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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
正式出版
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    Detecting crop phenology from vegetation index time-series data by improved shape model fitting in each phenological stage

    Liu, LicongCao, RuyinChen, JinShen, Miaogen...
    14页
    查看更多>>摘要：Crop phenology provides important information for crop growth management and yield estimations. The popular shape model fitting (SMF) method detects crop phenology from vegetation index (VI) time-series data, but it has two limitations. First, SMF assumes the same "relative position" of phenological stages for the pixels of the same crop type. This assumption is valid only if all target pixels, relative to the shape model, display a synchronized increase (or decrease) in length between any two phenological stages, which is uncommon in practice. Second, the variance in the resulting phenology estimates for a particular phenological stage is related to the stage itself; this makes it challenging to simulate spatial and temporal variations in crop phenology using SMF. Here, we address both limitations by developing the shape model fitting by the Separate phenological stage method ("SMF-S"). SMF-S uses a modified fitting function and an iterative procedure to match the shape model with the VI time series for each phenological stage in an adaptive local window. Comparisons between SMF-S and SMF in simulation experiments show the superior performance of SMF-S in different scenarios, regardless of noise. Comparisons involving winter wheat field observations from the North China Plain showed that the RMSE values averaged over nine phenological stages were smaller for SMF-S (RMSE = 9.5 d) than for SMF (RMSE = 13.4 d) and one variant of SMF (the shape model with accumulated growing degree days (SM-AGDD); RMSE=33.6 d). Moreover, SMF-S better described the spatial variations (i.e., variance) in the results and captured the temporal shifts in multiple phenological stages. In the derived regional phenology maps of winter wheat on the North China Plain, SMF-S generated more reasonable spatial patterns, whereas SMF underestimated (overestimated) the variance in the early (late) phenological stages. We expect that the improved crop phenology estimates obtained with SMF-S could benefit various agricultural activities.
      原文链接:
    • NSTL
    • Elsevier

    Eutrophication state in the Eastern China based on Landsat 35-year observations

    Xue, KunHu, MinqiMa, RonghuaXiong, Junfeng...
    18页
    查看更多>>摘要：Eutrophication of the eastern plain lake (EPL) region has a significant impact on the sustainable economic development and is closely related to the shortage of water resources in China. Remote sensing provides an effective tool for quantifying the trophic state of inland waters by associating the trophic state index (TSI) with optically active water quality parameters. However, limited by the satellite coverage range and operation time, the long-term changes in the trophic state of the EPL region have not been thoroughly investigated. This study aims to fill this gap by generating a 35-year (1986-2020) TSI dataset of lakes in the eastern plain based on Landsat images. The TSI inversion algorithm based on the algal biomass index (ABI) was designed for Landsat series after consistency analysis. The seasonal variations of the TSI showed the highest TSI (62.0 +/- 11.4) in summer and the lowest TSI (51.6 +/- 8.0) in winter, with uncertainties caused by the limitation of ABI for extremely turbid waters and the number of Landsat seasonal images. The TSI of the EPLs increased over the past 35 years by about 8.2%. Four change patterns were defined for the long-term interannual TSI variations: increasing trend less than 50% (Mode 1) or more than 50% (Mode 2), breaking points that show a surge trend (Mode 3), and decreasing trend (Mode 4). The contribution of meteorological and anthropogenic factors was calculated using a generalized linear model, which revealed that the eutrophication of inland lakes in the EPL region is mainly affected by industrial wastewater discharge and urban expansion. The influence of these explanatory variables becomes more complex with an increase in lake area. Our research provides an estimation of the TSI for the first 35-year basin-scale in the EPL region and a comprehensive evaluation of the driving factors of inland water eutrophication. The results can be used for the effective management and restoration of lakes.
      原文链接:
    • NSTL
    • Elsevier

    High-resolution satellite images combined with hydrological modeling derive river discharge for headwaters: A step toward discharge estimation in ungauged basins

