Soil moisture and evapotranspiration (ET) is affected by both water and energy balances in the soilvegetation- atmosphere system, it involves many complex processes in the nexus of water and thermal cycles at the surface of the Earth. These impacts may affect the recharge of the upper Floridian aquifer. The advent of urban hydrology and remote sensing technologies opens new and innovative means to undertake eventbased assessment of ecohydrological effects in urban regions. For assessing these landfalls, the multispectral Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing images can be used for the estimation of such soil moisture change in connection with two other MODIS products - Enhanced Vegetation Index (EVI), Land Surface Temperature (LST). Supervised classification for soil moisture retrieval was performed for Tampa Bay area on the 2 kmx2km grid with MODIS images. Machine learning with genetic programming model for soil moisture estimation shows advances in image processing, feature extraction, and change detection of soil moisture. ET data that were derived by Geostationary Operational Environmental Satellite (GOES) data and hydrologic models can be retrieved from the USGS web site directly. Overall, the derived soil moisture in comparison with ET time series changes on a seasonal basis shows that spatial and temporal variations of soil moisture and ET that are confined within a defined region for each type of surfaces, showing clustered patterns and featuring space scatter plot in association with the land use and cover map. These concomitant soil moisture patterns and ET fluctuations vary among patches, plant species, and, especially, location on the urban gradient. Time series plots of LST in association with ET, soil moisture and EVI reveals unique ecohydrological trends. Such ecohydrological assessment can be applied for supporting the urban landscape management in hurricane-stricken regions.

Passive microwave soil moisture algorithms must account for vegetation attenuation of the signal in the retrieval process. One approach to accounting for vegetation is to use vegetation indices such as the Normalized Difference Vegetation Index (NDVI) to estimate the vegetation optical depth. The passive microwave sensor platforms typically do not include sensors for providing this information and the data must be acquired independently. This presents challenges to data processing and integration and concerns about data availability. As an alternative to routine updating of the NDVI, it is possible to use a global vegetation index climatology. This climatology is based on the long term set of observations from the MODIS instrument (10 years). A technique was developed to process the NASA NDVI and Enhanced Vegetation Index (EVI) data base to produce a 10-day annual cycle (climatology) for each 1 km pixel covering the Earth's land surface. Since our focus was on soil moisture, the classification rules and flags took this into consideration. Techniques developed for processing the indices, development of flags, and expected utilization in soil moisture retrieval algorithms are described.

In response to the increasing demand on in-season crop inventory, this study presents results of early season crop identification and acreage estimates based on a random forest classifier using RADARSAT-2 fine quad (FQ) SAR data. Thirty RADARSAT-2 FQ SAR scenes acquired over Indian Head, Canada, during the 2009 AgriSAR campaign led by the European Space Agency (ESA) were analyzed. Consistent with results from other researches, this study revealed that the highest classification accuracies are achieved in mid to late season (early July to mid August) when most of the crops experiencing vegetative growth and early reproduction. In addition by incorporating multi-beam images, an increase in classification accuracy of 2% to 12% can be achieved. For images acquired close in time, shallower incidence angles usually give better classification accuracy compared with steeper incidence angles. In order to achieve optimal classification performance, both multi-temporal and multi-beam acquisitions should be combined. For major crops such as canola, spring wheat, lentil, and field peas, over 85% accuracies can be reached early in the growing season (early July) when multi-temporal multi-beam RADARSAT-2 FQ data are used.

Vegetation canopy water content (CWC) is an important parameter for monitoring natural and agricultural ecosystems. Previous studies focused on the observation of annual or monthly variations in CWC but lacked temporal details to study vegetation physiological activities within a diurnal cycle. This study provides an evaluation of detecting vegetation diurnal water stress using airborne data acquired with the MASTER instrument. Concurrent with the morning and afternoon acquisitions of MASTER data, an extensive field campaign was conducted over almond and pistachio orchards in southern San Joaquin Valley of California to collect CWC measurements. Statistical analysis of the field measurements indicated a significant decrease of CWC from morning to afternoon. Field measured CWC was linearly correlated to the normalized difference infrared index (NDII) calculated with atmospherically corrected MASTER reflectance data using either FLAASH or empirical line (EL). Our regression analysis demonstrated that both atmospheric corrections led to a root mean square error (RMSE) of approximately 0.035 kg/m² for the estimation of CWC (R²=0.42 for FLAASH images and R²=0.45 for EL images). Remote detection of the subtle decline in CWC awaits an improved prediction of CWC. Diurnal CWC maps revealed the spatial patterns of vegetation water status in response to variations in irrigation treatment.

