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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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    A review of statistical methods used for developing large-scale and long-term PM2.5 models from satellite data

    Ma, ZongweiDey, SagnikChristopher, SundarLiu, Riyang...
    15页
    查看更多>>摘要：Research of PM2.5 chronic health effects requires knowledge of large-scale and long-term exposure that is not supported by newly established monitoring networks due to their sparse spatial coverage and lack of historical measurements. Estimating PM2.5 using satellite-derived aerosol optical depth (AOD) can be used to fill the data gap left by the ground monitors and extend the PM2.5 data coverage to suburban and rural areas over long time periods. Two approaches have been applied in large-scale and long-term satellite remote sensing of PM2.5, i.e., the scaling and statistical approaches. Compared to the scaling method, the statistical approach has greater prediction accuracy and has been widely used. There is a gap in the current literature and review papers on how the statistical methods work and specific considerations to best utilize them, especially for large-scale and longterm estimates. In this critical review, we summarize the evolution of large-scale and long-term PM2.5 statistical models reported in the literature. We describe the framework and guidance of large-scale and long-term satellite based PM2.5 modeling in data preparation, model development, validation, and predictions. Sample computer codes are provided to expedite new model-building efforts. We also include useful considerations and recommendations in covariates selection, addressing the spatiotemporal heterogeneities of PM2.5-AOD relationships, and the usage of cross validation, to aid the determination of the final model.
      原文链接:
    • NSTL
    • Elsevier

    Spatio-temporal variability of water use efficiency and its drivers in major forest formations in India

    Nandy, SubrataSaranya, M.Srinet, Ritika
    13页
    查看更多>>摘要：Forests play a significant role in mitigating the effects of climate change and regulating the biogeochemical cycles. Water use efficiency (WUE) is an important indicator that links the carbon and water cycles in terrestrial ecosystems. In the present study, WUE of major forest formations of India was calculated from 2003 to 2018 as the ratio of Moderate Resolution Spectroradiometer (MODIS) Gross Primary Productivity (GPP, MOD17A2H) to evapotranspiration (ET, MOD16A) using the Google Earth Engine platform. The spatial distribution of WUE was mapped and the inter-annual and monthly variations were analysed. The mean annual WUE of forests ranged from 1.78-2.02 gC kgH2O-1. It was observed that tropical thorn forests had the highest WUE (3.52 +/- 1.08 gC kgH2O-1) and moist alpine scrub had the lowest WUE (1.05 +/- 0.17gC kgH2O-1) among the forest type groups of India. WUE of forests showed an increasing trend with latitude and decreasing trend with elevation. To understand the influence of various bio-meteorological drivers on WUE and their importance in governing these influences, Random Forest (RF) algorithm was used. The bio-meteorological drivers were able to explain 65% of the variability in WUE. Temperature was identified as the most important driver in influencing the WUE of forests. Based on the findings of the study, it can be expected that the global increase in temperature would negatively affect the WUE of the major forest formations of India.
      原文链接:
    • NSTL
    • Elsevier

    High-throughput field phenotyping of soybean: Spotting an ideotype

    Roth, LukasBarendregt, ChristophBetrix, Claude-AlainHund, Andreas...
    13页
    查看更多>>摘要：Soybean is among the most important crops for food and feed production worldwide. Sustainable and local production in regions with marginal climates requires cold-adapted varieties that create high yield and protein content in a short vegetation period. Drone-based high-throughput field phenotyping methods allow monitoring the success and the developmental speed of genotypes in such target environments. This study exemplifies that such frequent and precise analyses of remotely sensed canopy growth traits can be used to derive the optimal genotype, a so-called ideotype, for a given mega-environment. For the case example of Switzerland, a country with a temperate oceanic climate, the results indicate that image-derived traits allow predicting yield and protein content from the dynamics of vegetative growth. Genotypes with early canopy cover produce high yield, whereas genotypes that show a prolonged duration until they have reached their final maximum of leaf area index are characterized by a high protein content. Analyses of early performance trial stage material indicate that there are genotypes that combine both features of growth dynamics. Whether these genotypes are then indeed successful in breeding programs remains to be investigated, since this also depends on disease resistance and other traits of those genotypes. Yet, overall, this study provides strong indications of the high value of high-throughput field phenotyping in the context of physiological and breeding-related analyses of crops.
      原文链接:
    • NSTL
    • Elsevier

