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Remote Sens., Volume 9, Issue 12 (December 2017)

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Cover Story (view full-size image) Release of methane (CH4) from the Arctic can affect global climate. Predicting future CH4 emissions [...] Read more.
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Open AccessArticle Analyzing the Long-Term Phenological Trends of Salt Marsh Ecosystem across Coastal LOUISIANA
Remote Sens. 2017, 9(12), 1340; https://doi.org/10.3390/rs9121340
Received: 14 October 2017 / Revised: 2 December 2017 / Accepted: 2 December 2017 / Published: 20 December 2017
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Abstract
In this study, we examined the phenology of the salt marsh ecosystem across coastal Louisiana (LA) for a 16-year time period (2000–2015) using NASA’s Moderate Resolution Imaging Spectroradiometer’s (MODIS) eight-day average surface reflectance images (500 m). We compared the performances of least squares
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In this study, we examined the phenology of the salt marsh ecosystem across coastal Louisiana (LA) for a 16-year time period (2000–2015) using NASA’s Moderate Resolution Imaging Spectroradiometer’s (MODIS) eight-day average surface reflectance images (500 m). We compared the performances of least squares fitted asymmetric Gaussian (AG) and double logistic (DL) smoothing functions in terms of increasing the signal-to-noise ratio from the raw phenology derived from the time-series composites. We performed derivative analysis to determine the appropriate start of season (SOS) and end of season (EOS) thresholds. After that, we extracted the seasonality parameters in TIMESAT, and studied the effect of environmental disturbances/anomalies on the seasonality parameters. Finally, we performed trend analysis using the derived seasonality parameters such as base green biomass (GBM) value, maximum GBM value, seasonal amplitude, and small seasonal integral. Based on root mean square error (RMSE) values and residual plots, we selected the best thresholds for SOS (5% of amplitude) and EOS (20% of amplitude), along with the best smoothing function. The selected SOS and EOS thresholds were able to capture the environmental disturbances that have affected the salt marsh ecosystem during the 16-year time period. Our trend analysis results indicate positive trends in the base GBM values in the salt marshes of LA. However, we did not notice as much of a positive trend in the maximum GBM levels. Hence, we observed mostly negative changes in the GBM amplitude and small seasonal integral values. These negative changes indicated the overall progressive decline in the rates of photosynthesis and biomass allocation in the LA salt marsh ecosystem, which is most likely due to elevated atmospheric carbon dioxide levels and sea level rise. The results illustrate both the relative efficiency of MODIS-based biophysical models for analyzing salt marsh phenology, and performances of the smoothing techniques in terms of improving the signal-to-noise ratio of the MODIS-derived phenology. Full article
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Open AccessArticle High-Resolution Mapping of Freeze/Thaw Status in China via Fusion of MODIS and AMSR2 Data
Remote Sens. 2017, 9(12), 1339; https://doi.org/10.3390/rs9121339
Received: 30 October 2017 / Revised: 30 November 2017 / Accepted: 13 December 2017 / Published: 20 December 2017
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Abstract
Transition of freeze/thaw (F/T) affects land-atmospheric interactions and other biospheric dynamics. Global F/T statuses are normally monitored using microwave remote sensing, but at coarse resolutions (e.g., 25 km). Integration of coarse microwave remote sensing data with finer satellite products represents an opportunity to
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Transition of freeze/thaw (F/T) affects land-atmospheric interactions and other biospheric dynamics. Global F/T statuses are normally monitored using microwave remote sensing, but at coarse resolutions (e.g., 25 km). Integration of coarse microwave remote sensing data with finer satellite products represents an opportunity to further enhance our ability to map F/T statuses regionally and globally. Here, we implemented and tested an approach to generate daily F/T status maps at a 5-km spatial resolution through the fusion of passive microwave data from AMSR2 and land surface temperature products from MODIS, using China as our study area for the year 2013 and 2014. Moreover, possible influences from elevation, vegetation, seasonality, etc., were also analyzed, as such analysis provides a direction to improve the approach. Overall, our freeze/thaw maps agreed well with ground reference observations, with an accuracy of ~86.6%. The new F/T maps helped to identify regions subject to frequent F/T transitions through the year, such as the Qinghai-Tibetan Plateau, Xinjiang, Gansu, Heilongjiang, Jilin, and Liaoning Province. This study indicates that the combination of AMSR2 and MODIS observations provides an effective method to obtain finer F/T maps (5-km or lower) for extensive regions. The finer F/T maps improve our knowledge of the F/T state detected by satellite remote sensing, and have a wide range of applications in regional studies considering land surface heterogeneity and models (e.g., community land models). Full article
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Open AccessArticle Short-Term Impacts of the Air Temperature on Greening and Senescence in Alaskan Arctic Plant Tundra Habitats
Remote Sens. 2017, 9(12), 1338; https://doi.org/10.3390/rs9121338