Leaf and canopy water contents provide information for leaf area index, vegetation biomass, and wildfire fuel moisture content. Hyperspectral retrievals of leaf and canopy water content are determined from the relationship of spectral reflectance and the specific absorption coefficient of water over the wavelength range of a water absorption feature. Vegetation water indices such as the Normalized Difference Water Index [NDWI = (R₈₅₀ - R₁₂₄₀)/(R₈₅₀ + R₁₂₄₀)] and Normalized Difference Infrared Index [NDII = (R₈₅₀ - R₁₆₅₀)/(R₈₅₀ + R₁₆₅₀)] may be calculated from multispectral sensors such as Landsat Thematic Mapper, SPOT HRG, or MODIS. Predicted water contents from hyperspectral data were much greater than measured water contents for both leaves and canopies. Furthermore, simulated spectral reflectances from the PROSPECT and SAIL models also had greater retrieved leaf and canopy water contents compared to the inputs. Used simply as an index correlated to leaf and canopy water contents, hyperspectral retrievals had better predictive capability than NDII or NDWI. Atmospheric correction algorithms estimate canopy water content in order to estimate the amount of water vapor. These results indicate that estimated canopy water contents should have a systematic bias, even though this bias does not affect retrieved surface reflectances from hyperspectral data. Field campaigns in a variety of vegetation functional types are needed to calibrate both hyperspectral retrievals and vegetation water indices.

Accurate assessment of vegetation canopy optical properties plays a critical role in monitoring natural and managed ecosystems under environmental changes. In this context, radiative transfer (RT) models simulating vegetation canopy reflectance have been demonstrated to be a powerful tool for understanding and estimating spectral bio-indicators. In this study, two narrow band spectroradiometers were utilized to acquire observations over corn canopies for two summers. These in situ spectral data were then used to validate a two-layer Markov chain-based canopy reflectance model for simulating the Photochemical Reflectance Index (PRI), which has been widely used in recent vegetation photosynthetic light use efficiency (LUE) studies. The in situ PRI derived from narrow band hyperspectral reflectance exhibited clear responses to: 1) viewing geometry which affects the light environment; and 2) seasonal variation corresponding to the growth stage. The RT model (ACRM) successfully simulated the responses to the viewing geometry. The best simulations were obtained when the model was set to run in the two layer mode using the sunlit leaves as the upper layer and shaded leaves as the lower layer. Simulated PRI values yielded much better correlations to in situ observations when the cornfield was dominated by green foliage during the early growth, vegetative and reproductive stages (r = 0.78 to 0.86) than in the later senescent stage (r = 0.65). Further sensitivity analyses were conducted to show the important influences of leaf area index (LAI) and the sunlit/shaded ratio on PRI observations.

Forest is important in global carbon cycle and has potential impact on global climatic change. Whether the soil moisture under forest area can be detected by microwave emission signature is unknown due to the dense forest cover. Also, the relationship between forest biomass and its microwave emissivity and transmissivity is of interest to be studied. The microwave emission contribution received by the radiometer above the forest canopy comes from both the soil surface and vegetation layer. In this study, a high-order emission model, Matrix-Doubling, was employed to simulate the emissivity of a young deciduous forest. A field experiment before and after watering the deciduous tree stand was carried in June 5, 2011 in Baoding, China to verify the model, and to measure the tree transmissivity. A tree was selected to be cut to measure the tree parameters and weighed its biomass. Assuming the forest as a non-scattering medium, the effective single-scattering albedo is obtained for 0th-order model by fitting the same emissivity from Matrix-Doubling model. For lower albedo which could be ignored, transmissivity of trees can be deduced by measured Brightness Temperatures before and after watering the underlying soil. The relationship between forest biomass and its transmissivity is presented in this paper.

