Investigating snowpack across scale in the northern Great Lakes–St. Lawrence forest region of Central Ontario,Canada

This study investigates scaling issues by evaluating snow processes and quantifying bias in snowpack properties across scale in a northern Great Lakes–St. Lawrence forest. Snow depth and density were measured along transects stratified by land cover over the 2015/2016 and 2016/2017 winters. Daily snow depth was measured using a time‐lapse (TL) camera at each transect. Semivariogram analysis of the transect data was conducted, and no autocorrelation was found, indicating little spatial structure along the transects. Pairwise differences in snow depth and snow water equivalent (SWE) between land covers were calculated and compared across scales. Differences in snowpack between forested sites at the TL points corresponded to differences in canopy cover, but this relationship was not evident at the transect scale, indicating a difference in observed process across scale. TL and transect estimates had substantial bias, but consistency in error was observed, which indicates that scaling coefficients may be derived to improve point scale estimates. TL and transect measurements were upscaled to estimate grid scale means. Upscaled estimates were compared and found to be consistent, indicating that appropriately stratified point scale measurements can be used to approximate a grid scale mean when transect data are not available. These findings are important in remote regions such as the study area, where frequent transect data may be difficult to obtain. TL, transect, and upscaled means were compared with modelled depth and SWE. Model comparisons with TL and transect data indicated that bias was dependent on land cover, measurement scale, and seasonality. Modelled means compared well with upscaled estimates, but model SWE was underestimated during spring melt. These findings highlight the importance of understanding the spatial representativeness of in situ measurements and the processes those measurements represent when validating gridded snow products or assimilating data into models.     

Observation-Based Estimates of Global Glacier Mass Change and Its Contribution to Sea-Level Change

Glaciers have strongly contributed to sea-level rise during the past century and will continue to be an important part of the sea-level budget during the twenty-first century. Here, we review the progress in estimating global glacier mass change from in situ measurements of mass and length changes, remote sensing methods, and mass balance modeling driven by climate observations. For the period before the onset of satellite observations, different strategies to overcome the uncertainty associated with monitoring only a small sample of the world’s glaciers have been developed. These methods now yield estimates generally reconcilable with each other within their respective uncertainty margins. Whereas this is also the case for the recent decades, the greatly increased number of estimates obtained from remote sensing reveals that gravimetry-based methods typically arrive at lower mass loss estimates than the other methods. We suggest that strategies for better interconnecting the different methods are needed to ensure progress and to increase the temporal and spatial detail of reliable glacier mass change estimates.     

Radar hydrology: rainfall estimation

Radar observations of rainfall and their use in hydrologic research provide the focus for the paper. Radar-rainfall products are crucial for input to runoff and flood prediction models, validation of satellite remote sensing algorithms, and for statistical characterization of extreme rainfall frequency. In this context we discuss the issues of radar-rainfall product development, and the theoretical and practical requirements of validating radar-rainfall maps and new radar technologies. We discuss a framework for reflectivity based rainfall estimation, including estimation of uncertainty of radar-rainfall estimates. Validation of radar-rainfall products is a major challenge for broad utilization of these products in hydrologic applications. In the discussion of radar-rainfall prediction we focus on orographically induced extreme rainfall and flooding, discuss the issues of detection, statistical sample size, and scale effects. We conclude the paper with a set of recommendations for research priorities and experimental requirements to address them.     

