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Remote Sensing of Biogeochemical Cycles
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Remote Sensing of Biogeochemical Cycles

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 May 2016) | Viewed by 35865

Special Issue Editors

Dr. Richard L. Miller

E-Mail Website
Guest Editor
Department of Geological Sciences and Institute for Coastal Science and Policy, East Carolina University, 101 Graham Hall, Greenville, NC 27858, USA
Interests: material transport; coastal processes; ocean color remote sensing; coupled land-ocean systems
Dr. Cheng-Chien Liu

E-Mail Website
Guest Editor
Department of Earth Sciences, Global Earth Observation and Data Analysis Center (GEODAC), National Cheng Kung University, No 1, Ta-Hsueh Road, Tainan City 700, Taiwan
Interests: remote sensing; ocean optics; geospatial information science; unmanned aerial vehicle; landslide susceptibility model
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biogeochemical cycles involve the interaction of biological, chemical, and geological processes, which determine the sources, sinks, and fluxes of elements through different reservoirs within and between ecosystems. Recently, considerable effort has been devoted to determining the amount of major bioactive elements in different reservoirs to gain a better understanding of the role that the land, ocean, and atmosphere play in global climate change. Remote sensing, using a wide range of technologies and approaches, has played a key role in this effort. Process oriented studies, which are designed to examine biogeochemical cycles, are inherently complex, generally multidisciplinary, and often require innovative technologies. This Special Issue seeks to highlight the use of remote sensing in studies that examine biogeochemical cycles—i.e., the mobilization, transport, transformation, and fate of material in different reservoirs.

Authors are encouraged to submit articles that demonstrate the use of remote sensing for biogeochemical cycles, as related to the following topics:

- material transport between major reservoirs
- use of data from multiple remote sensing instruments for ecosystem analysis
- carbon transport in coupled land-ocean coastal systems
- advances in remote sensing technologies for examining biogeochemical processes
- integration of field measurements and remote sensing
- application of remote sensing biogeochemical processes to resource management

Richard L. Miller
Cheng-Chien Liu
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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