The National Ecological Observatory Network (NEON) is a planned facility of the National Science Foundation with the mission to enable understanding and forecasting of the impacts of climate change, land use change and invasive species on continental-scale ecology. Airborne remote sensing plays a critical role by providing measurements at the scale of individual shrubs and larger plants over hundreds of square kilometers. The NEON Airborne Observation Platform is designed to bridge scales from organism and stand scales, as captured by plot and tower observations, to the scale of satellite based remote sensing. Fused airborne spectroscopy and waveform LiDAR is used to quantify vegetation composition and structure. Panchromatic photography at better than 30 cm resolution will retrieve fine-scale information on land use, roads, impervious surfaces, and built structures. NEON will build three airborne systems to allow for regular coverage of NEON sites and the capacity to respond to investigator requests for specific projects. The system design achieves a balance between performance and development cost and risk, taking full advantage of existing commercial airborne LiDAR and camera components. To reduce risk during NEON construction, an imaging spectrometer design verification unit is being developed at the Jet Propulsion Laboratory to demonstrate that operational and performance requirements can be met. As part of this effort, NEON is also focusing on science algorithm development, computing hardware prototyping and early airborne test flights with similar technologies. This paper presents an overview of the development status of the NEON airborne instrumentation in the context of the NEON mission.

A land-based infrared (IR) camera is used to detect endangered Southern Resident killer whales in Puget Sound, Washington, USA. The observations are motivated by a proposed tidal energy pilot project, which will be required to monitor for environmental effects. Potential monitoring methods also include visual observation, passive acoustics, and active acoustics. The effectiveness of observations in the infrared spectrum is compared to observations in the visible spectrum to assess the viability of infrared imagery for cetacean detection and classification. Imagery was obtained at Lime Kiln Park, Washington from 7/6/10-7/9/10 using a FLIR Thermovision A40M infrared camera (7.5-14μm, 37°HFOV, 320x240 pixels) under ideal atmospheric conditions (clear skies, calm seas, and wind speed 0-4 m/s). Whales were detected during both day (9 detections) and night (75 detections) at distances ranging from 42 to 162 m. The temperature contrast between dorsal fins and the sea surface ranged from 0.5 to 4.6 °C. Differences in emissivity from sea surface to dorsal fin are shown to aid detection at high incidence angles (near grazing). A comparison to theory is presented, and observed deviations from theory are investigated. A guide for infrared camera selection based on site geometry and desired target size is presented, with specific considerations regarding marine mammal detection. Atmospheric conditions required to use visible and infrared cameras for marine mammal detection are established and compared with 2008 meteorological data for the proposed tidal energy site. Using conservative assumptions, infrared observations are predicted to provide a 74% increase in hours of possible detection, compared with visual observations.

Excessive nutrients, which may be represented as Total Nitrogen (TN) and Total Phosphorus (TP) levels, in natural water systems have proven to cause high levels of algae production. The process of phytoplankton growth which consumes the excess TN and TP in a water body can also be related to the changing water quality levels, such as Dissolved Oxygen (DO), chlorophyll-a, and turbidity, associated with their changes in absorbance of natural radiation. This paper explores spatiotemporal nutrient patterns in Tampa Bay, Florida with the aid of Moderate Resolution Imaging Spectroradiometer or MODIS images and Genetic Programming (GP) models that are deigned to link those relevant water quality parameters in aquatic environments.

The identification and real time detection of explosives and hazardous materials are of great interest to the Army and environmental monitoring/protection agencies. The application and efficiency of the remote Raman spectroscopy system for real time detection and identification of explosives and other hazardous chemicals of interest, air pollution monitoring, planetary and geological mineral analysis at various standoff distances have been demonstrated. In this paper, we report the adequacy of stand-off Raman system for remote detection and identification of chemicals in water using dissolved sodium nitrate and ammonium nitrate for concentrations between 200ppm and 5000ppm. Nitrates are used in explosives and are also necessary nutrients required for effective fertilizers. The nitrates in fertilizers are considered as potential sources of atmospheric and water pollution. The standoff Raman system used in this work consists of a 2-inch refracting telescope for collecting the scattered Raman light and a 785nm laser operating at 400mW coupled with a small portable spectrometer.

