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Arctic sea ice change is one of the critical factors affecting the global climate environment; hence, it is crucial to obtain sea ice thickness with high accuracy while studying polar and global climate change. In the process of using altimetry data to estimate sea ice thickness, the mean sea surface height will bring greater uncertainty to the extraction of sea ice freeboard, which will affect the accuracy of sea ice thickness. Also, the influence of snow cover on radar signal penetration will bring greater uncertainty to sea ice thickness. In this paper, we present a processing chain for sea ice thickness estimation. First, we compare the effect of four different MSS models on the freeboard estimation. Then, considering the incomplete penetration of radar signals and the different speeds of radar signals penetrating the snow layer and the vacuum, the traditional sea ice thickness model is optimized to obtain the sea ice thickness. Compared with the Operation IceBridge (OIB) sea ice thickness, the accuracy of sea ice thickness obtained by the optimized model is better than 0.350 m, with a root mean square error of 0.260 m and a mean bias of 0.048 m. The comparison results show that the combination of the latest DTU18 MSS model and sea ice thickness optimization model effectively improve the accuracy of sea ice thickness. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) is an important variable for controlling the energy exchange between earth's surface and atmosphere. Accurate estimation of the distribution of SSM and its dynamic changes is increasingly essential in the study of hydrology, ecology, agriculture, geological hazard monitoring, and global climate change. This paper presents a new model for SSM retrieval (named, KSSM) through assimilating soil porosity with a two-dimensional feature space composed of land surface temperature and vegetation index (LST-VI). The KSSM model takes into account the physical properties of soil porosity, so it is suitable for SSM retrieval of various soil types in different regions. The KSSM model was validated using the measured data, and a comparison analysis with other data was conducted. Through the sensitivity analysis, it is concluded that the error of SSM retrieved by the KSSM model is less than 0.04 m3 m−3 and the KSSM model is robust. High accuracy of volumetric SSM estimation, error within 10% compared with the measured data, can be achieved by the KSSM model without the need for an in-situ SSM. [ABSTRACT FROM AUTHOR]

Lakes are important to the healthy functioning of the urban ecosystem. The urban lakes in Wuhan, China, which is known as 'city of hundreds of lakes', are facing substantial threats mainly due to rapid urbanization. This paper focused on detecting the spatial and temporal change of urban lakes in Wuhan, using a long time series of Landsat and HJ-1A remotely sensed data from 1987 to 2016. The permanent disappearance and seasonal fluctuation of 28 main urban lakes were analysed, and their relationships with climatic change and human activities were discussed. The results show that most lakes in Wuhan had shrunk over the past 30 years resulting in a permanent change from water to land. The shrinkage was also most apparent in the central region of the city. Seasonal fluctuations of lake area were evident for most lakes but the relative important driving variable of lake area change varied between sub-periods of time for different lakes. The explanatory power of impervious surface to five-year permanent water change is 91.75%, suggesting that urbanization – as increasing impervious surface – had led to the shrinkage of urban lakes in Wuhan. In all, 128.28 km2 five-year permanent water disappeared from 1987 to 2016. [ABSTRACT FROM AUTHOR]

