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Sustained ocean color monitoring is vital to understanding the marine ecosystem. It has been identified as an Essential Climate Variable (ECV) and is a vital parameter in understanding long-term climate change. Furthermore, observations can be beneficial in observing oil spills, harmful algal blooms and the health of fisheries. Space-based remote sensing, through MERIS, SeaWiFS and MODIS instruments, have provided a means of observing the vast area covered by the ocean which would otherwise be impossible using ships alone. However, the large pixel size makes measurements of lakes, rivers, estuaries and coastal zones difficult. Furthermore, retirement of a number of widely used and relied upon ocean observation instruments, particularly MERIS and SeaWiFS, leaves a significant gap in ocean color observation opportunities This paper presents an overview of the SeaHawk mission, a collaborative effort between Clyde Space Ltd., the University of North Carolina Wilmington, Cloudland Instruments, and Goddard Spaceflight Center, funded by the Gordon and Betty Moore Foundation. The goal of the project is to enhance the ability to observe ocean color in high temporal and spatial resolution through use of a low-cost, next-generation ocean color sensor flown aboard a CubeSat. The final product will be 530 times smaller (0.0034 vs 1.81m 3 ) and 115 time less massive (3.4 vs 390.0kg) but with a ground resolution 10 times better whilst maintaining a signal/noise ratio 50% that of SeaWiFs. This paper will describe the objectives of the mission, outline the payload specification and the spacecraft platform to support it.

Proceedings of SPIE - The International Society for Optical Engineering, Volume 9229, 2014, Article number 922912 Accurate assessment of water use is an important issue in a globally changing climate and environment, where water is becoming a scarce but essential resource. The concept "?Water Footprint (WF) of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage, respectively. This indicator provides valuable information for a global assessment of how water resources are used. Remote sensing (RS) provides physically-based, worldwide, and consistent spatial information over space and time, and has been used in hydrological applications in order to estimate relevant variables at different temporal and spatial scales. The paper focuses on exploring and exploiting the potential of using RS techniques and data for WF assessment in agriculture. Based on recent papers initiated in this research topic the investigation focuses on how variables needed in the calculation of water footprint are obtained (based on non RS and on RS approaches), on identifying the inputs required for estimating the WF of crops and whether it is feasible to integrate various RS approaches. The results of this study demonstrate the usefulness of satellite data for water footprint assessment, which were obtained by the Remote Sensing Working Group in the framework of the ESSEM COST Action ES1106, "Assessment of EUROpean AGRIculture WATer use and trade under climate change"? (EURO-AGRIWAT). © 2014 SPIE.

Climate variability and change are risk factors for climate sensitive activities such as forestry. Managing these risks requires “climate knowledge”, i.e. a sound understanding of causes and consequences of climate variability and knowledge of potential management options that are suitable in light of the climatic risks posed. Thresholding based on biophysical variables derived from time series satellite data is a new approach to classifying forest land cover via remote sensing through use of Normalized Difference Vegetation Index –NDVI and Enhanced vegetation Index- EVI. This paper aims to assess spatio-temporal forest changes through applied time-series Landsat TM, ETM, MODIS Terra/Aqua and IKONOS satellite remote sensing data of Cernica forest area near Bucharest, Romania, during 1990-2011 period. Additional forest biophysical parameter Land Surface Temperature/Emissivity - LST was considered in this paper. This information is complemented by in-situ monitoring data (field measurements, data collection) together with modeling tools (theoretical developments, mathematical and bio-geophysical parameterization, statistics of the main urban structures in their interdependency) and spatial analysis methods.

Understanding the earth's climate and collecting the requisite signatures over the next 10, 20, 30 years is a shared mandate by many of the world's governments. But there remains a daunting challenge to bridge scientific missions to 'operational' systems that truly support the demands of decision makers, scientific investigators and global users' requirements for trusted data. For this Part III paper, we will examine the required components of a coupled modeling framework to perform, with benefit of adjoint constraints , optimal forward modeling of the climate's GHG's for both demonstration and verification. Interrogating such forward modeling in detail will help uncover the most efficient and sufficient set of critical climate parameters & metrics needed to systematic capture and attribute climate monitoring environmental records. This in turn would allow globally trusted algorithms to produce climate products that the world's governments can use to most accurately assess man's impacts on earth's climate and promote informed decisions sustaining the earth's ability to support life. This paper is the climate modeling based extension to two earlier papers from 2009 & 2010.

This paper apply an statistical technique to correct radiometric data measured by Advanced Very High Resolution Radiometers(AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellites(POES). This paper study Normalized Difference Vegetation Index (NDVI) stability in the NOAA/NESDIS Global Vegetation Index (GVI) data for the period 1982-2003. AVHRR weekly data for the five NOAA afternoon satellites NOAA-7, NOAA-9, NOAA-11, NOAA-14, and NOAA-16 are used for the China dataset, for it includes a wide variety or different ecosystems represented globally. GVI has found wide use for studying and monitoring land surface, atmosphere, and recently for analyzing climate and environmental changes. Unfortunately the POES AVHRR data, though informative, can not be directly used in climate change studies because of the orbital drift in the NOAA satellites over these satellites' life time. This orbital drift introduces errors in AVHRR data sets for some satellites. To correct this error of satellite data, this paper implements Empirical Distribution Function (EDF) which is a statistical technique to generate error free long-term time-series for GVI data sets. We can use the same methodology globally to create vegetation index to improve the climatology.

This paper apply an statistical technique to correct radiometric data measured by Advanced Very High Resolution Radiometers(AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellites(POES). This paper study Normalized Difference Vegetation Index (NDVI) stability in the NOAA/NESDIS Global Vegetation Index (GVI) data for the period 1982-2003. AVHRR weekly data for the five NOAA afternoon satellites NOAA-7, NOAA-9, NOAA-11, NOAA-14, and NOAA-16 are used for the China dataset, for it includes a wide variety or different ecosystems represented globally. GVI has found wide use for studying and monitoring land surface, atmosphere, and recently for analyzing climate and environmental changes. Unfortunately the POES AVHRR data, though informative, can not be directly used in climate change studies because of the orbital drift in the NOAA satellites over these satellites' life time. This orbital drift introduces errors in AVHRR data sets for some satellites. To correct this error of satellite data, this paper implements Empirical Distribution Function (EDF) which is a statistical technique to generate error free long-term time-series for GVI data sets. It allows one to represent any global ecosystem from desert to tropical forest and to correct deviations in satellite data due to orbit degradation. The corrected datasets can be used as proxy to study climate change, epidemic analysis, and drought prediction etc.