Carbon Capture and Sequestration (CCS) is widely accepted as a means to reduce and eliminate the fossil fuel CO₂ (ff- CO₂) emissions from coal fired power plants. Success of CCS depends on near zero leakage rates over decadal time scales. Currently no commercial methods to determine leakage of ff-CO₂ are available. The Global Monitor Platform (GMP) field analyzer provides high precision analysis of CO2 isotopes [¹²C (99%), ¹³C (<1%), ¹⁴C (1.2x10^-10 %)] that can differentiate between fossil and biogenic CO₂ emissions. Fossil fuels contain no ¹⁴C; their combustion should lower atmospheric amounts on local to global scales. There is a clear mandate for monitoring, verification and accounting (MVA) of CCS systems nationally and globally to verify CCS integrity, treaty verification (Kyoto Protocol) and to characterize the nuclear fuel cycle. Planetary Emissions Management (PEM), working with the National Secure Manufacturing Center (NSMC), has the goal of designing, ruggedizing and packaging the GMP for field deployment. The system will conduct atmosphere monitoring then adapt to water and soil evaluations. Measuring ¹⁴CO₂ in real time will provide quantitative concentration data for ff-CO₂ in the atmosphere and CCS leakage detection. Initial results will be discussed along with design changes for improved detection sensitivity and manufacturability.

The sediment concentration in river flow is very important in monitoring of water quality, operation of the hydraulic facilities, and management of water resources. Commonly used sampling method is time consuming, labor intensive, and providing only point data at gauging station. This study is presenting a remote sensing approach to quantify suspended sediment concentration (SSC) of the high turbid flow in the Yellow River in China, where the high sediment transportation from severe soil erosion is a big environmental concern. The approach was based on public accessible satellite images and surface networking monitoring data. With the longest time series records, the Landsat EMT+ images were chosen to establish the remote sensing approach. Daily sediment records from 2 hydrological stations from 1999 to 2008 in the middle part Yellow River were associated with available satellite imaginary. The water reflectance was retrieved from the Landsat images by using an effective easy-to-use atmospheric correction method. Correlation among water reflectance at band 1 to 4, particle size of suspended sediment and SSC are analyzed to establish the SSC indices. According to the significance of relation between SSC and the water reflectance at different bands of Landsat data, regression models between SSC and water reflectance was developed. The model was calibrated by the daily sediment records from surface observation.

Water vapor represents a small but environmentally significant constituent of the atmosphere. This study retrieved columnar water vapor (CWV) with the 939.3 nm band of a Multi-filter Rotating Shadowband Radiometer (MFRSR) using the modified Langley technique from September 23, 2004 to June 20, 2005 at the XiangHe site.To improve the credibility, the MFRSR results were compared with those obtained from the AERONET (AErosol RObotic NETwork) CIMEL sun-photometer measurements, co-located at the XiangHe site, and the Moderate Resolution Imaging Spectroradiometer (MODIS) Near-Infrared Total Precipitable Water Product (MOD05), respectively. These comparisons show a good agreement in terms of correlation coefficients, slopes, and offsets, revealing that the accuracy of CWV estimation using the MFRSR instrument is reliable and suitable for extended studies in northern China.

This paper presents a new drought assessment method by spatially and temporally integrating the regional water stress index (RWSI) with the temperature vegetation dryness index (TVDI). With the aid of LANDSAT TM/ETM data, we were able to retrieve the land-use and land-cover (LULC), vegetation indices (VIs), and land surface temperature (LST), and derive three types of TVDI, including TVDI_SAVI, TVDI_ANDVI and TVDI_MSAVI, for the drought impact assessment in a well-developed coastal region, northern China. The classification of four drought impact categories associated with the RWSI values enables us to refine the spatiotemporal relationship between the LST and the VIs in a greater detail. Holistic drought impact assessment between 1987 and 2000 in our study area was carried out by linking RWSI with TVDIs group wise.

