首页期刊导航|Remote Sensing of Environment
期刊信息/Journal information
Remote Sensing of Environment
Elsevier
Remote Sensing of Environment

Elsevier

0034-4257

Remote Sensing of Environment/Journal Remote Sensing of EnvironmentSCIISTPEI
正式出版
收录年代

    Information fusion for GNSS-R wind speed retrieval using statistically modified convolutional neural network

    Guo, WenfeiDu, HaoGuo, ChiSouthwell, Benjamin J....
    17页
    查看更多>>摘要：Spaceborne global navigation satellite system reflectometry (GNSS-R) has recently been applied for wind speed retrieval over oceans, where the wind speed is often retrieved using features extracted from delay-Doppler map (DDM) and empirical geophysical model functions (GMFs). However, it is challenging to utilize the other factors related to the GNSS-R process, such as the geometry and sea state, as the input in GMF given their complicated effects. The use of a fully connected network (FCN) has been recently proposed, but the overfitting occurs at high wind speed due to the non-uniform wind distribution, and some information is undesirably forfeited due to artificially extracting features from DDMs. To this end, we propose a deep learning-based end-to-end modified convolutional neural network (CNN) model, which applies the cumulative distribution function (CDF) matching. A multimodal approach is utilized where the convolutional layers extract effective DDM features at first. Then, they are fused with the auxiliary information, including the geolocation of the specular point, incidence angle, range corrected gain (RCG), uncertainty of bistatic radar cross section (BRCS), and significant wave height (SWH). Further, multiple fully connected layers compose the remainder of the network's layers. At the last step, CDF matching is applied to correct the system deviation of CNN winds. We found that the root mean square error (RMSE) and bias of wind speed retrievals are 1.53 m/s and - 0.097 m/s, respectively, within 0-25 m/s when the proposed method is used. Notably, the bias is reduced by 51%, compared with the FCN architecture, while the RMSE of the retrievals at 12-25 m/s is improved by 12%. Moreover, the RMSE and bias against the incidence angle differ no more than 0.35 m/s and 0.078 m/s, while those against RCG differ less than 0.24 m/s and 0.061 m/s, respectively. Time-series analyses indicated that the wind speed retrievals of the proposed model are in line with the referenced wind speeds. We identified a temporal increase of the retrieval bias, driven by the downward trend of DDM observation in the cyclone global navigation satellite system (CYGNSS) Version 2.1 inadequately calibrated products.
      原文链接:
    • NSTL
    • Elsevier

    Assessment of OLCI-A and OLCI-B radiometric data products across European seas

    Zibordi, GiuseppeMelin, FredericTalone, MarcoCazzaniga, Ilaria...
    26页
    查看更多>>摘要：The Ocean and Land Color Instruments (OLCI) operated onboard the Copernicus Sentinel-3 satellites are providing globally distributed Ocean Color Radiometry (OCR) data products of relevance for environmental and climate applications. This work summarizes results on the assessment of fundamental OCR data from the Operational Baseline 3 Collection OL_L2M.003.01 of OLCI-A and OLCI-B onboard Sentinel-3A and Sentinel-3B, respectively. Evaluated products are the satellite derived normalized water-leaving radiance L-WN(lambda), aerosol optical depth at 865 nm tau(a)(865) and angstrom ngstrom exponent alpha determined in the near-infrared spectral region. The analyses were performed relying on in situ reference data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) from sites representative of diverse water types. The comparison of OLCI-A and OLCI-B with AERONET-OC L-WN(lambda) for oligotrophic/mesotrophic waters shows cross-mission consistent spectral median percent differences (i.e., biases) varying within +/- 6% at the blue-green center-wavelengths. The analysis of data from regions characterized by optically complex waters, however, displays systematic negative biases for both OLCI-A and OLCI-B further increasing for waters dominated by chromophoric dissolved organic matter, thus suggesting a dependence of the atmospheric correction on water type. The direct inter-comparison of OLCI-B and OLCI-A L-WN(lambda) from the Tandem Phase characterized by Sentinel-3B and Sentinel-3A flying 30 s apart on the same orbit, shows spectral median percent differences lower than +/- 1% in the 412-560 nm interval, of approximately +5% at 620 and 665 nm, and-7% at 400 nm. However, outside the Tandem Phase, the inter-comparison of OLCI-B and OLCI-A data products indicates large and systematic differences explained by a notable dependence on the viewing angle. The evaluation of tau(a)(865) and alpha across different geographic regions exhibits overestimated values between +48 and + 79% for the former and underestimated values between-28% and -41% for the latter. A complementary evaluation of OCR data products from the Visible Infrared Imager Radiometer Suite on board the Suomi National Polar-orbiting Partnership (VIIRS-S), proposed as a further in-direct term of reference for OLCI-A and OLCI-B data, shows large underestimates of L-WN(lambda) with respect to the in situ reference data in the various water types at 410 nm. Nevertheless, opposite to OLCI-A and OLCI-B data products, absolute differences between VIIRS-S and in situ reference data do not reveal any large or systematic dependence on water type and satellite viewing angle. Overall results suggest the need for further developing the OLCI-A and OLCI-B atmospheric correction, possibly improving the capability to identify aerosol types and to model scattering processes.
      原文链接:
    • NSTL
    • Elsevier

