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Remote Sensing of Environment
Elsevier
Remote Sensing of Environment

Elsevier

0034-4257

Remote Sensing of Environment/Journal Remote Sensing of EnvironmentSCIISTPEI
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    Towards consistent assessments of in situ radiometric measurements for the validation of fluorescence satellite missions

    Buman, BastianHueni, AndreasColombo, RobertoCogliati, Sergio...
    17页
    查看更多>>摘要：The upcoming Fluorescence Explorer (FLEX) satellite mission aims to provide high quality radiometric measurements for subsequent retrieval of sun-induced chlorophyll fluorescence (SIF). The combination of SIF with other observations stemming from the FLEX/Sentinel-3 tandem mission holds the potential to assess complex ecosystem processes. The calibration and validation (cal/val) of these radiometric measurements and derived products are central but challenging components of the mission. This contribution outlines strategies for the assessment of in situ radiometric measurements and retrieved SIF. We demonstrate how in situ spectrometer measurements can be analysed in terms of radiometric, spectral and spatial uncertainties. The analysis of more than 200 k spectra yields an average bias between two radiometric measurements by two individual spectrometers of 8%, with a larger variability in measurements of downwelling radiance (25%) compared to upwelling radiance (6%). Spectral shifts in the spectrometer relevant for SIF retrievals are consistently below 1 spectral pixel (up to 0.75). Found spectral shifts appear to be mostly dependent on temperature (as measured by a temperature probe in the instrument). Retrieved SIF shows a low variability of 1.8% compared with a noise reduced SIF estimate based on APAR. A combination of airborne imaging and in situ non-imaging fluorescence spectroscopy highlights the importance of a homogenous sampling surface and holds the potential to further uncover SIF retrieval issues as here shown for early evening acquisitions. Our experiments clearly indicate the need for careful site selection, measurement protocols, as well as the need for harmonized processing. This work thus contributes to guiding cal/val activities for the upcoming FLEX mission.
      原文链接:
    • NSTL
    • Elsevier

    Estimate of daytime single-layer cloud base height from advanced baseline imager measurements

    Lin, HanLi, ZhenglongLi, JunZhang, Feng...
    15页
    查看更多>>摘要：Cloud base height (CBH) is an important parameter to describe cloud state and is highly related to the vertical motions in the atmosphere. CBH information is critical for both aviation safety and synoptic analysis. In this study, daytime CBH is estimated directly from Geostationary Operational Environmental Satellite-R Series (GOES-16) Advanced Baseline Imager (ABI) level lb data and the European Centre for Medium-Range Weather Forecasts' (ECMWF) fifth generation reanalysis (ERAS) data using the Gradient Boosted Regression Trees (GBRT) machine learning technique. The CBH estimate algorithm, which is named as GETCBH, covers the same areal extent as the full disk of the ABI/GOES-16 and only for single-layer clouds. The 2-years of CBH measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite is used as the label (which is the true value/class of the model output for regression/classification problem in machine learning terminology). A quality flag algorithm using another machine learning technique, the Gradient Boosted Decision Trees machine learning technique is developed to provide a confidence level for the CBH estimate. The evaluations show an overall root mean square error (RMSE) of 1.87 km and Pearson's correlation coefficient (Pearson's r) of 0.92 before any quality control. After excluding CBH estimates with low confidence (19.2% of all samples), the RMSE is reduced to 1.14 km, Pearson's r increases to 0.97, and 96% of the estimates are within 2 km of the CALIOP results. By analyzing model bias and feature importance, cloud phase information has the biggest impact on the CBH estimate, although all input features have positive impact on the estimate accuracy. Limited by the penetrability of CALIOP, GETCBH is valid for clouds with COD < 8.5. The CBH estimates have reduced accuracy (Pearson's r of 0.88) for optically thin clouds (clouds with cloud optical depth [COD] < 0.1) where little cloud information is contained in the ABI measurements, as well as for optically thick clouds (clouds with COD >= 3) where a larger proportion of opaque clouds is excluded. Furthermore, for the GBTCBH model using 9 months of CloudSat measurements as label, the CBH estimates are improved with an RMSE of 1.41 km and Pearson's r of 0.92. In a case study of Hurricane Dorian, CBHs for most of the single-layer clouds are successfully estimated with small errors and flagged with high confidence, for both high and low clouds. Deep convective clouds and multi-layer clouds, both of which are not included in the training, are reasonably flagged as low confidence with large CBH estimate errors. In this particular case, 65% of cloudy pixels have CBH estimate with high confidence in the scene. Daytime CBH with high spatial (2 km) and temporal (10 min) resolution can be derived from ABI measurements using this methodology.
      原文链接:
    • NSTL
    • Elsevier