Received: 31 October 2017 / Revised: 12 December 2017 / Accepted: 14 December 2017 / Published: 20 December 2017
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Abstract
Climate change is warming the temperatures and lengthening the Arctic growing season with potentially important effects on plant phenology. The ability of plant species to acclimate to changing climatic conditions will dictate the level to which their spatial coverage and habitat-type dominance is
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Climate change is warming the temperatures and lengthening the Arctic growing season with potentially important effects on plant phenology. The ability of plant species to acclimate to changing climatic conditions will dictate the level to which their spatial coverage and habitat-type dominance is different in the future. While the effect of changes in temperature on phenology and species composition have been observed at the plot and at the regional scale, a systematic assessment at medium spatial scales using new noninvasive sensor techniques has not been performed yet. At four sites across the North Slope of Alaska, changes in the Normalized Difference Vegetation Index (NDVI) signal were observed by Mobile Instrumented Sensor Platforms (MISP) that are suspended over 50 m transects spanning local moisture gradients. The rates of greening (measured in June) and senescence (measured in August) in response to the air temperature was estimated by changes in NDVI measured as the difference between the NDVI on a specific date and three days later. In June, graminoid- and shrub-dominated habitats showed the greatest rates of NDVI increase in response to the high air temperatures, while forb- and lichen-dominated habitats were less responsive. In August, the NDVI was more responsive to variations in the daily average temperature than spring greening at all sites. For graminoid- and shrub-dominated habitats, we observed a delayed decrease of the NDVI, reflecting a prolonged growing season, in response to high August temperatures. Consequently, the annual C assimilation capacity of these habitats is increased, which in turn may be partially responsible for shrub expansion and further increases in net summer CO2 fixation. Strong interannual differences highlight that long-term and noninvasive measurements of such complex feedback mechanisms in arctic ecosystems are critical to fully articulate the net effects of climate variability and climate change on plant community and ecosystem processes. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Open AccessArticle Experimental Evaluation of Several Key Factors Affecting Root Biomass Estimation by 1500 MHz Ground-Penetrating Radar
Remote Sens. 2017, 9(12), 1337; https://doi.org/10.3390/rs9121337
Received: 3 November 2017 / Revised: 11 December 2017 / Accepted: 17 December 2017 / Published: 20 December 2017
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Abstract
Accurate quantification of coarse roots without disturbance represents a gap in our understanding of belowground ecology. Ground penetrating radar (GPR) has shown significant promise for coarse root detection and measurement, however root orientation relative to scanning transect direction, the difficulty identifying dead root
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Accurate quantification of coarse roots without disturbance represents a gap in our understanding of belowground ecology. Ground penetrating radar (GPR) has shown significant promise for coarse root detection and measurement, however root orientation relative to scanning transect direction, the difficulty identifying dead root mass, and the effects of root shadowing are all key factors affecting biomass estimation that require additional research. Specifically, many aspects of GPR applicability for coarse root measurement have not been tested with a full range of antenna frequencies. We tested the effects of multiple scanning directions, root crossover, and root versus soil moisture content in a sand-hill mixed oak community using a 1500 MHz antenna, which provides higher resolution than the oft used 900 MHz antenna. Combining four scanning directions produced a significant relationship between GPR signal reflectance and coarse root biomass (R2 = 0.75) (p < 0.01) and reduced variability encountered when fewer scanning directions were used. Additionally, significantly fewer roots were correctly identified when their moisture content was allowed to equalize with the surrounding soil (p < 0.01), providing evidence to support assertions that GPR cannot reliably identify dead root mass. The 1500 MHz antenna was able to identify roots in close proximity of each other as well as roots shadowed beneath shallower roots, providing higher precision than a 900 MHz antenna. As expected, using a 1500 MHz antenna eliminates some of the deficiency in precision observed in studies that utilized lower frequency antennas. Full article
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Open AccessArticle High Spatial Resolution Visual Band Imagery Outperforms Medium Resolution Spectral Imagery for Ecosystem Assessment in the Semi-Arid Brazilian Sertão
Remote Sens. 2017, 9(12), 1336; https://doi.org/10.3390/rs9121336
Received: 17 October 2017 / Revised: 3 December 2017 / Accepted: 10 December 2017 / Published: 20 December 2017
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Abstract
Semi-arid ecosystems play a key role in global agricultural production, seasonal carbon cycle dynamics, and longer-run climate change. Because semi-arid landscapes are heterogeneous and often sparsely vegetated, repeated and large-scale ecosystem assessments of these regions have to date been impossible. Here, we assess
