This PDF file contains the front matter associated with SPIE Proceedings Volume 9610, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

In the present work, the potential ecotourism sites in Wadi Wurayha, Fujairah, United Arab Emirates (UAE), are identified and prioritized using Geographic Information System (GIS). The identification criteria are based on landscape or naturalness characteristics; visibility and land use or land cover, the topography characteristics; elevation and slope, the accessibility characteristics; distance from roads, and community characteristics; settlement size. After developing the list of ecotourism criteria, GIS techniques were used to measure the ranking of different sites according to the set criteria and thus identify those with the best potential. Subsequently, the land suitability map for ecotourism was created. The degree of suitability of each factor was classified as highly suitable, moderately suitable, marginally suitable, and not suitable for ecotourism. Based on the suitability map, the areas of high ecotourism potential are located in protected areas. The methodology proposed was useful in identifying ecotourism sites by linking the criteria deemed important with the actual resources of the area. This study results helped to identify optimal use of the land for tourism facilities development and ecotourism resource utilization within Wadi Wurayha area in the near future. Additionally, the results from the present work can also serve as a starting point for more detailed studies in the future.

Cropland is the major source of carbon lost to the atmosphere and contribute directly to emissions of greenhouse gases. There is, however, large potential for cropland to reduce its carbon ux to the atmosphere and sequester soil carbon through soil and crop managements. The managements include no-tillage, perennial and/or deep root crops, irrigation, and organic fertilization etc. But these estimations over cropland remain largest uncertain among all other terrestrial biomes. In most models in CMIP5, the cropland is generally treated similarly as grassland without accounting for realistic crop phenology and physiology processes and crop and soil manage- ments. In this study, we will evaluate how well cropland is represented in CMIP5 simulations and how to improve the representations and reduce the uncertainties over cropland. We will compare the modeled biogeochemical variables against multiple observational data including various remote sensing products and in-situ data.

Surface ultraviolet (UV) observations can be obtained from satellite or ground observations. This study uses data fusion to combine the advantages from both sources of observations, aiming at achieving a better estimate of surface UV. In this study, ensemble methods were used to estimate the covariances, which are the most important components in data fusion. The combined UV observations not only have the same coverage as satellite data, but also improve their regional accuracy around the ground observatories.

The Mw 9.0 earthquake that struck Japan in 2011 was followed by a large-scale tsunami in the Tohoku region. The damage in the coastal plane was extensively displayed through many satellite images. Furthermore, satellite imaging is requested for the ongoing evaluation of the restoration process. The reconstruction of the urban structure, farmlands, grassland, and coastal forest that collapsed under the large tsunami requires effective long-term monitoring. Moreover, the post-tsunami land cover dynamics can be effectively modeled using time-constrained satellite data to establish a prognosis method for the mitigation of future tsunami impact. However, the remote satellite capture of a long-term restoration process is compromised by accumulating spatial resolution effects and seasonal influences. Therefore, it is necessary to devise a method for data selection and dataset structure. In the present study, the restoration processes were investigated in four years following the disaster in a part of the Sendai plain, northeast Japan, from same-season satellite images acquired by different optical sensors. Coastal plains struck by the tsunami are evaluated through land-cover classification processing using the clustering method. The changes in land cover are analyzed from time-series optical images acquired by Landsat-5/TM, 7/ETM+, 8/OLI, EO-1/ALI, and ALOS-1/AVNIR-2. The study reveals several characteristics of the change in the inundation area and signs of artificial and natural restoration.

Spartina alterniflora is one of the most serious invasive species in the coastal saltmarshes of China. An accurate quantitative estimation of its canopy leaf chlorophyll content is of great importance for monitoring plant physiological state and vegetation productivity. Hyperspectral reflectance data representing a range of canopy chlorophyll content were simulated by using the PROSAIL radiative transfer model at a 1nm sampling interval, which was based on prior knowledge of S.alterniflora. A set of indices was tested for estimating canopy chlorophyll content. Subsequently, validation were performed for testing the performance of indices, based on the PROSAIL model using in situ data measured by a Spectroradiometer with spectral range of 350-2500nm in a late autumn in a sub-tropical estuarine marsh. PROSAIL simulations showed that the most readily available indices were not good to be directly used in canopy chlorophyll estimation of S.alterniflora. The modified Chlorophyll Absorption in Reflectance Index MCARI[705,750] was linear related to the canopy chlorophyll content (R²=0.94) , but did not achieve a satisfactory estimation results with a high RMSE (RMSE=0.95 g.m^-2). We optimized the index MCARI[705,750] by introducing a scale conversion coefficient to the formula to solve data units inconsistent, which is between the practical application unit and the unit used in the process of establishing the index, and balance scale transformation through radiative transfer models and examing corresponding canopy reflectance index values. We proposed index Optimized modified Chlorophyll Absorption in Reflectance Index OMCARI[705, 750]. The results showed that the index OMCARI[705, 750] had higher precision of prediction of chlorophyll for S.alterniflora (R²=0.94,RMSE=0.41 g.m^-2 ).