    Monitoring permafrost changes in central Yakutia using optical and polarimetric SAR data

    Park, Sang-EunJung, Yoon TaekKim, Hyun-Cheol
    17页
    查看更多>>摘要：The changes in the permafrost environment have been of interest as a sensitive indicator of changes in global climate conditions. Since changes in the soil and ecosystem of the permafrost active layer are spatially and temporally complex depending on many environmental factors, it is not easy to grasp climate-induced changes occurring in coupled atmospheric-ecological-geocryological systems. To understand the changes in the permafrost active layer, spatially detailed monitoring methods such as multi-spectral optical and Synthetic Aperture Radar (SAR) remote sensing technologies have been extensively applied to the permafrost observation. Optical and SAR systems observe different permafrost features due to significant differences in electromagnetic wave frequencies and imaging mechanisms. Therefore, most studies used optical and SAR data separately according to the purpose and characteristics of each study. The objective of this study is to explore the possibility of combined interpretation of optical and SAR data for identifying and understanding spatiotemporal details of the short- and long-term changes occurring in the permafrost active layer. Multi-spectral optical images acquired during the thawing period and L-band polarimetric SAR images acquired during the freezing period are used in this study in order to examine ecological characteristics and cryogenic processes, respectively. The result of analyzing the relationship between information obtained from optical and SAR sensors revealed that there was a significant correlation between winter changes in scattering properties observed in SAR data and summer land cover changes observed in optical data. The scattering characteristics of winter soil were found to be particularly related to the ecosystem changes in areas that can be explained by the thermokarst development process. Additional data from independent sources, such as elevation data, meteorological data, and long-term optical data, consistently supported the relationship between the winter SAR observations and the thermokarst-related ecosystem changes. The experimental results also elucidated that polarimetric scattering mechanism indicators representing the signal depolarization and surface roughness properties played an important role in deriving information related to the permafrost process from the winter SAR data.
      原文链接:
    • NSTL
    • Elsevier

    DTM extraction from DSM using a multi-scale DTM fusion strategy based on deep learning

    Amirkolaee, Hamed AminiArefi, HosseinAhmadlou, MohammadRaikwar, Vinay...
    26页
    查看更多>>摘要：Extraction of digital terrain model (DTM) from Digital Surface Model (DSM) still faces many problems in a complex scene with geometric ambiguities such as steep slope forested environments and contiguous non-ground regions. In this paper, an approach based on deep learning is proposed to generate DTM directly from DSM without applying filtering methods for eliminating non-ground pixels. In this regard, first, in the preprocessing step, the data is prepared for entering into the proposed deep network. Then, a hybrid deep convolutional neural network (HDCNN) is proposed which is a combination of the U-net architecture and residual networks. In this network, effective features are generate4d in different scales during the downsampling process from the input DSM and the DTM is extracted during the upsampling process. To rectify the results, a multi-scale fusion strategy is proposed to produce the final DTM by fusing the generated DTMs at different scales and with different spatial shifts. The performance of the proposed approach is analyzed by implementing four different evaluation scenarios in five different datasets. The evaluation results demonstrated significant performance and high generalizability of the proposed approach. The proposed network also outperforms the deep learning-based filtering methods and two reference DTM extraction algorithms especially in challenging regions.
      原文链接:
    • NSTL
    • Elsevier