Owing to the influence of global change, land cover and land use have changed significantly over the last decade in the cold and arid regions of China, such as Madoi County which is located in the source area of the Yellow River. In this paper, land-use/cover change and landscape dynamics are investigated using satellite remote sensing (RS) and a geographical information system (GIS). The objectives of this paper are to determine land-use/cover transition rates between different cover types in the Madoi County over 10 years e.g., from 1990 to 2000. Second, the changes of landscape metrics using various indices and models are quantified. The impact factors of LUCC (Land-Use land cover Change) are systematically identified by integrating remote sensing as well as statistical data, including climate, frozen soil, hydrological data and the socio-economic data. Using 30 m×30 m spatial resolution Landsat (Enhanced) Thematic Mapper (TM/ETM+) data in our study area, nine land cover classes can be discriminated. Our results show that Grassland, Marshes and Water Bodies decrease notably, while oppositely, Sands - Gobi and Barren land increase significantly. The number of lakes with an acreage larger than six hectares decreased from 405 in 1990 to 261 in 2000. Numerous small lakes dried out. The area of grassland with a high cover fraction decreased as well, while the surface area of grassland with a medium level of cover fraction increased. The medium cover fraction grassland mainly originates from high cover fraction grassland. The desertification of land is a serious issue. (ii) The inter-transformations between Grasslands, Barren Land, Sands, Gobi, Water Bodies and Marshes are remarkable. The Shannon-Weaver Diversity Index (SWDI), the Evenness Index (EI) and the extent of Landscape Heterogeneity (LH) has improved. Marshes have become more fragmented hence, with less connected patches. (iii) In the recent 30 years, average annual temperature, the power of evaporation and the index of dryness did increase significantly. Moreover, soil moisture content (SMC) decreased and the drought trend accelerated. The degradation of frozen soil has impacted on the decrease of surface water area and induced a drop in groundwater levels. Monitoring LUCC in sensitive regions would not only benefit from a study of vulnerable ecosystems in cold and high altitude regions, but would provide scientifically based decision-making tools for local governments as well. [ABSTRACT FROM AUTHOR]

The detection of land-cover change over large areas using short time series is a challenging and important task in global change studies. This paper introduces a novel method, called the Segment-based Detection of Trends and Change (SDTC), to determine areas that are undergoing land-cover changes. The method is illustrated in the State of Alaska over the 2001–2009 time period using three time series derived from Moderate Resolution Spectroradiometer (MODIS) imagery: the normalized difference vegetation index (NDVI), albedo, and land surface temperature (LST). SDTC extends seasonal trend analysis (STA) by using segmentation in the detection process to combine multiple variables and to incorporate the spatial context. Segments labelled as ‘change’ correspond to groups of adjacent pixels with a majority of pixels undergoing significant temporal trends as defined by the Mann–Kendall (MK) procedure and STA. Segments correspond to landscape units in a less arbitrary manner than pixels because they represent groupings of adjacent areas undergoing similar temporal behaviour. Findings indicate that the use of MK in conjunction with segmentation exploits more fully the richness of the spatial context in the process of detection. Results suggest that SDTC is a useful method for detecting and characterizing land-cover change. The technique is conservative and eliminates the ‘salt and pepper’ effect by filtering noise and identifying areas of change. Using SDTC, we found that most areas of change in Alaska undergo between one and three significant changes and that increasing LST seasonality constitutes the most widespread type of change. [ABSTRACT FROM PUBLISHER]

Feature tracking is an efficient way to derive glacier velocity. It is based on a cross-correlation algorithm that seeks offsets of the maximal correlation windows on repeated satellite images. In this paper we demonstrate that different window sizes lead to different velocities. The averaged velocity gradient (AVG) method is proposed to improve window sizes in feature tracking and to obtain the most suitable flow field. The AVG method measures velocity variation between adjacent windows on the whole glacier in the image. Different window sizes lead to different AVG values, and the best-size window corresponds to the value where the AVG changes from abrupt to gradual. Using improved feature tracking, two flow fields of the same glacier are acquired with Advanced Land Observing Satellite (ALOS) optical and synthetic aperture radar (SAR) data, respectively. The advantages, application conditions, accuracy and disadvantages of the two kinds of data using the feature tracking method are discussed. [ABSTRACT FROM AUTHOR]

This study aims to assess CO2 levels in China, based on the dry air column-averaged mixing ratios of CO2 measured by the Scanning Imaging Absorption spectrometer for Atmospheric CartograpHY (SCIAMACHY) instrument on board ENVISAT and emission inventories. A land cover and statistical data set, the Regional emission inventory in Asia (REAS), was also used in this study. The results show that the spatial variations of the CO2 concentration in the whole of China are obvious, and with clear seasonal fluctuation during 2003-2005. The monthly averaged variability of CO2 over China fluctuates, with the peak occurring in April to May and the trough occurring in August to October. The largest peak-to-trough amplitude was about 16 ppm in this period. The spatial and temporal variations of CO2 had a strong correlation with anthropogenic CO2 emissions and regional terrestrial ecosystem, as well as local meteorological conditions. This paper illustrates that satellite data are promising for obtaining a comprehensive understanding of the variations of atmospheric CO2 concentrations and of CO2 emissions to the atmosphere. [ABSTRACT FROM AUTHOR]

