Integration of MODIS-derived metrics to assess interannual variability in snowpack, lake ice, and NDVI in southwest Alaska

Impacts of global climate change are expected to result in greater variation in the seasonality of snowpack, lake ice, and vegetation dynamics in southwest Alaska. All have wide-reaching physical and biological ecosystem effects in the region. We used Moderate Resolution Imaging Spectroradiometer (MODIS) calibrated radiance, snow cover extent, and vegetation index products for interpreting interannual variation in the duration and extent of snowpack, lake ice, and vegetation dynamics for southwest Alaska. The approach integrates multiple seasonal metrics across large ecological regions.Throughout the observation period (2001-2007), snow cover duration was stable within ecoregions, with variable start and end dates. The start of the lake ice season lagged the snow season by 2 to 3 months. Within a given lake, freeze-up dates varied in timing and duration, while break-up dates were more consistent. Vegetation phenology varied less than snow and ice metrics, with start-of-season dates comparatively consistent across years. The start of growing season and snow melt were related to one another as they are both temperature dependent. Higher than average temperatures during the El Niño winter of 2002-2003 were expressed in anomalous ice and snow season patterns. We are developing a consistent, MODIS-based dataset that will be used to monitor temporal trends of each of these seasonal metrics and to map areas of change for the study area.     

Technical note. Illustration of the influence of shadowing on high latitude information derived from satellite imagery

Two separate types of contamination by shadowing have been identified following an analysis of two Thematic Mapper scenes from the Arctic. The well-established effect of orographic shadowing is particularly important for cryospheric surfaces. Cirrus clouds, often very difficult to identify by automated techniques, also cast shadows which decrease the radiances detected from snow and ice surfaces. These effects are illustrated here in relation to snow mapping algorithms.     

Application of the dense medium radiative transfer theory for calculating microwave emissivities of different sea ice types

The dense medium radiative transfer theory (DMRT) was used to calculate microwave emissivities of different undeformed sea ice types in the Arctic and Antarctic. The computed results were compared with measurements. More often taken for describing snow, we show that the DMRT can be applied to both sea ice and snow cover. While doing so the choice of appropriate parameters needed in the DMRT is discussed. As a result a multi-layered, uniform model for a variety of sea ice types including snow cover is obtained.     

The physical,radiative and microwave scattering characteristics of melt ponds on Arctic landfast sea ice

Melt ponds are an important characteristic of Arctic sea ice because of their control on the surface radiation balance. Little is known about the physical nature of these features and to date there is no operational method for detection of their formation or estimation of their aerial fraction. Coincident in situ observations, aerial surveys and synthetic aperture radar data from a field site in Arctic Canada are compared in an evaluation of the physical, radiative and electrical properties of melt ponds on first-year and multiyear sea ice. Results show that the interrelationships between the thermal diffusivity and conductivity of the snow cover control the mechanisms of snow ablation. Aerial fractions of snow patches, and light and dark coloured melt ponds, show considerable variation both as a function of proximity to land and due to ice type. First-year sea ice is shown to have a water background with discrete snow patches distributed throughout. Multiyear sea ice consists of discrete 'particles' within a snow background. Morphological measurements indicate that snow patches range in size with average areas of from 5 to 20m2 . Pond sizes over multiyear sea ice are also highly variable with averages ranging from 15 to 20m2. The integrated shortwave albedo was measured in the field and averaged to: snow patches (0.64 0.07); light melt ponds (0.29 0.04); and dark melt ponds (0.14 0.03). Snow patch size statistics explained a statistically significant proportion of the surface shortwave albedo. We found that microwave scattering could be used to obtain a measure of the onset ofmelt and had utility in detecting subtle details ofthe thermodynamic transition from winter through early melt into pond formation. We formalized a statistical relationship between microwave scattering and surface climatological albedo (sigma-alpha relationship). We found the relationship valid only for landfast firstyear sea ice under windy conditions. We conclude with a discussion of the role of surface wind stress and diurnal cycling in specification of the sigma-alpha relationship.     