II. Snow hydrology processes and remote sensing

In the last 20 years remote sensing research has led to significant progress in monitoring and measuring certain snow hydrology processes. Snow distribution in a drainage basin can be adequately assessed by visible sensors. Although there are still some interpretation problems, the NOAA-AVHRR sensor can provide frequent views of the areal snow cover in a basin, and snow cover maps are produced operationally by the National Weather Service on about 3000 drainage basins in North America. Measurement of snow accumulation or snow water equivalent with microwave remote sensing has great potential because of the capabilities for depth penetration, all-weather observation and night-time viewing. Several critical areas of research remain, namely, the acquisition of snow grain size information for input to microwave models and improvement in passive microwave resolution from space. Methods that combine both airborne gamma ray and visible satellite remote sensing of the snowpack with field measurements also hold promise for determining areal snow water equivalent. Some remote sensing techniques can also be used to detect different stages of snow metamorphism. Various aspects of snowpack ripening can be detected using microwave and thermal infra-red capabilities. The capabilities for measurement of snow albedo and surface temperature have direct application in both snow metamorphism and snowpack energy balance studies. The potentially most profitable research area here is the study of the bidirectional reflectance distribution function to improve snow albedo measurements. Most of the remote sensing capabilities in snow hydrology have been developed for improving snowmelt-run-off forecasting. Most applications have used the input of snow cover extent to deterministic models, both of the degree day and energy balance types. Snowmelt-run-off forecasts using satellite derived snow cover depletion curves and the models have been successfully made. As the extraction of additional snow cover characteristics becomes possible, remote sensing will have an even greater impact on snow hydrology. Important remote sensing capabilities will become available in the next 20 years through space platform observing systems that will improve our capability to observe the snowpack on an operational basis.     

Calibration of hydrological models in ungauged basins based on satellite radar altimetry observations of river water level

This paper proposes a new orientation to address the problem of hydrological model calibration in ungauged basin. Satellite radar altimetric observations of river water level at basin outlet are used to calibrate the model, as a surrogate of streamflow data. To shift the calibration objective, the hydrological model is coupled with a hydraulic model describing the relation between streamflow and water stage. The methodology is illustrated by a case study in the Upper Mississippi Basin using TOPEX/Poseidon (T/P) satellite data. The generalized likelihood uncertainty estimation (GLUE) is employed for model calibration and uncertainty analysis. We found that even without any streamflow information for regulating model behavior, the calibrated hydrological model can make fairly reasonable streamflow estimation. In order to illustrate the degree of additional uncertainty associated with shifting calibration objective and identifying its sources, the posterior distributions of hydrological parameters derived from calibration based on T/P data, streamflow data and T/P data with fixed hydraulic parameters are compared. The results show that the main source is the model parameter uncertainty. And the contribution of remote sensing data uncertainty is minor. Furthermore, the influence of removing high error satellite observations on streamflow estimation is also examined. Under the precondition of sufficient temporal coverage of calibration data, such data screening can eliminate some unrealistic parameter sets from the behavioral group. The study contributes to improve streamflow estimation in ungauged basin and evaluate the value of remote sensing in hydrological modeling. Copyright © 2011 John Wiley & Sons, Ltd.     

Evaluation of the SMOS and SMAP soil moisture products under different vegetation types against two sparse in situ networks over arid mountainous watersheds,Northwest China

Assessment of the suitability of satellite soil moisture products at large scales is urgently needed for numerous climatic and hydrological researches, particularly in arid mountainous watersheds where soil moisture plays a key role in landatmosphere exchanges. This study presents evaluation of the SMOS(L2) and SMAP(L2_P_E and L2_P) products against ground-based observations from the Upstream of the Heihe River Watershed in situ Soil Moisture Network(UHRWSMN) and the Ecological and Hydrological Wireless Sensor Network(EHWSN) over arid high mountainous watersheds, Northwest China.Results show that all the three products are reliable in catching the temporal trend of the in situ observations at both point and watershed scales in the study area. Due to the uncertainty in brightness temperature and the underestimation of effective temperature, the SMOS L2 product and both the SMAP L2 products show "dry bias" in the high, cold mountainous area. Because of the more accurate brightness temperature observations viewing at a constant angle and more suitable estimations of single scattering albedo and optical depth, both the SMAP L2 products performed significantly better than the SMOS product.Moreover, comparing with station density of in situ network, station representation is much more important in the evaluation of the satellite soil moisture products. Based on our analysis, we propose the following suggestions for improvement of the SMOS and SMAP product suitability in the mountainous areas: further optimization of effective temperature; revision of the retrieval algorithm of the SMOS mission to reduce the topographic impacts; and, careful selection of in situ observation stations for better representation of in situ network in future evaluations. All these improvements would lead to better applicability of the SMOS and SMAP products for soil moisture estimation to the high elevation and topographically complex mountainous areas in arid regions.     