    Benchmarking algorithm changes to the Snow CCI plus snow water equivalent product

    Mortimer, C.Mudryk, L.Derksen, C.Brady, M....
    14页
    查看更多>>摘要：The European Space Agency (ESA) Snow Climate Change Initiative (CCI+) provides long-term, global time series of daily snow cover fraction and snow water equivalent (SWE). The Snow CCI+ SWE Version 1 (CCIv1) product is built on the GlobSnow algorithm, which combines passive microwave (PMW) data with in situ snow depth (SD) measurements to estimate SWE. While CCIv1 remains algorithmically similar to the most recent GlobSnow product (GlobSnow Version 3), Snow CCI+ SWE Version 2 (CCIv2) incorporates two notable differences. CCIv2 uses updated PMW data from the NASA MEaSUREs Calibrated Passive Microwave Daily EASE-Grid 2.0 Earth Science Data Record and is generated in EASE-Grid 2.0 with 12.5 km grid spacing. It also adjusts SWE retrievals in post-processing by incorporating spatially and temporally varying snow density information. Due to the phased product development framework CCI+ employs, proposed changes between CCIv1 and CCIv2 were implemented in a series of step-wise developmental datasets. Using these developmental datasets, we analyze how changes to input PMW and SD data and the snow density parameterization affect the resulting SWE product. Using in situ snow courses as reference data, we demonstrate that the correlation and RMSE of the CCIv2 developmental product improved 18% (0.10) and 12% (5 mm), respectively, relative to CCIv1. The timing of peak snow mass is shifted two weeks later and a temporal discontinuity in the monthly northern hemisphere snow mass time series associated with the shift from the Special Sensor Microwave/Imager (SSM/I) to the Special Sensor Microwave Imager/Sounder (SSMIS) in 2009 is also removed.
      原文链接:
    • NSTL
    • Elsevier

    Estimating global downward shortwave radiation from VIIRS data using a transfer-learning neural network

    Wang, DongdongLi, RuohanLiang, ShunlinJia, Aolin...
    16页
    查看更多>>摘要：In recent years, machine learning (ML) has been successfully used in estimating downward shortwave radiation (DSR). To achieve global estimations, traditional ML models need sufficient ground measurements covering various atmospheric and surface conditions globally, which is difficult to accomplish. Training on the simulated data of a radiative transfer model (RTM) is a possible solution, but widely used RTMs ignore some complex cloud conditions which brings bias to simulations. In this study, a neural network applied with the transfer-learning (TL) concept is introduced to utilize both radiative transfer simulations and ground measurement data, achieving global DSR estimation with only top-of-atmosphere and surface albedo at local solar noon as inputs. The proposed method estimates both instantaneous and daily DSR from Visible Infrared Imaging Radiometer Suite (VIIRS) data at 750-m resolution, and both the estimates are validated by 40 independent stations globally. The root mean-square error and relative root mean square error of instantaneous DSR validation over 25 Baseline Surface Radiation Network, seven Surface Radiation Network, and eight Greenland Climate Network stations in 2013 were 91.2 (16.1%), 106.3 (18.3%), 75.0 (24.2%) W/m2, respectively, and the daily validation achieved 30.8 (15.5%), 33.5 (17.6%), and 31.3 (14.4) W/m2, respectively. The proposed method presents significant high accuracy over polar regions and similar performances over other areas compared with traditional ML models, physics models (e.g., look-up tables and direct estimations), and existing DSR products. The algorithm is also applied to VIIRS swath data to test its global efficacy. Instantaneous mapping captures the spatial pattern of the cloud-mask product, and daily mapping shows spatial patterns similar to the Clouds and the Earth's Radiant Energy System Synoptic TOA and surface fluxes and clouds product, but with more detail. Further analysis indicates that model performance is less sensitive to the quantity of training data after TL has been incorporated. This study demonstrates the advantages of TL on boosting both the generality and accuracy of DSR estimation, which can potentially be applied to other variable retrievals.
      原文链接:
    • NSTL
    • Elsevier

