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Chen J.,Beijing Normal University | Zhu X.,Beijing Normal University | Zhu X.,Ohio State University | Vogelmann J.E.,Eros | And 3 more authors.
Remote Sensing of Environment | Year: 2011

The scan-line corrector (SLC) of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor failed in 2003, resulting in about 22% of the pixels per scene not being scanned. The SLC failure has seriously limited the scientific applications of ETM+ data. While there have been a number of methods developed to fill in the data gaps, each method has shortcomings, especially for heterogeneous landscapes. Based on the assumption that the same-class neighboring pixels around the un-scanned pixels have similar spectral characteristics, and that these neighboring and un-scanned pixels exhibit similar patterns of spectral differences between dates, we developed a simple and effective method to interpolate the values of the pixels within the gaps. We refer to this method as the Neighborhood Similar Pixel Interpolator (NSPI). Simulated and actual SLC-off ETM+ images were used to assess the performance of the NSPI. Results indicate that NSPI can restore the value of un-scanned pixels very accurately, and that it works especially well in heterogeneous regions. In addition, it can work well even if there is a relatively long time interval or significant spectral changes between the input and target image. The filled images appear reasonably spatially continuous without obvious striping patterns. Supervised classification using the maximum likelihood algorithm was done on both gap-filled simulated SLC-off data and the original "gap free" data set, and it was found that classification results, including accuracies, were very comparable. This indicates that gap-filled products generated by NSPI will have relevance to the user community for various land cover applications. In addition, the simple principle and high computational efficiency of NSPI will enable processing large volumes of SLC-off ETM+ data. © 2011.

Friedl M.A.,Boston University | Sulla-Menashe D.,Boston University | Tan B.,Earth Resources Technology Inc. | Schneider A.,University of Wisconsin - Madison | And 3 more authors.
Remote Sensing of Environment | Year: 2010

Information related to land cover is immensely important to global change science. In the past decade, data sources and methodologies for creating global land cover maps from remote sensing have evolved rapidly. Here we describe the datasets and algorithms used to create the Collection 5 MODIS Global Land Cover Type product, which is substantially changed relative to Collection 4. In addition to using updated input data, the algorithm and ancillary datasets used to produce the product have been refined. Most importantly, the Collection 5 product is generated at 500-m spatial resolution, providing a four-fold increase in spatial resolution relative to the previous version. In addition, many components of the classification algorithm have been changed. The training site database has been revised, land surface temperature is now included as an input feature, and ancillary datasets used in post-processing of ensemble decision tree results have been updated. Further, methods used to correct classifier results for bias imposed by training data properties have been refined, techniques used to fuse ancillary data based on spatially varying prior probabilities have been revised, and a variety of methods have been developed to address limitations of the algorithm for the urban, wetland, and deciduous needleleaf classes. Finally, techniques used to stabilize classification results across years have been developed and implemented to reduce year-to-year variation in land cover labels not associated with land cover change. Results from a cross-validation analysis indicate that the overall accuracy of the product is about 75% correctly classified, but that the range in class-specific accuracies is large. Comparison of Collection 5 maps with Collection 4 results show substantial differences arising from increased spatial resolution and changes in the input data and classification algorithm. © 2009 Elsevier Inc.

Skarke A.,Mississippi State University | Ruppel C.,U.S. Geological Survey | Kodis M.,Brown University | Brothers D.,U.S. Geological Survey | Lobecker E.,Earth Resources Technology Inc.
Nature Geoscience | Year: 2014

Methane emissions from the sea floor affect methane inputs into the atmosphere, ocean acidification and de-oxygenation, the distribution of chemosynthetic communities and energy resources. Global methane flux from seabed cold seeps has only been estimated for continental shelves, at 8 to 65 Tg CH 4 yr -1, yet other parts of marine continental margins are also emitting methane. The US Atlantic margin has not been considered an area of widespread seepage, with only three methane seeps recognized seaward of the shelf break. However, massive upper-slope seepage related to gas hydrate degradation has been predicted for the southern part of this margin, even though this process has previously only been recognized in the Arctic. Here we use multibeam water-column backscatter data that cover 94,000 km 2 of sea floor to identify about 570 gas plumes at water depths between 50 and 1,700 m between Cape Hatteras and Georges Bank on the northern US Atlantic passive margin. About 440 seeps originate at water depths that bracket the updip limit for methane hydrate stability. Contemporary upper-slope seepage there may be triggered by ongoing warming of intermediate waters, but authigenic carbonates observed imply that emissions have continued for more than 1,000 years at some seeps. Extrapolating the upper-slope seep density on this margin to the global passive margin system, we suggest that tens of thousands of seeps could be discoverable. © 2014 Macmillan Publishers Limited. All rights reserved.