    Landsat-based monitoring of southern pine beetle infestation severity and severity change in a temperate mixed forest

    Meng, RanGao, RenjieZhao, FengHuang, Chengquan...
    17页
    查看更多>>摘要：The recent northward expansion of Southern Pine Beetle (SPB) outbreaks associated with warming winters has caused extensive tree mortality in temperate pine forests, significantly affecting forest dynamics, structure, and functioning. Spatially-explicit early warning and detection of SPB-induced tree mortality is critical for timely and sustainable forest management practices. The unique contributions of remote sensing technologies to mapping the location, extent, and severity of beetle outbreaks, as well as assisting in analyzing the potential drivers for outbreak predictions, have been well recognized. However, little is known about the performance of moderate resolution satellite multispectral imagery for early warning and detection of SPB-induced tree mortality. Thus, we conducted this study, as the first attempt, to capture the spatial-temporal patterns of SPB infestation severity at the regional scale and to understand the underlying environmental drivers in a spatially-explicit manner. First, we explored the spectral signatures of SPB-killed trees based on 30-m plot measurements and Landsat-8 imagery. Then, to improve detection accuracy for areas with low-moderate SPB infestation severity, we added spectraltemporal anomaly information in the form of a linear trend of the spectral index trajectory to a previously developed approach. The best overall accuracy increased from 84.7% to 90.1% and the best Macro F1 value increased from 0.832 to 0.900. Next, we compared the performances of spectral indices in mapping SPB infestation severity (i.e., % red stage within the 30-m grid cell). The results showed that the combination of Normalized Difference Moisture Index and Tasseled Cap Greenness had the best performance for mapping SPB infestation severity (2016: R2 = 0.754; RSME = 15.7; 2017: R2 = 0.787; RSME = 12.4). Finally, we found that climatic and landscape variables can explain the detected patterns of SPB infestation from 2014 to 2017 in our study area (R2 = 0.751; RSME = 9.67), providing valuable insights on possible predictors for early warning of SPB infestation. Specifically, in our study area, winter dew point temperature was found to be one of the most important predictors, followed by SPB infestation locations in the previous year, canopy cover of host species, elevation, and slope. In the context of continued global warming, our study not only provides a novel framework for efficient, spatially-explicit, and quantitative measurements of forest damage induced by SPB infestation over large scales, but also uncovers opportunities to predict future SPB outbreaks and take precautions against it.
      原文链接:
    • NSTL
    • Elsevier

    Multi-decadal analysis of high-resolution albedo changes induced by urbanization over contrasted Chinese cities based on Landsat data

    Guo, TianciHe, TaoLiang, ShunlinRoujean, Jean-Louis...
    19页
    查看更多>>摘要：Surface albedo is a key parameter in the surface energy balance and has been identified as a primary essential climate variable (ECV). Variations in surface albedo can be used as a diagnostic tool for local climate change. This is particularly true in urban areas, where the impacts of land cover conversion due to increasing anthropogenic demands can be examined using surface albedo changes. Most of the previous studies of albedo in cities have relied on coarse-resolution datasets with short time spans and have disregarded continuous monitoring. In addition, it is still unclear which urbanization processes are involved and what effects they have on surface albedo over long time periods. This study aimed to identify the contribution of increasing urbanization to the regional climate by analyzing spatial and temporal changes in surface albedo. Assigning albedo values to land cover types is useful for determining the level of transformation and their impacts in various Chinese cities that underwent specific evolutions between 1986 and 2018. The Direct Estimation (DE) approach was modified to estimate the daily mean surface albedo at 30 m based on Landsat observations. It resulted root-mean-square errors (RMSEs) of less than 0.044 and bias about 0.006 between observations and model estimations. Such accuracy obtained after correcting the orbital drift of the Landsat satellite, was deemed satisfactory for detecting potential changes in albedo. Major findings are: 1) A notable trend was found over the past 33 years of 11 major Chinese cities, i.e. population about 10 million and more, with a general albedo increase from satellite observations. The higher resolution Landsat dataset showed a trend 3 times larger than the Global Land Surface Satellites (GLASS) product, which outlines the need for analyzing high resolution imagery in priority for reliable estimate of albedo over heterogeneous urban landscapes. 2) An increase in albedo infers a negative radiative forcing at an average rate of -2.771 W/m2 per decade, thereby indicating a cooling effect for most Chinese cities. 3) Changes in surface albedo were also closely linked to landscape transformation, clearly observed using the 30 m resolution of the Landsat data. 4) Throughout the study period, surface albedo exhibited a temporal U-shaped curve in built-up areas under development, with albedo peaks in old and newly built districts and a decrease in albedo between the two eras.
      原文链接:
    • NSTL
    • Elsevier