    Forest structure and solar-induced fluorescence across intact and degraded forests in the Amazon

    Pinage, Ekena RangelBell, David M.Longo, MarcosGao, Sicong...
    15页
    查看更多>>摘要：Tropical forest degradation (e.g., anthropogenic disturbances such as selective logging and fires) alters forest structure and function and influences the forest's carbon sink. In this study, we explored structure-function relationships across a variety of degradation levels in the southern Brazilian Amazon by 1) investigating how forest structural properties vary as a function of degradation history using airborne lidar data; 2) assessing the effects of degradation on solar-induced chlorophyll fluorescence (SIF) seasonality using TROPOMI data; and 3) quantifying the contribution of structural variables to SIF using multiple regression models with stepwise se-lection of lidar metrics. Forest degradation history was obtained through Landsat time-series classification. We found that fire, logging, and time since disturbance were major determinants of forest structure, and that forests affected by fires experienced larger variability in leaf area index (LAI), canopy height and vertical structure relative to logged and intact forests. Moreover, only recently burned forests showed significantly depressed SIF during the dry season compared to intact forests. Canopy height and the vertical distribution of foliage were the best predictors of SIF. Unexpectedly, we found that wet-season SIF was higher in active regenerating forests (~ 4 years after fires or logging) compared with intact forests, despite lower LAI. Our findings help to elucidate the mechanisms of carbon accumulation in anthropogenically disturbed tropical forests and indicate that they can capture large amounts of carbon while recovering.
      原文链接:
    • NSTL
    • Elsevier

    What lies beneath: Vertical temperature heterogeneity in a Mediterranean woodland savanna

    Johnston, Miriam R.Andreu, AnaVerfaillie, JosephBaldocchi, Dennis...
    17页
    查看更多>>摘要：As the availability of satellite and airborne thermal infrared remote sensing (TIR-RS) data increases and their spatial, temporal, and spectral resolutions improve, researchers are finding diverse applications for TIR-RS measurements. TIR-RS is now commonly applied in regional- and continental-scale analyses, such as those focused on fire and surface energy balance. However, its application lags in plant physiology and ecology, for which a finer-scale understanding of plant canopy temperatures would be useful to elucidate plant water dynamics, for example. In particular, while methods to disaggregate TIR-RS pixels in horizontal space have advanced, possible vertical stratification of plant canopy temperature and its implications for understanding the correspondence between TIR-RS and finer-scale, field-based thermal measurements (e.g. made with a thermal camera) remain unexplored. Here, we use data from a thermal camera deployed concurrently with the recent ECOSTRESS mission to quantify vertical temperature gradients within tree canopies and temperatures of overvs. under-story plants in a Mediterranean woodland savanna. We then leverage diverse ancillary data to maximize the geometric comparability of ECOSTRESS and thermal camera measurements, in order to assess the extent to which the two forms of thermal measurements correspond. Specifically, we ask: (1) What are the patterns of intra-canopy and over- vs. under-story vertical temperature in a Mediterranean woodland savanna?, and (2) How can vertically-resolved, but spatially-limited field-based temperature measurements be reconciled with spatiallyextensive, but surface-only, temperature measurements of a space-borne remote sensor? We found consistent patterns of vertical thermal heterogeneity both within tree canopies and between ecosystem over- and understories. The daytime difference between the top and bottom thirds of blue oak canopies was, on average, 0.48 degrees C - and sometimes several times larger. Notably, canopy tops are cooler, likely associated with the understory grass reaching daytime temperatures often exceeding over-story temperatures by 10 degrees C. Given the consistency of the intra-canopy temperature gradients, we expected the ECOSTRESS sensor would be in better agreement with camera measurements of canopy tops than bulk canopies or canopy bottoms. However, withincanopy gradients were overwhelmed by other sources of disagreement between the measurements, in part associated with upscaling camera measurements across space. Overall, thermal camera and ECOSTRESS measurements were largely in agreement at night (pixel RMSE = 1.1 degrees C), but they were more divergent during times of low (but >0 W/m2) and high incoming solar radiation (daytime pixel RMSE = 3.5 degrees C).
      原文链接:
    • NSTL
    • Elsevier

