This PDF file contains the front matter associated with SPIE Proceedings Volume 8513, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

^{Fuel moisture content (FMC) is an important variable for predicting the occurrence and spread of wildfire. Foliar FMC was calculated as the ratio of leaf foliar water content (Cw) and dry matter content (Cm). Recently, the normalized dry matter index (NDMI) was developed for the remote sensing of Cm using high-spectral resolution data. This study explored the potential for remote sensing of FMC using the ratio of various vegetation water indices with NDMI. For leaf-scale simulations, all index ratios were significantly related to FMC. For canopy-scale simulations, ratio indices of the normalized difference infrared index (NDII) and normalized difference water index (NDWI) with NDMI predicted FMC with R2 values of 0.900 and 0.864, respectively. NDII/NDMI determined from leaf reflectance data had the highest correlation with FMC. Further investigation needs to be conducted to evaluate the effectiveness of this approach at canopy scales with airborne remote sensing data.}

The beetle, Agrilus planipennis Fairmaire, was introduced to Michigan in 2002 and has since spread to many other states. In recent years, it has been reported in parts of New York. The fluctuations in satellite data signal associated with indices describing ash tree health, such as leaf area index (LAI) and Normalized Difference Vegetation Index (NVDI) as reported by the MODIS, have been studied. The fraction of Photosynthetically Active Radiation (FPAR) data was also studied. MODIS hyperspectral data, as calibrated to winged aircraft hyperspectral data, was used for ash tree characterization.

In order to improve the accuracy of solar radiation related parameters’ for crop modeling, a new calibration method (Multi-Channel Calibration) for Multi-Filter Rotating Shadow-band Radiometer (MFRSR) is proposed. It uses the Angstrom Law that links aerosol optical depth (AOD) at multiple wavelengths as the primary constraint. It also uses the bi-channel Langley Regression to provide an additional constraint. Starting with any initial guess of calibration coefficient (V₀) at 870 nm, two consecutive steps, both involves calling trust region based non-linear optimization module (CONDOR), are implemented to solve (1) the intermediate parameter Angstrom coefficient and the set of biased V₀s at other channels corresponding to the initial one at 870 nm channel; and (2) the final V₀s of all permissible channels. The result shows that Unlike Langley method, the Multi-Channel Calibration method return V₀ at all permissible channels. Besides, the new method can converge to the same (less than 0.5%) final V₀s with the starting guess in a wide range. Most important, the comparison between AODs derived from those final V₀s and those of AERONET sunphotometers suggests the upper limit of the error of those final V₀s is less than 1.03%, which is a great improvement over the Langley V₀s (7.45%).

This paper was to assess the reliability of RADARSAT-2 quad-polarization SAR data in rice mapping and yield estimation. Five scenes of RADARSAT-2 images were acquired in the rice season of 2011 in Jiangsu Province, China. Ground experiments were conducted in accordance with the acquisition dates. For rice mapping, optimal dual-polarization combination was obtained by ratio change detection. The accuracy of rice mapping by HV/HH reaches 79.2% and by VV/HH reaches 84.9%. For rice yield estimation, an improved scheme based on assimilation method has been put forward. ORYZA2000 model was coupled with an empirical rice backscattering model to simulate the dual-polarization ratios. SCE-UA optimization algorithm is employed to determine the optimal set of input parameters during the re-initialization process. As a result, an improved accuracy has been confirmed.

