sensors-logo

Journal Browser

Journal Browser

Special Issue "Advances in Quantitative Remote Sensing: Past, Present and Future"

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

Deadline for manuscript submissions: closed (30 April 2019).

Special Issue Editor

Guest Editor
Dr. Ghassem R. Asrar Website E-Mail
Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD 20740, USA
Phone: 301 405 5077
Interests: Earth system science; remote sensing; data assimilation; biosphere-atmosphere interactions

Special Issue Information

Dear Colleagues,

The overall intent of this Special Issue is to demonstrate significant advances in the field of Earth remote sensing during recent decades, and exciting scientific and technical challenges and opportunities ahead. This Special Issue is therefore devoted to innovative papers based on multi-sensors and/or multispectral remotely sensed observations of the Earth system towards an understanding of the fundamental processes that control its key components (atmosphere, land, oceans, and polar regions) and their interactions, which in turn control the variability and change of these components, and the entire Earth system. Such knowledge and resulting data are essential for developing and evaluating the performance of the Earth system models used for the prediction of weather conditions, and water and energy cycles that have profound societal importance. Specifically, papers on the use of observations from long-term records (e.g. Landsat and EOS), multiple satellites (EOS A- and B-Trains, and combined optical and microwave measurements), and combined polar and geostationary satellites are welcomed. Studies focused on innovative use of remotely sensed observations in deriving useful information (e.g. sun induced florescence, vegetation chemistry and composition, species habitat, landscape level ecosystems genotype and phenotype) are also invited. We also welcome papers focused on the next generation of Earth-observing sensors/satellites that build on previous generations and/or introduce new ones based on a combinations of sensors and satellites (e.g. micro-sensor/satellite constellations).

Dr. Ghassem R. Asrar
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 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

  • Earth remote sensing
  • micro-satellite constellations
  • multi-spectral remote sensing
  • Earth system science
  • landscape genotype and phenotype mapping
  • innovation in remote sensing applications
  • next generation, Earth-observing sensors/satellites

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Long-Term Spatiotemporal Variations in Soil Moisture in North East China Based on 1-km Resolution Downscaled Passive Microwave Soil Moisture Products
Sensors 2019, 19(16), 3527; https://doi.org/10.3390/s19163527 - 12 Aug 2019
Abstract
It is very important to analyze and monitor agricultural drought to obtain high temporal-spatial resolution soil moisture products. To overcome the deficiencies of passive microwave soil moisture products with low resolution, we construct a spatial fusion downscaling model (SFDM) using Moderate Resolution Imaging [...] Read more.
It is very important to analyze and monitor agricultural drought to obtain high temporal-spatial resolution soil moisture products. To overcome the deficiencies of passive microwave soil moisture products with low resolution, we construct a spatial fusion downscaling model (SFDM) using Moderate Resolution Imaging Spectroradiometer (MODIS) data. To eliminate the inconsistencies in soil depth and time among different microwave soil moisture products (Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) and its successor (AMSR2) and the Soil Moisture Ocean Salinity (SMOS)), a time series reconstruction of the difference decomposition (TSRDD) method is developed to create long-term multisensor soil moisture datasets. Overall, the downscaled soil moisture (SM) products were consistent with the in situ measurements (R > 0.78) and exhibited a low root mean square error (RMSE < 0.10 m3/m3), which indicates good accuracy throughout the time series. The downscaled SM data at a 1-km spatial resolution were used to analyze the spatiotemporal patterns and monitor abnormal conditions in the soil water content across North East China (NEC) between 2002 and 2018. The results showed that droughts frequently appeared in western North East China and southwest of the Greater Khingan Range, while drought centers appeared in central North East China. Waterlogging commonly appeared in low-terrain areas, such as the Songnen Plain. Seasonal precipitation and temperature exhibited distinct interdecadal characteristics that were closely related to the occurrence of extreme climatic events. Abnormal SM levels were often accompanied by large meteorological and natural disasters (e.g., the droughts of 2008, 2015, and 2018 and the flooding events of 2003 and 2013). The spatial distribution of drought in this region during the growing season shows that the drought-affected area is larger in the west than in the east and that the semiarid boundary extends eastward and southward. Full article
(This article belongs to the Special Issue Advances in Quantitative Remote Sensing: Past, Present and Future)
Show Figures