Land cover (LC) refers to the physical state of the Earth's surface such as soil, vegetation and water, etc. However, most LC features occur at spatial scales much finer than the resolution of the primary remote sensing satellites. In this paper, we explore the possibility of collaborative sparse unmixing for estimation and quantification of LC classes at subpixel level to obtain abundance maps in both the unconstrained and constrained forms (with abundance nonnegativity and abundance sum-to-one constraints imposed). Firstly, computer simulated noise-free and noisy data (Gaussian noise of different noise variance: 2, 4, 8, 16, 32, 64, 128 and 256) were unmixed with a set of global endmembers (substrate, vegetation and dark objects) in the NASA Earth Exchange. In the second set of experiments, a spectrally diverse collection of 11 cloud free scenes of Landsat-5 TM data representing an agricultural set-up in Fresno, California, USA were unmixed and validated using ground vegetation cover. Finally, Landsat-5 TM data for an area of San Francisco (an urbanized landscape), California, USA were used to assess the algorithms and compared with the fractional estimates of World View-2 data (2 m spatial resolution) for validation. The results were evaluated by using descriptive statistics, correlation coefficient, RMSE, probability of success and bivariate distribution function. With computer-simulated data, both unconstrained and constrained solutions gave excellent results up to a certain noise variance, beyond which the performance in classification gradually decreased. For the agricultural setup, mean absolute error (MAE) of vegetation fraction between actual and estimated abundance values was 0.08 for both unconstrained and constrained case, and for the urban landscape, average MAE of the three classes considered was 0.73 for unconstrained and 0.07 for the constrained solution.

Solar radiation inputs drive many processes in terrestrial ecosystem models. The processes (e.g. photosynthesis) account for most of the fluxes of carbon and water cycling in the models. It is thus clear that errors in solar radiation inputs cause key model outputs to deviate from observations, parameters to become suboptimal, and model predictions to loose confidence. However, errors in solar radiation inputs are unavoidable for most model predictions since models are often run with observations with spatial or / and temporal gaps. As modeled processes are non-linear and interacting with each other, it is unclear how much confidence most model predictions merits without examining the effects of those errors on the model performance. In this study, we examined the effects using a terrestrial ecosystem model, DayCent. DayCent was parameterized for annual grassland in California with six years of daily eddy covariance data totaling 15,337 data points. Using observed solar radiation values, we introduced bias at four different levels. We then simultaneously calibrated 48 DayCent parameters through inverse modeling using the PEST parameter estimation software. The bias in solar radiation inputs affected the calibration only slightly and preserved model performance. Bias slightly worsened simulations of water flux, but did not affect simulations of CO₂ fluxes. This arose from distinct parameter set for each bias level, and the parameter sets were surprisingly unconstrained by the extensive observations. We conclude that ecosystem models perform relatively well even with substantial bias in solar radiation inputs. However, model parameters and predictions warrant skepticism because model parameters can accommodate biases in input data despite extensive observations.

Rice production in Bangladesh is a crucial part of the national economy and providing about 70 percent of an average citizen’s total calorie intake. The demand for rice is constantly rising as the new populations are added in every year in Bangladesh. Due to the increase in population, the cultivation land decreases. In addition, Bangladesh is faced with production constraints such as drought, flooding, salinity, lack of irrigation facilities and lack of modern technology. To maintain self sufficiency in rice, Bangladesh will have to continue to expand rice production by increasing yield at a rate that is at least equal to the population growth until the demand of rice has stabilized. Accurate rice yield prediction is one of the most important challenges in managing supply and demand of rice as well as decision making processes. Artificial Neural Network (ANN) is used to construct a model to predict Aus rice yield in Bangladesh. Advanced Very High Resolution Radiometer (AVHRR)-based remote sensing satellite data vegetation health (VH) indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) are used as input variables and official statistics of Aus rice yield is used as target variable for ANN prediction model. The result obtained with ANN method is encouraging and the error of prediction is less than 10%. Therefore, prediction can play an important role in planning and storing of sufficient rice to face in any future uncertainty.