An objective algorithm for reconstructing the three-dimensional ocean temperature field based on Argo profiles and SST data

While global oceanic surface information with large-scale, real-time, high-resolution data is collected by satellite remote sensing instrumentation, three-dimensional (3D) observations are usually obtained from in situ measurements, but with minimal coverage and spatial resolution. To meet the needs of 3D ocean investigations, we have developed a new algorithm to reconstruct the 3D ocean temperature field based on the Array for Real-time Geostrophic Oceanography (Argo) profiles and sea surface temperature (SST) data. The Argo temperature profiles are first optimally fitted to generate a series of temperature functions of depth, with the vertical temperature structure represented continuously. By calculating the derivatives of the fitted functions, the calculation of the vertical temperature gradient of the Argo profiles at an arbitrary depth is accomplished. A gridded 3D temperature gradient field is then found by applying inverse distance weighting interpolation in the horizontal direction. Combined with the processed SST, the 3D temperature field reconstruction is realized below the surface using the gridded temperature gradient. Finally, to confirm the effectiveness of the algorithm, an experiment in the Pacific Ocean south of Japan is conducted, for which a 3D temperature field is generated. Compared with other similar gridded products, the reconstructed 3D temperature field derived by the proposed algorithm achieves satisfactory accuracy, with correlation coefficients of 0.99 obtained, including a higher spatial resolution (0.25° × 0.25°), resulting in the capture of smaller-scale characteristics. Finally, both the accuracy and the superiority of the algorithm are validated.     

Predicting global landslide spatiotemporal distribution： Integrating landslide susceptibility zoning techniques and real-time satellite rainfall estimates

Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration thresholds and information related to land surface susceptibility. However, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides due to the lack of sufficient ground-based observing network in many parts of the world. Recent advances in satellite remote sensing technology and increasing availability of high-resolution geospatial products around the globe have provided an unprecedented opportunity for such a study. In this paper, a framework for developing a preliminary real-time prediction system to identify where rainfall-triggered landslides will occur is proposed by combining two necessary components： surface landslide susceptibility and a real-time space-based rainfall analysis system （http：//trmm.gsfc.nasa.gov）. First, a global landslide susceptibility map is derived from a combination of semi-static global surface characteristics （digital elevation topography, slope, soil types, soil texture, land cover classification, etc.） using a GIS weighted linear combination approach. Second, an adjusted empirical relationship between rainfall intensity-duration and landslide occurrence is used to assess landslide hazards at areas with high susceptibility. A major outcome of this work is the availability for the first time of a global assessment of landslide hazards, which is only possible because of the utilization of global satellite remote sensing products. This preliminary system can be updated continuously using the new satellite remote sensing products. This proposed system, if pursued through wide interdisciplinary efforts as recommended herein, bears the promise to grow many local landslide hazard analyses into a global decision-making support system for landslide disaster preparedness and mitigation activities across the world.     

An estimate of the suspended particulate matter (SPM) transport in the southern North Sea using SeaWiFS images,in situ measurements and numerical model results

A study is presented where satellite images (SeaWiFS), in situ measurements (tidal cycle and snapshot) and a 2D hydrodynamic numerical model have been combined to calculate the long term SPM (Suspended Particulate Matter) transport through the Dover Strait and in the southern North Sea. The total amount of SPM supplied to the North Sea through the Dover Strait is estimated to be 31.74×10⁶ t. The satellite images provide synoptic views of SPM concentration distribution but do not take away the uncertainty of SPM transport calculation. This is due to the fact that SPM concentration varies as a function of tide, wind, spring-neap tidal cycles and seasons. The short term variations (tidal, spring-neap tidal cycle) have not been found in the satellite images, however seasonal variations are clearly visible. Furthermore the SPM concentration in the satellite images is generally lower than in the in situ measurements. The representativness of SPM concentration maps derived from satellites for calculating long term transports has therefore been investigated by comparing the SPM concentration variability from the in situ measurements with those of the remote sensing data. The most important constraints of satellite images are related to the fact that satellite data is evidence of clear sky conditions, whereas in situ measurements from a vessel can be carried out also during rougher meteorological conditions and that due to the too low time resolution of the satellite images the SPM concentration peaks are often missed. It is underlined that SPM concentration measurements should be carried out during at least one tidal cycle in high turbidity areas to obtain representative values of SPM concentration.     