The northwestern Hebei mountains area is located in the transitional area from the subhumid and temperate zone to the semiarid zone, pertaining to the ecologically fragile area. Closing to Beijing and Tianjin, the area is in the windward and upriver area of the two cities. It is of great significance to study the climate change in this area for the purpose of understanding the law of local ecological development and constructing the Beijing-Tianjin ecological protective barrier. Based on the average monthly temperatures and precipitation data collected by three weather stations in the northwestern mountains area between 1956 and 2001, this paper studied the climate changes in this area over the past 46 years, by analyzing the average temperature anomaly and precipitation anomaly of yearly and quarterly, calculating the five years sliding average value of them, and applying the way of univariate linear regression to get linear regression equation of the five years sliding average value. Preliminary research was conducted on the average temperature anomaly and precipitation anomaly values and their development trends with the adoption of correlation analysis. Discussion on factors causing the climate change was carried out. The result shows that the average temperature was on an upward trend in the period 1956-2001 at a rate of 0.41 deg. Celcius /10a, with the highest growth rate seen in winter. In contrast, the annual precipitation was on decrease during the same period at a rate of 7.765mm/10a, with the highest rate seen in summer. The area under research is turning warmer and drier. The correlation analysis indicates no apparent correlation between the upward annual average temperature and the downward precipitation, which should be regarded as a process of independent change. This paper also explores the possible causes that have affected the climate in this area, including the global warming, the climate change in a larger area such as the circulation of water vapor in East Asia as well as the changes to local ecological environment such as pollution, greenhouse gas emissions, increased particulate matters in the atmosphere, ecological damages, etc.

The aim of the paper is mountain forest landuse/cover changes analysis due to anthropogenic and climatic stressors. Our analysis indicates a potentially application of threshold techniques to land-cover classification and changes analysis due to climatic effects for Romanian Carpathian mountain forest ecosystem. The climate of the Carpathians is moderately cool and humid, with both temperature and precipitation strongly correlated with elevation. Extreme climatic events and anthropogenic effects have a strong impact on forest ecosystem. Specific aim of this paper is to assess, forecast, and mitigate the risks of air pollution and climatic changes and extreme events on mountain forest ecosystem in Prahova Valley, Romanian Carpathians test area and to provide early warning strategies on the basis of spectral information derived from Landsat TM, ETM and MODIS satellite data over 1987- 2007 period. The paper aims to describe observed trends and potential impacts based on scenarios from simulations with regional climate models and other downscaling procedures.

Tibetan Plateau serves as the sources of several big rivers such as Yangtze River and Yellow River. Due to the high elevation of the plateau, it has profound thermal and dynamical influence on both local and global climate and atmospheric circulation. Land surface temperature (LST) plays a significant role in climate change and glacier melting. In this paper we present our study on mapping land surface temperature variations for the years 2005-2006 in the plateau using MODIS satellite data. Since the plateau has a very rough ground surface that is difficult to estimate the necessary parameters for the mapping, we have developed a practical approach for LST retrieval from the MODIS thermal band data. The approach was alternated from the split window algorithm proposed by Qin et al. (2001) for NOAA-AVHRR data. Detailed methods for atmospheric transmittance and ground emissivity have been presented in the paper. Results from our study indicate that ground temperature in the plateau is featured with obvious spatial and temporal variations. Generally the temperature in winter and spring is less than 0°C and it is also not very high in summer, due to the high altitude. Because of topological form, Chaidamu Basin of the plateau has the highest temperature in summer, usually high up to above 40°C. Our study provides an alternative to understand the environmental changes in the plateau that shape significant impacts on atmospheric dynamics of East Asia and South Asia.

During the past few years, the European Space Agency has launched several projects related to forest fires from global to local scales. The ATSR World Fire Atlas (WFA) project started in late 1995 and is still running today. It provides the longest time series ever produced on the global distribution of active fires. The WFA consists of 12 years of coherent and consistent data sets. The night time fire occurrences are derived from the 3.7 micron channel on-board the ATSR instrument series hosted by the ERS-2 and ENVISAT satellites. The ATSR WFA products were validated first in 1998 with the support of IGBP and more recently by extensive comparisons with existing data sets on forest fires events. A smooth transition from the ERS-2 ATSR-2 to the ENVISAT AATSR has been performed in January 2003 and the quality of the WFA products continuity verified. The ATSR WFA products are available in near real time since May 2006. The distribution of the ATSR WFA products will be thoroughly analyzed in this paper and a synthesis of the work performed by more than 900 registered users will be presented. The GlobCarbon project started in early 2003 with the objective to develop a service for the production of multi-year / multi-sensors global level 3 Land products to be used as input to carbon assimilation models. Understanding the spatial and temporal variation in carbon fluxes is essential to constrain models that predict climate change. However our current knowledge of these spatial and temporal patterns is uncertain, particularly over land. One of the bio-geophysical parameters that the GlobCarbon project aims to measure is the fully calibrated estimate of the burned areas quasi-independent of the original satellite sensor. These burned areas estimates are used in dynamic global vegetation models, a central component of the IGBP-IHDP-WCRP Global Carbon Cycle Joint Project. The service will feature global estimates of burned areas amongst other variables from 1998 to 2007, derived from Earth Observation sensors (ERS-2 ATSR-2, ENVISAT AATSR and SPOT VEGETATION). Finally the RISK-EOS project started in 2003 under the framework of the Global Monitoring for Environment and Security (GMES) initiative, with the objective to establish a network of European service providers for the provision of geoinformation services in support to the risk management of meteorological hazards (floods and fires). The Fire component of RISK-EOS feature two main services: the Burn Scar Mapping (BSM) service that provides some seasonal mapping of forests and semi-natural burned areas at high spatial resolution (minimum mapping unit of 3 to 5 ha); and the Regional Fire Monitoring (RFM) service that provides near real time observation of active fires, based on middle resolution satellite data (AQUA/TERRA MODIS and MSG SEVIRI). The RISK-EOS BSM service builds on the achievements of ITALSCAR, a demonstration project for the yearly mapping of burned areas in Italy, using the LANDSAT Thematic Mapper sensor. The paper will provide a synthesis of the RISK-EOS products validation and utility reports collected during the 2006 summer season.