The concentration of total organic carbon (TOC) in surface waters is subject to seasonal variation, as well as abrupt changes in concentration due to events. In drinking water treatment, TOC is a precursor to disinfection byproducts such as total trihalomethanes (TTHM). With the aid of an early warning system for the detection of TOC concentrations, water treatment operators could make more informed decisions and adjust the treatment processes to minimize the generation of disinfection byproducts. In this paper, a near real-time monitoring system is explored using the Integrated Data Fusion and Machine-learning (IDFM) technique to predict the spatial distribution of TOC in a lake based upon surface reflectance data from satellite imagery. Landsat 5 TM and MODIS Terra satellite imagery can be acquired free of charge, yet MODIS has coarse spatial resolution, while Landsat has a lengthy 16 day revisit time. This difficulty is solved using data fusion algorithms to fuse the fine spatial resolution of Landsat with the daily revisit time of MODIS to generate a synthetic image with both high spatial and temporal resolution. To demonstrate the capabilities of IDFM, this case study uses the fused surface reflectance band data and applied machine-learning techniques to reconstruct the spatiotemporal distribution of TOC in Harsha Lake, which serves as the source water intake for the McEwen Water Treatment Plant in Ohio. The results of this application of IDFM were analyzed using 4 statistical indices, which indicated that the Artificial Neural Network model is capable of reconstructing TOC concentrations throughout the lake.

The commercial development of microbolometer uncooled long-wave thermal infrared imagers in conjuncture with advanced radiometric calibration methods developed at Montana State University has led to new uses of thermal imagery in remote sensing applications. A novel use of these calibrated imagers is imaging of vegetation for CO₂ gas leak detection. During a four-week period in the summer of 2011, a CO₂ leak was simulated in a test field run by the Zero Emissions Research and Technology Center in Bozeman, Montana. Thermal infrared images were acquired, along with visible and near-infrared reflectance images, of the exposed vegetation and healthy control vegetation. The increased root-level CO₂ concentration causes plant stress that results in reduced thermal regulation of the vegetation, which is detectable as an increased diurnal variation of infrared emission. . In a linear regression, the infrared data were found to have a strong coefficient of determination and clearly show the effect of the CO₂ on the vegetation.

We have carried out comparative simulation studies to establish the advantages and limitations of alternative spectral regions and measurement wavelengths being investigated by different groups for the sensing of CO₂ and O₂ for potential use in the ASCENDS mission implementation. Our studies are based on the lidar modeling framework we developed specifically for ASCENDS which may be further applied to similar missions relying on the active sensing approach from space or aircraft. The modeling framework performs standard lidar sensitivity calculations, and also includes analysis of weighting functions and the effects of laser wavelength instabilities. As such, the factors considered in the analysis include the general LIDAR sensitivity, shapes of the weighting functions, as well as the added error due to the laser wavelength jitter in the selected spectral bands and wavelength regions. In particular, the studies were performed for the 1.26 – 1.27 micron and the A-band of oxygen, as well as the 1.57 and 2.06 micron bands of carbon dioxide. Additionally, the analysis is based on a range of satellite datasets and models to also take into account a variety of spacial and temporal variations in surface and atmospheric parameters. The results of our comparative studies for alternative spectral bands will be presented including the quantitative estimates of required constraints on selected system parameters to achieve the desired accuracy of ~0.3% in XCO₂ measurements.

The growth of forest is critically vulnerable to the change in rainfall and radiation than in air temperature. The amount of rainfall and cloudiness in the northeast region of the United States is assumed to be strongly affected by the Atlantic sea surface temperature (SST). The observational investigation of the relation between the greenness of three undisturbed forested areas in the Atlantic region and Atlantic SST is fundamental to understand the response of terrestrial ecosystems to climate change. Such teleconnection signals may also entail the change of natural variability associated with several hydrological parameters such as rainfall and runoff. We conducted short-term environmental change quantification using MODIS satellite imageries supplemented by NEXRAD data. Wavelet analysis was employed to derive climate signals and embedded patterns over the timescale to illuminate the propagation effects of climate teleconnection.

With the analysis of SWIR and NIR spectral space, and based on the Short Wave Perpendicular Water Stress Index (SPSI) of which was constructed by Abduwasit Ghulam, the SPSI was applied in the soil moisture retrieval during the wheat growing period in April with full cover condition. The result showed there is a high correlation coefficient between SPSI and soil moisture in depth of 0-30 cm, and has been tested that the SPSI is an effectiveness index in the soil moisture retrieval under full cover condition.