To provide a reference for canopy parameters inversion, sensitivity analysis of plant canopy parameters based on remote sensing model is a prerequisite for the inversion. Because the local sensitivity analysis do not consider the coupling effect among the parameters, the EFAST (i.e., Extended Fourier Amplitude Sensitivity Test), a global sensitivity analysis, can be used not only for the analysis of each parameter, but also consider the interacted effect among each parameter. Based on PROSAIL model, the paper focused on the parameters’ sensitivity by using simulated data and EFAST method. The results showed that the EFAST considered not only the contribution of single parameter, but also the interactive effects among each parameter, and four parameters, leaf area index (LAI), leaf mesophyll structure (N), the controller factor of the average leaf slope (LIDFa) and soil moisture condition (psoil) had great effect on the canopy reflectance in the whole wavelength from 400 to 2500 nm than other canopy parameters, and the EFAST method enlarged the contribution of some parameters that had little effects.

Based on SPOT VEGETATION NDVI time-series data, multi-phase China’s land use / land cover (LULC) data were extracted in this study, where land use degree method and land dynamic degree method were used to analyze the spatial and temporal change characteristics of China’s LULC in the latest decade. Moreover, bookkeeping model was applied to analyze the response of China's carbon sink to LUCC. Research conclusions were achieved as follows. China's annual vegetation carbon sink was 0.22- 0.32PgC/year, equivalent to 26% -28% of China's industrial CO₂ emissions over the same period. Dynamic changes in woodland and grassland led to carbon sink changed in 11.4-15.7TgC, and the increased carbon sink due to LUCC offset 1.3-1.4% of China’s industrial CO₂ emissions.

Identification of teleconnection patterns at a local scale is challenging, largely due to the coexistence of non-stationary and non-linear signals embedded within the ocean-atmosphere system. This study develops a method to overcome the problem of non-stationarity and nonlinearity and investigates how the non-leading teleconnection signals as well as the known teleconnection patterns can affect precipitation over three pristine sites in the United States. It is presented here that the oceanic indices which affect precipitation of specific site do not have commonality in different seasons. Results also found cases in which precipitation is significantly affected by the oceanic regions of two oceans within the same season. We attribute these cases to the combined physical oceanic-atmospheric processes caused by the coupled effects of oceanic regions. Interestingly, in some seasons, different regions in the South Pacific and Atlantic Oceans show more salient effects on precipitation compared to the known teleconnection patterns. Results highlight the importance of considering the seasonality scale and non-leading teleconnection signals in climate prediction.

The most important aggregate measure of the long run health of the productive component of the agricultural economy is agricultural total factor productivity (TFP). Between 1948 and 2011, average annual input growth in US agriculture averaged approximately 0.07% while annual average output growth averaged roughly 1.5%. That translates into an annual average agricultural TFP growth rate of approximately 1.43%. That growth has led to a remarkable expansion of the productive ability of the US agricultural sector. However, climate change poses unprecedented challenges to U.S. agricultural production because of the sensitivity of agricultural productivity and costs to changing climate conditions. Some studies have examined the effect of climate change on U.S. agriculture. But none has investigated how climate affects the overall U.S. agricultural productivity. This study intends to find out climate change impacts on U.S. agricultural TFP change (TFPC). By correlation analysis with data in 1979-2005, we found that precipitation and temperature had significant positive or negative correlations with U.S. agricultural TFPC. Those correlation coefficients ranged from -0.8 to 0.8. And significant correlations, whether positive or negative, existed in different regions and different seasons. This is important information for policy-makers in decisions to support U.S. agriculture sustainability.

Remotely sensed reflectance parameters from corn and soybean surfaces can be correlated to crop production. Surface reflectance of a typical Upper Midwest corn /soybean region in central Iowa across multiple years reveal subtle dynamics in vegetative surface response to a continually varying climate. From 2006 through 2014 remotely sensed data have been acquired over production fields of corn and soybeans in central IA, U.S.A. with the fields alternating between corn and soybeans. The data have been acquired using ground-based radiometers with 16 wavebands covering the visible, near infrared, shortwave infrared wavebands and combined into a series of vegetative indices. These data were collected on clear days with the goal of collecting data at a minimum of once per week from prior to planting until after fall tillage operations. Within each field, five sites were established and sampled during the year to reduce spatial variation and allow for an assessment of changes in the vegetative indices throughout the growing season. Ancillary data collected for each crop included the phenological stage at each sampling date along with biomass sampled at the onset of the reproductive stage and at physiological maturity. Evaluation of the vegetative indices for the different years revealed that patterns were related to weather effects on corn and soybean growth. Remote sensing provides a method to evaluate changes within and among growing seasons to assess crop growth and development as affected by differences in weather variability.