According to the theory of abrupt climatic change, the regional economic development process in Jiangsu province since 1978 is divided into three periods in this paper. Then the paper employs urban centrality index and time cost for transportation, instead of the city scale and the distance factor, to modify the original potential model. Based on the grid arithmetic of GIS methodology, the paper points out the ranges of urban economic effect regions (UEERs) for central cities at different rank in Jiangsu province of main years. Accordingly, Jiangsu province can be separated into different rank of urban economic effect regions. Consequently, the characteristics of UEERs in Jiangsu province since 1978 can be summarized as below: (1) the change in spatial structure from South-North to East-West, (2) the expanding tendency to north for UEERs in the south, (3) the emergence of new central cities in southern Jiangsu province. Furthermore, the mechanism of evolution of UEERs in Jiangsu is also analyzed in this paper, and the development of transportation axes, the diversities of economic development level and the disparities among the input of social product factors are the three main reasons.

In recent years, the impacts of natural disaster are more and more severe on coastal lowland areas. Aim to the threat of climate change and sea level rise, the natural disaster reduction in coastal lowland areas is paid highly attention. Based on a number of literatures, the paper summarizes the categories and characteristic of natural disasters emerging in coastal lowland areas, such as windstorm and storm surge, hurricanes and hurricane winds, tsunamis and floods, and analyzes the most devastating natural disasters in coastal lowland in the world 2005. The paper also summarizes the effects of typhoons on the coastal lowland areas of China in 2005 and review to analyze the natural disaster mitigation measures and its researches. At last, the paper discusses the vulnerability assessment and response strategies.

The state-of-the-art electro-optical sensors being designed for today's space-based environmental applications require a complete characterization and thorough calibration. This is especially true for sensors designed to assess global climate change, which require very small uncertainties. This paper describes a system-level approach that addresses each phase of calibration, from planning to on-orbit operations. This approach encourages early planning and continuity of effort throughout the lifetime of the project (pre- and post-flight) to promote an optimum calibration approach that will minimize uncertainty for the intended application. This paper also discusses considerations for component level characterization, ground calibration and standards, in-flight calibration sources and trending, and in-flight validation assessment.

The paper deals with climate change and global warning and concentrates on the various forms of renewable energy, mainly Photovoltaics, Solar Thermal, Biomass, Geothermal, Wave, Hydro and Wind Energy. Specific examples will be placed on Photovoltaic applications and the future prospects of this discipline to meet 20% of the electricity generated on a global scale by the year 2020. The paper discusses the various fundings and programs available around the world in renewable energy and concludes that renewable energy is the best energy strategy for any country.

This paper is to answer the question “How can inter- and intra-annual variability in the ocean be leveraged by the submarine Force?” through quantifying inter- and intra-annual variability in (T, S) fields and in turn underwater acoustic characteristics such as transmission loss, signal excess, and range of detection. The Navy’s Generalized Digital Environmental Model (GDEM) is the climatological monthly mean data and represents mean annual variability. An optimal spectral decomposition method is used to produce a synoptic monthly gridded (SMG) (T, S) dataset for the world oceans with 1o×1o horizontal resolution, 28 vertical levels (surface to 3,000 m depth), monthly time increment from January 1945 to December 2014 now available at the NOAA/NCEI website: http://data.nodc.noaa.gov/cgi- bin/iso?id=gov.noaa.nodc:0140938. The sound velocity decreases from 1945 to 1975 and increases afterwards due to global climate change. Effect of the inter- and intra-annual (T, S) variability on acoustic propagation in the Yellow Sea is investigated using a well-developed acoustic model (Bellhop) in frequencies from 3.5 kHz to 5 kHz with sound velocity profile (SVP) calculated from GDEM and SMG datasets, various bottom types (silty clay, fine sand, gravelly mud, sandy mud, and cobble or gravel) from the NAVOCEANO‘s High Frequency Environmental Algorithms (HFEVA), source and receiver depths. Acoustic propagation ranges are extended drastically due to the inter-annual variability in comparison with the climatological SVP (from GDEM). Submarines’ vulnerability of detection as its depth varies and avoidance of short acoustic range due to inter-annual variability are also discussed.

The paper presents lecture materials given at the Nineteenth International Conference and School on Quantum Electronics “Laser Physics and Applications” (19th ICSQE) in 2016, Sozopol, Bulgaria and contains the results of the 10-year research of Belarusian Antarctic expeditions to study the atmospheric aerosol and Earth surface in Antarctica. The works focus on the studying variability and trends of aerosol, cloud and snow characteristics in the Antarctic and the links of these processes with the long range transport of atmospheric pollutants and climate changes.

Agricultural land use conversion is inevitable to meet the needs of growing population, together with climate change issue which has become global concern. This research aims at investigating the impact of climate change on agriculture by identifying land use conversion in part of Central Java, Indonesia, namely Tegal District. Research was carried out in August 2014 until March 2015.This is a survey research with explorative descriptive method, data processing using ENVI 4.5. and ArcGIS 10.1. The satellite image of Landsat was analyzed by determining and comparing the land use changes of the last 20 years, then the interview data with farmers was analyzed using logistic regression. The results showed that many lands converted into settlement, with increasing rate in 2003-2014 was almost twice than 1994-2003, while the reduce of irrigation rice field lands are lower in the period of 2003-2014 than 1994-2003. It is presumed that the factors encourage irrigation rice field land conversion are erratic rainfall, floods in the 1990s, and water lack in the 2000s. This paper discusses briefly about agricultural land use conversion as the impact of the past and current climate variability on farm land.

Climate change affects forest both directly and indirectly through disturbances, that are a natural and integral part of forest ecosystems, and climate change can alter these natural interactions. Forest vegetation characteristics, including land cover and phenology, affect processes such as water cycle, absorption and re-emission of solar radiation, momentum transfer, carbon cycle, and latent and sensible heat fluxes. The climate system responds in complex ways to changes in forcing that may be natural or human-induced. Drastic climate change over the last decades has greatly increased the importance of forest land cover changes monitoring through time-series satellite data. Satellite based derived biophysical parameters for assessment of climate impacts on forest vegetation have to meet particularly high quality requirements. Forest vegetation and climate interact through a series of complex feedbacks, which are not very well understood. Satellite remote sensing is suited tool to assess the main phenological events based on tracking significant changes on temporal trajectories of Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and GPP (Gross Primary Production), which are key biophysical variables for studying land surface processes and surface-atmosphere interactions for forested areas. The aim of this paper was to investigate their pattern dynamics due to the impact of climate variations on a periurban forest Branesti-Cernica, placed to the North-Eastern part of Bucharest city, Romania. The forest vegetation analysis was based on derived biogeophysical parameters from time-series satellite remote sensing MODIS Terra/Aqua and NOAA AVHRR data and in-situ monitoring ground data (as air temperature, aerosols distribution, relative humidity, etc.) over 2002–2014 period.

