This PDF file contains the front matter associated with SPIE Proceedings Volume 10405 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Cloud screening is an essential procedure for in-situ calibration and atmospheric properties retrieval on (UV-)MultiFilter Rotating Shadowband Radiometer [(UV-)MFRSR]. Previous study has explored a cloud screening algorithm for direct-beam (UV-)MFRSR voltage measurements based on the stability assumption on a long time period (typically a half day or a whole day). To design such an algorithm requires in-depth understanding of radiative transfer and delicate data manipulation. Recent rapid developments on deep neural network and computation hardware have opened a window for modeling complicated End-to-End systems with a standardized strategy. In this study, a multi-layer dynamic bidirectional recurrent neural network is built for determining the cloudiness on each time point with a 17-year training dataset and tested with another 1-year dataset. The dataset is the daily 3-minute cosine corrected voltages, airmasses, and the corresponding cloud/clear-sky labels at two stations of the USDA UV-B Monitoring and Research Program. The results show that the optimized neural network model (3-layer, 250 hidden units, and 80 epochs of training) has an overall test accuracy of 97.87% (97.56% for the Oklahoma site and 98.16% for the Hawaii site). Generally, the neural network model grasps the key concept of the original model to use data in the entire day rather than short nearby measurements to perform cloud screening. A scrutiny of the logits layer suggests that the neural network model automatically learns a way to calculate a quantity similar to total optical depth and finds an appropriate threshold for cloud screening.

Vegetation often exists as patch in arid and semi-arid region throughout the world. Vegetation patch can be effectively monitored by remote sensing images. However, not all satellite platforms are suitable to study quasi-circular vegetation patch. This study compares fine (GF-1) and coarse (CBERS-04) resolution platforms, specifically focusing on the quasicircular vegetation patches in the Yellow River Delta (YRD), China. Vegetation patch features (area, shape) were extracted from GF-1 and CBERS-04 imagery using unsupervised classifier (K-Means) and object-oriented approach (Example-based feature extraction with SVM classifier) in order to analyze vegetation patterns. These features were then compared using vector overlay and differencing, and the Root Mean Squared Error (RMSE) was used to determine if the mapped vegetation patches were significantly different. Regardless of K-Means or Example-based feature extraction with SVM classification, it was found that the area of quasi-circular vegetation patches from visual interpretation from QuickBird image (ground truth data) was greater than that from both of GF-1 and CBERS-04, and the number of patches detected from GF-1 data was more than that of CBERS-04 image. It was seen that without expert’s experience and professional training on object-oriented approach, K-Means was better than example-based feature extraction with SVM for detecting the patch. It indicated that CBERS-04 could be used to detect the patch with area of more than 300 m², but GF-1 data was a sufficient source for patch detection in the YRD. However, in the future, finer resolution platforms such as Worldview are needed to gain more detailed insight on patch structures and components and formation mechanism.

In neural networks, the training/predicting accuracy and algorithm efficiency can be improved significantly via accurate input feature extraction. In this study, some spatial features of several important factors in retrieving surface ultraviolet (UV) are extracted. An extreme learning machine (ELM) is used to retrieve the surface UV of 2014 in the continental United States, using the extracted features. The results conclude that more input weights can improve the learning capacities of neural networks.

The USDA UV-B Monitoring and Research Program (UVMRP) comprises of 36 climatological sites along with 4 long-duration research sites, in 27 states, one Canadian province, and the south island of New Zealand. Each station is equipped with an Ultraviolet multi-filter rotating shadowband radiometer (UV-MFRSR) which can provide response-weighted irradiances at 7 wavelengths (300, 305.5, 311.4, 317.6, 325.4, and 368 nm) with a nominal full width at half maximun of 2 nm. These UV irradiance data from the long term monitoring station at Mauna Loa, Hawaii, are used as input to a retrieval algorithm in order to derive high time frequency total ozone columns. The sensitivity of the algorithm to the different wavelength inputs is tested and the uncertainty of the retrievals is assessed based on error propagation methods. For the validation of the method, collocated hourly ozone data from the Dobson Network of the Global Monitoring Division (GMD) of the Earth System Radiation Laboratory (ESRL) under the jurisdiction of the US National Oceanic & Atmospheric Administration (NOAA) for the period 2010-2015 were used.