This study aims to retrieve aerosol optical depth (AOD) from ground-based MultiFilter Rotating Shadowband Radiometer (MFRSR) measurements at Xianghe site in Hebei Province from September 2004 to October 2005. Based on Langley regression, calibrations of MFRSR are carried out and then AODs at Xianghe is derived. In order to evaluate the precision of retrieved AOD, correlations between MFRSR AODs and Aerosol Robotic Network (AERONET) AODs which has been generally approved and used are analyzed. The result suggests that MFRSR AODs and AERONET AODs have a significant linear correlation. The correlation coefficients at 500nm, 670nm and 870nm band are 0.9764, 0.9712 and 0.954, respectively. Meanwhile, comparisons between Moderate resolution imaging spectroradiometer (MODIS) AOD at Xianghe site and MFRSR AODs are carried out. Finally, monthly mean MODIS AODs in the study area are derived from September 2004 to August 2005. Moreover, their spatial distribution and monthly variations are demonstrated.

High precision grids of meteorological data are essential input parameters for most kinds of large-scale global models. Improvements on data accuracy can make models running more effectively and exactly. At present, IDW, Kriging and Splines are often used as common interpolation methods, but for meteorological data their interpolation accuracy is not high enough and the interpolated raster images are sometimes too rough. This paper attempts to use ANUSPLIN, spatial interpolation software based on the theory of thin plate smoothing spline interpolation, to interpolate average temperature and precipitation in different time scales as daily, monthly, annual, with source data from 71 meteorological stations in Anhui Province. Before interpolation, experiments on different ANUSPLIN models were implemented with a combination of three variants (Longitude, Latitude and Elevation) to ensure the best one correspond each source data in different scales, the results showed that CO2 (elevation as a covariate and the order of spline is 2) model fits daily and monthly temperature data, CO3 model is effective for monthly and annual precipitation data. A comparison between the interpolated surfaces using ordinary kriging method and ANUSPLIN showed the latter one performs more accuracy and smoothness in all the time scales of temperature and precipitation: the mean error of daily mean temperature interpolation can be reduced by 0.103 centi-degree, monthly one by 0.091 centi-degree, annual one by 0.078 centidegree, monthly precipitation interpolation mean error can be reduced by 4.649mm, annual one by 22.194mm. The high precision of interpolated data can meet the need of many climatic and ecological models.

As an index to estimate regional air quality, aerosol plays an important role in air pollution. MODIS has a fast access to a wide range of aerosol data, however, there is no guarantee for the accuracy of its AOD product in China. This paper attempts to evaluate the applicability of MODIS AOD product in the lower and middle reaches of Yangtze River supported by AERONET data from Lin'an, Taihu and Hefei sites. The author matched AERONET AOD within ±30 min of MODIS overpass times with MODIS AOD over one pixel-size centered on the AERONET site, then analyzed and evaluated the fitting results. The results indicated that MODIS AOD is well correlated with AERONET AOD from 2007 to 2008, except R² is about 0.55 in 0.66μm band at Taihu site. MODIS AOD is underestimated at Lin'an site, while overestimated at the others. Similarity exists in the quality of MODIS AOD between Terra and Aqua, and the estimation of AOD by Terra was higher than that by Aqua. MODIS AOD in 0.47μm band has an advantage over that in 0.66μm as far as product accuracy is concerned. All the year, MODIS AOD is the best in quality in spring at Taihu site.