Multi-Filter Rotating Shadowband Radiometer (MFRSR) and its UV version (UV-MFRSR) are ground-based instruments for measuring solar UV and VIS radiation, deployed together in field at most USDA UV-B Monitoring and Research Program (UVMRP) sites. The performance of the traditional calibration method, Langley Analysis (LA), varies with MFRSR channels and sites, resulting in less confidence in some irradiance products. A two-stage calibration method is developed. We attributed the variation in Langley Analysis performance to the monotonically changing total optical depth (TOD) in the cloud screened points. Constant TOD is an assumption in LA. Since (1) aerosol is the main source of TOD variation at the 368nm channel and (2) UV-MFRSR measures direct normal and diffuse horizontal simultaneously, we used the radiative transfer model (i.e. MODTRAN) to create the look-up table of the ratio of direct normal and diffuse (DDR) with respect to aerosol optical depth (AOD) and solar zenith angle to evaluate the quality of the Langley Offset (V_LO) by giving lower weights to V_LO generated from points with monotonic AOD variation. With one or two calibrated channels as Reference Channels (RC), the most stable points in RC were selected and LA was applied on those time points to generate V_LO at the adjacent un-calibrated channel. The test of this method on the UV-B program site at Homestead, Florida showed that (1) The long-term trend of the original LA VLO is impacted by the monotonic changing in AOD at 368nm channel; and (2) more clustered and abundant V_LO at all channels are generated compared with the original Langley method.

Vegetation stress detection is of great importance to many agricultural and ecological studies. Vegetation water stress is commonly encountered in many areas. The accurate detection of water stress may enable more efficient use of limited water resource. Plant leaf water content is also one of the primary factors indicating vegetation health condition. In this study, a polarized laser at 532-nm was used to study water stress from plant leaves. Polarimetric measurements of the backscattered light were conducted. Preliminary study indicates that depolarization ratio is a good indicator of water stress in the studied case. In addition, an overall increasing trend of depolarization ratio under water stress condition was also observed.

Experimental studies of optical parameters of different atmospheric bioindicators (arboreous and terricolous types of plants) have been performed with Raman spectroscopy. The change in the optical parameters has been explored for the objects under direct light exposure, as well as for the objects placed in the shade. The age peculiarities of the bioindicators have also been taken into consideration. It was established that the statistical variability of optical parameters for arboreous bioindicators was from 9% to 15% and for plants from 4% to 8.7%. On the basis of these results dandelion (Taraxacum) was chosen as a bioindicator of atmospheric emissions.

Thermal emissivity can be used to determine the moisture content in soils, but it is strongly influenced by the kind of soil and the organic matter content. These experiments were performed by recording infrared images of the wet soils as a function of water loss. Samples with different organic matter content were wet until reach the field capacity; then, a sequence of thermal images was acquired to follow the different stages of drying process of the studied samples. The emissivity was calculated indirectly by measuring the reflection and absorption of the samples.

Classification, as well as finding representative spectrum, is always one of the major applications of satellite hyperspectral image data. A new method to find the set of representative spectrums was introduced in this study. It was firstly used to classify forest types in Tianmu Mountain National Nature Reserve by using Landsat TM images. This method is generic and can be applied to the data set that is hard to find one representative data for one class.

The Weather Research and Forecast Model (WRF) version 3.5 has been used in this study to simulate a heavy rainfall event during the Meiyu season that occurred between 1 and 2 July 2014 over the Yangtze River valley (YRV) in China. The WRF model is driven by the National Centers for Environmental Predictions (NCEP) Final (FNL) global tropospheric analysis data, and eight WRF nested experiments using four different microphysics (MP) schemes and two cumulus parameterizations (CP) are conducted to evaluate the effects of these microphysics and cumulus schemes on heavy rainfall predictions over YRV region. The four MPs selected in this study are Lin et al., WRF Single-Moment 3-class scheme (WSM3), WRF Single-Moment 5-class scheme (WSM5) and WRF Single-Moment 6-class scheme (WSM6), and the two CPs are Kain-Fristch (KF) and Betts-Miller-Janjic (BMJ) schemes. Sensitivity studies showed that all MPs coupling with KF and BMJ CP schemes can well capture the major rain belt from the northeast to southwest with three rainfall centers, but largely overestimate the rainfall near the border between Anhui and Hubei provinces along with the Yellow Sea shore, which produce an opposite trend compared to the observations. Large discrepancies are also presented in WRF simulations of heavy rainfall centers regarding their locations and magnitudes. All MPs coupling with KF CP scheme produced the rainfall areas shifting towards east compared to the observations, while all MPs with BMJ CP scheme tend to better predict the rainfall patterns with slightly more fake precipitation centers. Among all the experiments, the BMJ cumulus scheme has superiority in simulating the Meiyu rainfall over the KF scheme, and the WSM5–BMJ combination shows the best predictive skills.