    Far-field bistatic scattering simulation for rice crop biophysical parameters retrieval using modified radiative transfer model at X- and C-band

    Yadav, Suraj A.Prasad, RajendraYadav, Vijay P.Verma, Bhagyashree...
    12页
    查看更多>>摘要：Dual-polarimetric (i.e., HH and VV) scattering responses at X-and C-bands from indigenously designed far-field bistatic specular (bi-spec) scatterometer acquired over the entire rice crop phenology have been analyzed using a modified parametric radiative transfer model (MRTM). The scattering responses are examined over a wideranging bi-spec incidence angle varying from 20? to 60? at 10? intervals. Furthermore, optimization of the bispec scatterometer system showed high sensitivity at 40? specular angle of incidence based on the correlation analysis between the measured value of bi-spec scattering coefficient (sigma(0)(Measured)) and vegetation biophysical parameters such as leaf area index (LAI) and plant water content (PWC). The MRTM implied to investigate the dominance of surface (sigma(0)(Surface)) and vegetation((sigma)(0)(Vegetation)) specular scattering components within the total value of simulated bi-spec scattering coefficient (sigma(0)(Simulated)) in forward scattering alignment (FSA) convention. The vegetation phase function (VPF) and a bi-directional reflectance distribution function (BRDF) are parameterized to approximate scattering responses from the vegetation volume layer and the surface beneath vegetation. In addition, empirical frequency-specific parameters (i.e., b(1 & nbsp;)and b(2)) are used to simulate temporal dynamics of sigma(0)(Simulated) using a linear relationship between vegetation optical depth (VOD) with LAI and PWC. The model and empirical frequency-specific parameters are calibrated using a constrained non-linear least square optimization algorithm, and the results are validated against the value of sigma(0)(Measured). According to the simulation findings, the total specular scattering decomposition offers a robust model for interpreting time-series microwave scattering scenarios through vegetation in the FSA convention. Moreover, as compared to C-band, the inverse modeling of MRTM showed high retrieval accuracies of LAI at VV polarization and PWC at HH polarization for the X-band.
      原文链接:
    • NSTL
    • Elsevier

    Different responses of surface freeze and thaw phenology changes to warming among Arctic permafrost types

    Chen, XingJeong, SujongPark, Chang-EuiPark, Hoonyoung...
    10页
    查看更多>>摘要：Arctic permafrost surface freeze-thaw (FT) changes related to warming could regulate the magnitude of global warming by altering the terrestrial carbon cycle and energy balances. This study investigated the sensitivity of surface FT changes to warming over Arctic permafrost regions by analyzing long-term changes in surface FT phenology from satellite remote sensing and meteorological variables from the climate data for the period from 1979 to 2017. Averaging over the entire Arctic permafrost regions, spring thawed date apparently advanced by -2.05 days decade-1, whereas autumn frozen date showed weak delaying trend of 0.83 days decade-1, implying the lengthening of the thawed season. Dividing the regions by permafrost types, advancing trends of thawed dates in continuous and high ice content permafrost areas (-2.57 and -2.70 days decade-1) were stronger than those over the discontinuous and low ice content permafrost areas (-1.61 and -1.73 days decade-1). The difference in changes in spring thawed dates between the regions is attributed to the difference in absolute magnitude of warming trends (e.g., 0.72 degrees C decade- 1 for continuous vs. 0.44 degrees C decade- 1 for discontinuous). However, the temperature sensitivity over discontinuous (low ice content) permafrost areas was 23% (10%) stronger than that over continuous (high ice content) permafrost areas for thawed date. In case of autumn, delaying trends of frozen dates were smaller over continuous and high ice content areas (0.69 and 0.74 days decade-1) than those over discontinuous and low ice content areas (1.01 and 0.88 days decade-1). This is mainly explained by the difference in temperature sensitivity (e.g., 1.57 days degrees C- 1 for continuous vs. 2.18 days degrees C- 1 for discontinuous) to warming between the regions rather than the difference in the absolute warming trends between the regions (e.g., 0.91 degrees C decade- 1 for continuous vs. 0.51 degrees C decade- 1 for discontinuous). The stronger temperature sensitivity of discontinuous and low ice content permafrost could be related to the lower demand of latent heat for the phase change of ground ice (or water). Overall, our results suggest that discontinuous and low ice content permafrost are more vulnerable to atmospheric warming. In addition to the magnitude of warming, the sensitivity to warming also needs to be considered when predicting permafrost FT changes.
      原文链接:
    • NSTL
    • Elsevier