    Huang, QiLong, DiHan, ZhongyingHan, Pengfei...
    18页
    查看更多>>摘要：The reliance on ground-based measurements has been a long-standing barrier toward deriving river discharge for ungauged headwaters, which are quite sensitive to climate change due to relatively high contributions of snow and glacier meltwater to river discharge. In addition, information pertaining to discharge is scarce and inconsistent across the globe due to economic, geographic, and political reasons. Therefore, understanding spatiotemporal dynamics of river discharge in ungauged headwaters is extremely challenging, particularly in high mountain regions such as the Tibetan Plateau (TP). This study presents a methodology of estimating daily continuous discharge using multisource remote sensing and a hydrological model that can simulate cryospheric processes including snow and glacier accumulation and melt. The hydrological model is forced and calibrated purely by satellite-observed information, particularly by river widths derived from high-spatial-resolution images without using in situ river discharge measurements. Poorly gauged or ungauged headwaters with river widths on the order of 100 m, including the Lhasa (the tributary of the upper Brahmaputra River), Salween, Mekong, and Yangtze rivers originating on the TP were chosen to test the methodology. At-a-section river widths were derived from high-resolution satellite images (i.e., IKONOS, QuickBird, RapidEye-1/2/4/5, GeoEye-1, and WorldView-2) and Landsat archives. As exemplified by the Nash-Sutcliffe efficiency coefficient values (NSE) (up to 0.8 and higher than 0.5 overall), simulated daily continuous discharge was reliable in the four testbeds with different river bathymetry, narrow river widths, and complex terrain. Uncertainties associated with the accuracy, number, and variability of calibration references (i.e., remotely sensed widths) and optimization algorithms were fully discussed. This study highlights the potential of deriving daily continuous discharge without a priori information on river flow, paving the way for discharge estimation in ungauged headwaters globally and providing important implications for satellite-based discharge estimation in narrow rivers across high-mountain regions.
      原文链接:
    • NSTL
    • Elsevier

    A new approach for 2-D and 3-D precise measurements of ground deformation from optimized registration and correlation of optical images and ICA-based filtering of image geometry artifacts

    Aati, SaifMilliner, ChrisAvouac, Jean-Philippe
    20页
    查看更多>>摘要：High resolution satellite images with improved spatial and temporal resolution provide unprecedented opportunities to monitor Earth Surface changes in 2D and 3D due, for example, to earthquakes, sand dune migration, ice flow, or landslides. The volume of imagery available for such measurements is rapidly growing but the exploitation of these data is challenging due to the various sources of geometric distortions of the satellite imagery. Here we propose a new approach to extract high-quality surface displacement in 3D based on the correlation of multi-date and multi-platform high resolution optical imagery. We additionally show that when a large enough volume of data is available, it is possible to separate the deformation signal from the artifacts due to the satellite jitter and misalignment of the CCDs, which, together with topographic artifacts, are the main source of noise in the measurements. Our method makes use of a reference DEM, but the outcome is independent of the characteristics of the chosen DEM. We use the case-example of the ground deformation caused by the Ridgecrest earthquake sequence to assess the performance of our proposed approach. We show that it outperforms the more standard approach which combines 2-D correlation and DEM differencing. With our technique, we were able to generate high quality measurements of coseismic ground displacement with GSD of 2.4 m, and uncertainties at the 90% confidence level on the NS, EW and vertical displacement measurements of 0.6 m, 0.7 m, and 0.6 m respectively.
      原文链接:
    • NSTL
    • Elsevier

    Special issue on remote sensing of greenhouse gas emissions

    Thorpe, Andrew K.Dennison, Philip E.Guanter, LuisFrankenberg, Christian...
    2页
    查看更多>>摘要：Greenhouse gases are an essential component in Earth's energy budget, and increasing concentrations of greenhouse gases in Earth's atmosphere are responsible for the primary forcing of increasing global temperature. Quantification of greenhouse gas emissions is a rapidly evolving application of remote sensing, spanning a wide range in remote sensing technologies, wavelengths, platforms, and spatial resolutions. This special issue provides 23 new papers covering the breadth of remote sensing of greenhouse gas emissions. Our summary provides a brief description of each of the papers in the special issue.
      原文链接:
    • NSTL
    • Elsevier

    Estimating 1 km gridded daily air temperature using a spatially varying coefficient model with sign preservation