Urbanization may be considered the most significant anthropogenic force that has brought about fundamental changes in urban land cover and landscape pattern around the globe, being one of the crucial issues of global change in the 21st century affecting urban ecosystem. In the physical climate system, land surface albedo determines the radiation balance of the surface and affects the surface temperature and boundary-layer structure of the atmosphere. Due to anthropogenic and natural factors, urban land covers changes result is the land surfaces albedo changes. The main aim of this paper is to investigate the albedo patterns dynamics due to the impact of atmospheric pollution and climate variations on land cover of Bucharest metropolitan area, Romania based on satellite remote sensing MODIS Terra/Aqua (Moderate Imaging Spectroradiometer) data over 2000–2014 time period. This study is based on MODIS derived biogeophysical parameters land surface BRDF/albedo products and in-situ monitoring ground data (as air temperature, aerosols distribution, relative humidity, etc.). For urban land cover changes over the same investigated period have been used also IKONOS satellite data. Due to deforestation in the periurban areas albedo changes appear to be the most significant biogeophysical effect in temperate forests. As the physical climate system is very sensitive to surface albedo, urban/periurban vegetation systems could significantly feedback to the projected climate change modeling scenarios through albedo changes.

This paper presents the prediction models which analyze and compute the CO 2 emission in Malaysia. Each prediction model for CO 2 emission will be analyzed based on three main groups which is transportation, electricity and heatproduction as well as residential buildings and commercial and public services. The prediction models were generated using data obtained from World Bank Open Data. Best subset method will be used to remove irrelevant data andfollowed by multi linear regression to produce the prediction m odels. From the results, high R-square (prediction) valu ewas ob tained and this implies that the models are reliable to predict the CO 2 emission by using specific data. In addition, the CO 2 emissions from these three groups are forecasted using trend analysis plots for observation purpose. Keywords: CO 2 , best subsets, multi linear regression , trend analysis plots, Malaysia. 1. INTRODUCTION Since the Industrial revolution in 19th cen turies, the world is experiencing huge increases in technological advancementas well as great innovations and significantly improved our lives. However, at the same time, those great inventions,innovations and technologies are bringing unwanted side effects to our world. One of the unwanted side effect is CO2 emissions which caused climate change around the globe. This global threat ening issue has brought the awareness from various organization, research community and government since 1900s

Rice is a dominant food crop of Bangladesh accounting about 75 percent of agricultural land use for rice cultivation and currently Bangladesh is the world’s fourth largest rice producing country. Rice provides about two-third of total calorie supply and about one-half of the agricultural GDP and one-sixth of the national income in Bangladesh. Aus is one of the main rice varieties in Bangladesh. Crop production, especially rice, the main food staple, is the most susceptible to climate change and variability. Any change in climate will, thus, increase uncertainty regarding rice production as climate is major cause year-to-year variability in rice productivity. This paper shows the application of remote sensing data for estimating Aus rice yield in Bangladesh using official statistics of rice yield with real time acquired satellite data from Advanced Very High Resolution Radiometer (AVHRR) sensor and Principal Component Regression (PCR) method was used to construct a model. The simulated result was compared with official agricultural statistics showing that the error of estimation of Aus rice yield was less than 10%. Remote sensing, therefore, is a valuable tool for estimating crop yields well in advance of harvest, and at a low cost.

Snow cover is an important parameter in the hydrological applications and global climate change research. Accurate snow cover information in daily basis is significant in weather forecasting, hydrological model and other applications. High temporal resolution of geostationary data can provide snow cover maps with less cloud obscuration. In this paper, Fengyun-2 geostationary satellites (FY-2D and FY-2E) and Multi-functional Transport Satellite-2 (MTSAT-2) data were compared and used in snow cover mapping over China. FY-2D, FY-2E and MTSAT-2 data calibrated by GSICS was compared firstly. Then we used the same snow cover algorithm to test the performance of the three geostationary satellites on January and February, 2013 over China. Meteorological station observations were utilized to validate the snow cover maps of FY-2D, FY-2E and MTSAT-2. Results indicated that FY-2D and FY-2E presented similar and good performance over China, with overall accuracy about 92%. On the other hand, the overall accuracy of MTSAT-2 was approximately 88%, which was lower than FY-2D and FY-2E. Further calibration of the MTSAT-2 data with FY-2D/E should be considered in future study.

Climate variability represents the ensemble of net radiation, precipitation, wind and temperature characteristic for a region in a certain time scale (e.g.monthly, seasonal annual). The temporal and/or spatial sensitivity of forest vegetation dynamics to climate variability is used to characterize the quantitative relationship between these two quantities in temporal and/or spatial scales. So, climate variability has a great impact on the forest vegetation dynamics. Forest vegetation phenology constitutes an efficient bio-indicator of climate and anthropogenic changes impacts and a key parameter for understanding and modeling vegetation-climate interactions. Satellite remote sensing is a very useful tool to assess the main phenological events based on tracking significant changes on temporal trajectories of forest biophysical parameters like as Normalized Difference Vegetation Index (NDVIs) and Leaf Aria Index (LAI), which requires time-series data with good time resolution, over homogeneous area, cloud-free and not affected by atmospheric and geometric effects and variations in sensor characteristics (calibration, spectral responses). This paper will quantify this impact over a forest ecosystem Cernica- Branesti placed in the North-Eastern part of Bucharest town, Romania, with NDVI and LAI parameters extracted from MODIS Terra and NOAA AVHRR satellite images in synergy with meteorological data over 2000-2013 periods. For investigated test area, considerable NDVI and LAI decline have been observed during heat wave and drought events of 2003, 2007 and 2012 years. Under water stress conditions, it is evident that environmental factors such as soil type, parent material, and topography are not correlated with NDVI dynamics.