Normalized Difference Vegetation Index (NDVI) is one of the most widely used indicators for monitoring the vegetation coverage in land surface. The time series features of NDVI are capable of reflecting dynamic changes of various ecosystems. Calculating NDVI via Moderate Resolution Imaging Spectrometer (MODIS) and other wide-swath remotely sensed images provides an important way to monitor the spatial and temporal characteristics of large-scale NDVI. However, difficulties are still existed for ecologists to extract such information correctly and efficiently because of the problems in several professional processes on the original remote sensing images including radiometric calibration, geometric correction, multiple data composition and curve smoothing. In this study, we developed an efficient and convenient online toolbox for non-remote sensing professionals who want to extract NDVI time series with a friendly graphic user interface. It is based on Java Web and Web GIS technically. Moreover, Struts, Spring and Hibernate frameworks (SSH) are integrated in the system for the purpose of easy maintenance and expansion. Latitude, longitude and time period are the key inputs that users need to provide, and the NDVI time series are calculated automatically.

Bohai Sea is a semi-enclosed inland sea with serious environmental problems. Harmful algal blooms (HABs) in Bohai Sea happen almost every year covering a large area for a long duration. Real time detection of the HABs can significantly reduce economic loss and assure human safety. Remote sensing technology can monitor the sea surface over a large area and detect HABs. Geo-stationary Ocean Color Imager (GOCI) is the world's first geostationary ocean color imager with high spatial and temporal resolution for monitoring the Bohai Sea. Rapid scanning of the GOCI allows enough cloud-free observations to accumulate for detection of HABs. Many approaches exist for detecting the HABs with GOCI data, but the approaches are rarely validated.. In this paper, an Aureococcus anophagefferens bloom that happened in Qinhuangdao is used to evaluate several HAB detecting approaches: abnormal chlorophyll concentration, red tide index (RI) and MODIS red tide index (MRI). Validations with field observations showed that the HAB was best detected with MRI, second with chlorophyll concentration abnormity and worst with RI. These results show that the MRI best detects the Aureococcus anophagefferens algae.

Researches on soil moisture and salinity are mainly concerned in the observation of soil salinization. Passive microwave remote sensing data can reflect the dielectric properties of different land surfaces. Meanwhile, soil dielectric properties have high sensitivity to changes of soil moisture and salinity. As a result, passive microwave remote sensing data can be used for retrieval of surface soil moisture and salinity. Western Jilin Province of China was selected as the study area in this paper. By the iteration between the rough surface reflectivity calculated based on satellite data and the rough surface reflectivity calculated based on land surface model, three parameters, including surface roughness parameter, soil moisture and salinity can be retrieved simultaneously. After comparing the retrieval results to the field measurement of salinity and FY satellite soil moisture product, the experimental results show that the surface roughness parameters of the study area were concentrated in the vicinity of 0.31. The error between the average of retrieved salinity and the average of the field measurement of salinity was about 10.52g/kg. The error between the average of retrieved soil moisture and the average of FY satellite soil moisture product was approximately 0.005cm³/cm³.

In this paper, with four remote sensing images from the 1980 to 2010 periods and the coastal survey data as data sources, then integrated use remote sensing and GIS technology, the Efficient Ecological Economic Zone of the Yellow River Delta's coastline and sea reclamation changes were extracted by the means of visual interpretation and the artificial vector method. The conclusions are as follows: The coastline of this study area showed a rising trend during 1980 to 2010, the silty coastline showed a reduction trend while the artificial coastline showed an increasing trend, natural and social factors together determined the evolution of coastline. The reclamation area was the largest during 1980 to 1990 and the area was the smallest during 1990 to 2000, demographic factors and economic factors are the most prominent driving reasons of the reclamation. This paper can provide data support and services for the study area to implement management and sustainable development more efficiently.