    Zhang, TaoZhou, YuyuWang, LiZhao, Kaiguang...
    16页
    查看更多>>摘要：Near-surface air temperature (Ta) is one of the key variables in a variety of studies such as hydrological modeling, assessment of heat waves, and energy modeling. Among existing methods, statistical algorithms are suitable for integrating auxiliary spatial data with station-based Ta data to produce gridded Ta over large areas. However, existing statistical algorithms (e.g., Geographically Weighted Regression (GWR)) cannot always correctly capture and preserve relationships between Ta and explanatory variables, which may increase un-certainties of relevant applications based on the estimated Ta with abnormal spatial patterns. This issue is mainly caused by the lack of enough observations due to the limited spatial coverage of weather stations, leading to abnormal relationships between Ta and explanatory variables. In order to address this issue, in this study, we introduced a new method named the Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP) to estimate gridded Ta using gridded land surface temperature (LST) and elevation as explanatory variables with presetting positive and negative signs for coefficients, respectively. Using this method, first, we calculated the preset parameters of the bivariate spline surface. Second, we used the input data at weather stations and con-strained least squares regression to obtain the coefficient surface for both the explanatory variables (i.e., elevation and LST) and the intercept. Third, we calculated the gridded Ta using the 1 km gridded LST and elevation data, and the estimated spatially varying coefficient surfaces. We evaluated the model performance for estimating 1 km gridded daily maximum and minimum Ta (i.e., Tmax and Tmin) data in mainland China from 2003 to 2016 using 10-fold cross-validation and compared its performance with the GWR model. The average root mean square error (RMSE) and mean absolute error (MAE) based on the SVCM-SP are 1.75 C and 1.22 C for Tmax, and 1.82 C and 1.30 C for Tmin, respectively. The SVCM-SP method showed better performance than the GWR in terms of accuracy, computing efficiency, and has more interpretable coefficients for explanatory variables to get more realistic spatial pattern of gridded Ta. More important, the sign preservation of the SVCM-SP method can mitigate the issue of abnormal relationships between Ta and explanatory variables in the traditional methods such as GWR, and therefore will contribute to future studies in developing better gridded air temperature or relevant data products.
      原文链接:
    • NSTL
    • Elsevier

    A deep neural network based SMAP soil moisture product

    Gao, LunGao, QiangZhang, HankuiLi, Xiaojun...
    15页
    查看更多>>摘要：In this paper, it is demonstrated that while satellite soil moisture (SM) retrievals often have minimum biases, reanalysis data can capture more temporal variability of SM, especially for non-cropland areas - when validated against in situ measurements. Accordingly, this paper presents a deep neural network (DNN) that utilizes the merits of a suite of existing satellite and reanalysis products to produce a new SM product with minimum (maximum) bias (correlation) - using NASA's Soil Moisture Active Passive (SMAP) data and ERA5 reanalysis. The benchmark of the network is a bias-adjusted SM with maximum correlation with in situ data over each land cover type. The mean of the benchmark data is adjusted to the product that exhibits a minimum bias over each land-cover type. Consistent with the laws of L-band microwave propagation in soil and canopy, the input variables of DNN include polarized SMAP brightness temperatures, incidence angle, vegetation scattering albedo, surface roughness parameter, surface water fraction, effective soil temperatures, bulk density, clay fraction, and vegetation optical depth from the normalized difference vegetation index (NDVI) climatology. The DNN is trained and validated using two years (04/2015-03/2017) of global data and deployed for assessment of its performance from 04/2017 to 03/2021. The testing results against in situ measurements demonstrate that the DNN outputs typically exhibit improved error quality metrics over most land-cover types and climate regimes and can properly capture SM temporal dynamics, beyond each SMAP product across regional to continental scales.
      原文链接:
    • NSTL
    • Elsevier

    Cross-sensor domain adaptation for high spatial resolution urban land-cover mapping: From airborne to spaceborne imagery

    Wang, JunjueMa, AilongZhong, YanfeiZheng, Zhuo...
    18页
    查看更多>>摘要：Urban land-cover information is essential for resource allocation and sustainable urban development. Recently, deep learning algorithms have shown promising results in land-cover mapping with high spatial resolution (HSR) imagery. However, the limitation of the annotation and the divergence of the multi-sensor images always challenge the transferability of deep learning, thus hindering city-level or national-level mapping. In this paper, we propose a scheme to leverage small-scale airborne images with labels (source) for unlabeled large-scale spaceborne image (target) classification. Considering the sensor characteristics, a Cross-Sensor Land-cOVEr framework, called LoveCS, is introduced to address the difficulties of the spatial resolution inconsistency and spectral differences. As for the structural design, cross-sensor normalization is proposed to automatically learn sensor-specific normalization weights, thereby narrowing the spectral differences hierarchically. Furthermore, a dense multi-scale decoder is proposed to effectively fuse the multi-scale features from different sensors. As for the model optimization, self-training domain adaptation is adopted, and multi-scale pseudo-labeling is proposed to reduce the scale divergence brought by the spatial resolution inconsistency. The effectiveness of LoveCS was tested on data from the three cities of Nanjing, Changzhou, and Wuhan in China. The comprehensive results all show that LoveCS is superior to the existing domain adaptation methods in cross-sensor tasks, and has good generalizability. Compared with the existing land-cover products, the obtained results have the highest accuracy and spatial resolution (1.0 m). Overall, LoveCS provides a new perspective for large-scale land-cover mapping based on limited HSR images.
      原文链接:
    • NSTL
    • Elsevier