Using a remote sensing field experiment to investigate flux-footprint relations and flux sampling distributions for tower and aircraft-based observations

During a remote sensing field experiment conducted in the Southern Great Plains in 1997 (SGP97), tower and aircraft-based flux observations were collected over one of the main study sites in central Oklahoma. This is an agricultural region and contains primarily grassland/pasture and winter wheat, which was recently harvested leaving a significant number of fields either as wheat stubble or plowed bare soil. Multi-spectral data obtained by aircraft provided high-resolution (30 m) spatially-distributed vegetation cover and surface temperature information over the study area. The spatial variations in these surface states strongly affect the partitioning of surface fluxes between sensible and latent heat. These data, together with coarser resolution (5 km) satellite data, are used in a remote sensing-based energy balance modeling system that disaggregates flux estimates from 5 km to 30-m resolution. The resulting high-resolution flux maps provide a means for evaluating whether tower and aircraft-based flux measurements sample a full range in flux conditions for this landscape. In addition, this remote sensing-based modeling system can be used to investigate the influence of variability in these key surface states on tower and aircraft measurements through flux-footprint modeling. Under the light wind and unstable conditions that existed during the observations, highest correlation between aircraft and modeled estimated heat and water vapor fluxes were obtained using different flux-footprint estimates. More specifically, the source area for heat was estimated to be much closer to the aircraft flight line than for water vapor.     

Combining MODIS and AMSR‐E observations to improve MCD43A3 short‐time snow‐covered Albedo estimation

Land surface albedo plays an important role in the radiation budget and global climate models. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) provide 16‐day albedo product with 500‐m resolution every 8 days (MCD43A3). Some in‐situ albedo measurements were used as the true surface albedo values to validate the MCD43A3 product. As the 16‐day MODIS albedo retrievals do not include snow observations when there is ephemeral snow on the ground surface in a 16‐day period, comparisons between MCD43A3 and 16 day averages of field data do not agree well. Another reason is that the MODIS cannot detect the snow when the area is covered by clouds. The Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) data are not affected by weather conditions and are a good supplement for optical remote sensing in cloudy weather. When the surface is covered by ephemeral snow, the AMSR‐E data can be used as the additional information to retrieve the snow albedo. In this study, we developed an improved method by using the MODIS products and the AMSR‐E snow water equivalent (SWE) product to improve the MCD43A3 short‐time snow‐covered albedo estimation. The MODIS daily snow products MOD10A1 and MYD10A1 both provide snow and cloud information from observations. In our study region, we updated the MODIS daily snow product by combining MOD10A1 and MYD10A1. Then, the product was combined with the AMSR‐E SWE product to generate new daily snow‐cover and SWE products at a spatial resolution of 500 m. New SWE datasets were integrated into the Noah Land Surface Model snow model to calculate the albedo above a snow surface, and these values were then utilized to improve the MODIS 16‐day albedo product. After comparison of the results with in‐situ albedo measurements, we found that the new corrected 16‐day albedo can show the albedo changes during the short snowfall season. For example, from January 25 to March 14, 2007 at the BJ site, the albedo retrieved from snow‐free observations does not indicate the albedo changes affected by snow; the improved albedo conforms well to the in‐situ measurements. The correlation coefficient of the original MODIS albedo and the in‐situ albedo is 0.42 during the ephemeral snow season, but the correlation coefficient of the improved MODIS albedo and the in‐situ albedo is 0.64. It is concluded that the new method is capable of capturing the snow information from AMSR‐E SWE to improve the short‐time snow‐covered albedo estimation. Copyright © 2012 John Wiley & Sons, Ltd.     