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Semi-arid ecosystems play a key role in global agricultural production, seasonal carbon cycle dynamics, and longer-run climate change. Because semi-arid landscapes are heterogeneous and often sparsely vegetated, repeated and large-scale ecosystem assessments of these regions have to date been impossible. Here, we assess the potential of high-spatial resolution visible band imagery for semi-arid ecosystem mapping. We use WorldView satellite imagery at 0.3–0.5 m resolution to develop a reference data set of nearly 10,000 labeled examples of three classes—trees, shrubs/grasses, and bare land—across 1000 km 2 of the semi-arid Sertão region of northeast Brazil. Using Google Earth Engine, we show that classification with low-spectral but high-spatial resolution input (WorldView) outperforms classification with the full spectral information available from Landsat 30 m resolution imagery as input. Classification with high spatial resolution input improves detection of sparse vegetation and distinction between trees and seasonal shrubs and grasses, two features which are lost at coarser spatial (but higher spectral) resolution input. Our total tree cover estimates for the study area disagree with recent estimates using other methods that may underestimate treecover because they confuse trees with seasonal vegetation (shrubs and grasses). This distinction is important for monitoring seasonal and long-run carbon cycle and ecosystem health. Our results suggest that newer remote sensing products that promise high frequency global coverage at high spatial but lower spectral resolution may offer new possibilities for direct monitoring of the world’s semi-arid ecosystems, and we provide methods that could be scaled to do so. Full article
(This article belongs to the Special Issue Google Earth Engine Applications)
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Open AccessArticle Performance Assessment of Tailored Split-Window Coefficients for the Retrieval of Lake Surface Water Temperature from AVHRR Satellite Data
Remote Sens. 2017, 9(12), 1334; https://doi.org/10.3390/rs9121334
Received: 18 October 2017 / Revised: 2 December 2017 / Accepted: 14 December 2017 / Published: 20 December 2017
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Abstract
Although lake surface water temperature (LSWT) is defined as an essential climate variable (ECV) within the global climate observing system (GCOS), current satellite-based retrieval techniques do not fulfill the GCOS accuracy requirements. The split-window (SW) retrieval method is well-established, and the split-window coefficients
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Although lake surface water temperature (LSWT) is defined as an essential climate variable (ECV) within the global climate observing system (GCOS), current satellite-based retrieval techniques do not fulfill the GCOS accuracy requirements. The split-window (SW) retrieval method is well-established, and the split-window coefficients (SWC) are the key elements of its accuracy. Performances of SW depends on the degree of SWC customization with respect to its application, where accuracy increases when SWC is tailored for specific situations. In the literature, different SWC customization approaches have been investigated, however, no direct comparisons have been conducted among them. This paper presents the results of a sensitivity analysis to address this gap. We show that the performance of SWC is most sensitive to customizations for specific time-windows (Sensitivity Index SI of 0.85) or spatial extents (SI 0.27). Surprisingly, the study highlights that the use of separated SWC for daytime and night-time situations has limited impact (SI 0.10). The final validation with AVHRR satellite data showed that the subtle differences among different SWC customizations were not traceable to the final uncertainty of the LSWT product. Nevertheless, this study provides a basis to critically evaluate current assumptions regarding SWC generation by directly comparing the performance of multiple customization approaches for the first time. Full article
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Open AccessFeature PaperArticle A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data
Remote Sens. 2017, 9(12), 1333; https://doi.org/10.3390/rs9121333
Received: 30 September 2017 / Revised: 13 December 2017 / Accepted: 14 December 2017 / Published: 20 December 2017
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Abstract
Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in
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Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. We present a novel method to fully reconstruct MODIS daily LST products for central Europe at 1 km resolution and globally, at 3 arc-min. We combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. The reconstructed MODIS LST for central Europe was calibrated to air temperature data through linear models that yielded R2 values around 0.8 and RMSE of 0.5 K. This new method proves to scale well for both local and global reconstruction. We show examples for the identification of extreme events to demonstrate the ability of these new LST products to capture and represent spatial and temporal details. A time series of global monthly average, minimum and maximum LST data and long-term averages is freely available for download. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessTechnical Note Land-Air Interactions over Urban-Rural Transects Using Satellite Observations: Analysis over Delhi, India from 1991–2016
Remote Sens. 2017, 9(12), 1283; https://doi.org/10.3390/rs9121283