A fully-integrated, Hyperspectral optical and SAR (Synthetic Aperture Radar) constellation of small earth observation satellites will be deployed over multiple launches from last December to next five years. The Constellation is expected to comprise a minimum of 16 satellites (8 SAR and 8 optical ) flying in two orbital planes, with each plane consisting of four satellite pairs, equally-spaced around the orbit plane. Each pair of satellites will consist of a hyperspectral/mutispectral optical satellite and a high-resolution SAR satellite (X-band) flying in tandem. The constellation is expected to offer a number of innovative capabilities for environment monitoring. As a pre-launch experiment, two hyperspectral earth observation minisatellites, Spark 01 and 02 were launched as secondary payloads together with Tansat in December 2016 on a CZ-2D rocket. The satellites feature a wide-range hyperspectral imager. The ground resolution is 50 m, covering spectral range from visible to near infrared (420 nm - 1000 nm) and a swath width of 100km. The imager has an average spectral resolution of 5 nm with 148 channels, and a single satellite could obtain hyperspectral imagery with 2.5 million km2 per day, for global coverage every 16 days. This paper describes the potential applications of constellation image in environment monitoring.

In this presentation, two advanced feature extraction methods with fast and deep learning algorithms will be discussed for environmental monitoring in all-weather conditions with convergent and divergent thinking. One is the newly developed novel Spectral Information Adaptation and Synthesis Scheme (SIASS) and the other is the newly invented SMart Information Reconstruction (SMIR) method to support the Integrated Data Fusion and Mining (IDFM) research. Whereas the former is organized to generate cross-mission consistent ocean color reflectance by merging observations from several different satellites to recover the cloudy pixels, the latter is designed to reconstruct cloud contaminated pixel values from the time-space-spectrum continuum with the aid of a machine learning tool. For the purpose of demonstration, Lake Nicaragua located at Central America is selected as a study site which is a very cloudy area year round. In this case study, merging observations from MODIS-Terra, MODIS-Aqua, and VIIRS over Lake Nicaragua will be presented for the 2012-2015 time period. Then the performance of SMIR will be performed after the merging operation by reconstructing the missing remote sensing reflectance values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite over Lake Nicaragua. The SIASS algorithm is proven to have the capability not only in eliminating incompatibilities for those matchup bands but also in reconstructing spectral information for those mismatching bands among sensors. For the recovery of those missing pixel values after merging three satellite images, experimental results from SMIR show that the extreme learning machine may perform well with simulated memory effect due to linking the complex time-space-spectrum continuum between cloud-free and cloudy pixels. Final water quality assessment will be generated based on the integrative algorithm of the two with bio-optical models for eutrophication assessment in Lake Nicaragua.

The USDA UV-B Monitoring and Research Program (UVMRP) is an ongoing effort aiming to establish a valuable, longstanding database of ground-based ultraviolet (UV) solar radiation measurements over the US. Furthermore, the program aims to achieve a better understanding of UV variations through time, and develop a UV climatology for the Northern American section. By providing high quality radiometric measurements of UV solar radiation, UVMRP is also focusing on advancing science for agricultural, forest, and range systems in order to mitigate climate impacts. Within these foci, the goal of the present study is to investigate, analyze, and validate the accuracy of the measurements of the UV multi-filter rotating shadowband radiometer (UV-MFRSR) and Yankee (YES) UVB-1 sensor at the high altitude, pristine site at Mauna Loa, Hawaii. The response-weighted irradiances at 7 UV channels of the UV-MFRSR along with the erythemal dose rates from the UVB-1 radiometer are discussed, and evaluated for the period 2006-2015. Uncertainties during the calibration procedures are also analyzed, while collocated groundbased measurements from a Brewer spectrophotometer along with model simulations are used as a baseline for the validation of the data. Besides this quantitative research, the limitations and merits of the existing UVMRP methods are considered and further improvements are introduced.

The main purpose of this research is to determine the anomalies regarding with the coal mining operations in an abandoned coal mine site in central Anatolia by multi-temporal image analysis of Landsat 4-5 surface reflectance data. A well-known anomaly detection algorithm, Reed-Xioli (RX), which calculates square of Mahalanobis metrics to calculate the likelihood ratios by normalizing the difference between the test pixel and the background to allocate anomaly pixels, is implemented across the time series. The experimental results reveal especially the profound land use – land cover change in time series, pointing out critically abandoned regions that need immediate rehabilitation action. The rate of anomaly scores together with their relation to mine development over the focused time spectrum discloses a linearity trend as of the operations are ceased at the end of 1990s, which is indicative of the capacity of the applied method. The performance of the algorithm is also quantified with Receiver Operating Characteristics (ROC) curves and precisionrecall graphs to quantify its capability on Landsat Thematic Mapper (TM) multispectral image series. The resulting plots show the increasing capability of the hyperspectral anomaly detection technique in multi-temporal data set, with a steady and slight increase in performance between 2000 and 2012 after the end of the mining activities, which substantiates the success of global RX algorithm to identify the mining-induced land use and land cover anomalies.

