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Hypertemporal Land Remote Sensing with Third-Generation Geostationary Earth Orbit (GEO) Satellites

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (1 July 2023) | Viewed by 12318

Special Issue Editors

Dr. Hiroki Yoshioka

E-Mail Website
Guest Editor
Department of Information Science and Technology, Aichi Prefectural University, Nagakute Aichi 480-1198, Japan
Interests: optical remote sensing of land surfaces; retrieval of biophysical parameters; radiative transfer theory; vegetation isoline theory
Dr. Tomoaki Miura

E-Mail Website
Guest Editor
Department of Natural Resources and Environmental Management, University of Hawai‘i at Mānoa, Honolulu, HI 96822, USA
Interests: environment; spatial analysis; climate change; remote sensing; satellite image analysis; vegetation; landscape ecology; time series; vegetation mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Kazuhito Ichii

E-Mail Website
Guest Editor
Center for Environmental Remote Sensing (CEReS), Chiba Uiversity, Inage-ku, Chiba 263-8522, Japan
Interests: terrestrial carbon cycle; vegetation monitoring; model–data integration

Special Issue Information

Geostationary Earth orbit (GEO) satellites have long been utilized, mainly in the field of atmospheric sciences and especially for meteorological purposes. During the decade starting in 2002, the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) series of GEO satellites, which differ from typical low Earth orbit (LEO) satellites, demonstrated the potential for land remote sensing using datasets with a “hypertemporal” resolution. The promising results obtained using SEVIRI drove the emergence of “third-generation” GEO sensors characterized by advancements with higher temporal, spatial, and spectral resolutions. Especially over the past seven years, a number of GEO satellites carrying sensors with improved land monitoring capabilities have been launched from several countries. The first of these was Himawari-8 (Japan) launched in 2014, followed in 2016 by INSAT-3DR (India), Himawari-9 (Japan), GEOS-16 (USA), and FY-4 (China). In 2018, two more satellites, GOES-17 (USA) and GEO-KOMPSAT-2A (South Korea), were launched. The deployment of these satellites raises the prospect of a new era of land remote sensing based on hypertemporal datasets from third-generation GEO sensors.

This Special Issue focuses on recent advances in land remote sensing using advanced GEO sensors. It aims to capture the current status of research in this area, covering topics ranging from fundamentals to applications, and to nurture discussions on future prospects in the field of hypertemporal land remote sensing. It welcomes manuscripts that address issues related to broad aspects of terrestrial monitoring from GEO satellites.

Dr. Hiroki Yoshioka
Dr. Tomoaki Miura
Dr. Kazuhito Ichii
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

  • Third-generation geostationary Earth orbit (GEO) satellites
  • Hypertemporal land remote sensing
  • GEO-LEO intercomparison and data fusion
  • Cloud mask and atmospheric correction
  • Geometric correction and accuracy estimation
  • Parameter retrieval
  • Land surface temperature
  • Land surface phenology
  • Vegetation change
  • Primary production
  • Carbon cycle
  • BRDF model
  • Disaster monitoring
  • Surface classification

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Published Papers (4 papers)