In this paper, 10-day (ten-day) spatio-temporal response of vegetation to the change of temperature and precipitation was analyzed in spring, summer, autumn and whole year in Xinjiang, China during the period of 1998-2009 based on the SPOT VEGETATION-NDVI data and 10-day average temperature or precipitation data observed by 54 meteorological stations in Xinjiang through correlation analysis. The results show that the response of 10-day NDVI to temperature was more significant than that to precipitation, and the maximal response of vegetation to temperature and precipitation lagged for two 10-day periods. Seasonally, the effect of temperature and precipitation on vegetation NDVI was the highest in autumn, then in spring, and it was the lowest in summer. The response of vegetation to 10-day change of meteorological factors was positive in spring, the affecting duration was long, and it was relatively short in autumn and summer. Spatially, the 10-day maximal response of NDVI to temperature in northern Xinjiang was higher than that in southern Xinjiang. The results indicated that interannual change of temperature was not the dominant factor affecting the change of vegetation NDVI in Xinjiang, but the decrease of annual precipitation was the main factor resulting in the fluctuation of vegetation coverage. 10-day average temperature was an important factor to promote vegetation growth in Xinjiang within a year, but the effect of precipitation on vegetation growth within a year was not strong.

Increased C_O2 (carbon dioxide) has been considered as one of key factors of global warming. Intending to describe the capability of CO₂ measurement by space-borne sensors quantitatively, this paper compares two data sets of CO₂ monthly products retrieved from AIRS and SCIAMACHY over China from 2003 to 2005. The increasing trend of CO₂ concentration can be detected consistently from both of the data sets. However, the seasonal variation of AIRS CO₂ is larger than SCIAMACHY CO₂ because the former represents CO₂ existing in the mid-troposphere while the latter represents in the lower-troposphere. CO₂ 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 CO₂ 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.

The fluctuations of leaf area index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) as reported by the MODIS 8-day product MOD15A2 over a section of Harriman State Park, New York were studied with reference to another nearby local park. The area selected for study, a seven km square grid with one km resolution, is known for its biodiversity. Time series data points were generated using the sums of the grid's 49 pixel measurements for each of the 46 entries that make up the annual time series. A quadratic relation has been observed that suggests that LAI/FPAR is proportional to FPAR if FPAR is considered as the forcing parameter via chlorophyll (a, b, c, d and f), in an application model for the study of biodiversity. The LAI annual time series from 2000 to 2009 follows the corresponding FPAR annual time series as expected, but with different proportionality ratios in different seasons. The fractal analysis results of the time series data suggest that the LAI sequences have a lower fractal dimension (~1.35) than those of the FPAR sequences (~1.55), consistent with the idea that biological systems are capable of regulating fluctuation. The regression of LAI sequence fractal dimension versus FPAR sequence fractal dimension exhibits an R-square of about 0.7 (N =10). The observed regression outlier for the year 2009 could be indicative of the presence of additional factors. Synchrotron EXAFS and XANES investigations of leaf samples reveal data consistent with metal absorption under stress. Further studies of absorption under stress using remote sensing data are warranted.

Based on the Aster LAI estimation, the main object of this paper is to generate the high spatial and high temporal resolution LAI product. One method is proposed to get high spatial and temporal resolution LAI product by fusing MODIS LAI product and Aster LAI. In this method, the LULC data is used to register with MODIS data, then the percentage of classes of PFT classification in the MODIS pixel can be calculated. And the multi-year mean MODIS LAI values are the background data, the Aster LAI is used to adjust this curve of multi-year mean MODIS LAI. And we validate LAI with high spatial and high temporal resolution using the measured data that is not to be used as the training data. The results is good and can meet our study needs.

Jing-Hang Great Canal is the earliest canal in the world, it has an important role in the transportation between south and north China during almost 2,000 years. But with the development new technologies, the Great Canal has lost its role from 19th century. Especially in Nanwang part of canal, it has disappeared as a dry drain now. In order to find the reason of Nanwang canal disappeared from 1700 gradually, we put forward a new method to find the change of it. In this paper ,we first use an old map which draw in 300 years ago to obtain the old time environment status and correct it into nowadays remote sensing data to reveal the old sub-rivers which has run into canal in the old time. We also use some history materials to get the social information such as population data, county and village data. Second we using present remote sensing data to extract river, farmland data, we also collect the population data now in this area. In the end we compared this two period data to find the different hydrographic net in 300 years. The result will give us the answer for the canal change and give us a hint for reconstruction the Great Canal in the future.