The western region of Jilin Province is an important part of fragile ecological environment in Northeast China where the soil salinization problem is particularly obvious. Meanwhile, it belongs to a typical snow-covered area and has a northerly continental monsoon climate, with long, cold winters and short, warm summers. It has one single large snowfall period of six month. Therefore, in this paper, the western Jilin Province was selected as the study area and divided into five land surface types including water bodies, grassland, farmland, slight saline-alkali land, moderate and severe saline-alkali land. Furthermore, the two snow depth retrieval algorithms of Chang algorithm and FY3B operational retrieval algorithm were validated and analyzed by using FY-3B/MWRI passive microwave remote sensing data. The main research focused on the analysis of the snow depth covered on the other four different land surface types except water bodies. Based on the five years' observation data from 2011 to 2015, the changes of snow depth on the four land surface types were analyzed and compared with that of MODIS 09A1 snow cover data. The analysis results demonstrated that the snow depth in farmland type is greater than that in grassland type. In addition, the snow depth in slight saline-alkali land type is greater than that in the moderate and severe saline-alkali land type. The study results also showed that the snow depth of Chang's algorithm is more accurate than that of FY3B operational retrieval algorithm in the study area. This research provided important information to the research of snow depth in saline-alkali land area.

With 4 periods of remote sensing images as data sources, based on Geo-information TuPu analysis method, coastline change information mapping of Shandong province during the past 30 years is established using ArcGIS software, and then this kind of information was studied deeply. The conclusions are as follows: 1) During the past 30 years, coastline of Shandong province shows an increasing trend; the centre of increasing shifts eastward gradually; different areas have different increasing situations. 2) During the past 30 years, coastline change degree of Shandong province is basically stable, change fiercely areas concentrate on the Yellow River estuary while Zhaoyuan, Penglai and Longkou have a rather slow degree. 3) From 1980 to 1990, coastline of Shandong province retreats to land quickly, draws back slowly from 1990 to 2000 while advances to sea rapidly entering into 21st century; Dongying has a backward trace for 30 years, Zhaoyuan and Laiyang have been basically unchanged, Yantai, Rizhao and Jiaonan have a obvious trend of advancing to sea.

The aim of this work is to use narrow band normalized difference vegetation indices to compare the estimations of chlorophyll contents at foliar level and canopy level, through a large number of simulated canopy reflectance spectra under different chlorophyll contents based on PROSPECT model and SAIL model. 10 narrow band NDVIs were selected at the identified ranges that can effectively assess foliar chlorophyll content. We analyzed the correlations between canopy chlorophyll contents and the ten narrow band NDVIs firstly, and then analyze these indices’ sensitivities to all canopy parameters, the adaptation of the 10 narrow band NDVIs used in assessing the canopy chlorophyll content were evaluated finally. We found that only two narrow band NDVIs (i.e., NDVI(875, 725) and NDVI(900,720)) can be applied for the estimation of chlorophyll contents at canopy level.

In this paper, the AOD over Beijing on Sep. 4th and Oct. 6th, 2014 are retrieved by applying DDV method using Landsat8/OLI data. Both cases show that retrieved AOD over Beijing has a higher value over urban areas and a relative lower value over mountainous area, and the distribution of AOD is greatly influenced by the land cover type. The ground-based PM2.5 data is also used to validate the derived AOD, and there is a good agreement between the AOD and PM2.5 concentrations with a high correlation coefficient of 0.8742. The calculated AOD reflects the detailed information of the atmosphere over Beijing due to its higher spatial resolution, and it is concluded that the DDV algorithm can be applied to Landsat8/OLI to retrieve AOD over Beijing.

With the increasingly prevalent and far-reaching application of remote sensing, several algorithms have been put forward for land surface temperature retrieval. However, there is still no consensus on the calculation of land surface emissivity (LSE), which is one of the significant parameters in land surface temperature (LST) retrieval. In this paper, two methods of estimating LSE based on thematic mapper data were introduced: Van’s empirical formula method and the mixed pixels method. Based on the detailed introduction to Van’s empirical formula and the mixed pixels decomposing method in computing surface emissivity, Landsat-8 thermal infrared data and the radiative transfer equation method were used to obtain the land surface temperature in Taihu region. In this paper, atmospheric parameters are based on real-time atmospheric profile to reduce the LST error brought by the atmospheric profile. Two figures were acquired, which represented the LST of Van’s empirical formula and the mixed pixels decomposing method respectively. The relationship between land surface temperature and land cover was also studied.