Passive microwave signature observations of the Baltic Sea ice

Passive microwave signatures of various Baltic Sea ice types and open water leads were measured in the spring of 1995 and in March 1997 with airborne non‐imaging microwave radiometers (MWR) operating in the frequency range from 6.8 to 36.5?GHz. The MWR datasets were assigned by video imagery into open water leads and various ice type categories. The ground data provided further classification into dry, moist and wet snow sub‐categories. The datasets were used to study the behaviour of the brightness temperature and polarization ratio as a function of frequency and the degree of ice deformation; additionally, the dimensionality of multichannel datasets, classification of surface types, and suitability of the SSM/I and AMSR‐E data and NASA Team and Bootstrap ice concentration algorithms for the mapping of the Baltic Sea ice were examined. The results indicate that open water leads can be distinguished from sea ice regardless of the snow cover wetness, using even single‐channel MWR data. Classification of ice types is possible only under dry snow condition. Determination of the ice type concentrations from the coarse‐resolution space‐borne MWR data is not feasible, because the mean signatures for various ice types are very close to each other. The results also suggest that the SSM/I and AMSR‐E data and the NASA Team and Bootstrap algorithms can be used to map total ice concentration after modifications of open water and sea ice reference signatures.     

A new low-salinity sea-ice model for UHF radiometry

Abstract

A first-year sea-ice model, consisting of the dielectric properties and the geometry of ice and snow cover, has been used for interpreting brightness-temperature measurements of low-salinity sea ice. Dielectric measurements were made on about 400 ice samples, the salinity of which ranged from 0·1 to 1·5‰ by weight and the temperature from ?18 to ?01°C. Measurements were made with the electric field at angles of 0, 30 and 90° to the horizontal in the natural ice field. The effect of the fluctuations in the properties of ice and snow within the radiometer antenna beam has been included in the theoretical brightness-temperature model.     

Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data

We have produced the first 30 m resolution global land-cover maps using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. We have classified over 6600 scenes of Landsat TM data after 2006, and over 2300 scenes of Landsat TM and ETM+ data before 2006, all selected from the green season. These images cover most of the world's land surface except Antarctica and Greenland. Most of these images came from the United States Geological Survey in level L1T (orthorectified). Four classifiers that were freely available were employed, including the conventional maximum likelihood classifier (MLC), J4.8 decision tree classifier, Random Forest (RF) classifier and support vector machine (SVM) classifier. A total of 91,433 training samples were collected by traversing each scene and finding the most representative and homogeneous samples. A total of 38,664 test samples were collected at preset, fixed locations based on a globally systematic unaligned sampling strategy. Two software tools, Global Analyst and Global Mapper developed by extending the functionality of Google Earth, were used in developing the training and test sample databases by referencing the Moderate Resolution Imaging Spectroradiometer enhanced vegetation index (MODIS EVI) time series for 2010 and high resolution images from Google Earth. A unique land-cover classification system was developed that can be crosswalked to the existing United Nations Food and Agriculture Organization (FAO) land-cover classification system as well as the International Geosphere-Biosphere Programme (IGBP) system. Using the four classification algorithms, we obtained the initial set of global land-cover maps. The SVM produced the highest overall classification accuracy (OCA) of 64.9% assessed with our test samples, with RF (59.8%), J4.8 (57.9%), and MLC (53.9%) ranked from the second to the fourth. We also estimated the OCAs using a subset of our test samples (8629) each of which represented a homogeneous area greater than 500 m?×?500 m. Using this subset, we found the OCA for the SVM to be 71.5%. As a consistent source for estimating the coverage of global land-cover types in the world, estimation from the test samples shows that only 6.90% of the world is planted for agricultural production. The total area of cropland is 11.51% if unplanted croplands are included. The forests, grasslands, and shrublands cover 28.35%, 13.37%, and 11.49% of the world, respectively. The impervious surface covers only 0.66% of the world. Inland waterbodies, barren lands, and snow and ice cover 3.56%, 16.51%, and 12.81% of the world, respectively.     

Analysis of coastal ice cover using ERS-I SAR data

An applications demonstration of the use of Synthetic Aperture Radar (SAR) data in an operational selling is being conducted by the National Oceanic and Atmospheric Administration (NOAA) CoastWatch Program. In the development phase of this demonstration, case studies were conducted to assess the utility of SAR data for monitoring coastal ice in the Bering Sea, icebergs from calving glaciers in Prince William Sound, and lake ice in the Great Lakes. ERS-l SAR data was used in these studies. Results showed that depending on size and sea state icebergs could be detected from background and computer enhanced in the imagery, thaI SAR data can supplement and enhance the utility of satellite visible and infrared data sources for coastal ice monitoring, and that Greal Lakes ice cover can be classified by ice type and mapped in the SAR data using image processing techniques. Cloud cover was a common problem. Based on the further development of automated analysis algorithms and the increase in frequency of SAR coverage, the all-weather, day/night viewing capabilities of SAR make it a unique and valuable tool for operational ice detection and monitoring.     