    Oceanic internal wave amplitude retrieval from satellite images based on a data-driven transfer learning model

    Zhang, XudongWang, HaoyuWang, ShuoLiu, Yanliang...
    15页
    查看更多>>摘要：Internal waves (IW) are characterized by a large-amplitude, long-wave crest, and long-propagation distance. They are widespread in the global ocean. Amplitude is an essential IW parameter and is difficult to derive from the IW surface signatures in satellite images. A laboratory experiment and combined satellite/in-situ measurements were carried out to build two internal wave datasets (888 pairs of lab data and 121 pairs of synchronous in-situ data and satellite images). To efficiently use the lab data, we implemented a transfer learning model to retrieve IW amplitude from satellite images. The model is a purely data-driven model pre-trained with lab data and re-trained with satellite/in-situ data. A short connection was incorporated into the transfer learning framework to reduce information loss. Bias correction was adopted to improve the model performance. After the correction, the root mean square error (RMSE) of the estimated IW amplitude decreased from 12.09 m (17.84 m) to 9.59 m (11.59 m), the mean relative error decreased from 21% (27%) to 18% (16%), and the correlation coefficients improved from 0.81 (0.72) to 0.89 (0.90) on the test (training) dataset. For IWs with amplitude exceeding 100 m, the model can be expected to get an absolute error of 10 m. The mean relative error decreased with the increase in IW amplitudes. Comparisons with other algorithms demonstrate that the proposed model is efficient for IW studies. We applied the model to 156 satellite images containing IW signatures in the Andaman Sea, finding that large-amplitude IWs were mainly located at the water depth between 200 m and 1000 m on the continental slope. When considering one-pixel input errors for the peak-to-peak (PP) distance, the model shows large tolerance with the errors. Compared with the KdV equation-based method, the developed model was more accurate.
      原文链接:
    • NSTL
    • Elsevier

    An observation-based approach to calculating ice-shelf calving mass flux

    Evans, EleriFraser, Alexander D.Cook, SueColeman, Richard...
    10页
    查看更多>>摘要：In order to determine whether the calving flux of an ice shelf is changing, the long-term calving flux needs to be established. Methods used to estimate the calving flux either take into account non-steady-state behaviour by capturing movement of the calving-front location (e.g., using satellite observations), or they assume the calving front is stationary and that the ice is in steady state (e.g., flux-gate methods). Non-steady-state methods are hampered by the issue of temporal aliasing, i.e., when the satellite observation frequency is insufficient to capture the cyclic nature of the calving-front position. Methods that assume a steady state to estimate the calving flux accrue uncertainties if the ice shelf changes its physical state. In order to overcome these limitations we propose and implement a new observation-based approach that combines a time series of calving-front locations with a flux-gate method. The approach involves the creation of a unique semi-temporal domain as a mechanism to overcome the issue of temporal aliasing, and only requires easily accessible ice thickness and surface velocity estimates of the ice shelf. This approach allows for complex calving-front geometries and captures calving events of all sizes that are visible within the satellite imagery. Application of the approach allows the long-term average calving flux to be estimated (provided sufficient temporal coverage by satellite imagery), as well as identification of the minimum temporal baseline needed to produce a representative estimate of the long-term average calving flux, for any ice shelf. Implementation of the approach to multiple ice shelves would enable comparisons to be made regarding the spatial variability in the long-term calving flux of Antarctica's ice shelves, thereby highlighting calving regime change around the continent.
      原文链接:
    • NSTL
    • Elsevier