The aim of this work was to predict responses of reference evapotranspiration (ET_o) to perturbations of four climatic variables in Shandong province, China. For this purpose, ET_o was estimated based on the FAO-56 Penman–Monteith equation, a non-dimensional relative sensitivity coefficient was employed. Climatic variables (i.e., daily air temperature, sunshine duration, wind speed and daily relative humidity) at 12 meteorological stations covering whole area (1960 to 2013) were collected firstly and used for the analysis. Results showed that ET_o had positive sensitivities to air temperature, sunshine duration and wind speed, opposite to what were observed to relative humidity. The sensitivity of climatic parameters to ET_o showed a decreasing trend: relative humidity> >sunshine duration>wind speed > air temperature. The sensitivity coefficients of different factors varied in time and space. From 1960 to 2013, the sensitivity coefficient of sunshine duration (S_n) showed a downward trend at a rate of (-4.3e-4)/a. The sensitivity coefficient of wind speed (S_WS) and relative humidity (S_RH) increased at a rate of (3.9e-4)/a and (1.9e-3)/a respectively, while the sensitivity coefficient of air temperature (S_T) waved with a tiny decrease trend. The values of S_T and S_n in southern were larger than in northern region. The values of S_WS in southern and northeast region were smaller than that in the northern area. S_RH in the central region was lower than other area, opposite to what were observed in coastal areas.

This study evaluates the accuracy of total ozone column derived from Ozone Monitoring Instruments (OMI) with two algorithms: OMI Total Ozone Mapping Spectrometer (OMI-TOMS) and OMI Differential Optical Absorption Spectroscopy (OMI-DOAS), compared to ground-based Brewer and Dobson spectrophotometers located at eight China stations from July 2009 to December 2013, including Xianghe, Kunming, Mt.Waliguan, Lhasa, Taipei, Chengkung, Cape D'Aguilar and Longfengshan. Results showed that the agreement between OMI ozone data and ground-based measurements is excellent. Total ozone columns from both OMI-TOMS and OMI-DOAS data are on average about 1.5% lower than ground-based data. For both OMI ozone data products the SZA dependence of the mean relative differences (RD) between satellite data and the ground-based data is relative obvious when the SZA is larger than 50°. Similar to the SZA, the satellite view zenith angle (VZA) dependence of the mean relative differences (RD) between satellite and ground is relatively markedly when the VZA is smaller than 10° in eight stations. Finally, the dependence of the mean relative differences (RD) (-4.28% to 0.818%) between OMI-DOAS data and ground-based data for the total ozone column is remarkable. While for OMI-TOMS data the dependence is not obvious (the RD value varies from -3.30% to -0.676%).

This study analyzed grassland gross primary production (GPP) estimated by the Temperature and Greenness (TG) model and the Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm along the mean precipitation gradient and as a function of interannual variability in site-level precipitation. The calibrated TG model and MODIS algorithm appeared to provide accurate GPP estimations at three study sites with varying precipitation. However, the evaluation for each site/year demonstrated the variations of the accuracy of GPP estimates among different sites and years. GPP were overestimated at the driest site among three study sites, and during the dry years of the semiarid site. Both models provided more accurate GPP estimates for the wet site and during the wet and normal years of the semiarid sites. Calibrating both models for each site/year showed that the parameters of both models varied among sites and years, especially for the TG model. The relationship between flux-tower GPP observations and (scaled EVI *scaled LST) for the TG model and the relationship between GPP observations and (fPAR*PAR*T_min scalar*VPD scalar) for the MODIS algorithm were different during green-up and dry-down period of grassland during the dry years at semiarid sites. This result implied that different relationships at different growing stages might be one of the major reasons for the overestimation of GPP by the TG model and the MODIS algorithm for semiarid grassland where water is a limiting resource. Thus, both TG model and MODIS algorithm should be used with caution in the arid and semiarid grassland regions

This paper uses PROSAIL model to simulate vegetation canopy reflectance under different chlorophyll contents and Leaf area index (LAI). The changes of NDVIs with different LAIs and chlorophyll contents are analyzed. A simulated spectral dataset was built firstly by using PROSIAL vegetation radiative transfer model with various vegetation chlorophyll concentrations and leaf area index. The responses of NDVIs to LAIs are quantitatively analyzed further based on the dataset. The results show that chlorophyll contents affect canopy reflectance mainly in visible band. Canopy reflectance decreases with an increasing chlorophyll content. Under the same LAI value, NDVI values increase with an increase chlorophyll contents. Under constant content of chlorophyll, NDVIs increases with an increasing LAI. When the value of LAI is less than5, the canopy reflectance is significantly affected by soil background. When value of LAI is higher than5, the earth surface is almost completely covered with vegetation. The increase in LAI has little effect on canopy reflectance and NDVIs consequently. NDVIs increases with the adding of chlorophyll content, when chlorophyll is higher than 40, the rangeability of NDVIs is becoming stable.