Received: 28 September 2017 / Revised: 23 November 2017 / Accepted: 7 December 2017 / Published: 20 December 2017
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Abstract
Over the past four decades Delhi, India, has witnessed rapid urbanization and change in land use land cover (LULC) pattern, with most of the cultivable areas and wasteland being converted into built-up areas. Presently around 40% land is under built-up area, a drastic
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Over the past four decades Delhi, India, has witnessed rapid urbanization and change in land use land cover (LULC) pattern, with most of the cultivable areas and wasteland being converted into built-up areas. Presently around 40% land is under built-up area, a drastic rise of 30% from 1977. The effect of changing LULC, at a local scale, on various variables-land surface temperature (LST), normalized difference vegetation index (NDVI), emissivity, albedo, evaporation, Bowen ratio, and planetary boundary layer (PBL) height, from 1991–2016, is investigated. To assess the spatio-temporal dynamics of land-air interactions, we select two different 100 km transects covering the NE-SW and NW-SE expanse of Delhi and its adjoining areas. High NDVI and emissivity is found for regions with green cover and drastic reduction is noted in built-up area clusters. In both of the transects, land surface variations manifest itself in patterns of LST variation. Parametric and non-parametric correlations are able to statistically establish the land-air interactions in the city. NDVI, an indirect indicator for LULC classes, significantly helps in understanding the modifications in LST and ultimately air temperature. Significant, strong positive relationships exist between skin temperature and evaporation, skin temperature and PBL height, and PBL height and evaporation, providing insights into the meteorological changes that are associated with urbanization. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau
Remote Sens. 2017, 9(12), 1332; https://doi.org/10.3390/rs9121332
Received: 20 October 2017 / Revised: 6 December 2017 / Accepted: 17 December 2017 / Published: 19 December 2017
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Abstract
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 algorithms were developed using a linear regression model (LR), a linear spectral
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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 algorithms were developed using a linear regression model (LR), a linear spectral mixture analysis model (LSMA) and a back-propagation artificial neural network model (BP-ANN) based on MODIS data (version 006) and unmanned aerial vehicle (UAV) data. The accuracies of the three models were validated against Landsat 8 Operational Land Imager (OLI) snow-cover maps (Landsat 8 FSC) and compared with the MODIS global FSC product (MOD10A1 FSC, version 005) for the purpose of finding the optimal algorithm for FSC extraction for the Tibetan Plateau. The results showed that (1) the overall retrieval results of the LR and BP-ANN models based on MODIS and UAV data were relatively similar to the OLI snow-cover maps; the accuracy and stability were greatly improved, with even some reduction in errors; compared to the Landsat 8 FSC, the correlation coefficients (r) were 0.8222 and 0.8445 respectively and the root-mean-square errors (RMSEs) were 0.2304 and 0.2201, respectively. (2) The accuracy and stability of the fully constrained LSMA model using the pixel purity index (PPI) endmember extraction method based only on MODIS data suffered the worst performance of the three models; r was only 0.7921 and the RMSE was as large as 0.3485. There were some serious omission phenomena in the study area, specifically for the largest mean absolute error (MAE = 0.2755) and positive mean error (PME = 0.3411). (3) The accuracy of the MOD10A1 FSC product was much lower than that of the LR and BP-ANN models, although its accuracy slightly better that of the LSMA based on comprehensive evaluation of six accuracy indices. (4) The optimal model was the BP-ANN model with combined inputs of surface reflectivity data (R1–R7), elevation (DEM) and temperature (LST), which can easily incorporate auxiliary information (DEM and LST) on the basis of (R1–R7) during the relationship training period and can effectively improve the accuracy of snow area monitoring—it is the ideal algorithm for retrieving FSC for the Tibetan Plateau. Full article
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Open AccessArticle Comparative Study on Assimilating Remote Sensing High Frequency Radar Surface Currents at an Atlantic Marine Renewable Energy Test Site
Remote Sens. 2017, 9(12), 1331; https://doi.org/10.3390/rs9121331
Received: 18 October 2017 / Revised: 7 December 2017 / Accepted: 12 December 2017 / Published: 19 December 2017
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Abstract
A variety of data assimilation approaches have been applied to enhance modelling capability and accuracy using observations from different sources. The algorithms have varying degrees of complexity of implementation, and they improve model results with varying degrees of success. Very little work has