The present work aims to provide a hybrid platform capable of complementary and sensitive detection of β-lactam antibiotics, ampicillin in particular. The use of an aptamer specific to ampicillin assures good selectivity and sensitivity for the detection of ampicillin from different matrice. This new approach is dedicated for a portable, remote sensing platform based on low-cost, small size and low-power consumption solution. The simple experimental hybrid platform integrates the results from the D-shape surface plasmon resonance plastic optical fiber (SPR-POF) and from the electrochemical (bio)sensor, for the analysis of ampicillin, delivering sensitive and reliable results. The SPR-POF already used in many previous applications is embedded in a new experimental setup with fluorescent fibers emitters, for broadband wavelength analysis, low-power consumption and low-heating capabilities of the sensing platform.

It has been a longstanding problem for current weather/climate models to accurately predict summer heavy precipitation over the Yangtze-Huaihe Region (YHR) which is the key flood-prone area in China with intensive population and developed economy. Large uncertainty has been identified with model deficiencies in representing precipitation processes such as microphysics and cumulus parameterizations. This study focuses on examining the effects of microphysics parameterization on the simulation of different type of heavy precipitation over the YHR taking into account two different cumulus schemes. All regional persistent heavy precipitation events over the YHR during 2008-2012 are classified into three types according to their weather patterns: the type I associated with stationary front, the type II directly associated with typhoon or with its spiral rain band, and the type III associated with strong convection along the edge of the Subtropical High. Sixteen groups of experiments are conducted for three selected cases with different types and a local short-time rainstorm in Shanghai, using the WRF model with eight microphysics and two cumulus schemes. Results show that microphysics parameterization has large but different impacts on the location and intensity of regional heavy precipitation centers. The Ferrier (microphysics) –BMJ (cumulus) scheme and Thompson (microphysics) – KF (cumulus) scheme most realistically simulates the rain-bands with the center location and intensity for type I and II respectively. For type III, the Lin microphysics scheme shows advantages in regional persistent cases over YHR, while the WSM5 microphysics scheme is better in local short-term case, both with the BMJ cumulus scheme.

The Aleutian Low is one of the principal causal factors of the weather and climate systems of the Northern Hemisphere.Based on reanalysis datasets provided by the National Centers for Environmental Prediction (NCEP) from 1970 to 2005, the climatological features of Aleutian low in winter were characterized. It is shown from the study results that in the late 1970s, the winter Aleutian low’s intensity changed from weak to strong. Then, the relationship between Aleutian low and sea surface heat flux in the North Pacific was analyzed by singular value decomposition (SVD) and correlation analysis. Aleutian low’s intensity was positively correlated with the sea surface heat flux in the central North Pacific, and negatively correlated with the sea surface heat flux in the west coast of North America.

Coastal wetland is a net carbon sink with a high carbon density. However, coastal reclamation directly changes the structure of coastal wetland ecosystem and consequent carbon sink function. The aim of this work was to estimate the reclamation-induced carbon loss in coastal wetlands using time series GF-1 WVF data. For this purpose, GF-1 WVF imageries of 2013 (before reclamation) and 2017 (after reclamation) in the Yangtze Estuary were collected and analyzed combined with field monitoring. Results showed that the converted coastal wetland area occupied up to 61.60% between 2013 and 2017. Carbon estimation indicated that the coastal wetland before reclamation had greater potential contribution to the global warming mitigation than the wetland reclamation to other land cover types. Finally the vulnerability of carbon stores and uncertain analysis with remote sensing technology in coastal wetlands environment were discussed. We emphasized that long-term monitoring of coastal wetlands and its carbon dynamic are urgently needed, because so many uncertain factors exist in short-term monitoring.