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Research

20 pages, 91408 KiB  
Article
Orthorectification of Data from the AHI Aboard the Himawari-8 Geostationary Satellite
by Masayuki Matsuoka and Hiroki Yoshioka
Remote Sens. 2023, 15(9), 2403; https://doi.org/10.3390/rs15092403 - 4 May 2023
Cited by 1 | Viewed by 2678
Abstract
The use of geostationary meteorological satellites for land remote sensing has attracted much attention after the launch of the Himawari-8 satellite equipped with a sensor with enhanced land observation capabilities. In the context of land remote sensing, geolocation errors are often a critical [...] Read more.
The use of geostationary meteorological satellites for land remote sensing has attracted much attention after the launch of the Himawari-8 satellite equipped with a sensor with enhanced land observation capabilities. In the context of land remote sensing, geolocation errors are often a critical issue, especially in mountainous regions, where a precise orthorectification process is required to maintain high geometric accuracy. The present work addresses the issues related to orthorectification of the new-generation geostationary Earth orbit (GEO) satellites by applying an algorithm known as the ray-tracing indirect method to the data acquired by the Advanced Himawari Imager (AHI) aboard the Himawari-8 satellite. The orthorectified images of the AHI were compared with data from the Sentinel-2 Multispectral Instrument (MSI). The comparison shows a clear improvement of the geometric accuracy, especially in high-elevation regions located far from the subsatellite point. The results indicate that approximately 7.3% of the land pixels are shifted more than 3 pixels during the orthorectification process. Furthermore, the maximum displacement after the orthorectification is up to 7.2 pixels relative to the location in the original image, which is of the Tibetan Plateau. Moreover, serious problems caused by occlusions in the images of GEO sensors are clearly indicated. It is concluded that special caution is needed when using data from GEO satellites for land remote sensing in cases where the target is in a mountainous region and the pixels are located far from the subsatellite point. Full article
(This article belongs to the Special Issue Hypertemporal Land Remote Sensing with Third-Generation Geostationary Earth Orbit (GEO) Satellites)
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27 pages, 8603 KiB  
Article
A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations
by Weile Wang, Yujie Wang, Alexei Lyapustin, Hirofumi Hashimoto, Taejin Park, Andrew Michaelis and Ramakrishna Nemani
Remote Sens. 2022, 14(4), 964; https://doi.org/10.3390/rs14040964 - 16 Feb 2022
Cited by 7 | Viewed by 2892
Abstract
This study developed a new atmospheric correction algorithm, GeoNEX-AC, that is independent from the traditional use of spectral band ratios but dedicated to exploiting information from the diurnal variability in the hypertemporal geostationary observations. The algorithm starts by evaluating smooth segments of the [...] Read more.
This study developed a new atmospheric correction algorithm, GeoNEX-AC, that is independent from the traditional use of spectral band ratios but dedicated to exploiting information from the diurnal variability in the hypertemporal geostationary observations. The algorithm starts by evaluating smooth segments of the diurnal time series of the top-of-atmosphere (TOA) reflectance to identify clear-sky and snow-free observations. It then attempts to retrieve the Ross-Thick–Li-Sparse (RTLS) surface bi-directional reflectance distribution function (BRDF) parameters and the daily mean atmospheric optical depth (AOD) with an atmospheric radiative transfer model (RTM) to optimally simulate the observed diurnal variability in the clear-sky TOA reflectance. Once the initial RTLS parameters are retrieved after the algorithm’s burn-in period, they serve as the prior information to estimate the AOD levels for the following days and update the surface BRDF information with the new clear-sky observations. This process is iterated through the full time span of the observations, skipping only totally cloudy days or when surface snow is detected. We tested the algorithm over various Aerosol Robotic Network (AERONET) sites and the retrieved results well agree with the ground-based measurements. This study demonstrates that the high-frequency diurnal geostationary observations contain unique information that can help to address the atmospheric correction problem from new directions. Full article
(This article belongs to the Special Issue Hypertemporal Land Remote Sensing with Third-Generation Geostationary Earth Orbit (GEO) Satellites)
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31 pages, 7077 KiB  
Article
Evaluation of BRDF Information Retrieved from Time-Series Multiangle Data of the Himawari-8 AHI
by Xiaoning Zhang, Ziti Jiao, Changsen Zhao, Jing Guo, Zidong Zhu, Zhigang Liu, Yadong Dong, Siyang Yin, Hu Zhang, Lei Cui, Sijie Li, Yidong Tong and Chenxia Wang