Fires are one of the main causes of environmental alteration in Mediterranean forest ecosystems. Albedo varies and evolves seasonally based on solar illumination. It is greatly influenced by changes on vegetation: vegetation growth, cutting/planting forests or forest fires. This work analyzes albedo variations due to a large forest fire that occurred on 19- 21 September 2012 in northwestern Spain. From this area, albedo post-fire images (immediately and 1-year after fire) were generated from Landsat 7 Enhanced Thematic Mapper (ETM+) data. Specifically we considered total shortwave albedo, total-, direct-, and diffuse-visible, and near-infrared albedo. Nine to twelve weeks after fire, 111 field plots were measured (27 unburned plots, 84 burned plots). The relationship between albedo values and thematic class (burned/unburned) was evaluated by one-way analysis of variance. Our results demonstrate that albedo changes were related to burned/unburned variable with statistical significance, indicating the importance of forestry areas as regulators of land surface energy fluxes and revealing the potential of post-fire albedo for assessing burned areas. Future research, however, is needed to evaluate the persistence of albedo changes.

This paper proposes an improved statistical method for fusing carbon dioxide (CO₂) data retrieved from two major instruments, the Greenhouse gases Observing SATellite (GOSAT) and the Atmospheric Infrared Sounder (AIRS). These two datasets were fused to obtain CO₂ concentrations near the surface, which is a region that is especially important for studies on carbon sources and sinks. Overall, the CO₂ monthly average values from GOSAT are all lower than those from AIRS from 2010 to 2012. The datasets show the similar seasonal cycles of carbon dioxide and show an increasing trend with a determination coefficient of 0.45. A strong correlation was determined by adding the climatic factors as independent variables for regression analysis. The correlation coefficients between the CO₂ values from AIRS and GOSAT significantly increased in response. The true CO₂ data processes were then predicted using the fixed rank kriging method. This showed that the data-fusion CO₂ product provides more reasonable information and that the corresponding mean squared prediction errors are smaller than those from the single GOSAT CO₂ dataset.

The atmospheric pollution and air quality issues are getting worse in China, the formation mechanism of aerosols and their environment effects attracted more and more attention. Aerosol Optical Depth (AOD) is one of the most important parameters which can indicate the atmospheric turbidity and aerosol load. High-quality AOD data are significant for the study in the atmospheric environment (i.e., air quality). This paper used MODIS/Terra AOD in 2008 to improve the coverage of MODIS/Aqua AOD, which was based on linear regression analysis model. RMSE between estimation value and Aqua_AOD detected through satellite is 0.132. The average value of test data was 0.812. The average of regression result was 0.807. It showed that the regression model between AOD_Terra and AOD_Aqua worked well. Also, we built two sets of estimation models (MODIS AOD and OMI AOD) through stepwise regression analysis model. One is using OMI AOD and meteorological elements to estimate MODIS AOD. The value of RMSE was 0.113, which represents 13.916% of the average(R²=0.782). The other one is using MODIS AOD and meteorological elements to estimate OMI AOD. RMSE of the model is 0.132, which represents 18.182% of the average (R²=0.726).

In this work the results of research of heavy metals impact on aquatic plants with Raman spectroscopy are shown. The peculiarity of Raman spectrum under the influence of heavy metals has been experimentally established. Optical coefficient, determining heavy metals impact on aquatic plants was introduced. It was defined as correlation of Raman intensity values on wave numbers 1547 cm^-1, 1522cm^-1 to intensity band value at 1600 cm^-1. Microscopic analysis of aquatic plants under the influence of heavy metals has been conducted.

The Moderate Resolution Imaging Spectroradiometer (MODIS) data has a high temporal resolution, which, at present, is an ideal data source in simulative monitoring of regional-scale changes in surface energy and water. However, the spatial resolution of its thermal infrared band is relatively low (1 km). The Landsat TM/ETM+ data have a high spatial resolution, but their single thermal infrared bands can lead to the fact that the inversion accuracy for the surface temperature is not high, and that the time resolution is low. This limits its application in the surface evapotranspiration (ET) monitoring. Combining TM/ETM + visible wave band with MODIS thermal infrared wave band, this paper discusses a multi-scale remote sensing method to estimate regional surface ET. On the basis of space enhancement method, the vegetation index estimated by TM/ETM + enhances the surface temperature scale with the inversion of MODIS to a 30 m resolution, which aims to improve the estimation accuracy of ET in the non-uniform surface mixed-pixel. The results show that this method has a higher accuracy of ET estimation compared with the method of only using MODIS or ETM+ data. Moreover, it can obtain a more obvious effect on scale correction in the uneven land surface or various surface covering types, and the corrected ET is close to the observation result.