Comparison of Envisat radar and airborne laser altimeter measurements over Arctic sea ice

Sea ice thickness is a crucial, but very undersampled cryospheric parameter of fundamental importance for climate modeling. Advances in satellite altimetry have enabled the measurement of sea ice freeboard using satellite microwave altimeters. Unfortunately, validation of these new techniques has suffered from a lack of ground truth measurements. Therefore, an airborne campaign was carried out in March 2006 using laser altimetry and photo imagery to validate sea ice elevation measurements derived from the Envisat/RA-2 microwave altimeter.We present a comparative analysis of Envisat/RA-2 sea ice elevation processing with collocated airborne measurements collected north of the Canadian Archipelago. Consistent overall relationships between block-averaged airborne laser and Envisat elevations are found, over both leads and floes, along the full 1300 km aircraft track. The fine resolution of the airborne laser altimeter data is exploited to evaluate elevation variability within the RA-2 ground footprint. Our analysis shows good agreement between RA-2 derived sea ice elevations and those measured by airborne laser altimetry, particularly over refrozen leads where the overall mean difference is about 1 cm. Notwithstanding this small 1 cm mean difference, we identify a larger elevation uncertainty (of order 10 cm) associated with the uncertain location of dominant radar targets within the particular RA-2 footprint. Sources of measurement uncertainty or ambiguity are identified, and include snow accumulation, tracking noise, and the limited coverage of airborne measurements.     

Observations of the snowcover on sea ice in the Gulf of Bothnia

Abstract

During March 1988, detailed measurements of the physical properties and depth distributions of the snow cover on the sea ice in the Gulf of Bothnia were made as part of BEPERS-88 (Bothnian Experiment in Preparation for ERS-1). The observations included profiles of the density, crystal structure. salinity, temperature, and brine volume (at 1-2 cm depth intervals), and 1084 snow depth measurements. These data are presented along with discussions of the implications of the measured properties for the interpretation of SAR imagery, and the use of laser profilometry for remotely estimating ice surface roughnesses.     

C波段SAR山区积雪面积提取研究

合成孔径雷达(SAR)不仅具有穿云透雾,全天候观测地表的能力,而且可穿透地表覆盖一定深度获取地表覆盖物内部特征信息。利用2011年10景ENVISAT\|ASAR可变极化模式精细图像(ASA_APP_1P)数据,分析比较了黑河上游祁连山冰沟流域不同时段积雪SAR后向散射特性,应用同期的MODIS积雪面积产品确定研究区积雪的累积和消融背景信息。研究表明:由于融雪期积雪含水量上升,SAR图像后向散射系数相比干雪或无雪图像明显降低,经过分析认为广泛应用的-3 dB阈值会明显低估湿雪覆盖范围,-2 dB阈值更适合该地区湿雪面积参数提取。山区积雪融化过程中低海拔区域积雪融化而高海拔山区积雪仍可能为干雪,在提取湿雪像元的基础上,根据Sigmoid函数阈值获取的像元湿雪百分比及DEM信息来提取干雪像元,最终获取整个流域积雪面积信息。通过与Landsat ETM+图像积雪面积分类结果进行比较,总体精度达到78%。积雪累积和消融背景信息的分析表明:误差主要源于流域东北部与西北部低海拔区域积雪快速消融。     

Flood detection and monitoring with the Autonomous Sciencecraft Experiment onboard EO-1

In this paper, we present a new way of detecting and monitoring flooding through the Autonomous Sciencecraft Experiment (ASE) [Chien, S. T., Debban, C., Yen, R., Sherwood, R. Castano, B., & Cichy, A. G. et al. (2001). ASC Science Study Report, available from http://ASE.jpl.nasa.gov], which is part of the Space Technology 6 effort under NASA's New Millennium Program. Recent autonomy experiments conducted on Earth Observing 1 (EO-1) using the ASE flight software have demonstrated the ability of several science algorithms to successfully classify key features including flood-induced changes, in hyperspectral images captured by the EO-1 Hyperion instrument. Furthermore, onboard science analysis on the classified images has been performed, and then used to modify an operational plan without interaction from the ground (Sherwood, R., Chien, S., Tran, D., Cichy, B., Castano, R., Davies, A., et al. (2004). Preliminary results of the autonomous sciencecraft experiment. In: Proceedings of the IEEE Aerospace Conference, Big Sky, MT). These algorithms are used to downlink science data only when change occurs, and to detect features of scientific interests such as flooding, volcanic eruptions, and the formation and breakup of sea ice. The purpose of this paper is to demonstrate the success of ASE and its implications on detecting, mapping, and monitoring transient processes such as flooding autonomously from space. Mapping of water inundation and its change through time is part of our focus in studying transient processes from space.In 2004, hyperspectral data were acquired from the Hyperion instrument for target areas around the world that have a high potential for flooding to develop and test floodwater classifiers. In addition, classifier thresholds were determined from both normal flows and possible flood conditions. The paper introduces the development, testing, and success of the ASE software in detecting and reacting to flooding in near real-time. ASE is now operational and flight-tested, and, thus, ready to use for space-borne reconnaissance. Successful demonstration of the floodwater classifiers includes the capture of a rare flooding event of the Australian Diamantina River during ground testing in February 2004, and the detection of flood-related changes along the Brahmaputra River in Bangladesh and the Yukon River in Alaska during onboard testing on EO-1 in 2005. Both of these detections led to triggered responses onboard the spacecraft, which included acquiring additional Hyperion scenes. These results pave the way for future smart reconnaissance missions of transient processes on Earth and beyond. It is hoped that ASE will become a default in future missions to increase the science return by introducing spacecraft autonomy for detection and monitoring of science events, which otherwise would be discovered too late or altogether missed.     