Remote Sens. 2022, 14(1), 139; https://doi.org/10.3390/rs14010139 - 29 Dec 2021
Cited by 5 | Viewed by 2744
Abstract
Recently, much attention has been given to using geostationary Earth orbit (GEO) meteorological satellite data for retrieving land surface parameters due to their high observation frequencies. However, their bidirectional reflectance distribution function (BRDF) information content with a single viewing angle has not been [...] Read more.
Recently, much attention has been given to using geostationary Earth orbit (GEO) meteorological satellite data for retrieving land surface parameters due to their high observation frequencies. However, their bidirectional reflectance distribution function (BRDF) information content with a single viewing angle has not been sufficiently investigated, which lays a foundation for subsequent quantitative estimation. In this study, we aim to comprehensively evaluate BRDF information from time-series observations from the Advanced Himawari Imager (AHI) onboard the GEO satellite Himawari-8. First, ~6.2 km monthly multiangle surface reflectances from POLDER onboard a low-Earth-orbiting (LEO) satellite with good angle distributions over various land types during 2008 were used as reference data, and corresponding 0.05° high-quality MODIS (i.e., onboard LEO satellites) and AHI datasets during four months in 2020 were obtained using cloud and aerosol property products. Then, indicators of angle distribution, BRDF change, and albedos were retrieved by the kernel-driven Ross-Li BRDF model from the three datasets, which were used for comparisons over different time spans. Generally, the quality of sun-viewing geometries varies dramatically for accumulated AHI observations according to the weight-of-determination, and wide-ranging anisotropic flat indices are obtained. The root-mean-square-errors of white sky albedos between AHI and MODIS half-month data are 0.018 and 0.033 in the red and near-infrared bands, respectively, achieving smaller values of 0.004 and 0.007 between the half-month and daily AHI data, respectively, due to small variances in sun-viewing geometries. The generally wide AHI BRDF variances and good consistency in albedo with MODIS show their potential for retrieving anisotropy information and albedo, while angle accumulation quality of AHI time-series observations must be considered. Full article
(This article belongs to the Special Issue Hypertemporal Land Remote Sensing with Third-Generation Geostationary Earth Orbit (GEO) Satellites)
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20 pages, 2585 KiB  
Article
Development and Demonstration of a Method for GEO-to-LEO NDVI Transformation
by Kenta Obata, Kenta Taniguchi, Masayuki Matsuoka and Hiroki Yoshioka
Remote Sens. 2021, 13(20), 4085; https://doi.org/10.3390/rs13204085 - 13 Oct 2021
Cited by 6 | Viewed by 2282
Abstract
This study presents a new method that mitigates biases between the normalized difference vegetation index (NDVI) from geostationary (GEO) and low Earth orbit (LEO) satellites for Earth observation. The method geometrically and spectrally transforms GEO NDVI into LEO-compatible GEO NDVI, in which GEO’s [...] Read more.
This study presents a new method that mitigates biases between the normalized difference vegetation index (NDVI) from geostationary (GEO) and low Earth orbit (LEO) satellites for Earth observation. The method geometrically and spectrally transforms GEO NDVI into LEO-compatible GEO NDVI, in which GEO’s off-nadir view is adjusted to a near-nadir view. First, a GEO-to-LEO NDVI transformation equation is derived using a linear mixture model of anisotropic vegetation and nonvegetation endmember spectra. The coefficients of the derived equation are a function of the endmember spectra of two sensors. The resultant equation is used to develop an NDVI transformation method in which endmember spectra are automatically computed from each sensor’s data independently and are combined to compute the coefficients. Importantly, this method does not require regression analysis using two-sensor NDVI data. The method is demonstrated using Himawari 8 Advanced Himawari Imager (AHI) data at off-nadir view and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at near-nadir view in middle latitude. The results show that the magnitudes of the averaged NDVI biases between AHI and MODIS for five test sites (0.016–0.026) were reduced after the transformation (<0.01). These findings indicate that the proposed method facilitates the combination of GEO and LEO NDVIs to provide NDVIs with smaller differences, except for cases in which the fraction of vegetation cover (FVC) depends on the view angle. Further investigations should be conducted to reduce the remaining errors in the transformation and to explore the feasibility of using the proposed method to predict near-real-time and near-nadir LEO vegetation index time series using GEO data. Full article
(This article belongs to the Special Issue Hypertemporal Land Remote Sensing with Third-Generation Geostationary Earth Orbit (GEO) Satellites)
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