On Creating Global Gridded Terrestrial Water Budget Estimates from Satellite Remote Sensing

The increasing availability and reliability of satellite remote sensing products [e.g., precipitation (P), evapotranspiration (ET), and the total water storage change (TWSC)] make it feasible to estimate the global terrestrial water budget at fine spatial resolution. In this study, we start from a reference water budget dataset that combines all available data sources, including satellite remote sensing, land surface model (LSM) and reanalysis, and investigate the roles of different non-satellite remote sensing products in closing the terrestrial water budget through a sensitivity analysis by removing/replacing one or more categories of products during the budget estimation. We also study the differences made by various satellite products for the same budget variable. We find that the gradual removal of non-satellite data sources will generally worsen the closure errors in the budget estimates, and remote sensing retrievals of P, ET, and TWSC together with runoff (R) from LSM give the worst closure errors. The gauge-corrected satellite precipitation helps to improve the budget closure (4.2–9 % non-closure errors of annual mean precipitation) against using the non-gauge-corrected precipitation (7.6–10.4 % non-closure errors). At last, a data assimilation technique, the constrained Kalman filter, is applied to enforce the water balance, and it is found that the satellite remote sensing products, though with worst closure, yield comparable budget estimates in the constrained system to the reference data. Overall, this study provides a first comparison between the water budget closure using the satellite remote sensing products and a full combination of remote sensing, LSM, and reanalysis products on a quasi-global basis. This study showcases the capability and potential of the satellite remote sensing in closing the terrestrial water budget at fine spatial resolution if properly constrained.     

A retrieval algorithm for satellite remote sensing of the nighttime global distribution of the sodium layer

A new retrieval method for satellite remote sensing of global sodium during nighttime is proposed. This method uses satellite limb observations of ozone density, neutral temperature, and Na D nightglow volume emission rate to retrieve sodium density. Our calculations show that, in the ideal condition in which there is no measurement noise, the retrieval error in the main region of sodium layer is very small (less than 1%). The retrieval error is mainly determined by the uncertainties in the observations of ozone, temperature and the Na D-line nightglow emission.     

Reconstruction of MODIS total suspended matter time series maps by DINEOF and validation with autonomous platform data

In situ measurements of total suspended matter (TSM) over the period 2003–2006, collected with two autonomous platforms from the Centre for Environment, Fisheries and Aquatic Sciences (Cefas) measuring the optical backscatter (OBS) in the southern North Sea, are used to assess the accuracy of TSM time series extracted from satellite data. Since there are gaps in the remote sensing (RS) data, due mainly to cloud cover, the Data Interpolating Empirical Orthogonal Functions (DINEOF) is used to fill in the TSM time series and build a continuous daily “recoloured” dataset. The RS datasets consist of TSM maps derived from MODIS imagery using the bio-optical model of Nechad et al. (Rem Sens Environ 114: 854–866, 2010). In this study, the DINEOF time series are compared to the in situ OBS measured in moderately to very turbid waters respectively in West Gabbard and Warp Anchorage, in the southern North Sea. The discrepancies between instantaneous RS, DINEOF-filled RS data and Cefas data are analysed in terms of TSM algorithm uncertainties, space–time variability and DINEOF reconstruction uncertainty.     

Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm

There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.     

遥感与实测地表温度的对比分析及在地震研究中的意义

伴随着空间观测技术的发展,卫星热红外遥感在地震领域受到越来越多的关注,同时存在许多基础性工作亟待完善.本文以由卫星遥感影像与实际测量两种不同方法获取的地表温度为基础,选取2006年3月～2008年2月近2年的数据,进行遥感与实测地表温度之间的对比研究.分析结果表明遥感与实测地表温度之间:夜间差值比白天要小,白天的相关性...     

