The wider Zagreb area is considered one of the few seismically active areas in the Republic of Croatia. During the period 1880–1906, moderate to strong seismic activity with three earthquakes magnitude ML ≥ 6 occurred on the NW-SE striking Kašina Fault and since then, the area has not experienced earthquakes exceeding magnitude ML = 5. In order to estimate the ongoing interseismic strain accumulation along the fault, we analyze Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) and Environmental Satellite (Envisat)-Advanced Synthetic Aperture Radar (ASAR) datasets acquired over the period 2007–2010 and 2002–2010, respectively. The data were analyzed using small baseline interferometry (SBI) technique and indicate very slow surface deformations in the area, within ±3.5 mm/year, which are in a good agreement with previous geodetic studies. Interseismic strain accumulation analysis was conducted on two 14 km long segments of the Kašina Fault, seismically active in the South and stable in the North. The analysis indicates an ongoing interseismic strain accumulation of 2.3 mm/year on the Southern segment and no detectable strain accumulation on the Northern segment. Taking into consideration the lack of moderate to strong seismic activity in the past 113 years combined with the preliminary geodetic analysis from this study, we can conclude that the Southern segment of the Kašina Fault has the potential to generate earthquake magnitude Mw < 6. Full article
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