    Ocean color algorithms to estimate the concentration of particulate organic carbon in surface waters of the global ocean in support of a long-term data record from multiple satellite missions

    Stramski, DariuszJoshi, IshanReynolds, Rick A.
    21页
    查看更多>>摘要：As the concentration of particulate organic carbon (POC) in the surface ocean plays a key role in marine biogeochemical cycles and ecosystems, its assessment from satellite observations of the global ocean is of significant interest. To achieve a global multi-decadal data record of POC by merging observations from multiple satellite ocean color missions, we formulated a new suite of empirical POC algorithms for several satellite sensors. For the algorithm development we assembled a field dataset of concurrent POC and remote-sensing reflectance, R-rs(lambda), measurements collected in all major ocean basins encompassing tropical, subtropical, and temperate latitudes as well as the northern and southern polar latitudes. This dataset is characterized by a globally-representative probability distribution of POC with a broad range of values between about 10 and 1000 mg m(-3). This development dataset was created with the use of additional inclusion and exclusion criteria based on well-assured and documented consistency of measurement protocols as well as specific bio-optical and particle characteristics of seawater which are consistent with vast areas of open-ocean pelagic environments. To formulate the algorithms the development dataset was subject to parametric regression analysis. Overall we evaluated over seventy formulas for estimating POC from R-rs(lambda) using seven distinctly different algorithmic categories, each with a fundamentally different definition of independent variable involving R-rs(lambda). Through the goodness-of-fit analysis, we selected the best candidate POC algorithms, referred to as the hybrid algorithms, which are tuned specifically for the spectral bands of SeaWiFS, MODIS, VIIRS, MERIS, and OLCI satellite sensors. These hybrid algorithms consist of two components, the MBR (Maximum Band Ratio)-OC4 cubic polynomial function and BRDI (Band Ratio Difference Index) quintic polynomial function. The MBR-OC4 uses four spectral bands and the BRDI three spectral bands from the blue-green spectral region. The MBR-OC4 algorithm is used for POC > 25 mg m(-3) and the BRDI for POC < 15 mg m(-3). In the transition region the weighting approach is applied to POC derived from the two algorithmic formulas. While the main role of the BRDI is to improve POC estimates in ultraoligotrophic waters where POC is very low, the MBR-OC4 provides improvements, compared with the predecessor algorithms, over a broader range of POC but especially for relatively high POC values. A preliminary analysis of field-satellite matchup datasets based on SeaWiFS and MODIS-Aqua observations shows improved performance of hybrid algorithms compared with current standard algorithms for both SeaWiFS and MODIS. In addition, a reasonable consistency is demonstrated between POC derived from hybrid algorithms applied to example satellite observations with SeaWiFS, MODIS-Aqua, and VIIRS-SNPP. The suite of newly developed algorithms provides the potential next generation version of global algorithms that better represents the spatial and temporal variability within a broader range of POC than the predecessor global algorithms, while also offering a capability to generate a long-term sensor-to-sensor consistent data record of POC that begins with the launch of SeaWiFS mission in 1997.
      原文链接:
    • NSTL
    • Elsevier

    Improving surface soil moisture retrievals through a novel assimilation algorithm to estimate both model and observation errors