India's climate is changing gradually from one year to another. A significant increase in surface air temperature in different Indian regions was observed from 1998 onwards, which became all the more pronounced in 2009. Monthly mean maximum and minimum temperatures were higher than normal values over the whole of India, accompanied by a sharp increase in the duration and frequency of hot days and heatwave conditions. In the light of these observations the paper is aimed at investigating the spatial and temporal variation of air temperature at different altitudes and the interannual trend for different months over the Indian region for a period of 108 years from 1901 to 2008. It is observed that the variation in air temperature is not uniform at all places in India. The yearly mean air temperature for the period 1948-2008 exhibits an increasing trend at all atmospheric layers from the surface up to 10 km. The recently observed increase in surface air temperature and longer periods of heatwaves and hot days in India may be due to a higher occurrence of dust storms and forest fires and the influence of local factors, such as a sharp population growth accompanied by uncontrolled urbanization and rapid industrialization, resulting in high levels of pollution and imbalances in the regional climate. [ABSTRACT FROM AUTHOR]

In this paper, we quantified vegetation variations in the Qaidam Basin from 1982 to 2003 by using growing-season NDVI sequences, which were defined as the summation of monthly NDVI values from May to September, and were calculated pixel-by-pixel from a successive 8-km NDVI dataset. We adopt linear regressions to examine the trends in growing-season NDVI and the trends in climate (temperature, precipitation and sunshine duration) during this period in an attempt to depict their temporal and spatial variability. Our results indicate that climate in the Qaidam Basin has homogeneously warmed at a rate of about 0.6°C/decade during the study period, with significant trends in monthly mean temperatures in April-September. However, there were no statistically significant trends observed in precipitation and sunshine duration. We found positive growing-season NDVI trends in 31.6% of the vegetated lands in 1982-2003 and in 24.1% over the first half period, 1982-1992. In addition, few areas were shown to have negative trends during these periods. In 1993-2003, however, the percentage of land with a positive trend decreased to 13.1%, and the percentage of vegetated land with a negative trend increased to 10.2%. Growing-season NDVI trends show both temporal and spatial variability. Areas with negative trends are distributed mostly at lower elevations and near oasis boundaries, and areas with positive trends at higher elevations. Using correlation analyses we estimated the relationship between growing-season NDVI and the climatic factors with the consideration of duration and lagging effects. The results suggest that growing-season NDVI trends are more correlated to temperature increases in growing-season months when compared to variations in precipitation and sunshine duration; however increased precipitation amounts within May-August can also facilitate vegetation growth in some of this arid basin. However, we found no significant correlations between growing-season NDVI and temperature in the non-trend areas, which account for the majority of the vegetated land. We suggest that the variability in vegetation responses to the observed warming climates results from the differences in background thermal condition and moisture availability, which depend on elevation and other factors, such as hydrological conditions. [ABSTRACT FROM AUTHOR]

Although the Montreal Protocol now controls the production and emission of ozone depleting substances, the timing of ozone recovery is unclear. There are many other factors affecting the ozone layer, in particular climate change is expected to modify the speed of re-creation of the ozone layer. Therefore, long-term observations are needed to monitor the further evolution of the stratospheric ozone layer. Measurements from satellite instruments provide global coverage and are supplementary to selective ground-based observations. The combination of data derived from different space-borne instruments is needed to produce homogeneous and consistent long-term data records. They are required for robust investigations including trend analysis. For the first time global total ozone columns from three European satellite sensors GOME (ERS-2), SCIAMACHY (ENVISAT), and GOME-2 (METOP-A) are combined and added up to a continuous time series starting in June 1995. On the one hand it is important to monitor the consequences of the Montreal Protocol and its amendments; on the other hand multi-year observations provide the basis for the evaluation of numerical models describing atmospheric processes, which are also used for prognostic studies to assess the future development. This paper gives some examples of how to use satellite data products to evaluate model results with respective data derived from observations, and to disclose the abilities and deficiencies of atmospheric models. In particular, multi-year mean values derived from the Chemistry-Climate Model E39C-A are used to check climatological values and the respective standard deviations. [ABSTRACT FROM AUTHOR]

