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

Deadline for manuscript submissions: closed (23 March 2020) | Viewed by 31801

Special Issue Editor

Dr. Ghassem R. Asrar

E-Mail Website
Guest Editor
Universities Space Research Association, 7178 Columbia Gateway Drive, Columbia, MD 21046, USA
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

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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

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Published Papers (6 papers)

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Editorial

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4 pages, 162 KiB  
Editorial
Advances in Quantitative Earth Remote Sensing: Past, Present and Future
by Ghassem R. Asrar
Sensors 2019, 19(24), 5399; https://doi.org/10.3390/s19245399 - 7 Dec 2019
Cited by 6 | Viewed by 2928
Abstract
A combination of multispectral visible, infra-red and microwave sensors on the constellation of international Earth-observing satellites are providing unprecedented observations for all Earth domains over multiple decades (i.e., atmosphere, land, oceans and polar regions). This Special Issue of Sensors is dedicated to papers [...] Read more.
A combination of multispectral visible, infra-red and microwave sensors on the constellation of international Earth-observing satellites are providing unprecedented observations for all Earth domains over multiple decades (i.e., atmosphere, land, oceans and polar regions). This Special Issue of Sensors is dedicated to papers that describe such advances in the field of Earth remote sensing and their applications to advance understanding of Earth’s planetary system and applying the resulting knowledge and information to meet the societal needs during recent decades. The papers accepted and published in this issue convey the exciting scientific and technical challenges and opportunities for remote sensing of all domains of Earth system, including terrestrial, aquatic and coastal ecosystems; bathymetry of coasts and islands; oceans and lakes; measurement of soil moisture and land surface temperature that affects both water resources and food production; and advances in use of sun-induced fluorescence (SIF) in measuring and monitoring the contribution of terrestrial vegetation in the cycling of carbon in Earth’s system. Measurements of SIF, for example, has had a profound impact on the field of terrestrial ecosystems research and modelling. The Earth Polychromatic Imaging Camera (EPIC) instrument on the Deep Space Climate Observatory (DSCVR) satellite located at the Sun–Earth Lagrange Point One, about 1.5 million miles away from Earth, is providing unique observations of the Earth’s full sun-lit disk from pole-to-pole and minute-by-minute, which overcomes a major limitation in temporal coverage of Earth by other polar-orbiting Earth-observing satellites. Active and passive microwave remote sensing instruments allow all-weather measurements and monitoring of clouds, weather phenomena, land-surface temperature and soil moisture by overcoming the presence of clouds that affect measurements by visible and infrared sensors. The use of powerful in-space lasers is allowing scientists and engineers to measure and monitor rapidly changing ice sheets in polar regions and mountain glaciers. These sensors and their measurements that are deployed on major space-based observatories and small- and micro-satellites, and the scientific knowledge they provide, are enhancing our understanding of planet Earth and development of Earth system models that are used increasingly to project future conditions due to Earth’s rapidly changing environmental conditions. Such knowledge and information are benefiting people, businesses and governments worldwide. Full article
(This article belongs to the Special Issue Advances in Quantitative Remote Sensing: Past, Present and Future)

Research

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18 pages, 8749 KiB  
Article
Long-Term Spatiotemporal Variations in Soil Moisture in North East China Based on 1-km Resolution Downscaled Passive Microwave Soil Moisture Products
by Xiangjin Meng, Kebiao Mao, Fei Meng, Xinyi Shen, Tongren Xu and Mengmeng Cao
Sensors 2019, 19(16), 3527; https://doi.org/10.3390/s19163527 - 12 Aug 2019
Cited by 13 | Viewed by 3447
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)
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21 pages, 3944 KiB  
Article
SIFSpec: Measuring Solar-Induced Chlorophyll Fluorescence Observations for Remote Sensing of Photosynthesis
by Shanshan Du, Liangyun Liu, Xinjie Liu, Jian Guo, Jiaochan Hu, Shaoqiang Wang and Yongguang Zhang
Sensors 2019, 19(13), 3009; https://doi.org/10.3390/s19133009 - 8 Jul 2019
Cited by 62 | Viewed by 7693
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)
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20 pages, 5458 KiB  
Article
Deep Learning Convolutional Neural Network for the Retrieval of Land Surface Temperature from AMSR2 Data in China
by Jiancan Tan, Nusseiba NourEldeen, Kebiao Mao, Jiancheng Shi, Zhaoliang Li, Tongren Xu and Zijin Yuan
Sensors 2019, 19(13), 2987; https://doi.org/10.3390/s19132987 - 6 Jul 2019
Cited by 55 | Viewed by 6148
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)
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20 pages, 7557 KiB  
Article
Improved Bathymetric Mapping of Coastal and Lake Environments Using Sentinel-2 and Landsat-8 Images
by Ali P. Yunus, Jie Dou, Xuan Song and Ram Avtar
Sensors 2019, 19(12), 2788; https://doi.org/10.3390/s19122788 - 21 Jun 2019
Cited by 76 | Viewed by 6719
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)
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11 pages, 3434 KiB  
Article
Remote Sensing of Daytime Water Leaving Reflectances of Oceans and Large Inland Lakes from EPIC onboard the DSCOVR Spacecraft at Lagrange-1 Point
by Bo-Cai Gao, Rong-Rong Li and Yuekui Yang
Sensors 2019, 19(5), 1243; https://doi.org/10.3390/s19051243 - 12 Mar 2019
Cited by 7 | Viewed by 4137
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)
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