    Tian, JiaxinQin, JunYang, KunZhao, Long...
    13页
    查看更多>>摘要：Soil moisture controls the land surface water and energy budget and plays a crucial role in land surface processes. Based on certain mathematical rules, data assimilation can merge satellite observations and land surface models, and produce spatiotemporally continuous profile soil moisture. The two mainstream assimilation algorithms (variational-based and sequential-based) both need model error and observation error estimates, which greatly impact the assimilation results. Moreover, the performance of land data assimilation relies heavily on the specification of model parameters. However, it is always challenging to specify these errors and model parameters. In this study, a dual-cycle assimilation algorithm was proposed for addressing the above issue. In the inner cycle, the Ensemble Kalman Filter (EnKF) is run with parameters of both model and observation operators and their errors, which are provided by the outer cycle. Both the analyzed state variable and the innovation are reserved at each analysis moment. In the outer cycle, the innovation time series kept by the inner cycle are fed into a likelihood function to adjust the values of parameters of both the model and observation operators and their errors through an optimization algorithm. A series of assimilation experiments were first performed based on the Lorenz-63 model. The results illustrate that the performance of the dual-cycle algorithm substantially surpasses those of both the classical parameter calibration and the standard EnKF. Subsequently, the Advanced Microwave Scanning Radiometer of earth Observing System (AMSR-E) brightness temperatures were assimilated into the simple biosphere model scheme version 2 (SiB2) with a radiative transfer model as the observation operator in two experimental areas, namely Naqu on the Tibetan Plateau and a Coordinate Enhanced Observing (CEOP) reference site in Mongolia. The results indicate that the dual-cycle assimilation algorithm can simultaneously estimate model parameters, observation operator parameters, model error, and observation error, thus improving surface soil moisture estimation in comparison with other assimilation algorithms. Since the dualcycle assimilation algorithm can estimate the observation errors, it provides the potential for assimilating multi-source remote sensing data to generate physically consistent land surface state and flux estimates.
      原文链接:
    • NSTL
    • Elsevier

    Mapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery

    Morresi, DonatoMarzano, RaffaellaLingua, EmanueleMotta, Renzo...
    16页
    查看更多>>摘要：Deriving burn severity from multispectral satellite data is a widely adopted approach to infer the degree of environmental change caused by fire. Burn severity maps obtained by thresholding bi-temporal indices based on pre- and post-fire Normalized Burn Ratio (NBR) can vary substantially depending on temporal constraints such as matched acquisition and optimal seasonal timing. Satisfying temporal requirements is crucial to effectively disentangle fire and non-fire induced spectral changes and can be particularly challenging when only a few cloud-free images are available. Our study focuses on 10 wildfires that occurred in mountainous areas of the Piedmont Region (Italy) during autumn 2017 following a severe and prolonged drought period. Our objectives were to: (i) generate reflectance composites using Sentinel-2 imagery that were optimised for seasonal timing by embedding spatial patterns of long-term land surface phenology (LSP); (ii) produce and validate burn severity maps based on the modelled relationship between bi-temporal indices and field data; (iii) compare burn severity maps obtained using either a pair of cloud-free Sentinel-2 images, i.e. paired images, or reflectance composites. We proposed a pixel-based compositing algorithm coupling the weighted geometric median and thematic spatial information, e.g. long-term LSP metrics derived from the MODIS Collection 6 Land Cover Dynamics Product, to rank all the clear observations available in the growing season. Composite Burn Index data and bi-temporal indices exhibited a strong nonlinear relationship (R-2 > 0.85) using paired images or reflectance composites. Burn severity maps attained overall classification accuracy ranging from 76.9% to 83.7% (Kappa between 0.61 and 0.72) and the Relative differenced NBR (RdNBR) achieved the best results compared to other bi-temporal indices (differenced NBR and Relativized Burn Ratio). Improvements in overall classification accuracy offered by the calibration of bi-temporal indices with the dNBR offset were limited to burn severity maps derived from paired images. Reflectance composites provided the highest overall classification accuracy and differences with paired images were significant using uncalibrated bi-temporal indices (4.4% to 5.2%) while they decreased (2.8% to 3.2%) when we calibrated bi-temporal indices derived from paired images. The extent of the high severity category increased by similar to 19% in burn severity maps derived from reflectance composites (uncalibrated RdNBR) compared to those from paired images (calibrated RdNBR). The reduced contrast between healthy and burnt conditions associated with suboptimal seasonal timing caused an underestimation of burnt areas. By embedding spatial patterns of long-term LSP metrics, our approach provided consistent reflectance composites targeted at a specific phenological stage and minimising non-fire induced inter-annual changes. Being independent from the multispectral dataset employed, the proposed pixel-based compositing approach offers new opportunities for operational change detection applications in geographic areas characterised by persistent cloud cover.
      原文链接:
    • NSTL
    • Elsevier