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A variety of data assimilation approaches have been applied to enhance modelling capability and accuracy using observations from different sources. The algorithms have varying degrees of complexity of implementation, and they improve model results with varying degrees of success. Very little work has been carried out on comparing the implementation of different data assimilation algorithms using High Frequency radar (HFR) data into models of complex inshore waters strongly influenced by both tides and wind dynamics, such as Galway Bay. This research entailed implementing four different data assimilation algorithms: Direct Insertion (DI), Optimal Interpolation (OI), Nudging and indirect data assimilation via correcting model forcing into a three-dimensional hydrodynamic model and carrying out detailed comparisons of model performances. This work will allow researchers to directly compare four of the most common data assimilation algorithms being used in operational coastal hydrodynamics. The suitability of practical data assimilation algorithms for hindcasting and forecasting in shallow coastal waters subjected to alternate wetting and drying using data collected from radars was assessed. Results indicated that a forecasting system of surface currents based on the three-dimensional model EFDC (Environmental Fluid Dynamics Code) and the HFR data using a Nudging or DI algorithm was considered the most appropriate for Galway Bay. The largest averaged Data Assimilation Skill Score (DASS) over the ≥6 h forecasting period from the best model NDA attained 26% and 31% for east–west and north–south surface velocity components respectively. Because of its ease of implementation and its accuracy, this data assimilation system can provide timely and useful information for various practical coastal hindcast and forecast operations. Full article
(This article belongs to the Special Issue Radar Remote Sensing of Oceans and Coastal Areas)
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Open AccessArticle Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification
Remote Sens. 2017, 9(12), 1330; https://doi.org/10.3390/rs9121330
Received: 12 November 2017 / Revised: 2 December 2017 / Accepted: 14 December 2017 / Published: 19 December 2017
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Abstract
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 spectral feature extraction is considered as a sequence learning problem, and
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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 spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN), a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. In addition, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a Softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with six state-of-the-art methods, including the popular 3D-CNN model, on three widely used HSIs (i.e., Indian Pines, Pavia University, and Kennedy Space Center). The obtained results show that Bi-CLSTM can improve the classification performance by almost 1.5 % as compared to 3D-CNN. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Response of Grassland Degradation to Drought at Different Time-Scales in Qinghai Province: Spatio-Temporal Characteristics, Correlation, and Implications
Remote Sens. 2017, 9(12), 1329; https://doi.org/10.3390/rs9121329
Received: 28 October 2017 / Revised: 4 December 2017 / Accepted: 17 December 2017 / Published: 19 December 2017
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Abstract
Grassland, as the primary vegetation on the Qinghai-Tibet Plateau, has been increasingly influenced by water availability due to climate change in last decades. Therefore, identifying the evolution of drought becomes crucial to the efficient management of grassland. However, it is not yet well
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Grassland, as the primary vegetation on the Qinghai-Tibet Plateau, has been increasingly influenced by water availability due to climate change in last decades. Therefore, identifying the evolution of drought becomes crucial to the efficient management of grassland. However, it is not yet well understood as to the quantitative relationship between vegetation variations and drought at different time scales. Taking Qinghai Province as a case, the effects of meteorological drought on vegetation were investigated. Multi-scale Standardized Precipitation Evapotranspiration Index (SPEI) considering evapotranspiration variables was used to indicate drought, and time series Normal Difference Vegetation Index (NDVI) to indicate the vegetation response. The results showed that SPEI values at different time scales reflected a complex dry and wet variation in this region. On a seasonal scale, more droughts occurred in summer and autumn. In general, the NDVI presented a rising trend in the east and southwest part and a decreasing trend in the northwest part of Qinghai Province from 1998 to 2012. Hurst indexes of NDVI revealed that 69.2% of the total vegetation was positively persistent (64.1% of persistent improvement and 5.1% of persistent degradation). Significant correlations were found for most of the SPEI values and the one year lagged NDVI, indicating vegetation made a time-lag response to drought. In addition, one month lagged NDVI made an obvious response to SPEI values at annual and biennial scales. Further analysis showed that all multiscale SPEI values have positive relationships with the NDVI trend and corresponding grassland degradation. The study highlighted the response of vegetation to meteorological drought at different time scales, which is available to predict vegetation change and further help to improve the utilization efficiency of water resources in the study region. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Water Resources in a Changing Climate)
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Open AccessArticle GAN-Assisted Two-Stream Neural Network for High-Resolution Remote Sensing Image Classification
Remote Sens. 2017, 9(12), 1328; https://doi.org/10.3390/rs9121328
Received: 17 October 2017 / Revised: 30 November 2017 / Accepted: 16 December 2017 / Published: 18 December 2017
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Abstract