24 pages, 9499 KiB  
Article
Evaluation of MODIS Gross Primary Production across Multiple Biomes in China Using Eddy Covariance Flux Data
by Hongji Zhu, Aiwen Lin, Lunche Wang, Yu Xia and Ling Zou
Remote Sens. 2016, 8(5), 395; https://doi.org/10.3390/rs8050395 - 13 May 2016
Cited by 40 | Viewed by 8163
Abstract
MOD17A2 provides near real-time estimates of gross primary production (GPP) globally. In this study, MOD17A2 GPP was evaluated using eddy covariance (EC) flux measurements at eight sites in five various biome types across China. The sensitivity of MOD17A2 to meteorological data and leaf [...] Read more.
MOD17A2 provides near real-time estimates of gross primary production (GPP) globally. In this study, MOD17A2 GPP was evaluated using eddy covariance (EC) flux measurements at eight sites in five various biome types across China. The sensitivity of MOD17A2 to meteorological data and leaf area index/fractional photosynthetically active radiation (LAI/FPAR) products were examined by introducing site meteorological measurements and improved Global Land Surface Satellite (GLASS) LAI products. We also assessed the potential error contributions from land cover and maximum light use efficiency (εmax). The results showed that MOD17A2 agreed well with flux measurements of annual GPP (R2 = 0.76) when all biome types were considered as a whole. However, MOD17A2 was ineffective for estimating annual GPP at mixed forests, evergreen needleleaf forests and croplands, respectively. Moreover, MOD17A2 underestimated flux derived GPP during the summer (R2 = 0.46). It was found that the meteorological data used in MOD17A2 failed to properly estimate the site measured vapor pressure deficits (VPD) (R2 = 0.31). Replacing the existing LAI/FPAR data with GLASS LAI products reduced MOD17A2 GPP uncertainties. Though land cover presented the fewest errors, εmax prescribed in MOD17A2 were much lower than inferred εmax calculated from flux data. Thus, the qualities of meteorological data and LAI/FPAR products need to be improved, and εmax should be adjusted to provide better GPP estimates using MOD17A2 for Chinese ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Biogeochemical Cycles)
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19 pages, 6835 KiB  
Article
High-Resolution Classification of South Patagonian Peat Bog Microforms Reveals Potential Gaps in Up-Scaled CH4 Fluxes by use of Unmanned Aerial System (UAS) and CIR Imagery
by Jan R. K. Lehmann, Wiebke Münchberger, Christian Knoth, Christian Blodau, Felix Nieberding, Torsten Prinz, Verónica A. Pancotto and Till Kleinebecker
Remote Sens. 2016, 8(3), 173; https://doi.org/10.3390/rs8030173 - 25 Feb 2016
Cited by 55 | Viewed by 10679
Abstract
South Patagonian peat bogs are little studied sources of methane (CH4). Since CH4 fluxes can vary greatly on a small scale of meters, high-quality maps are needed to accurately quantify CH4 fluxes from bogs. We used high-resolution color infrared [...] Read more.
South Patagonian peat bogs are little studied sources of methane (CH4). Since CH4 fluxes can vary greatly on a small scale of meters, high-quality maps are needed to accurately quantify CH4 fluxes from bogs. We used high-resolution color infrared (CIR) images captured by an Unmanned Aerial System (UAS) to investigate potential uncertainties in total ecosystem CH4 fluxes introduced by the classification of the surface area. An object-based approach was used to classify vegetation both on species and microform level. We achieved an overall Kappa Index of Agreement (KIA) of 0.90 for the species- and 0.83 for the microform-level classification, respectively. CH4 fluxes were determined by closed chamber measurements on four predominant microforms of the studied bog. Both classification approaches were employed to up-scale CH4 closed chamber measurements in a total area of around 1.8 hectares. Including proportions of the surface area where no chamber measurements were conducted, we estimated a potential uncertainty in ecosystem CH4 fluxes introduced by the classification of the surface area. This potential uncertainty ranged from 14.2 mg·m−2·day−1 to 26.8 mg·m−2·day−1. Our results show that a simple classification with only few classes potentially leads to pronounced bias in total ecosystem CH4 fluxes when plot-scale fluxes are up-scaled. Full article
(This article belongs to the Special Issue Remote Sensing of Biogeochemical Cycles)
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15 pages, 12352 KiB  
Article
Diurnal Variability of Turbidity Fronts Observed by Geostationary Satellite Ocean Color Remote Sensing
by Zifeng Hu, Delu Pan, Xianqiang He and Yan Bai
Remote Sens. 2016, 8(2), 147; https://doi.org/10.3390/rs8020147 - 16 Feb 2016
Cited by 41 | Viewed by 7590
Abstract
Monitoring front dynamics is essential for studying the ocean’s physical and biogeochemical processes. However, the diurnal displacement of fronts remains unclear because of limited in situ observations. Using the hourly satellite imageries from the Geostationary Ocean Color Imager (GOCI) with a spatial resolution [...] Read more.
Monitoring front dynamics is essential for studying the ocean’s physical and biogeochemical processes. However, the diurnal displacement of fronts remains unclear because of limited in situ observations. Using the hourly satellite imageries from the Geostationary Ocean Color Imager (GOCI) with a spatial resolution of 500 m, we investigated the diurnal displacement of turbidity fronts in both the northern Jiangsu shoal water (NJSW) and the southwestern Korean coastal water (SKCW) in the Yellow Sea (YS). The hourly turbidity fronts were retrieved from the GOCI-derived total suspended matter using the entropy-based algorithm. The results showed that the entropy-based algorithm could provide fine structure and clearly temporal evolution of turbidity fronts. Moreover, the diurnal displacement of turbidity fronts in NJSW can be up to 10.3 km in response to the onshore-offshore movements of tidal currents, much larger than it is in SKCW (around 4.7 km). The discrepancy between NJSW and SKCW are mainly caused by tidal current direction relative to the coastlines. Our results revealed the significant diurnal displacement of turbidity fronts, and highlighted the feasibility of using geostationary ocean color remote sensing technique to monitor the short-term frontal variability, which may contribute to understanding of the sediment dynamics and the coupling physical-biogeochemical processes. Full article
(This article belongs to the Special Issue Remote Sensing of Biogeochemical Cycles)
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17 pages, 1476 KiB  
Article
World’s Largest Macroalgal Blooms Altered Phytoplankton Biomass in Summer in the Yellow Sea: Satellite Observations
by Qianguo Xing, Chuanmin Hu, Danling Tang, Liqiao Tian, Shilin Tang, Xiao Hua Wang, Mingjing Lou and Xuelu Gao
Remote Sens. 2015, 7(9), 12297-12313; https://doi.org/10.3390/rs70912297 - 21 Sep 2015
Cited by 68 | Viewed by 8017
Abstract
Since 2008, the world’s largest blooms of the green macroalgae, Ulva prolifera, have occurred every summer in the Yellow Sea, posing the question of whether these macroalgal blooms (MABs) have changed the phytoplankton biomass due to their perturbations of nutrient dynamics. We [...] Read more.
Since 2008, the world’s largest blooms of the green macroalgae, Ulva prolifera, have occurred every summer in the Yellow Sea, posing the question of whether these macroalgal blooms (MABs) have changed the phytoplankton biomass due to their perturbations of nutrient dynamics. We have attempted to address this question using long-term Moderate Resolution Imaging Spectroradiometer (MODIS) observations. A new MODIS monthly time-series of chlorophyll-a concentrations (Chl-a, an index of phytoplankton biomass) was generated after removing the macroalgae-contaminated pixels that were characterized by unexpectedly high values in the daily Chl-a products. Compared with Chl-a during July of 2002–2006 (the pre-MAB period), Chl-a during July of 2008–2012 (the MAB period) exhibited significant increases in the offshore Yellow Sea waters (rich in macroalgae), with mean Chl-a increased by 98% from 0.64 µg/L to 1.26 µg/L in the study region. In contrast, no significant Chl-a changes were observed during June between the two periods. After analyzing sea surface temperature, photosynthetically available radiation, and nutrient availability, we speculate that the observed Chl-a changes are due to nutrient competition between macroalgae and phytoplankton: during the MAB period, the fast-growing macroalgae would uptake the increased nutrients from the origin of Jiangsu Shoal; thus, the nutrients available to phytoplankton were reduced, leading to no apparent increases in biomass in the offshore Yellow Sea in June. Full article
(This article belongs to the Special Issue Remote Sensing of Biogeochemical Cycles)
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