The land surface temperature (LST) plays an important role in the process of interaction between surface and atmosphere. It is widely need in meteorology, geology, hydrology, ecological and many other fields. This article uses the ETM+ data of February 16th, 2002 and August 27th, 2002, using the single window algorithm to retrieve the LST in the southern area of Gansu province. First step is removing cloud for image. Secondly, classifies the type of surface by dividing into three types of water surface, snow surfaces (winter) and natural surface. Then, estimate the emissivity according to the classification in order to calculate surface temperature. Through the analysis of spatial distribution of land surface temperature in the study area, the result shows QinZhiHao's single window algorithm is consistent with the reality.

Based on medification of crop model WOFOST, a winter wheat growth model was applied in Yucheng region of Shandong Province in the North China Plain. Combination method of remote sensing information with crop model in water stress production level was studied. Through coupling remote sensing information, crop model was optimized by reestimating its parameters and initial conditions. A new method of regional remote sensing combining crop model was established and its application was studied. This method has highly potential application in crop growth monitoring and yield forecasting.

Crop model is a powerful tool in crop growth monitoring and yield forecasting, however crop model is developed based on single point scale, due to regional differentiation、field variation and other reasons lead to input parameters and initial conditions which required by crop model simulation are hard to obtain, the application of crop model has been greatly limited in the regional scale, the introduction of remote sensing will solve this problem, remote sensing is combined with the crop model WOFOST, using the state variable retrieved by remote sensing to optimize crop model simulation, revaluing the sensitive parameters and initial conditions which needed in crop model on the region scale, in order to take the advantage of crop model in the area.This study is on the basis of adaptive adjustment and amendment of crop model WOFOST, build a winter wheat growth simulation model which is suitable for Yucheng, Shandong; Using the field experiment data calibration and validation the WOFOST model, discussed the method which combined crop simulation model and remote sensing under water stress level, using remote sensing calibrated some key processes of crop simulation or reinitialize、parameterize the crop simulation model in order to achieve the optimization model; Explored some reasonable and practical method of remote sensing information application in crop simulation at regional scale, with more research, make it possible to monitor regional crop growth and forecast the output.

Land-use and land-cover change has been a research focus in global environmental change. Recent research found that land-use change could influence the structure of biogeochemical spheres as well as material and energy recycle directly or indirectly. Land-use dynamic models are considered as an effective technique to study the processes of land-use modification. The objective of this paper is to compare two widely use land-use dynamic models, CLUE-S and Dinamica EGO, from the perspective of land-use change amount, spatial characteristics, and their utility. A case study was conducted to examine the ascendants of each model and Kappa coefficient was used to compare the simulation accuracy. The modelling experiments reflected that the predictions of land-use change based on CLUE-S and Dinamica EGO matched broadly with actual situation. CLUE-S was better in overall accuracy whereas the Markov process in Dinamica EGO could precisely predict the amount of land-use change. Moreover, the spatial pattern of simulation map based on Dinamica EGO was more consistent with empirical result. Both results indicate their possible further applicability for forecasting future land-use change and corresponding studies.

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%.

Carbon dioxide (CO2) is one of major green house gases affecting global climate. Biomass burning caused by fire is an important emission source of CO2 in the atmosphere. CO2 concentration retrieved from Atmospheric Infrared Sounder (AIRS) and fire pixel counts (FPC) obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) from 2003 to 2010 over China were obtained and analyzed. The characteristics of correlation between CO and FPC were analyzed in time series. To investigate the spatial characteristics of correlation between CO2 and fire, energy fires emitted based on the Global Fire Emissions Database v3 (GFED3) was used. CO2 concentration was steadily increased in both daytime and nighttime. The seasonal distribution of CO2 concentration and FPC had the similar pattern as the highest value appeared in Spring and lowest value in Autumn. What’s more, the changes of the aggregated CO2 concentration had a good agreement with the changes of the total FPC. However, the concentration of CO2 emitted from fires was low except Heilongjiang province. And the tempo-spatial characteristic of CO2 and FPC were similar with each other. It was different with characteristic of correlation between CO2 and FPC in whole country.

