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

Deadline for manuscript submissions: closed (1 April 2021) | Viewed by 8511

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Special Issue Editors

Prof. Dr. Hyungjun Kim

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Guest Editor

The University of Tokyo, Japan
Interests: climate forcing and land feedback; coupled natural–human systems and sustainable development; remote sensing hydrology; big data–model integration
Special Issues, Collections and Topics in MDPI journals

Dr. John T. Reager

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Guest Editor

Jet Propulsion Laboratory/NASA, USA
Interests: application of satellite gravimetry for terrestrial hydrology; influence of subsurface water storage on hydrologic extremes; global water cycle variability and sea level rise
Special Issues, Collections and Topics in MDPI journals

Prof. Jin-Ho Yoon

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Guest Editor

Gwangju Institute of Science and Technology, Gwangju, Korea
Interests: climate extremes; climate dynamics; geoengineering
Special Issues, Collections and Topics in MDPI journals

Dr. Yadu Pokhrel

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Guest Editor

Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA
Interests: hydrological modeling; human impacts on the water cycle; water resource sustainability; food–energy–water nexus
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During recent years, the book-keeping records of extreme events and disasters have been replaced year to year. Because of significant advances over the past decade in modeling and remote sensing capacity for climate–hydrology–human interactions, our understanding of causes and impacts of such extreme events and disasters has improved considerably.

This Special Issue, jointly organized by Atmosphere, Remote Sening, and Water, aims to solicit original scientific contributions from the broader communities related to climate and atmospheric sciences, hydrology, and remote sensing, on the following topics: (1) Variability of climate forcing and hydrological feedback, (2) detection/attribution of extreme events, and impact assessment, (3) modeling of interactions between nature and human society, and (4) remote sensing hydrology, and data–model integration.

Studies that focus on modeling and/or monitoring behaviors as coupled natural–human systems against extreme climatic perturbation from multiscale perspectives are particularly encouraged, but studies related to the general areas of climate and hydrological extremes, climate change and impact assessments, sustainability science, numerical model development, and development of remote sensing algorithms are equally welcome.

According to the aims and scope of the hosting journals and the topic of the study, a manuscript can be submitted to the most appropriate journal among Atmosphere, Remote Sening, and Water.

Prof. Dr. Hyungjun Kim
Dr. John T. Reager
Prof. Jin-Ho Yoon
Prof. Dr. Yadu Pokhrel
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

Extreme events
Natural and human systems
Hydrological modeling
Remote sensing hydrology

Published Papers (2 papers)

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Research

21 pages, 6366 KiB

Open AccessArticle

Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems

by Haemi Park, Wataru Takeuchi and Kazuhito Ichii

Remote Sens. 2020, 12(2), 250; https://doi.org/10.3390/rs12020250 - 10 Jan 2020

Cited by 12 | Viewed by 4246

Abstract

Tropical peatland ecosystems are known as large carbon (C) reservoirs and affect spatial and temporal patterns in C sinks and sources at large scales in response to climate anomalies. In this study, we developed a satellite data-based model to estimate the net biosphere exchange (NBE) in Indonesia and Malaysia by accounting for fire emissions (FE), ecosystem respiration (R_e), and gross primary production (GPP). All input variables originated from satellite-based datasets, e.g., the precipitation of global satellite mapping of precipitation (GSMaP), the land surface temperature (LST) of the moderate resolution imaging spectroradiometer (MODIS), the photosynthetically active radiation of MODIS, and the burned area of MODIS fire products. First, we estimated the groundwater table (GWT) by incorporating LST and precipitation into the Keetch–Byram Drought Index (KBDI). The GWT was validated using in-situ measurements, with a root mean square error (RMSE) of 24.97 cm and an r-squared (R²) of 0.61. The daily GWT variations from 2002 to 2018 were used to estimate respiration (R_e) based on a relationship between the in situ GWT and flux-tower-observed R_e. Fire emissions are a large direct source of CO₂ from terrestrial ecosystems into the atmosphere and were estimated by using MODIS fire products and estimated biomass. The GPP was calculated based on the MODIS GPP product after parameter calibration at site scales. As a result, averages of long-term (17 years) R_e, GPP, FE, and NBE from whole peatlands in the study area (6°N–11°S, 95–141°E) were 66.71, 39.15, 1.9, and 29.46 Mt C/month, respectively. We found that the NBE from tropical peatlands in the study area was greater than zero, acting as a C source. R_e and FE were influenced by El Niño, and the value of the NBE was also high in the El Niño period. In future studies, the status of peatland degradation should be clarified in detail to accurately estimate the C budget by applying appropriate algorithms of R_e with delineations of types of anthropogenic impacts (e.g., drainages and fires). Full article

(This article belongs to the Special Issue Modeling and Monitoring Climate Extremes and Impacts on Natural-Human Systems)

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20 pages, 6137 KiB

Open AccessArticle

Estimating Live Fuel Moisture Using SMAP L-Band Radiometer Soil Moisture for Southern California, USA

by Shenyue Jia, Seung Hee Kim, Son V. Nghiem and Menas Kafatos

Remote Sens. 2019, 11(13), 1575; https://doi.org/10.3390/rs11131575 - 3 Jul 2019

Cited by 27 | Viewed by 3862

Abstract

Live fuel moisture (LFM) is a field-measured indicator of vegetation water content and a crucial observation of vegetation flammability. This study presents a new multi-variant regression model to estimate LFM in the Mediterranean ecosystem of Southern California, USA, using the Soil Moisture Active Passive (SMAP) L-band radiometer soil moisture (SMAP SM) from April 2015 to December 2018 over 12 chamise (Adenostoma fasciculatum) LFM sites. The two-month lag between SMAP SM and LFM was utilized either as steps to synchronize the SMAP SM to the LFM series or as the leading time window to calculate the accumulative SMAP SM. Cumulative growing degree days (CGDDs) were also employed to address the impact from heat. Models were constructed separately for the green-up and brown-down periods. An inverse exponential weight function was applied in the calculation of accumulative SMAP SM to address the different contribution to the LFM between the earlier and present SMAP SM. The model using the weighted accumulative SMAP SM and CGDDs yielded the best results and outperformed the reference model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Visible Atmospherically Resistance Index. Our study provides a new way to empirically estimate the LFM in chaparral areas and extends the application of SMAP SM in the study of wildfire risk. Full article

(This article belongs to the Special Issue Modeling and Monitoring Climate Extremes and Impacts on Natural-Human Systems)

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