This paper examines three empirically based methods of monitoring forest growth between 1991 and 2000 from airborne synthetic aperture radar (SAR). In the first method, height change and volume change between 1991 and 2000 were estimated from the mean L-band stand backscatter difference between the two dates. Height change and volume change over the 9-year period were estimated with an accuracy of 0.23 m and 15 m3 ha-1, respectively, when the stands were below saturation point for the first date. The accuracy of the results was lower for stands beyond saturation in both data sets. In the second method, the height change is calculated from the estimated stand height in 2000 minus the estimated stand height in 1991. The second method produced poorer results than the first method, but better results than predicted by the error propagation equation. The difference between the observed accuracy and the expected error (based on the error propagation equation) appears to be due to a systematic bias in both the 1991 and 2000 estimates, as the residuals are correlated for stands below 20 years old (r = 0.71 for stand volume residuals). The third experiment investigates the utility of data from two dates to classify the stands into three age classes. The results show that, with two images separated by 9 years, 85% of stands were correctly classified compared with 69% for a single date L-HV image. [ABSTRACT FROM AUTHOR]

This paper describes a method to detect the impact of rainfall anomalies on vegetation phenology, in terms of timing (phase) and greenness, by using Fourier series to fit a time series of Normalized Difference Vegetation Index (NDVI) observations. The study was conducted in the northern semiarid region of Argentina, where rainfall is the driving factor of vegetation phenology. A 9-year time series of monthly NDVI Global Area Coverage (GAC) images, obtained with the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR), was split into nine series of 12-monthly images, each corresponding to a yearly growth cycle. A Fast Fourier Transform (FFT) algorithm was applied to each cycle, and derived parameters were analysed according to rainfall anomalies for irrigated and rainfed crops, grasslands and native forest. Derived Fourier parameters were: mean NDVI, amplitude and phase. Both negative and positive rainfall anomalies had a significant impact on the Fourier parameters. Amplitude and phase were the most sensitive parameters. Droughts modified the monomodal structure of the yearly cycle by decreasing the contribution of the 12-month periodic component and increasing the contribution of the 6-month component. The impact of drought on the Fourier parameters was highest for rainfed crops. Yearly values of Fourier parameters for grasslands and native forest were affected by prevailing hydrological conditions over the previous year. [ABSTRACT FROM AUTHOR]

The effects of climate change on northern vegetation productivity need to be fully understood in order to reduce uncertainties in predicting vegetation distributions under different climate warming scenarios. Knowledge of the relationship between northern climate and vegetation productivity will also help provide a better understanding of changes in vegetation distributions as an indicator of climate change and variability. Vegetation productivity and biomass have been monitored using long-term satellite earth observations, mostly using the Normalized Difference Vegetation Index (NDVI), as a cumulative indicator of all effects resulting from processes related to climate change, including changes in temperature, precipitation, and disturbance. In this paper, the investigation is focused on the short-term effect of temperature anomalies on arctic and tree-line transition vegetation productivity in both dry and humid regions of Canada. The analysis shows that several land-cover types composed mainly of trees and shrubs exhibit a significant increase in NDVI with higher-than-normal temperatures in the preceding 10-40-day period, while land-cover types consisting of lichen and moss growing on mostly barren surfaces show a significant NDVI decrease with increased temperature. These trends are consistent with results reported in plot-warming experiments in the north, which have shown that certain vegetation communities increase, while others decrease in cover fraction and biomass in response to warming. When land cover is grouped into increasing and decreasing NDVI with temperature and stratified by dry and humid regions of Canada, much of the dry region of northern Canada does not exhibit significant NDVI response to preceding temperature anomalies. It is expected that in the absence of disturbance or other limiting factors, an increased frequency of elevated temperature anomalies may eventually contribute to changes in vegetation biomass. A map of land-cover types that have the potential to increase in biomass with climate warming and those that are vulnerable to decline is presented. [ABSTRACT FROM AUTHOR]