Principles and methods for the validation of quantitative remote sensing products

We first discuss the relativity of true value and homogeneity for quantitative remote sensing products (QRSPs), and then propose the definitions of eigenaccuracy and eigenhomogeneity under practical conditions. The eigenaccuracy and eigenhomogeneity for land surface crucial parameters such as albedo, leaf area index (LAI), and surface temperature are analyzed based on a series of experiments. Secondly, we point out the differences and similarities between the scale-free phenomena of the QRSPs and the measur...     

Data assimilation methods in the Earth sciences

Although remote sensing data are often plentiful, they do not usually satisfy the users’ needs directly. Data assimilation is required to extract information about geophysical fields of interest from the remote sensing observations and to make the data more accessible to users. Remote sensing may provide, for example, measurements of surface soil moisture, snow water equivalent, snow cover, or land surface (skin) temperature. Data assimilation can then be used to estimate variables that are not directly observed from space but are needed for applications, for instance root zone soil moisture or land surface fluxes. The paper provides a brief introduction to modern data assimilation methods in the Earth sciences, their applications, and pertinent research questions. Our general overview is readily accessible to hydrologic remote sensing scientists. Within the general context of Earth science data assimilation, we point to examples of the assimilation of remotely sensed observations in land surface hydrology.     

Evaluation of in situ and remote sensing sampling methods for SPM concentrations, Belgian continental shelf (southern North Sea)

Large sets of suspended particulate matter (SPM) concentration data from in situ and remote sensing (moderate resolution imaging spectroradiometer, MODIS) samplings in the Belgian nearshore area (southern North Sea) are combined in order to evaluate their heterogeneity and the sampling techniques. In situ SPM concentration measurements are from a vessel (tidal cycle) and from a tripod. During the tidal cycle measurements, vertical profiles of SPM concentration have been collected; these profiles have been used as a link between satellite surface and near-bed tripod SPM concentrations. In situ time series at fixed locations using a tripod are excellent witnesses of SPM concentrations under all weather conditions and may catch SPM concentration variability with a much finer scale. The heterogeneity has been statistically assessed by comparing the SPM concentration frequency distributions. Tidal cycle, tripod and MODIS datasets have different distributions and represent a different subpopulation of the whole SPM concentrations population. The differences between the datasets are related to meteorological conditions during the measurements; to near-bed SPM concentration dynamics, which are partially uncoupled from processes higher up in the water column; to the sampling methods or schemes and to measurement uncertainties. In order to explain the differences between the datasets, the tripod data have been subsampled using wave height conditions and satellite and tidal cycle sampling schemes. It was found that satellites and low-frequent tidal cycle measurements are biased towards good weather condition or spring–summer seasons (satellite). The data show that the mean surface SPM concentration derived from satellite data is slightly lower than from in situ tidal cycle measurements, whereas it is significantly lower than the mean SPM concentration interpolated to the water surface from the tripod measurements. This is explained by the errors arising from the interpolation along the vertical profiles, but also by the fact that satellite-measured signal saturates in the visible band used to retrieve SPM concentration in very turbid waters.     

Quantifying uncertainty in remotely sensed land cover maps

Remotely sensed land cover maps are increasingly used as inputs into environmental simulation models whose outputs inform decisions and policy-making. Risks associated with these decisions are dependent on model output uncertainty, which is in turn affected by the uncertainty of land cover inputs. This article presents a method of quantifying the uncertainty that results from potential mis-classification in remotely sensed land cover maps. In addition to quantifying uncertainty in the classification of individual pixels in the map, we also address the important case where land cover maps have been upscaled to a coarser grid to suit the users’ needs and are reported as proportions of land cover type. The approach is Bayesian and incorporates several layers of modelling but is straightforward to implement. First, we incorporate data in the confusion matrix derived from an independent field survey, and discuss the appropriate way to model such data. Second, we account for spatial correlation in the true land cover map, using the remotely sensed map as a prior. Third, spatial correlation in the mis-classification characteristics is induced by modelling their variance. The result is that we are able to simulate posterior means and variances for individual sites and the entire map using a simple Monte Carlo algorithm. The method is applied to the Land Cover Map 2000 for the region of England and Wales, a map used as an input into a current dynamic carbon flux model.