    Optimizing TRISHNA TIR channels configuration for improved land surface temperature and emissivity measurements

    Gamet, PhilippeJacob, FredericVidal, Thomas H. G.Olioso, Albert...
    16页
    查看更多>>摘要：In preparation of the Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) mission, we conducted a thorough analysis of sensitivity for the Temperature-Emissivity Separation (TES) method to the position of the four TRISHNA spectral channels, notably to find an optimal spectral configuration. To that purpose, we designed a fast-computing end-to-end simulator including several components, which we implemented to simulate both pixel-size TRISHNA measurements and land surface temperature (LST) retrievals. Firstly, simulations were conducted over a wide range of realistic scenarii, notably by including vegetation canopy-scale cavity effect. Secondly, the experimental design included the features of second generation Mercury-Cadmium-Telluride (MCT) cooled detectors with lower instrumental noises and finer channels. Thirdly, as opposed to previous studies that used predefined spectral configurations to determine the most suited one, we conducted an optimization of the spectral configuration by crossing, on a pair basis, several positions of the four TIR channels over a range of wavelengths. Fourthly, we quantified the TES sensitivity to atmospheric perturbations, by comparing LST retrievals with and without atmospheric noise. We observed an overall moderate sensitivity of TES LST retrievals to the spectral channel positions, with a maximum RMSE variation of 0.31 K within the atmospheric spectral windows. Furthermore, the TES method was sensitive to three main parameters, namely the instrumental noise, the atmospheric downwelling irradiance, and the transmittance due to ozone and water vapor, with RMSEs larger than 1 K for specific channel locations. Moreover, by considering possible superimposition of two channels, we noted that the TES method could achieve similar performance by considering three or four channels. Eventually, our study enabled us to recommend a new spectral configuration for the TRISHNA TIR instrument, that is more robust to atmospheric perturbations and to uncertainties on channel positions and bandwidths.
      原文链接:
    • NSTL
    • Elsevier

    Comprehensive LiDAR simulation with efficient physically-based DART-Lux model (I): Theory, novelty, and consistency validation

    Yang, XueboWang, YingjieYin, TiangangWang, Cheng...
    17页
    查看更多>>摘要：Light Detection And Ranging (LiDAR) remote sensing is increasingly needed to assess the 3D architecture of Earth's surface. Physically-based LiDAR radiative transfer (RT) models are essential tools for interpreting LiDAR signals, designing LiDAR systems, and validating information retrieval methods. Discrete Anisotropic Radiative Transfer (DART) is one of the most accurate and comprehensive 3D RT models that simulate LiDAR signals of urban and natural landscapes. Its physical modeling relies on a forward Monte Carlo mode optimized by a ray-tracking technique, also called DART-RC (Ray Carlo) mode. However, DART-RC is not adapted to simulate massive LiDAR signals of large landscapes due to its constraints of high memory demand and long computational time. Therefore, we developed a novel computationally efficient LiDAR modeling method based on a new DART modeling mode called DART-Lux. It simulates LiDAR signal by adapting the bidirectional path tracing algorithm of DART-Lux to the time and power measurements and by implementing the LiDAR instrument and multiple product outputs in DART-Lux. We verified the accuracy of DART-Lux for LiDAR modeling using DART-RC as a reference for several case studies with different LiDAR configurations (i.e., single-pulse waveform, multi-pulse point cloud, multi-pulse photon counting, with and without solar signal) on realistic scenes from the RAMI experiment. Results stress that i) DART-Lux is consistent with DART-RC, for example, R-2 = 1 and rRMSE = 0.21% for the waveform of a forest simulated with a huge number of rays; ii) DART-Lux converges faster than DART-RC: its processing time is usually about half that of DART-RC, and over ten times smaller if the solar signal is simulated; iii) DART-Lux memory usage can be a hundred times less than DART-RC. Also, several sensitivity studies with various sensor configurations and solar directions illustrate the usefulness of DART-Lux for impact studies. This new DART-Lux LiDAR model opens promising perspectives for large-scale LiDAR applications with 3D modeling. It is already part of the official DART version freely available to scientists (https://dart.omp.eu).
      原文链接:
    • NSTL
    • Elsevier

    Mapping causal agents of disturbance in boreal and arctic ecosystems of North America using time series of Landsat data