The potential of satellite data used for particular matter monitoring is a crucial subject in air quality research. PM10 is influenced by many meteorological factors and has a difference correlation with aerosol optical depth in different place. Geographically weighted regression (GWR) model have been proved to be an effective methods for spatial variation analysis. This paper presented results from a study of PM10 concentration from API in eastern China from 2005 to 2010. Wavelet analysis was used for analyzing the periodicity characteristics of PM10 and AOD. The correlations between PM10 and meteorological factors were also analyzed without AOD and with AOD added, respectively. Obvious spatial and seasonal non-stationary distributions of PM10 concentration were found with spatial auto-correlation analysis. PM10 concentration and AOD have similar periods and discontinuity characteristics in 41 months scale and 70 months scale. Correlation between PM10 concentration and meteorological factors were improved when AOD added as a factor, and the tempo-spatial distributions of the correlations were non-stationary in eastern China because of differences of the regional weather conditions and the pollution sources.

Population dynamics has major impacts on regional ecosystem and socioeconomic development. Its prediction has become a key step for assessing ecosystems and socioeconomic development. Using the population data of Yangtze River Delta, a model created by Back Propagation (BP) neural network were adapted to probe and describe the dynamic evolution, and the Moran Index was used in analyzing spatial autocorrelation quantitatively.

Transpiration, an essential component of surface evapotranspiration, is particularly important in the research of surface evapotranspiration in arid areas. The paper explores the spectral information of the arid vegetal evapotranspiration from a semi-empirical perspective by the measured data and the up-scaling method. The paper inverted the transpiration of Haloxylon ammodendronat at the canopy, pixel and regional scales in the southern edge of the Gurbantunggut desert in Xinjiang, China. The results are as follows: At the canopy scale, the optimal exponential model of the sap flow based on the hyperspectrum is Y = 3.65× SR(1580,1600) + 0.76, R2 = 0.72. At the pixel scale, there was a good linear relationship between the sap flow and the SR index, with a linear relationship of Y = 0.0787 X - 0.0724, R2 = 0.604. At the regional scale, based on the optimal exponential model and the EO-1 Hyperion remote sensing data, the transpiration of the study area was inverted. Comparing the results of the SEBAL and SEBS models, the errors of the simulation results were 12.66% and 11.68%. The paper made full use of the knowledge flow at different scales, bridging the scale difference in canopy and remote sensing images to avoid the information bottleneck in the up-scaling. However, there is much limit in the data acquirement, the endmembers determine, the temporal-spatial up-scaling, and the accuracy assessment to be improved in the future studies.

The stem sap flow exhibited a bi-peaked or multi-peaked curve, with lower values at night than during the day. The ambiguous noon-depression phenomenon usually occurs during 14:00~16:00 from mid-May to the early September. Under the same environmental conditions, the larger the stem diameter, the larger the stem sap flow, and the more obvious the ambiguous noon-depression phenomenon. The daily changes of the sap flow were highest in June and lowest in September. There were differences in the monthly mean value in different plants, which may result from the differences in the crown and the number of assimilation organ. The daily accumulation showed a “S” trend between May and the end of August, and showed a straight line with the same slope in September and October. The larger the stem diameter, the larger the daily water use and the accumulative rate were. The sap flow was influenced by meterological factors, it was positively correlated with solar radiation, air temperature and wind speed, and negatively correlated with the air relative humidity, in which the solar radiation had the greatest impact on the sap flow. Under the same environmental condition, the larger the stem diameter, the better the correlation was. The correlation was the largest water use in July, and least in May and October. The larger the stem diameter, the more the water consumption was.

By using MODIS data products, combined with DEM data, land use data, meteorological data, employed SEBAL model, light use efficiency model, PAR model and the algorithm of vegetation index , the parameters of ET (Evapotranspiration), NPP (Net Primary Product) , PAR (Photosynthetic Active Radiation), NDVI (Normalized Difference Vegetation Index) and EVI (Enhance Vegetation Index) in Haihe River Basin were estimated. The impacts of elevation and land cover change on ET, NPP , PAR , NDVI and EVI are analyzed.

