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Remote Sensing, Volume 9, Issue 12

December 2017 - 134 articles

Cover Story: Release of methane (CH4) from the Arctic can affect global climate. Predicting future CH4 emissions remains challenging because Arctic landscapes are characterized by high spatial heterogeneity with vegetation types, environmental conditions, and CH4 fluxes varying substantially over the meter scale. This large spatial heterogeneity requires the use of high resolution remote sensing imagery to upscale the chamber measurements to the ecosystem scale eddy covariance (EC) tower measurements. However, there is still disagreement on the methodologies used to perform this upscaling. We show that high resolution vegetation maps can be successfully integrated into both a simple upscaling technique and a more sophisticated footprint modelling analysis, and that these upscaled chamber-based CH4 fluxes using both methods showed good agreement with the EC flux estimates. View this paper
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Articles (134)

  • Article
  • Open Access
24 Citations
5,874 Views
19 Pages

Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau

  • Hui Liang,
  • Xiaodong Huang,
  • Yanhua Sun,
  • Yunlong Wang and
  • Tiangang Liang

19 December 2017

Moderate-resolution imaging spectroradiometer (MODIS) snow-cover products have relatively low accuracy over the Tibetan Plateau because of its complex terrain and shallow, fragmented snow cover. In this study, fractional snow-cover (FSC) mapping algo...

  • Article
  • Open Access
289 Citations
13,605 Views
18 Pages

19 December 2017

This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial features from hyperspectral images (HSIs). In the network, the issue of spectr...

  • Article
  • Open Access
42 Citations
7,626 Views
29 Pages

18 December 2017

Using deep learning to improve the capabilities of high-resolution satellite images has emerged recently as an important topic in automatic classification. Deep networks track hierarchical high-level features to identify objects; however, enhancing t...

  • Article
  • Open Access
56 Citations
12,247 Views
18 Pages

16 December 2017

Optical wavelength satellite data have directional reflectance effects over non-Lambertian surfaces, described by the bidirectional reflectance distribution function (BRDF). The Sentinel-2 multi-spectral instrument (MSI) acquires data over a 20.6° fi...

  • Article
  • Open Access
11 Citations
6,066 Views
20 Pages

Modeling the Observed Microwave Emission from Shallow Multi-Layer Tundra Snow Using DMRT-ML

  • Nastaran Saberi,
  • Richard Kelly,
  • Peter Toose,
  • Alexandre Roy and
  • Chris Derksen

16 December 2017

The observed brightness temperatures (Tb) at 37 GHz from typical moderate density dry snow in mid-latitudes decreases with increasing snow water equivalent (SWE) due to volume scattering of the ground emissions by the overlying snow. At a certain poi...

  • Article
  • Open Access
20 Citations
10,023 Views
38 Pages

16 December 2017

We have developed algorithms and procedures for calculating daily sea ice thickness (SIT) and open water–sea ice (OWSI) charts, based on the Moderate Resolution Imaging Spectroradiometer (MODIS), ice surface temperature (IST) (night-time only), and r...

  • Article
  • Open Access
35 Citations
7,422 Views
20 Pages

MODIS-Based Estimation of Terrestrial Latent Heat Flux over North America Using Three Machine Learning Algorithms

  • Xuanyu Wang,
  • Yunjun Yao,
  • Shaohua Zhao,
  • Kun Jia,
  • Xiaotong Zhang,
  • Yuhu Zhang,
  • Lilin Zhang,
  • Jia Xu and
  • Xiaowei Chen

16 December 2017

Terrestrial latent heat flux (LE) is a key component of the global terrestrial water, energy, and carbon exchanges. Accurate estimation of LE from moderate resolution imaging spectroradiometer (MODIS) data remains a major challenge. In this study, we...

  • Article
  • Open Access
16 Citations
6,779 Views
18 Pages

Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach

  • Pannimpullath R. Renosh,
  • Frédéric Jourdin,
  • Anastase A. Charantonis,
  • Khalil Yala,
  • Aurélie Rivier,
  • Fouad Badran,
  • Sylvie Thiria,
  • Nicolas Guillou,
  • Fabien Leckler and
  • Francis Gohin
  • + 1 author

15 December 2017

Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we...

  • Article
  • Open Access
11 Citations
7,464 Views
12 Pages

Utilization of Integrated Geophysical Techniques to Delineate the Extraction of Mining Bench of Ornamental Rocks (Marble)

  • Julián Martínez,
  • Violeta Montiel,
  • Javier Rey,
  • Francisco Cañadas and
  • Pedro Vera

15 December 2017

Low yields in ornamental rock mining remain one of the most important problems in this industry. This fact is usually associated with the presence of anisotropies in the rock, which makes it difficult to extract the blocks. An optimised planning of t...

  • Article
  • Open Access
28 Citations
9,539 Views
22 Pages

15 December 2017

Phytoplankton pigments absorb sunlight for photosynthesis, protect the chloroplast from damage caused by excess light energy, and influence the color of the water. Some pigments act as bio-markers and are important for separation of phytoplankton fun...

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Remote Sens. - ISSN 2072-4292