Figure 1

Open AccessArticle
SIFSpec: Measuring Solar-Induced Chlorophyll Fluorescence Observations for Remote Sensing of Photosynthesis
Sensors 2019, 19(13), 3009; https://doi.org/10.3390/s19133009 - 08 Jul 2019
Cited by 1
Abstract
Solar-induced chlorophyll fluorescence (SIF) is regarded as a proxy for photosynthesis in terrestrial vegetation. Tower-based long-term observations of SIF are very important for gaining further insight into the ecosystem-specific seasonal dynamics of photosynthetic activity, including gross primary production (GPP). Here, we present the [...] Read more.
Solar-induced chlorophyll fluorescence (SIF) is regarded as a proxy for photosynthesis in terrestrial vegetation. Tower-based long-term observations of SIF are very important for gaining further insight into the ecosystem-specific seasonal dynamics of photosynthetic activity, including gross primary production (GPP). Here, we present the design and operation of the tower-based automated SIF measurement (SIFSpec) system. This system was developed with the aim of obtaining synchronous SIF observations and flux measurements across different terrestrial ecosystems, as well as to validate the increasing number of satellite SIF products using in situ measurements. Details of the system components, instrument installation, calibration, data collection, and processing are introduced. Atmospheric correction is also included in the data processing chain, which is important, but usually ignored for tower-based SIF measurements. Continuous measurements made across two growing cycles over maize at a Daman (DM) flux site (in Gansu province, China) demonstrate the reliable performance of SIF as an indicator for tracking the diurnal variations in photosynthetically active radiation (PAR) and seasonal variations in GPP. For the O2–A band in particular, a high correlation coefficient value of 0.81 is found between the SIF and seasonal variations of GPP. It is thus concluded that, in coordination with continuous eddy covariance (EC) flux measurements, automated and continuous SIF observations can provide a reliable approach for understanding the photosynthetic activity of the terrestrial ecosystem, and are also able to bridge the link between ground-based optical measurements and airborne or satellite remote sensing data. Full article
(This article belongs to the Special Issue Advances in Quantitative Remote Sensing: Past, Present and Future)
Show Figures

Figure 1

Open AccessArticle
Deep Learning Convolutional Neural Network for the Retrieval of Land Surface Temperature from AMSR2 Data in China
Sensors 2019, 19(13), 2987; https://doi.org/10.3390/s19132987 - 06 Jul 2019
Cited by 1
Abstract
A convolutional neural network (CNN) algorithm was developed to retrieve the land surface temperature (LST) from Advanced Microwave Scanning Radiometer 2 (AMSR2) data in China. Reference data were selected using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to overcome the problem related [...] Read more.
A convolutional neural network (CNN) algorithm was developed to retrieve the land surface temperature (LST) from Advanced Microwave Scanning Radiometer 2 (AMSR2) data in China. Reference data were selected using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to overcome the problem related to the need for synchronous ground observation data. The AMSR2 brightness temperature (TB) data and MODIS surface temperature data were randomly divided into training and test datasets, and a CNN was constructed to simulate passive microwave radiation transmission to invert the surface temperature. The twelve V/H channel combinations (7.3, 10.65, 18.7, 23.8, 36.5, 89 GHz) resulted in the most stable and accurate CNN retrieval model. Vertical polarizations performed better than horizontal polarizations; however, because CNNs rely heavily on large amounts of data, the combination of vertical and horizontal polarizations performed better than a single polarization. The retrievals in different regions indicated that the CNN accuracy was highest over large bare land areas. A comparison of the retrieval results with ground measurement data from meteorological stations yielded R2 = 0.987, RMSE = 2.69 K, and an average relative error of 2.57 K, which indicated that the accuracy of the CNN LST retrieval algorithm was high and the retrieval results can be applied to long-term LST sequence analysis in China. Full article
(This article belongs to the Special Issue Advances in Quantitative Remote Sensing: Past, Present and Future)
Show Figures