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 the classification accuracy from low-level features is often disregarded. We therefore
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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 the classification accuracy from low-level features is often disregarded. We therefore proposed a two-stream deep-learning neural network strategy, with a main stream utilizing fine spatial-resolution panchromatic images to retain low-level information under a supervised residual network structure. An auxiliary line employed an unsupervised net to extract high-level abstract and discriminative features from multispectral images to supplement the spectral information in the main stream. Various feature extraction types from the neural network were selected and jointed in the novel net, as the combined high- and low-level features could provide a superior solution to image classification. In traditional convolutional neural networks, increased network depth might not influence the network performance perceptibly; however, we introduced a residual neural network to develop the expressive ability of the deeper net, increasing the role of net depth in feature extraction. To enhance feature robustness, we proposed a novel consolidation part in feature extraction. An adversarial net improved the feature extraction capabilities and aided digging the inherent and discriminative features from data, with increased extraction efficacy. Tests on satellite images indicated the high overall accuracy of our novel net, verifying that net depth or number of convolution kernels affected the classification capability. Various comparative tests proved the structural rationality for our two-stream structure. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Modeling the Observed Microwave Emission from Shallow Multi-Layer Tundra Snow Using DMRT-ML
Remote Sens. 2017, 9(12), 1327; https://doi.org/10.3390/rs9121327
Received: 1 November 2017 / Revised: 7 December 2017 / Accepted: 13 December 2017 / Published: 16 December 2017
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Abstract
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 point, however, as SWE increases,
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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 point, however, as SWE increases, the emission from the snowpack offsets the scattering of the sub-nivean emission. In tundra snow, the Tb slope reversal occurs at shallower snow thicknesses. While it has been postulated that the inflection point in the seasonal time series of observed Tb V 37 GHz of tundra snow is controlled by the formation of a thick wind slab layer, the simulation of this effect has yet to be confirmed. Therefore, the Dense Media Radiative Transfer Theory for Multi Layered (DMRT-ML) snowpack is used to predict the passive microwave response from airborne observations over shallow, dense, slab-layered tundra snow. Airborne radiometer observations coordinated with ground-based in situ snow measurements were acquired in the Canadian high Arctic near Eureka, NT, in April 2011. The DMRT-ML was parameterized with the in situ snow measurements using a two-layer snowpack and run in two configurations: a depth hoar and a wind slab dominated pack. With these two configurations, the calibrated DMRT-ML successfully predicted the Tb V 37 GHz response (R correlation of 0.83) when compared with the observed airborne Tb footprints containing snow pits measurements. Using this calibrated model, the DMRT-ML was applied to the whole study region. At the satellite observation scale, observations from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) over the study area reflected seasonal differences between Tb V 37 GHz and Tb V 19 GHz that supports the hypothesis of the development of an early season volume scattering depth hoar layer, followed by the growth of the late season emission-dominated wind slab layer. This research highlights the necessity to consider the two-part emission characteristics of a slab-dominated tundra snowpack at 37 GHz Tb. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Open AccessArticle MODIS-Based Estimation of Terrestrial Latent Heat Flux over North America Using Three Machine Learning Algorithms
Remote Sens. 2017, 9(12), 1326; https://doi.org/10.3390/rs9121326
Received: 3 October 2017 / Revised: 18 November 2017 / Accepted: 14 December 2017 / Published: 16 December 2017
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Abstract
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 estimated the daily LE for different
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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 estimated the daily LE for different plant functional types (PFTs) across North America using three machine learning algorithms: artificial neural network (ANN); support vector machines (SVM); and, multivariate adaptive regression spline (MARS) driven by MODIS and Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorology data. These three predictive algorithms, which were trained and validated using observed LE over the period 2000–2007, all proved to be accurate. However, ANN outperformed the other two algorithms for the majority of the tested configurations for most PFTs and was the only method that arrived at 80% precision for LE estimation. We also applied three machine learning algorithms for MODIS data and MERRA meteorology to map the average annual terrestrial LE of North America during 2002–2004 using a spatial resolution of 0.05°, which proved to be useful for estimating the long-term LE over North America. Full article
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