Snow accumulation has a very important influence on the natural environment and human activities. Meanwhile, improving the estimation accuracy of passive microwave snow depth (SD) retrieval is a hotspot currently. Northeastern China is a typical snow study area including many different land cover types, such as forest, grassland and farmland. Especially, there is relatively stable snow accumulation in January every year. The brightness temperatures which are observed by the Advanced Microwave Scanning Radiometer 2 (AMSR2) on GCOM-W1 and FengYun3B Microwave Radiation Imager (FY3B-MWRI) in the same period in 2013 are selected as the study data in the research. The results of snow depth retrieval using AMSR2 standard algorithm and Jiang’s FY operational algorithm are compared in the research. Moreover, to validate the accuracy of the two algorithms, the retrieval results are compared with the SD data observed at the national meteorological stations in Northeastern China. Furthermore, the retrieval SD is also compared with AMSR2 and FY standard SD products, respectively. The root mean square errors (RMSE) results using AMSR2 standard algorithms and FY operational algorithm are close in the forest surface, which are 6.33cm and 6.28cm, respectively. However, The FY operational algorithm shows a better result than the AMSR2 standard algorithms in the grassland and farmland surface. The RMSE results using FY operational algorithm in the grassland and farmland surface are 2.44cm and 6.13cm, respectively.

Snow cover information has great significance for monitoring and preventing snowstorms. With the development of satellite technology, geostationary satellites are playing more important roles in snow monitoring. Currently, cloud interference is a serious problem for obtaining accurate snow cover information. Therefore, the cloud pixels located in the MODIS snow products are usually replaced by cloud-free pixels around the day, which ignores snow cover dynamics. FengYun-2(FY-2) is the first generation of geostationary satellite in our country which complements the polar orbit satellite. The snow cover monitoring of Northeast China using FY-2G data in January and February 2016 is introduced in this paper. First of all, geometric and radiometric corrections are carried out for visible and infrared channels. Secondly, snow cover information is extracted according to its characteristics in different channels. Multi-threshold judgment methods for the different land types and similarity separation techniques are combined to discriminate snow and cloud. Furthermore, multi-temporal data is used to eliminate cloud effect. Finally, the experimental results are compared with the MOD10A1 and MYD10A1 (MODIS daily snow cover) product. The MODIS product can provide higher resolution of the snow cover information in cloudless conditions. Multi-temporal FY-2G data can get more accurate snow cover information in cloudy conditions, which is beneficial for monitoring snowstorms and climate changes.

Using Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager and Thermal Infrared Sensor imagery of the Yellow River Delta, this study analyzed the relationships between NDVI and LST (land surface temperature). Six Landsat images comprising two time series were used to calculate the land surface temperature and correlated vegetation indices. The Yellow River Delta area has expanded substantially because of the deposited sediment carried from upstream reaches of the river. Between 1986 and 2015, approximately 35% of the land use area of the Yellow River Delta has been transformed into salterns and aquaculture ponds. Overall, land use conversion has occurred primarily from poorly utilized land into highly utilized land. To analyze the variation of land surface temperature, a mono-window algorithm was applied to retrieve the regional land surface temperature. The results showed bilinear correlation between land surface temperature and the vegetation indices (i.e., Normalized Difference Vegetation Index, Adjusted-Normalized Vegetation Index, Soil-Adjusted Vegetation Index, and Modified Soil-Adjusted Vegetation Index). Generally, values of the vegetation indices greater than the inflection point mean the land surface temperature and the vegetation indices are correlated negatively, and vice versa. Land surface temperature in coastal areas is affected considerably by local seawater temperature and weather conditions.

Tidal flat area gains abundant natural resources. With the development of the coastal economy, tidal flat area possesses an unstable nature, thus of significant value for its study. Waterline extracting methods are essential to understand the dynamic change of tidal flat. In order to find a good method, we took Rudong County in Jiangsu Province as the research area, by using the HJ1A/1B images, waterlines are generated under the method of visual interpretation extraction, Canny edge detection, threshold segmentation and object-oriented classification. By contrast, the paper considered object-oriented classification as an effective method to extract waterlines.

In recent years, satellite remote sensing have been widely used in dynamic monitoring of Green Tide. However, the images captured by unmanned aerial vehicles (UAV) are rarely used in floating green tide monitoring. In this paper, a quad-rotor unmanned aerial vehicle was used to mapping the coverage of green tide on the seabeach in Haiyang with three algorithms based on RGB image.The conclusions are as follows: there is discrepancy in both maximum value band among RGB and the difference in the green band for a true color aerial photograph taken from a UAV; the best index for floating green tide mapping on seabeach is GLI. It is possible to have a comprehensive, objective and scientific understanding of the floating green tide mapping with aid of UAV based on RGB image in the seabeach.