    Zhang, YingtongWoodcock, Curtis E.Chen, ShijuanWang, Jonathan A....
    18页
    查看更多>>摘要：The arctic and boreal biomes are changing as temperatures increase, including changes in the type, frequency, intensity, and seasonality of disturbances. However, our understanding of the frequency, extent, and causes of disturbance events remains incomplete. Disturbances such as fire, forest harvest, drought, wind, flooding, and insects and pathogens occur at different frequencies and severities, posing challenges to characterize and assess them under a single framework. We used the Continuous Change Detection and Classification (CCDC) algorithm on all available Landsat observations from 1984 to 2014 to detect land cover and land condition change. We mapped the following causes of disturbances annually across the study domain of NASA's Arctic Boreal Vulnerability Experiment (ABoVE): fire, logging, and pest damage. Differences between Landsat Tasseled Cap (TC) values pre- and post-disturbance were used in a random forest classifier to map causal agents. For forested ecosystems, we mapped causal agents including fire, insect, and logging. In areas that were not forest before disturbance, only the fire class was mapped. The result shows that multidimensional spectral-temporal change information is useful for mapping the causes of disturbance in arctic and boreal biomes. We employed two rounds of post-processing and used the information obtained from the comparison between the map and reference data to improve the final map. The user's and producer's accuracies of an aggregated disturbance map were 94.6% (+/- 2.37%) and 89.3% (+/- 21.78%) (95% confidence intervals in parenthesis). When evaluating the causal agents, insect damage was found the most challenging to map and validate. We estimated that 10.8% of the ABoVE core domain was disturbed between 1987 and 2012, with a margin of error of 0.5% at the 95% confidence level. Rates of disturbance due to logging remained constant over time, while fires were more episodic, and insect damage was highest between 2005 and 2010. Overall, fires affected 8.8% of the study area, while logging was 1.4% and insect damage 0.6%. Our maps indicate that pest damage became a significant issue after 2000, but it was more severe for forest ecosystems in Western Canada than in Alaska.
      原文链接:
    • NSTL
    • Elsevier

    Clarifications on the equations and the sample number in triple collocation analysis using SST observations

    Tsamalis, Christoforos
    18页
    查看更多>>摘要：The triple collocation (or three-way error) analysis is a widely used approach to infer the random uncertainty of three datasets measuring the same geophysical variable. It has been applied primarily for studying the quality of satellite observations for several variables, including sea surface temperature (SST) among others. Different algorithms have been developed in the literature to calculate the random uncertainties with sometimes distinct assumptions of the underlying error model or different equations. Four of the available triple collocation (TC) algorithms have been examined here in order to clarify the assumptions behind them and investigate their mathematical equivalence. In addition, triple collocations of SST observations from drifting buoys, and satellite observations from the ATSRs and microwave (MW) imagers have been used to examine the divergence among the TC algorithms with real data. It has been found that the maximum absolute difference is less than 0.009 K for the data considered here, indicating that the choice of TC algorithm is not significant to first order. This result permitted the investigation of the impact of the number of collocations by performing statistical experiments with real observations selecting them randomly without replacement. It is established that the ideal number is at least 500. Numbers below 200 on average would underestimate the true value of random uncertainties, while for numbers of about 100 or below numerical instabilities appear preventing the convergence to a solution with this issue becoming more acute as the number becomes smaller. For the SST instruments considered, the dependence of the random uncertainty on the sample number is described adequately by a theoretical equation given by Zwieback et al. (2012). This theoretical equation has been used to study the robustness of the differences in AMSR-E random uncertainties between day and night. The differences are mainly due to wind speed dependence, manifested also as latitudinal dependence. This is further confirmed when examining the variability of the random uncertainties with wind speed and SST. The AATSR random uncertainty slightly decreases linearly with wind speed, while AMSR-E increases by up to 50% between low and high wind speeds. A non-negligible dependence with SST has been found for both AATSR and drifting buoys with higher random uncertainty for SST in the range 10 degrees-15 degrees C. The AMSR-E random uncertainty decreases linearly with SST, when SST is above about 15 degrees C. Obviously, the dependence of the random uncertainty with the geophysical variable, here SST, violates one of the assumptions behind TC. Nevertheless, this can be mitigated through the application of TC into intervals of the geophysical variable for which the independence from the random uncertainty holds. These practical results unveil the real power of TC which based on the use of three collocated datasets is able to characterise their random uncertainties in a robust way, something extremely difficult to achieve by employing only two datasets without prior knowledge.
      原文链接:
    • NSTL
    • Elsevier