Landslide phenomena constitute a major geological hazard in Greece and especially in the western part of the country as a result of anthropogenic activities, growing urbanization and uncontrolled land – use. More frequent triggering events and increased susceptibility of the ground surface to instabilities as consequence of climate change impacts (continued deforestation mainly due to the devastating forest wildfires and extreme meteorological events) have also increased the landslide risk. The studied landslide occurrence named “Platanos” has been selected within the framework of “Landslide Vulnerability Model – LAVMO” project that aims at creating a persistently updated electronic platform assessing risks related with landslides. It is a coastal area situated between Korinthos and Patras at the northwestern part of the elongated graben of the Corinth Gulf. The paper presents the combined use of geological-geotechnical insitu data, remote sensing data and GIS techniques for the evaluation of a subsurface geological model. High accuracy Digital Surface Model (DSM), airphotos mosaic and satellite data, with a spatial resolution of 0.5m were used for an othophoto base map compilation of the study area. Geological – geotechnical data obtained from exploratory boreholes were digitized and implemented in a GIS platform with engineering geological maps for a three – dimensional subsurface model evaluation. This model is provided for being combined with inclinometer measurements for sliding surface location through the instability zone.

Satellite remote sensing techniques play an important role in crop identification, acreage and production estimation, disease and stress detection, and soil and water resources characterization because they provide spatially explicit information and access to remote locations. The main objective of the study is to highlight the potential of using remote sensing techniques in the research field of water management, especially for “water footprint” assessment. In this paper, several vegetation indices (NDVI, NDWI, etc) and biophysical variables (LAI, fAPAR) are key variables to potentially be estimated by remote sensing and used in water footprint studies. The combination of these input parameters brings several limitations regarding the discrepancies in temporal and spatial resolution and data availability, which are described and discussed in detail. MODIS, Landsat, SPOT Vegetation and Meteosat data were used in order to estimate evapotranspiration and vegetation indices. The results of this study show the usefulness of satellite data for water footprint assessment and were obtained by the Remote Sensing Working Group in the framework of the ESSEM COST Action ES1106, “Assessment of EUROpean AGRIculture WATer use and trade under climate change” (EUROAGRIWAT).

The forest biome is vital to the health of the earth. Canada and the United States have a combined forest area of 4.68 Mkm 2 . The monitoring of these forest resources has become increasingly complex. Hyperspectral remote sensing can provide a wealth of improved information products to land managers to make more informed decisions. Research in this area has demonstrated that hyperspectral remote sensing can be used to create more accurate products for forest inventory (major forest species), forest health, foliar biochemistry, biomass, and aboveground carbon. Operationally there is a requirement for a mix of airborne and satellite approaches. This paper surveys some methods and results in hyperspectral sensing of forests and discusses the implications for space initiatives with hyperspectral sensing

The main purpose of eye-safe laser ceilometers is regular reporting of cloud base height, vertical visibility, and cloud cover. These instruments operate unattended in harsh weather conditions. The application of state-of-the-art electronics increases the quality of backscatter profiles and thus qualifies modern ceilometers for applications beyond cloud base detection. The single lens optics of the ceilometers introduced in this paper results in a compact and robust design and enables their application in campaigns monitoring climate change effects. That is why three of the German Terrestrial Environmental Observatories (TERENO) run by the Karlsruhe Institute of Technology are equipped with a ceilometer. The Technical University of Denmark (DTU) utilizes such an instrument to study arctic cloud formation at Station Nord, Greenland. Recent applications include site assessment for solar energy applications in the Arabic Peninsula and monitoring of Sahara dust cloud and biomass burning plume events over Germany. Backward trajectory calculations with the HYSPLIT trajectory model provided by the NOAA Air Resources Laboratory have been carried out to investigate possible sources, including wood fires in southern France and eruptions of the Eyjafjallajokull and Puyehue- Cordon Caulle volcanoes.

Oceans play a significant role in the global carbon cycle and climate change, and the most importantly it is a reservoir for plenty of protein supply, and at the center of many economic activities. Ocean health is important and can be monitored by observing different parameters, but the main element is the phytoplankton concentration (chlorophyll–a concentration) because it is the indicator of ocean productivity. Many methods can be used to estimate chlorophyll–a (Chl-a) concentration, among them, remote sensing technique is one of the most suitable methods for monitoring the ocean health locally, regionally and globally with very high temporal resolution. In this research, long term ocean health monitoring was carried out at the Bay of Bengal considering three facts i.e. i) very dynamic local weather (monsoon), ii) large number of population in the vicinity of the Bay of Bengal, and iii) the frequent natural calamities (cyclone and flooding) in and around the Bay of Bengal. Data (ten years: from 2001 to 2010) from SeaWiFS and MODIS were used. Monthly Chl–a concentration was estimated from the SeaWiFS data using OC4 algorithm, and the monthly sea surface temperature was obtained from the MODIS sea surface temperature (SST) data. Information about cyclones and floods were obtained from the necessary sources and in-situ Chl–a data was collected from the published research papers for the validation of Chl-a from the OC4 algorithm. Systematic random sampling was used to select 70 locations all over the Bay of Bengal for extracting data from the monthly Chl-a and SST maps. Finally the relationships between different aspects i.e. i) Chl-a and SST, ii) Chl-a and monsoon, iii) Chl-a and cyclones, and iv) Chl-a and floods were investigated monthly, yearly and for long term (i.e 10 years). Results indicate that SST, monsoon, cyclone, and flooding can affect Chl-a concentration but the effect of monsoon, cyclone, and flooding is temporal, and normally reduces over time. However, the effect of SST on Chl-a concentration can't be minimized very quickly although the change of temperature over this period is not very large.

This paper provides a coherent pattern identification analysis of coastal land use and land cover (LULC) under the impact of seawater intrusion. This study analysis applied the 4-, 3-, and 2-band false color composite Landsat satellite data to characterize the LULC in the study area. The evapotranspiration (ET) and heat fluxes were estimated by using the SEBAL model with two-time phase thermal infrared band images and regional surface parameters. Our findings are as follows: 1) Due to its distance from the sea, the vegetation index gradually increases as the level of land use gradually increased. 2) The different influences of seawater intrusion in the study area resulted in significantly different influences of land surface parameters (LST, Gn, MSAVI, and Uindex) on ET. There are a variety of types of relational patterns between parameters (LST, Gn, MSAVI and Uindex) and ET (positive, negative, and no relationship). 3) Seawater intrusion significantly affected the spatial pattern of LUCC, which evidently affected the spatial distribution of ET. The spatial distribution pattern and change characteristics of ET were formed by double driving forces of seawater intrusion and LUCC under the background effects of regional climate. © Copyright 2013 SPIE.