Figure 1

Open AccessArticle
Improved Bathymetric Mapping of Coastal and Lake Environments Using Sentinel-2 and Landsat-8 Images
Sensors 2019, 19(12), 2788; https://doi.org/10.3390/s19122788 - 21 Jun 2019
Cited by 1
Abstract
The bathymetry of nearshore coastal environments and lakes is constantly reworking because of the change in the patterns of energy dispersal and related sediment transport pathways. Therefore, updated and accurate bathymetric models are a crucial component in providing necessary information for scientific, managerial, [...] Read more.
The bathymetry of nearshore coastal environments and lakes is constantly reworking because of the change in the patterns of energy dispersal and related sediment transport pathways. Therefore, updated and accurate bathymetric models are a crucial component in providing necessary information for scientific, managerial, and geographical studies. Recent advances in satellite technology revolutionized the acquisition of bathymetric profiles, offering new vistas in mapping. This contribution analyzed the suitability of Sentinel-2 and Landsat-8 images for bathymetric mapping of coastal and lake environments. The bathymetric algorithm was developed using an empirical approach and a random forest (RF) model based on the available high-resolution LiDAR bathymetric data for Mobile Bay, Tampa Bay, and Lake Huron regions obtained from the National Oceanic and Atmospheric Administration (NOAA) National Geophysical Data Center (NGDC). Our results demonstrate that the satellite-derived bathymetry is efficient for retrieving depths up to 10 m for coastal regions and up to 30 m for the lake environment. While using the empirical approach, the root-mean-square error (RMSE) varied between 1.99 m and 4.74 m for the three regions. The RF model, on the other hand, provided an improved bathymetric model with RMSE between 1.13 m and 1.95 m. The comparative assessment suggests that Sentinel-2 has a slight edge over Landsat-8 images while employing the empirical approach. On the other hand, the RF model shows that Landsat-8 retrieves a better bathymetric model than Sentinel-2. Our work demonstrated that the freely available Sentinel-2 and Landsat-8 imageries proved to be reliable data for acquiring updated bathymetric information for large areas in a short period. Full article
(This article belongs to the Special Issue Advances in Quantitative Remote Sensing: Past, Present and Future)
Show Figures

Figure 1

Open AccessArticle
Remote Sensing of Daytime Water Leaving Reflectances of Oceans and Large Inland Lakes from EPIC onboard the DSCOVR Spacecraft at Lagrange-1 Point
Sensors 2019, 19(5), 1243; https://doi.org/10.3390/s19051243 - 12 Mar 2019
Cited by 1
Abstract
The NASA’s Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) satellite has been making multiple observations of the entire sunlit Earth in a given day from the Sun-Earth Largangian L1 point since the summer of 2015. EPIC contains [...] Read more.
The NASA’s Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) satellite has been making multiple observations of the entire sunlit Earth in a given day from the Sun-Earth Largangian L1 point since the summer of 2015. EPIC contains 10 narrow channels in the 317–780 nm solar spectral range. The data acquired with EPIC have already been used in a variety of scientific investigations, including the study of the global ozone levels, aerosol index and aerosol optical depth, UV reflectivity of clouds over land and ocean, cloud height over land and ocean, and vegetation indices. In this article, we report that EPIC data, particularly for the data measured with narrow channels centered near 443, 551, and 680 nm, can also have important applications in remote sensing of ocean color in different geographical regions. We have modified a version of a multi-channel atmospheric correction algorithm for Moderate Resolution Imaging SpectroRadiometer (MODIS) ocean color applications and adapted the algorithm for processing EPIC data. We present three case studies on water leaving reflectance retrievals from EPIC data acquired over a large turbid river, inland lakes, and oceans. We conclude that a future ocean color instrument on board a satellite at the L1 point, which provides continuous view of the full sunlit disk of the Earth, will complement and extend ocean color observations with the low Earth observing polar orbital and geostationary satellite instruments in both the spatial and time domains. Full article
(This article belongs to the Special Issue Advances in Quantitative Remote Sensing: Past, Present and Future)
Show Figures

Figure 1

Back to TopTop