    A two-step deep learning framework for mapping gapless all-weather land surface temperature using thermal infrared and passive microwave data

    Wu, PenghaiSu, YangDuan, Si-boLi, Xinghua...
    19页
    查看更多>>摘要：Blending data from thermal infrared (TIR) and passive microwave (PMW) measurements is a promising solution for generating the all-weather land surface temperature (LST). However, owing to swath gaps in PMW data and the resolution inconsistence between TIR and PWM data, spatial details are often incomplete or considerable losses are generated in the all-weather LST using traditional methods. This study was conducted to develop a two-step deep learning framework (TDLF) for mapping gapless all-weather LST over the China's landmass using MODIS and AMSR-E LST data. In the TDLF, a multi-temporal feature connected convolutional neural network bidirectional reconstruction model was developed to obtain the spatially complete AMSR-E LST. A multi-scale multi-temporal feature connected generative adversarial network model was then designed to blend spatially complete AMSR-E LST and cloudy-sky MODIS LST, and generate gapless all-weather LST data. Gapless allweather LST data were evaluated using six in-situ LST data from the Tibetan Plateau (TP) and the Heihe River Basin (HRB). The root mean squared errors (RMSEs) of the gapless all-weather LST were 1.71-2.0 K with determination coefficients (R-2) of 0.94-0.98 under clear conditions, and RMSEs of 3.41-3.87 K and R-2 of 0.88-0.94 were obtained under cloudy conditions. Compared to the existing PMW-based all-weather LSTs, the validation accuracy and image quality (such as spatial detail) of the generated gapless all-weather LSTs were superior. The TDLF does not require the use of any additional data and can potentially be implemented with other satellite TIR and PWM sensors to produce long-term, gapless, all-weather MODIS LST records on a global scale. Such a capability is beneficial for generating further gapless all-weather soil moisture and evapotranspiration datasets that can all be applied in global climate change research.
      原文链接:
    • NSTL
    • Elsevier

    Simulation of solar-induced chlorophyll fluorescence by modeling radiative coupling between vegetation and atmosphere with WPS

    Zhao, FengLi, ZhenjiangVerhoef, WoutFan, Chongrui...
    17页
    查看更多>>摘要：Recent advances in instruments and retrieval methods enable measurements of solar-induced chlorophyll fluorescence (SIF) across a wide range of scales. Radiative transfer (RT) models for simulating scattering and (re-) absorption of SIF provide a powerful tool to study the upscaling of SIF signal from leaf level to terrestrial ecosystems. Based on the Monte Carlo ray-tracing (MCRT) model, WPS (Weighted Photon Spread), we made major extensions with new functionalities and systematic evaluation of the new modules. By modeling the radiative coupling between atmosphere and land surface with the same MCRT method, the non-fluorescent and SIF radiance received by sensors can be simulated at levels from top-of canopy to top-of-atmosphere (TOA) in a coherent manner. New extension to represent the three-dimensional (3-D) canopies with geometrical primitives composed of turbid medium makes the hyperspectral simulation (especially SIF) for a sensor with medium spatial resolution at kilometer-scale feasible and practical. Evaluations through ROMC (Radiation transfer model intercomparison Online Model Checker) show that the accuracy of the new module of 3-D structure representation in WPS is within 1% of the reference solution. The spectra of TOA radiance and SIF and their components simulated at nadir by WPS agree closely with those simulated by the coupled SCOPE and MODTRAN models with the coefficient of determination (R-2) higher than 0.99 and the average absolute relative error (AARE) lower than 6.39%; for angular distributions of TOA radiance and SIF at 685 nm and 740 nm, R-2 is higher than 0.81 and AARE is lower than 6.94%. Comparisons of the spectra of TOA radiance and SIF and their components simulated at nadir by WPS and the DART model give R-2 higher than 0.99 and AARE lower than 3.5%; R-2 is higher than 0.92 and AARE is lower than 5.92% for the TOA angular simulations. The WPS model was also evaluated by hyperspectral measurements through unmanned aerial vehicle at different altitudes, which shows that WPS can reproduce the spectral features of a rapeseed crop. WPS can be used as a versatile tool to assess the impacts of various factors on the SIF signal and to evaluate the SIF retrieval methods under different conditions.
      原文链接:
    • NSTL
    • Elsevier