Remote sensing (RS) has long been a useful tool in global and regional applications. The Water Footprint (WF) of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage. RS provides new tools for global WF assessment and represents an innovative approach to regional and global irrigation mapping, enabling the estimation of green and blue water use. This paper presents an overview of the EU COST Action ES 1106 "Assessment of European agriculture water use and trade under climate change (EURO-AGRIWAT)", regarding the evaluation of the potential of remote sensing to improve the WF and Virtual Water Trade (VWT) assessment. The main objective is the analysis of the role of satellite data in the suitable models and indices concerned with the analysis of WF and VWT. The main tasks include: an inventory of the existing and near future satellite data records for several European regions that could be used for the WF and VWT assessment; the study of satellite data resolution requirements, in time and space; the analysis of the assimilation of satellite data into models for the determination of green and blue water use; conclusions and recommendations concerning the possibility to integrate remote sensing into WF and VWT accounting. The combination of RS data to assess the volume of irrigation applied, and the green and blue WF faces several limitations with respect to discrepancies in spatial and temporal resolution and data availability, which will be studied.

For the society it is of great interest to make cultural heritage accessible to the general public. The subsequent increase of museum loan services increases the risk of accelerated degeneration. Hence, in addition to the age related deterioration, transportation can be another source of damage. Despite modern packaging technologies, vibrations and environmental climate change can add up and damage the transported object. Besides obvious mechanical influencing quantities, a main concern is the detection of climate induced damages. Changes in the relative humidity cause inner strain, which may lead to defect formations and damages. White light fringe projection was applied to detect object changes due to variations of the relative humidity. First measurements indicated a sensitivity down to a relative humidity change of 6 %. Recently, the Stuppach Madonna, a painting by Matthias Grunewald (painted 1514 - 1516), was investigated with shearography after the return from an exhibition. The obtained shearograms revealed a variety of defects like bubbles, delaminations and tunnels caused by wood worms. Even the planking of the wooden panel and existing putty could be detected. This paper describes the first steps of a project with the aim of investigating and implementing a method to detect and classify transport related damages on works of art.

The “Global Change Observation Mission-Climate” (GCOM-C) is a project of Japan Aerospace Exploration Agency (JAXA) for the global and long-term observation of the Earth environment. The GCOM-C is a part of the JAXA’s GCOM mission which consists of two satellite series, GCOM-C and GCOM-W (Water), spanning three generations in order to perform uniform and stable global observations for 13 years. GCOM-C carries a multi-spectral optical radiometer named Second Generation Global Imager (SGLI), which will have special features of wide spectral coverage from 380nm to 12μm, a high spatial resolution of 250m, a field of view exceeding 1000km, two-direction simultaneous observation, and polarization observation. The GCOM-C mission aims to improve our knowledge on the global carbon cycle and radiation budget through high-accuracy observation of global vegetation, ocean color, temperature, cloud, aerosol, and snow and ice. As for the cryosphere products, not only snow and ice cover extent but also snow physical parameters are retrieved from SGLI data such as snow grain sizes at several surface levels (shallow layer, sub-surface layer, and the top surface), temperature, and mass fraction of impurity mixed in snow layer and so on. These snow physical parameters are important factors that determine spectral albedo and radiation budget at the snow surface. Thus it is essential to monitor those parameters from space in order to better understand snow metamorphosis and melting process and also to study the response of snow and sea-ice cover extent in the Polar Regions to a climate forcing such as global warming. This paper will summarize the SGLI cryospheric products and validation plans.

Recently, climate change and human activities accelerate hazards, such as deforestations, land slides, draughts, floods in Asian-Pacific countries are increased. To mitigate the hazards due to climate change and human activities, environmental monitoring has become important. Environment change monitoring by using space based technology are very important. Asian Pacific regional forum (APRSAF) agreed to host SAFE (Space Applications For Environment) initiatives. Under APRSAF, there are on-going several SAFE prototyping for water management, agriculture, fishery and coastal monitoring This paper describes SAFE overview and current situation of SAFE prototyping.

Recent climate models consistently project a decreasing trend of global tropical cyclone (TC) frequency in the future due to global warming. In our recent 228 year long simulations from 1872 to 2099, a decreasing trend of global TC frequency is found not only in the future but also in the past during the twentieth century. The decreasing trend of TC frequency is closely related to a decreasing trend of upward mass flux in the tropics, and it is in turn closely related to an increasing trend of dry static stability. Both decreasing trend of upward mass flux and increasing trend of dry static stability are simulated in all climate models. However, some observational studies indicated that the dry static stability was increasing at much smaller rate than the climate models or even decreasing during the last 30 years. In this paper, we explore possible causes of this apparent discrepancy between the observations and models by comparing the 228 year simulations with several reanalysis data. It is found that the difference between the model and reanalysis is within the uncertainties among the different reanalysis data.

Land is an indispensable natural resource for human, without which we cannot survive and develop. Land-use change, influenced by both natural environment and human activity, has a close relationship with food security, resource utilization, biodiversity and climate change. In order to understand the process and driving mechanism of land-use change, dynamic models were developed in these years, among which Dinamica EGO is a practical one and has been widely used in the world. In this paper, we aim to use Dinamica EGO to simulate the land-use of China in 2005 with data extracted from SPOT VGT NDVI. The real land-use map was compared with the simulation result so as to verify the feasibility of Dinamica EGO. Then we supposed three sceneries under which we could analyze the land-use change of China in 2020. Results indicated that: on the basis of no extreme natural disasters or exceptional policy fluctuation, the grassland area would reduce by 22.21 million hectares averagely. However forest would increase by 19.81 billion hectares on average. Water and unused land would probably remain stable as there was little change in three sceneries. Farmland areas showed a good agreement under these sceneries whereas the greatest difference in land-use area estimations lies in built-up with an uncertainty accounting for 1.67%.

Methane is an important greenhouse gas contributing to global climate change, and its warming effect is just second to carbon dioxide. Satellite remote sensing technique can obtain large scale distribution of trace gases, and it has been an important tool in the field of atmospheric observation. This paper presents the annual variations of methane in China based on the vertical columns of methane measured by the SCIAMACHY sensor on board ENVISAT. The variability of yearly averaged CH4 concentration in China and the whole world during 2003-2009 shows that the rapid growth of CH4 in China during 2005-2006 widened the difference between China’s CH4 level and the whole world’s level. China’s methane level has close relations with global climate change.