GEOLUE model was designed with Light Use Efficiency (LUE) mechanism and was validated with observed data and models comparison (GLOPEM, CASA, and CEVSA). We found that: GEOLUE model correctly simulates monthly, quarterly and annual variation of Net Primary Product (NPP) in different vegetation communities under monsoon climate. The spatial distribution of NPP simulated by GEOLUE matched up to 96.67% with that of forest and shrub land. The GEOLUE model perfectly simulated the seasonal characteristics and spatial pattern of biomass in different types of vegetation. The total amount NPP of China simulated by GEOLUE is 0.667GtC in spring, 1.365GtC in summer, 0.587GtC in autumn and 0.221GtC in winter. The average total NPP of China for 5 years is 2.84GtC / year.

The experimental study results of spectral characteristic change of different types of plants influenced by external factors (synthetic superficially active substances, salts of heavy metals and nitrate fertilizers) are presented. Differential optical factor was used as the monitored optical parameter that characterizes the chlorophyll concentration change. The differential backscatter method which has high test-sensitivity and provides with the most complete information on the plant condition was the main optical monitoring method. For understanding the mechanisms of external factor accumulation and influence on plants the confocal fluorescent microscopy method providing contrast micrographs of high resolution was used for microscopic analysis in the study. It was revealed that synthetic superficially active substances and heavy metal presence lead to quasilinear decrease of differential backscatter factor with time. It was shown that the presence of salts of heavy metals in a water solution leads to chlorophyll "binding" which is microscopically shown as their «adhesion» near the cell membranes. On the contrary, the presence of synthetic superficially active substances maintains the uniformity of chlorophyll distribution in a cell, but its concentration falls with increasing the concentration in a major emission. The latter depends on the fact that synthetic superficially active substances solubilize the cell membrane proteins, increasing its penetrability. It causes pigment release ("washing away") from a plant, thereby leading to differential optical factor change. It was shown that nitrate fertilizer presence leads to increase of differential backscatter factor with time which is microscopically connected to increase in chlorophyll concentration.

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.

A method for capturing the solar shape and location on occurrence of solar occultation is presented. On occurrence of occultation when the Sun is covered by atmosphere, the solar shape viewed by satellite-borne detector on certain orbit through atmosphere at different heights varies greatly due to such factor as the in homogeneity of atmosphere and cloud covering, etc. During the varying of heights of atmosphere, the gray image of Sun also changes, which even appears several parts in different size due to the disturbance of cloud layer. Based on which, the Paper proposes a method for capturing solar shape and intensity on occurrence of solar occultation. First, taking the solar grey image without atmospheric refraction effects as a reference; then the refraction angle of Sun ray after being refracted by atmosphere can be reversely calculated by using Abel integral function and the vertically distributed data of index of refraction; Last, the solar shape after passing atmosphere can be obtained by calculating the refraction angle of the ray on solar limb. We have obtained the image of solar shape and intensity at the occultation central point of contact from 5km to 60km when the detector is located at the defined satellite orbit (600km) by simulation. This method is of great significance for realizing the solar simulator which can reflect solar shape and intensity in a comparatively truly degree under the circumstance of existing various affecting factors for application in the fields like measuring and calibration of posture parts of satellite, remote sensing technology and material measure, etc.

Remote sensing data combined with crop model is an important application and development trend of current agricultural information technology, it can solve the problem that remote sensing or crop model cannot solve alone. In order to simulate crop growth and yield prediction in large scale, this paper using field test data to calibrate and validation the model parameters before apply to the winter wheat WOFOST model, than according to the actual environment of Xinxiang, simulate the growth in 3 different condition in the 2002-2003 growing season. Contrast the simulation value WOFOST model, using the Landsat-7 ETM retrieving leaf area index, define winter wheat’s growth condition in each pixel, the remote sensing information combined with crop model is accomplished at pixel scale. Based on the actual production of Xinxiang winter wheat in 2003,compare the simulate results with the corresponding parameter, results shows that the method of this study method is feasible.