利用地面观测实验评价湖冰对被动微波反演积雪算法的影响

通过对东北积雪实验观测数据和HUT(the Helsinki University of Technology)积雪-冰-水层模型模拟数据的比较分析,描述了积雪-冰-水系统的发射率特征.并于2010年1月21日～22日在吉林省松原市的松花江进行了积雪辐射计观测试验,通过对湖冰上的积雪的亮温观测和HUT模型模拟的亮温比较...     

A Modeling and Rendering Method for Snow by Using Metaballs

The display of natural scenes such as mountains, trees, the earth as viewed from space, the sea, and waves have been attempted. Here a method to realistically display snow is proposed. In order to achieve this, two important elements have to be considered, namely the shape and shading model of snow, based on the physical phenomenon. In this paper, a method for displaying snow fallen onto objects, including curved surfaces and snow scattered by objects, such as skis, is proposed. Snow should be treated as particles with a density distribution since it consists of water particles, ice particles, and air molecules. In order to express the material property of snow, the phase functions of the particles must be taken into account, and it is well-known that the color of snow is white because of the multiple scattering of light. This paper describes a calculation method for light scattering due to snow particles taking into account both multiple scattering and sky light, and the modeling of snow.     

Accuracy assessment of MODIS,NOAA and IRS data in snow cover mapping under Himalayan conditions

Snow cover information is an essential parameter for a wide variety of scientific studies and management applications, especially in snowmelt runoff modelling. Until now NOAA and IRS data were widely and effectively used for snow‐covered area (SCA) estimation in several Himalayan basins. The suit of snow cover products produced from MODIS data had not previously been used in SCA estimation and snowmelt runoff modelling in any Himalayan basin. The present study was conducted with the aim of assessing the accuracy of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions. The total SCA was estimated using these three datasets for 15 dates spread over 4 years. The results were compared with ground‐based estimation of snow cover. A good agreement was observed between satellite‐based estimation and ground‐based estimation. The influence of aspect in SCA estimation was analysed for the three satellite datasets and it was observed that MODIS produced better results. Snow mapping accuracy with respect to elevation was tested and it was observed that at higher elevation MODIS sensed more snow and proved better at mapping snow under mountain shadow conditions. At lower elevation, IRS proved better in mapping patchy snow cover due to higher spatial resolution. The temporal resolution of MODIS and NOAA data is better than IRS data, which means that the chances of getting cloud‐free scenes is higher. In addition, MODIS has an automated snow‐mapping algorithm, which reduces the time and errors incorporated during processing satellite data manually. Considering all these factors, it was concluded that MODIS data could be effectively used for SCA estimation under Himalayan conditions, which is a vital parameter for snowmelt runoff estimation.     

A smart classifier for extracting environmental data from digital image time-series: Applications for PhenoCam data in a tidal salt marsh

PhenoCams are part of a national network of automated digital cameras used to assess vegetation phenology transitions. Effectively analyzing PhenoCam time-series involves eliminating scenes with poor solar illumination or high cover of non-target objects such as water. We created a smart classifier to process images from the “GCESapelo” PhenoCam, which photographs a regularly-flooded salt marsh. The smart classifier, written in R, assigns pixels to target (vegetation) and non-target (water, shadows, fog and clouds) classes, allowing automated identification of optimal scenes for evaluating phenology. When compared to hand-classified validation images, the smart classifier identified scenes with optimal vegetation cover with 96% accuracy and other object classes with accuracies ranging from 86 to 100%. Accuracy for estimating object percent cover ranged from 74 to 100%. Pixel-classification with the smart classifier outperformed previous approaches (i.e. indices based on average color content within ROIs) and reduced variance in phenology index time-series. It can be readily adapted for other applications.     