Forest systems are all sensitive to climatic factors and extreme events and are likely to have different vulnerability thresholds according to the species, the amplitude, and the rate of climatic stressors. As a result of global climate change, there is growing evidence that some of the most severe weather events could become more frequent in Romania over the next 50 to 100 years. Effects of climate extremes on forests can have both short-term and long-term implications for standing biomass, tree health and species composition. Multispectral, multiresolution and multitemporal satellite imagery is used to classify and map various forest and spatio-temporal land-cover changes. In mountain forests, the more frequent occurrence of climatic changes may accelerate the replacement of sensitive tree species and reduce carbonstocks, and the projected slight increase in the frequency of extreme storms by the end of the century could increase the risk of windthrow. Mountain forest landscape pattern and the biogeophysical variables (NDVI, EVI) controlling observed patterns can be addressed using time series remote sensing satellite imagery. The specific aim of this paper was to: 1) quantify the changes and rates of change between 1990 and 2011 in vegetative composition across a forest landscape in Romanian Carpathians on Prahova Valley using Landsat TM/ETM, IKONOS, MODIS images; 2) examine the changes in landscape structure in relation with climatic changes and extreme events; 3) assess the climate risks and their potential impact on Romanian mountain forests; 4) analysis of mountain forest spatio-temporal land cover composition, pattern and structure.

The orchards are developing very fast in the northern China in recent years with the increasing demands on fruits in China. In most parts of the northern China, the risk of frost damage to fruit tree in early spring is potentially high under the background of global warming. The growing season comes earlier than it does in normal year due to the warm weather in earlier spring and the risk will be higher in this case. According to the reports, frost event in spring happens almost every year in Ningxia Region, China. In bad cases, late frosts in spring can be devastating all fruit. So lots of attention has been given to the study in monitoring, evaluating, preventing and mitigating frost. Two orchards in Ningxia, Taole and Jiaozishan orchards were selected as the study areas. MODIS data were used to monitor frost events in combination with minimum air temperature recorded at weather station. The paper presents the findings. The very good correlation was found between MODIS LST and minimum air temperature in Ningxia. Light, middle and severe frosts were captured in the study area by MODIS LST. The MODIS LST shows the spatial differences of temperature in the orchards. 10 frost events in April from 2000 to 2010 were captured by the satellite data. The monitoring information may be hours ahead circulated to the fruit farmers to prevent the damage and loss of fruit trees.

The Canadian Space Agency issued, in 2007, a Request for Proposals on The Atmospheric Processes on Climate and its Changes (APOCC). Innovative mission concepts were selected on the basis that they: Lead to new scientific understanding of atmospheric processes that regulate Earth's climate; Address questions of particular importance for northern latitudes; Build on Canada's experience and capacity in atmospheric science; Complement/ be synergistic with planned international missions. With the concept studies finished, the foreseen way forward is to issue support initiatives to develop those reformulated concepts that combine the most promising ideas in several affordable instrument contributions and Canadian spacecraft missions. This paper presents the rationale for the continued implementation of the APOCC initiative.

Increased C O2 (carbon dioxide) has been considered as one of key factors of global warming. Intending to describe the capability of CO 2 measurement by space-borne sensors quantitatively, this paper compares two data sets of CO 2 monthly products retrieved from AIRS and SCIAMACHY over China from 2003 to 2005. The increasing trend of CO 2 concentration can be detected consistently from both of the data sets. However, the seasonal variation of AIRS CO 2 is larger than SCIAMACHY CO 2 because the former represents CO 2 existing in the mid-troposphere while the latter represents in the lower-troposphere. CO 2 concentration reaches its yearly maximum in spring (April and May) and reaches its yearly minimum in late-autumn and winter (October to December and January) for both data sets. The coverage of AIRS monthly CO 2 is much better than that of SCIAMACHY over China and it shows that Xinjiang, Tibet, Inner Mongolia and northeast China have higher values than other regions in China especially in April and May due to local climate and vegetation growth process.

Begin the abstract two lines below author names and addresses. The abstract summarizes key findings in the paper. It is a paragraph of 200 words or less. For the keywords, select up to 8 key terms for a search on your manuscript's subject Using the yearly average rainfall and annual average temperature in Xingwen county during the period of 1959~2007, the correlation on average annual rainfall and temperature and the trend of change are analyzed by correlation analysis and grey sequence forecast. The results show that there was a negative correlation between average annual rainfall and temperature.The prediction results for future tendency show that the annual average rainfall will still keep downtrend, the annual average temperature will keep rising trend.The trend of change is mainly controlled by the global warming and the large-scale climate variations, and the results indicate that the trend of change is basically consistent with the trend of the Yangtze river basin.

Global warming and climate changes are changing the environment and therefore changing the distribution and behaviour of the plant species. Plant species often move and change their distributions as they find their original habitats are no longer suitable to their needs. It is therefore important to establish a statistical model to catch up the movement and patterns of the endangered species in order to effectively manage environmental protection under the inevitable biodiversity changes that are taking place. In this paper, we are focusing on the population category of rare Proteas that has an estimated population size from 1 to 10 per sample site, which is very small. We used the partial differential equation associated regression (PDEAR) model, which merges the partial differential equation theory, (statistical) linear model theory and random fuzzy variable theory together into a efficient small-sample oriented model, for the spatial pattern changing analysis. The regression component in a PDEAR model is in nature a special random fuzzy multivariate regression model. We developed a bivariate model for investigating the impacts from rainfall and temperature on the Protea species in average sense in the population size of 1 to 10, in the Cape Floristic Region, from 1992 to 2002, South Africa. Under same the average biodiversity structure assumptions, we explore the future spatial change patterns of Protea species in the population size of 1 to 10 with future (average) predicted rainfall and temperature. The spatial distribution and patterns are clearly will help us to explore global climate changing impacts on endangered species.

Wetlands play a key role in regional and global environments and are critically linked to many major issues such as climate change, water quality, the hydrological and carbon cycles, and wildlife habitat and biodiversity. Mapping wetlands and monitoring their change are a long-term task. Remote Sensing characters with macrocosm, dynamics, quantity, and comparability will largely favor wetland research, especially radar remote sensing, which is not limited by climate conditions, has been proved an effective tool in wetland monitoring. In this paper, the unique polarimetric data of RADARSAT-2 is investigated for wetland classification. The target decomposition is used for optimum characterization of wetland target scattering. In this study, it is shown that the polarimetric information provided by RADARSAT-2 permits discriminating eight classes of land surface, and leads to an effective unsupervised and supervised Wishart classification of Poyang Lake wetland. Hence, the combination of RADARSAT-2's polarimetric and all-weather capabilities should provide unique information for operational mapping and monitoring of wetlands.