The EU-CLOUDMAP project took place between 1997-2000 as a collaboration between five university and government research groups in the UK, Germany, the Netherlands and Switzerland. The original scientific motivation of the EU-CLOUDMAP Project was to improve the measurement and characterization of cirrus and contrail cloud properties. IPCC (Penner et al. 1999) demonstrated that contrail clouds could play a small but significant role in changing the radiative balance of the atmosphere based on work from one of the CLOUDMAP partners (DLR) using AVHRR data over Europe. However, the scope was broadened to include properties of clouds at all altitudes as (Cess et al. 1993) had shown that depending on how cloud processes are parameterised can lead to an order of magnitude difference in predictions of surface temperature due to changes in CO2 radiative forcing. This error is by far the largest uncertainty in making accurate forecasts of global warming. The primary technological motivation of the Project was to develop new techniques for deriving cloud-top properties (cloud-top height, amount, microphysics and winds) from a new series of meteorological sensors based on the use of either cloud-top stereo (ATSR-2 and MISR) or Oxygen A-band (MOS) and their application to the generation of new cloud climatology products. A secondary goal was to develop an automated technique, based on fuzzy logic, to detect contrails in non-thermal imagery where contrails can only be detected through their unique spatial characteristics. Validation of cloud products was perceived as a crucial central issue to any adoption of these products by the Numerical Weather Prediction (NWP) and climate forecasting communities. This presents unique challenges as validation must be conducted simultaneously with satellite data acquisition. Ground-based remote sensing techniques exploiting continuously operating radars and lidars were used and a new technique based on visible stereo digital cameras for retrieving cloud-base properties was pioneered. A technology spin-off from the CLOUDMAP work at UCL was the development of an uncooled thermal IR fish-eye lens camera for continuously monitoring cloud cover from the ground to replace existing manual observations of cloud cover (Chapman et al. 2007). Eight papers in this issue show some of the highlights of the project including fundamental aspects of the algorithms for retrieval of cloud-top height using stereo photogrammetry from ATSR-2 (Muller et al. 2007, Denis et al. 2007) and MOMS (Drescher 2007) and Oxygen A-band from MOS (Preusker et al. 2007). Seiz et al. (2007) describe the development of a novel technique for the automated retrieval of cloud-base height from stereo visible digital cameras. Naud et al. (2007) describe the validation of cloud-top heights from ATSR-2 and MOS using ground-based radar and lidar. Hetzheim (2007) shows how fuzzy logic systems can be employed for contrail detection when thermal imagery is not available using MOS and ATSR-2 to demonstrate his techniques. Finally, Meyer et al. (2007) show results from applying the thermal IR contrail detection technique to AVHRR over Asia. [ABSTRACT FROM AUTHOR]

Remote sensing plays a vital role in climate change studies by providing us with a record of 'snapshots' of the Earth's environment through time. Almost 30 years have past since the launch of the first routine and repetitive remote sensing systems. We are on the brink of being able to use remotely sensed imagery to define climatic normals using features of the Earth's surface environment as proxy indicators. Before we can do this, however, we need to develop new image analysis strategies—hypertemporal image analysis techniques—which we can use to measure variability and identify changes in long sequences of remotely sensed imagery. In this paper we explore one such technique: statistical time series analysis (TSA). We apply AutoRegressive-Moving Average (ARMA) time series models to a 108-month sequence of passive microwave images of Arctic sea ice concentrations. TSA is traditionally a manual technique applied to one or two temporal variables. Through this research we demonstrate how, by making two simple assumptions about the data, the approach can be automated to process the thousands of time series in a temporal image sequence. ARMA models are shown to be useful in deriving a spatial summary of the temporal characteristics of the modelled phenomenon, for identifying changes within that system, and for forecasting into the future with statistical confidence. [ABSTRACT FROM AUTHOR]

A call for papers regarding the topics listed in the 2011 Annual Conference on Earth Observation in a Changing World of the Remote Sensing and Photogrammetry Society (RSPSoc) including natural environment, climate change, and data management is presented.