Detection of small-scale roughness and refractive index of sea ice in passive satellite microwave remote sensing

Polar ice masses and sheets are sensitive indicators of climate change. Small-scale surface roughness significantly impacts the microwave emission of the sea ice/snow surface; however, published results of surface roughness measurements of sea ice are rare. Knowing the refractive index is important to discriminate between objects. In this study, the small-scale roughness and refractive index over sea ice are estimated with AMSR-E observations and a unique method. Consequently, the small-scale surface roughness of 0.25 cm to 0.5 cm at AMSR-E 6.9 GHz shows reasonable agreement with the results of known observations, ranging from 0.2 cm to 0.6 cm for the sea ice in the Antarctic and Arctic regions. The refractive indexes are retrieved from 1.6 to 1.8 for winter, from 1.2 to 1.4 for summer in the Arctic and the Antarctic, which are similar to those of the sea ice and results from previous studies. This research shows the physical characteristics of the sea ice edges and melting process. Accordingly, this investigation provides an effective procedure for retrieving the small-scale roughness and refractive index of sea ice and snow. Another advantage of this study is the ability to distinguish sea ice from the sea surface by their relative small-scale roughness.     

Integrated assessment on multi-temporal and multi-sensor combinations for reducing cloud obscuration of MODIS snow cover products of the Pacific Northwest USA

MODIS (Moderate Resolution Imaging Spectroradiometer) snow cover products, of daily, freely available, worldwide spatial extent at medium spatial resolution, have been widely applied in regional snow cover and modeling studies, although high cloud obscuration remains a concern in some applications. In this study, various approaches including daily combination, adjacent temporal deduction, fixed-day combination, flexible multi-day combination, and multi-sensor combination are assessed to remove cloud obscuration while still maintain the temporal and spatial resolutions. The performance of the resultant snow cover maps are quantitatively evaluated against in situ observations at 244 SNOTEL stations over the Pacific Northwest USA during the period of 2006-2008 hydrological years. Results indicate that daily Terra and Aqua MODIS combination and adjacent temporal deduction can reduce cloud obscuration and classification errors although an annual mean of 37% cloud coverage remains. Classification errors in snow-covered months are actually small and tend to underestimate the snow cover. Primary errors of MODIS daily, fixed and flexible multi-day combination products occur during transient months. Flexible multi-day combination is an efficient approach to maintain the balance between temporal resolution and realistic estimation of snow cover extent since it uses two thresholds to control the combination processes. Multi-sensor combinations (daily or multi-day), taking advantage of MODIS high spatial resolution and AMSR-E cloud penetration ability, provide cloud-free products but bring larger image underestimation errors as compared with their MODIS counterparts.     

An analysis of specialist and non-specialist user requirements for geographic climate change information

The EU EuroClim project developed a system to monitor and record climate change indicator data based on satellite observations of snow cover, sea ice and glaciers in Northern Europe and the Arctic. It also contained projection data for temperature, rainfall and average wind speed for Europe. These were all stored as data sets in a GIS database for users to download. The process of gathering requirements for a user population including scientists, researchers, policy makers, educationalists and the general public is described. Using an iterative design methodology, a user survey was administered to obtain initial feedback on the system concept followed by panel sessions where users were presented with the system concept and a demonstrator to interact with it. The requirements of both specialist and non-specialist users is summarised together with strategies for the effective communication of geographic climate change information.     

Investigation of C- and X-band backscattering signatures of Baltic Sea ice

Backscattering signatures of various Baltic Sea ice types and open water leads were measured with the helicopter-borne C- and X-band Helsinki University of Technology scatterometer (HUTSCAT) during six ice research campaigns in 1992–1997. The measurements were conducted at incidence angles of 23° and 45°. The HUTSCAT data were assigned by video imagery into various surface type categories. The ground data provided further classification of the HUTSCAT data into different snow wetness categories (dry, moist and wet snow). Various basic statistical parameters of backscattering signature data were used to study discrimination of open water leads and various ice types. The effect of various physical parameters (e.g. polarization, frequency, snow condition) on the surface type discrimination was investigated. The results from the data analysis can be used to help the development of sea ice classification algorithms for space-borne SAR data (e.g. Radarsat and Envisat). According to the results from the maximum likelihood classification it is not possible to reliably distinguish various surface types in the SAR images only by their backscatter intensity. In general, the best ice type discrimination accuracy is achieved with C-band VH-polarization σ° at an incidence angle of 45°.