For the characteristics of soil moisture in arid areas existence space heterogeneity, soil moisture remote sensing shall be based on what scales can meets the requirements do not break again operability being a big problem. The paper analyzes scale features of water heat energy parameters, introduces the scale curve. Then we analyze the relationship of spatial scale between spatial heterogeneity of surface parameters and remote sensing observational ability; believe that the intersection curve is helpful to determine the remote sensing observation scale of regional soil moisture. Therefore, study the spatial scale characteristics of surface parameters; in particular test the critical point of key parameters which is possible existence is very important. From case study, we quantitatively test the spatial scale from four aspects: Polygon scale analysis of land cover, pixel scale analysis of land cover, spatial semi-variance analysis of land attributes, multi-Scale consistency index; the suitable spatial scale is less than 1km,250m,1km and 240-480m. Integrated its results, the optimum soil moisture remote sensing research scale is between 300-1000m in arid area.

With the leaping of geological data, the demand to application of geological data is increasing and complex. How better to extract the excrescent information of geochemical anomalies and models of extracting altered information have been the important problem of geologists concerned. We analyzed the spectral characteristics of typical altered minerals, summarized the spectral response characteristics of typical altered minerals on ETM+. We contrasted three kinds of models of extracting altered information, they were enhancing model which was basis on spectrum characters, the threshold model which was basis on PCA and the SAM model which was basis on spectrum vectors, and we firstly built the MPS model on the base of analysis the method of eliminating familiar interference information. The research discovered the result of MPS model tallied with the location of the known gold spots, and avoided the obviously false abnormality information. And we made use of this model to extracting the altered information in whole study area.

The Advanced Very High Resolution Radiometer (AVHRR) sensors onboard The National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites have been measuring electromagnetic radiation emitted by the Earth in the visible (VIS), Near-Infrared (NIR) and Infrared (IR) portions of the electromagnetic spectrum for nearly 30 years. The Global Vegetation Index Vegetation Health product (GVI-x VH) developed from the AVHRR dataset includes the Brightness Temperature (BT) variable calculated from the IR channels, which in turn is used to estimate other environmental variables such as Sea Surface Temperature (SST), Land Surface Temperature (LST), Temperature Condition Index (TCI), and Vegetation Health Index (VTI) among others. However, the satellite measured IR radiances need to be corrected with sufficient accuracy to minimize the uncertainty introduced by a host of sources such as the atmosphere, stratospheric aerosols, and satellite orbital drift before being input into any algorithm to generate remotely sensed products. In this research we have applied a statistical technique based on Empirical Distribution Functions (EDF) to normalize the NOAA GVI-x BT records for the combined effect of the sources of uncertainty mentioned above, avoiding the need for physics based corrections. The normalized results are tested to verify that the normalization improves the data.

Multiple cropping index reflects the intensity of arable land been used by a certain planting system. The bond between multiple cropping index and NDVI time-series is the crop cycle rule, which determines the crop process of seeding, jointing, tasseling, ripeness and harvesting and so on. The cycle rule can be retrieved by NDVI time-series for that peaks and valleys on the time-series curve correspond to different periods of crop growth. In this paper, we aim to extract the multiple cropping index of China from NDVI time-series. Because of cloud contamination, some NDVI values are depressed. MVC (Maximum Value Composite) synthesis is used to SPOT-VGT data to remove the noise, but this method doesn't work sufficiently. In order to accurately extract the multiple cropping index, the algorithm HANTS (Harmonic Analysis of Time Series) is employed to remove the cloud contamination. The reconstructed NDVI time-series can explicitly characterize the biophysical process of planting, seedling, elongating, heading, harvesting of crops. Based on the reconstructed curve, we calculate the multiple cropping index of arable land by extracting the number of peaks of the curve for that one peak represents one season crop. This paper presents a method to extracting the multiple cropping index from remote sensing image and then the multiple cropping index of China is extracted from VEGETATION decadal composites NDVI time series of year 2000 and 2009. From the processed data, we can get the spatial distribution of tillage system of China, and then further discussion about cropping index change between the 10 years is conducted.