    Tracking the source direction of surface mass loads using vertical and horizontal displacements from satellite geodesy: A case study of the inter-annual fluctuations in the water level in the Great Lakes

    Wang, LinsongBevis, MichaelPeng, ZhenranKaban, Mikhail K....
    16页
    查看更多>>摘要：Climate oscillations with seasonal and longer periods drive surface water cycles and quasi-cycles at regional and global scales. Changes in terrestrial water storage produce responses in the Earth's gravitational field and crustal deformation. Here, we use techniques from the Global Positioning System (GPS) and the Gravity Recovery and Climate Experiment (GRACE) to reveal a normal pattern of inter-annual fluctuations in the water level in the North American Great Lakes (GL). The GRACE-estimated time series was in good agreement with in situ water level measurements from 2002 to 2018, especially in terms of phases. The amplitude of 3-4 inter-annual signals in water thickness was 4-6 cm, which is equivalent to a 50-75 km(3) oscillation in surface water volume within the entire GL. The slightly larger annual and inter-annual fluctuation amplitudes estimated using GRACE data indicate that the aquifer system of the GL and its surroundings also contributes to seasonal mass changes. After 2013, water levels in the GL region rose abruptly, and the water mass increased by nearly 270 km(3) until the end of 2018. We also used GPS-and GRACE-derived three-component displacements (vertical, northward, and eastward) to identify load patterns. GPS-and GRACE-estimated maximum probability source directions based on multi-channel singular spectrum analysis showed that most of the selected GPS sites point to the GL region, although the direction deviations of GPS results at a few sites are mainly caused by the combined effect of the local load and superposition of the distant GL. Our findings indicate that inter-annual displacement changes at different frequencies (1-to 8-year cycle) are primarily due to water volume fluctuations in the GL. The amplitudes estimated by GPS are greater than the GRACE-based and GL-modeled results at most stations, which reflects sensitivity differences between these geodetic solutions to the surface load, as hydrological processes in the local area around a station are difficult to identify using GRACE data with a resolution of similar to 300 km.
      原文链接:
    • NSTL
    • Elsevier

    A novel identification method for unrevealed mesoscale eddies with transient and weak features-Capricorn Eddies as an example

    Zhibing, LiZhiqiang, LiuXiaohua, WangJianyu, Hu...
    13页
    查看更多>>摘要：Traditional eddy detection methods can well identify eddies with long lifespans (usually >4 weeks) and strong hydrographic features. Eddies with shorter lifespans or intermittent features are arduous to detect and might be eliminated or misclassified during the detection. However, these eddies have been reported as a majority of the mesoscale eddies in global oceans. This study developed a novel eddy identification method for those eddies. Different from the traditional eddy detection performed based on a snapshot of observations in the horizontal plane, this novel identification method was developed on the evolution of eddies in the temporal dimension. It efficiently avoids the failure in detecting eddies with inconspicuous features during some stages of their evolutions and also eliminates excessive detection. The developed method was implemented to detect Capricorn Eddies based on 26 years of sea-level anomaly from satellite altimetry. Capricorn Eddies are transient and intermittent phenomena induced as a result of the East Australian Current colliding with the continental shelf edge near the Fraser Island, Australia. Using the new identification method, the characteristics of Capricorn Eddies, including their temporal and spatial scales, intensity preferences and evolution details, were resolved. Remarkable seasonal variations of the Capricorn Eddies were also revealed. Based on these findings, a comprehensive overview of Capricorn Eddies, which is crucial for further understanding the variabilities of regional biogeochemical processes and ocean ecosystems, was provided. The developed method will also provide potential insight into the unrevealed eddies in the global oceans.
      原文链接:
    • NSTL
    • Elsevier