    Extension of the Hapke model to the spectral domain to characterize soil physical properties

    Ding, AnxinMa, HanLiang, ShunlinHe, Tao...
    14页
    查看更多>>摘要：The Hapke bidirectional reflectance model has mainly been used in planetary remote sensing and has given rise to some studies in Earth science. However, it has not yet been comprehensively evaluated using data from different sources, and its ability to model reflectance spectra needs to be further explored. Therefore, the objective of this study was to develop a tangible parametric model of soil hyperspectral bidirectional reflectance via the evaluation and extension of the Hapke model (hereafter named the Hapke-HSR model). Comprehensive directional and spectral soil reflectance datasets, including satellite, field data, two spectral libraries, and simulated reflectance spectra, were used. First, the two widely used versions of the Hapke model, namely, the SOILSPECT and original Hapke models, were compared. Thereafter, the simplified SOILSPECT model was extended to characterize soil hyperspectral reflectance by deriving an approximate relationship between the single-scattering albedo and wavelength. We obtained the following results. (1) Both versions of the Hapke model agreed well in fitting soil bidirectional reflectance data. However, the SOILSPECT model (R-2 = 0.983-0.997, RMSE = 0.007-0.014) performed better than the original Hapke model (R-2 = 0.800-0.988, RMSE = 0.014-0.057) when both satellite and field data were used. (2) The Hapke-HSR model could effectively capture the characteristics of the soil hyperspectral reflectance (R-2 = 0.963-0.983 and RMSE = 0.018-0.028) based on both spectral libraries. The simulated reflectance spectra showed that the Hapke-HSR model can capture the soil moisture content variations (R-2 = 0.987, RMSE = 0.011). In addition, the residual prediction deviation (RPD) values of the Hapke-HSR model were greater than 3, indicating a high prediction accuracy. These findings demonstrate that the Hapke-HSR model performs well with respect to the characterization of soil hyperspectral directional reflectance.
      原文链接:
    • NSTL
    • Elsevier

    Combining transfer learning and hyperspectral reflectance analysis to assess leaf nitrogen concentration across different plant species datasets

    Wan, LiangZhou, WeijunHe, YongWanger, Thomas Cherico...
    18页
    查看更多>>摘要：Accurate estimation of leaf nitrogen concentration (LNC) is critical to characterize ecosystem and plant physiological processes for example in carbon fixation. Remote sensing can capture LNC, while interrelated traits and spectral diversity across plant species prevent development of transferable LNC assessment models based on leaf reflectance. Here, we developed a new transfer learning method by coupling transfer component analysis with the support vector regression, namely TCA-SVR, to transfer LNC assessment models across different plant species. We benchmarked the performance of TCA-SVR against a well-established partial least squares regression (PLSR) model with five remote sensing datasets on 60 plant species measured from three spectroradiometers with varied spectral resolutions and illumination and viewing angles. The result showed that leaf reflectance presented the high spectral diversity in different spectral regions, plant species, and growth stages. The combination of visible (VIS), near infrared (NIR), and shortwave infrared (SWIR) reflectance (e.g. 550-2300 nm) achieved the optimal LNC assessment across all datasets. Results on the testing datasets showed that the transferability of the PLSR models highly depended on the LNC distribution and spectral features, which were associated with the differences in plant species, spectral measurements, and growth conditions between datasets. These differences led to the large variations in LNC and leaf reflectance, which thus produced the overestimations and underestimations of LNC. Compared to the PLSR model, TCA-SVR greatly improved the transferability of the LNC assessment model by reducing the average root mean square error by 36.76%. Further, the implementation of modeling updating can help TCA-SVR learn the features related to the difference in plant species and LNC ranges by transferring samples from the target dataset to the source dataset. Our model updating approach improved the performance of TCA-SVR and only needed 5% of the off-site samples to supplement the source dataset to achieve an effective assessment of LNC. Refining the proposed method with new remote sensing datasets will aid rapid monitoring of plant nitrogen status and may improve carbon-nitrogen interactions in existing ecosystem models.
      原文链接:
    • NSTL
    • Elsevier