Zou X.,Nanjing University of Information Science and Technology | Zou X.,Florida State University | Lin L.,Joint Center for Satellite Data Assimilation | Lin L.,Earth Resources Technology Inc. | Weng F.,National Oceanic and Atmospheric Administration
IEEE Transactions on Geoscience and Remote Sensing | Year: 2014

The absolute accuracy of antenna brightness temperatures (TDR) from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-orbiting Partnership satellite is estimated using the Constellation Observing System for Meteorology, Ionosphere, and Climate radio occultation (RO) data as input to the U.S. Joint Center of Satellite Data Assimilation community radiative transfer model (RTF). It is found that the mean differences (e.g., biases) of observed TDR observations to GPS RO simulations are positive for channels 6, 10-13 with values smaller than 0.5 K and negative for channels 5, 7-9 with values greater than ${-}{0.7}~{\rm K}$. The bias distribution is slightly asymmetric across the scan line. A line-by-line RTF is used to further understand the sources of errors in forward calculations. It is found that, for some channels, the bias can be further reduced in a magnitude of 0.3 K if the accurate line-by-line simulations are used. With the high quality of GPS RO observations and the accurate RTF, ATMS upper level temperature sounding channels are calibrated with known absolute accuracy. After the bias removal in ATMS TDR data, it is shown that the distribution of residual errors for ATMS channels 5-13 is close to a normal Gaussian one. Thus, for these channels, the ATMS antenna brightness temperature can be absolutely calibrated to the sensor brightness temperature without a systematic bias. © 1980-2012 IEEE.

Ganguly S.,Bay Area Environmental Research Institute | Friedl M.A.,Boston University | Tan B.,Earth Resources Technology Inc. | Zhang X.,Earth Resources Technology Inc. | Verma M.,Boston University
Remote Sensing of Environment | Year: 2010

Information related to land surface phenology is important for a variety of applications. For example, phenology is widely used as a diagnostic of ecosystem response to global change. In addition, phenology influences seasonal scale fluxes of water, energy, and carbon between the land surface and atmosphere. Increasingly, the importance of phenology for studies of habitat and biodiversity is also being recognized. While many data sets related to plant phenology have been collected at specific sites or in networks focused on individual plants or plant species, remote sensing provides the only way to observe and monitor phenology over large scales and at regular intervals. The MODIS Global Land Cover Dynamics Product was developed to support investigations that require regional to global scale information related to spatio-temporal dynamics in land surface phenology. Here we describe the Collection 5 version of this product, which represents a substantial refinement relative to the Collection 4 product. This new version provides information related to land surface phenology at higher spatial resolution than Collection 4 (500-m vs. 1-km), and is based on 8-day instead of 16-day input data. The paper presents a brief overview of the algorithm, followed by an assessment of the product. To this end, we present (1) a comparison of results from Collection 5 versus Collection 4 for selected MODIS tiles that span a range of climate and ecological conditions, (2) a characterization of interannual variation in Collections 4 and 5 data for North America from 2001 to 2006, and (3) a comparison of Collection 5 results against ground observations for two forest sites in the northeastern United States. Results show that the Collection 5 product is qualitatively similar to Collection 4. However, Collection 5 has fewer missing values outside of regions with persistent cloud cover and atmospheric aerosols. Interannual variability in Collection 5 is consistent with expected ranges of variance suggesting that the algorithm is reliable and robust, except in the tropics where some systematic differences are observed. Finally, comparisons with ground data suggest that the algorithm is performing well, but that end of season metrics associated with vegetation senescence and dormancy have higher uncertainties than start of season metrics. © 2010 Elsevier Inc.

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