Time series of remote sensing data is widely used in vegetation change monitoring in regions of great environmental changes. In this paper, GIMMS dataset is used to extract temporal and spatial patterns of vegetation cover change in Huang-Huai-Hai Plain, China from 1993 to 2006. The results indicate that the total vegetation is increasing in this region. But the changing trend has spatial variations with some part improving and others deteriorating. The vegetation cover changed due to both human practices and climate changes.

Global three-dimensional cloud distributions and their properties are important information to estimate the earth radiation budget more precisely. The interactions between cloud particles and aerosols are also focused to improve accuracies of climate model. In order to meet expectations of scientists developing climate models for global warming problem, European and Japanese space agencies plan to launch a satellite called EarthCARE. The Cloud Profiling Radar (CPR), which will be the first millimeter-wave Doppler radar in space, is installed on this satellite as one of main sensors to observe clouds. This paper describes the latest design and development status of EarthCARE CPR.

Duststorms and sandstorms regularly devastate Northeast Asia and cause considerable damage to transportation system and public health; further, these events are conceived to be one of the very important indices for estimating the global warming and desertification. Previously, yellow sand events were considered natural phenomena that originate in deserts and arid areas. However, the greater scale and frequency of these events in recent years are considered to be the result of human activities such as overgrazing and over-cultivation. Japan, Korea, Cina and Mongolia are directly concerned to prevent and control these storms and have been able to some extent to provide forecasts and early warnings. In this framework, to improve the accuracy of forecasting , a compact and rugged eye safe lidar, the EZ LIDATM, developed together by Laboratoire des Sciences du Climat et l'Environnement (LSCE) (CEA-CNRS) and LEOSPHERE (France) to study and investigate structural and optical properties of clouds and aerosols, thanks to the strong know-how of CEA and CNRS in the field of air quality measurements and cloud observation and analysis, was deployed in Seoul, Korea in order to detect and study yellow sand events, thanks to its depolarization channel and scan capabilities. The preliminary results, showed in this paper, of this measurement campaign put in evidence that EZ Lidar, for its capabilities of operating unattended day and night under each atmospheric condition, is mature to be deployed in a global network to study long-range transport, crucial in the forecasting model.

Global climate change has attracted increasing attentions in recent years as a challenge facing human life. Accurately mapping, quantifying and monitoring changes in the physical characteristics of vegetation cover is a key element in the study of global climate change. The normalized difference vegetation index (NDVI) has been used extensively in ecosystem and climate monitoring. This paper examined vegetation change over the last 27 years (1981-2007) over Continental US. Using history of the values of the normalized difference vegetation index (NDVI) which is extracted from satellite sensor data acquired by the National Oceanic & Atmospheric Administration - Advanced Very High Resolution Radiometer (NOAA-AVHRR), long time series of satellite-derived vegetation index has been studied showing the increasing trend of vegetation index over Continental US from 1981 to 2007. The results demonstrated that the change of NDVI shows an increasing trend which might be induced by the global warming. The comparison of the land cover data in 1981 and 2001 validated the uptrend of NDVI.

Snow coverage plays a significant role in the Earth's water cycle and is sensitive and informative indicators of climate change. Recently, snow coverage in the Qilian Mountain great changed and caused great changes of the ecological environment in the Shiyang River. In this paper, remote sensing imagines were used to calculate the snow coverage, vegetation coverage and water resource. The result indicated that the entire area of the glacier snow was fluctuated on the Qilian Mountains region, having linear and increasing trend from 1997 to 2006. In the eastern and middle of Qilian Mountains, the snow coverage tends to decrease, but in the western, snow coverage tends to increase. As a whole, vegetation coverage is decreasing in Shiyang River Basin. Sparse vegetation and density vegetation were the worst degradation. Water resource in the downriver of Shiyang River (Hongya Mountain Reservoir) decreased.

Laccadive archipelago comprises of 1232 islands, 1196 in Maldives (Indian Ocean) and 36 in Lakshadweep (Arabian Sea). A total 210 islands (200 in Maldives and 10 in Lakshadweep) are inhabited with a population of about 300,000 and 60,000, respectively. These islands harbour various ecologically sensitive marine habitats (ESMH) of economic and socioeconomic values and of dynamic and productive nature. The biotic constituents of these habitats form roots and major contributors towards productivity stabilization and land formation. However, the existence of these ecologically sensitive regions, and hence of these islands are at risk from various environmental and climatic change issues. Considering the ecological importance and vulnerability to various problems, 'effective management' of these ecosystems must be a top priority and of a global concern. Remote sensing (RS) and GIS tools would be of a great use in the evaluation and formulation of strategies for their sustainable management. The paper describes different ecologically sensitive marine habitats (ESMH) from selected islands of Maldives and Lakshadweep, and application of RS and GIS for their evaluation, monitoring and management.

Arid and semi-arid ecosystems exhibit a spatially complex biogeophysical structure. According to arid western special climate-vegetation characters, using the remote sensing spectral mixture analysis. The study developed a modified production efficiency model Net Primary Productivity-Geography Processing Ecology Model (NPP-GPEM) appropriate for the arid area at regional scale based on the concept of radiation use efficiency. Combined with recent historical period climate data and two periods of land use data sets from remote sensing data, we test the net primary productivity (NPP) data sets over last 20 years in XinJiang of the North China modelled by the NPP-GPEM with the satellite data-driven for detecting the wide spread spatial temporal characteristics of the impacts of climate and land use change impact with the regional NPP. Our results shows that over past 20 years, the average annual temperature in the research region has remarkably increased by more than 0.056°C, but over the same period, there has been a 0.635mm increase in annual precipitation and increase in NPP by an annual rate of 0.142TgC. Taking use of 16 land covers of Xinjiang which was carried on the calculation of the matrix transfer, the study got the matrix of the NPP changes over past 20 years. The result found that the quicker trend increasing is the others woodlands (the 5.51gC/m2/yr) and the oasis farmland (4.32 gC/m2/yr). The pasture area is the biggest to about 29.3% of total area, the pasture growth is smaller than others, but its increase to the contribution rate of the annual NPP in the whole land ecosystem system biggest is pasture and agricultures to land use covers, that had the total annual NPP increase with 52.2% and 19.9% of the quantity respectively, the plant cover is only the shrub pant which has a absolute decrease ecosystem system in the type (- 14.39gC/m2); In fine, the paper mainly reveals the dissimilarities and conversion among the pasture, farmland, woodland and different covered pasture and other types. The correlation analysis between annual NPP and annual precipitation was highly consistent with plant cover spatially (R2=0.64, P