This paper conducted dynamic monitoring over the green tide (large green alga—Ulva prolifera) occurred in the Yellow Sea in 2014 to 2016 by the use of multi-source remote sensing data, including GF-1 WFV, HJ-1A/1B CCD, CBERS-04 WFI, Landsat-7 ETM+ and Landsta-8 OLI, and by the combination of VB-FAH (index of Virtual-Baseline Floating macroAlgae Height) with manual assisted interpretation based on remote sensing and geographic information system technologies. The result shows that unmanned aerial vehicle (UAV) and shipborne platform could accurately monitor the distribution of Ulva prolifera in small spaces, and therefore provide validation data for the result of remote sensing monitoring over Ulva prolifera. The result of this research can provide effective information support for the prevention and control of Ulva prolifera.

Unmanned aerial vehicle(UAV) have been increasingly used for natural resource applications in recent years as a result of their greater availability, the miniaturization of sensors, and the ability to deploy UAV relatively quickly and repeatedly at low altitudes. UAV remote sensing offer rich contextual information, including spatial, spectral and contextual information. In order to extract the information from these UAV remote sensing images, we need to utilize the spatial and contextual information of an object and its surroundings. If pixel based approaches are applied to extract information from such remotely sensed data, only spectral information is used. Thereby, in Pixel based approaches, information extraction is based exclusively on the gray level thresholding methods. To extract the certain features only from UAV remote sensing images, this situation becomes worse. To overcome this situation an object-oriented approach is implemented. By object-oriented thought, the coastal windbreak forest information are extracted by the use of UAV remote sensing images. Firstly, the images are segmented. And then the spectral information and object geometry information of images objects are comprehensively applied to build the coastal windbreak forest extraction knowledge base. Thirdly, the results of coastal windbreak forest extraction are improved and completed. The results show that better accuracy of coastal windbreak forest extraction can be obtained by the proposed method, in contrast to the pixel-oriented method. In this study, the overall accuracy of classified image is 0.94 and Kappa accuracy is 0.92.

This paper monitored the outbreak of green tide in the Yellow Sea, China, in 2014 based on GOCI remote sensing image and NDVI extraction method, combined with GIS (Geographical Information System) and visual interpretation technologies. The results show: the green tide is firstly found in the open waters near Yancheng, Jiangsu Province in mid May, and drifted from the southwest to the northeast direction. When reached the neighboring waters between Jiangsu and Shandong in early June, the green tide entered an outbreak stage and reached the maximum coverage area of 2206.54 km² in 18, June. In early July, the green tide began into a recession stage until all died in early August while its frontline preserved in Yantai – Weihai – Qingdao. Our work shows GOCI image with high temporal resolution is available for the study of migration path and drift speed of green tide.

Over the past decade, China has experienced a rapid increase in urbanization. The urban built-up areas (population) of Shanghai increased by 16.1% (22.9%) from 2006 to 2015. This study aims to analyze the variations of tropospheric NO2 over Yangtze River Delta region and the impacts of rapid urbanization during 2006-2015. The results indicate that tropospheric NO2 vertical column density (VCD) of all cities in the study area showed an increasing trend during 2006-2011 whereas a decreasing trend during 2011-2015. Most cities showed a lower tropospheric NO2 VCD value in 2015 compared to that in 2006, except for Changzhou and Nantong. Shanghai and Ningbo are two hotspots where the tropospheric NO2 VCD decreased most significantly, at a rate of 22% and 19%, respectively. This effect could be ascribed to the implementation of harsh emission control policies therein. Similar seasonal variability was observed over all cities, with larger values observed in the summer and smaller values shown in the winter. Further investigations show that the observed increasing trend of tropospheric NO2 during 2006-2011 could be largely explained by rapid urbanization linked to car ownership, GDP, power consumption, population and total industrial output. Such effect was not prominent after 2011, mainly due to the implementation of emission control strategies.

Fine particulates less than 2.5 microns in aerodynamic diameter (PM_2.5) has been widely considered to be one of the main pollutant threating human health. Ground-level PM_2.5 monitoring can provide accurate point data, but its value is hard to scale up to large scale. In this respects, satellite data with large coverage areas and long term range, could enhance our ability to estimate PM_2.5 concentration. In this study, a Multilinear correlation model (MLC) based on MODIS AOD level 2 data was developed to estimate PM_2.5 concentration in Northeastern China from 2013-2016, then ground-level PM_2.5 monitoring data from 15 stations covering study area were used for validation. Results showed that 1) the annual PM_2.5 is 63.98μg/m2, AOD values agreed well with estimated PM_2.5 concentration, 2) the spatial variations of PM_2.5 were not clear, while the temporal dynamic of PM_2.5 were observed, the highest values were observed in winter, opposite to what were observed in fall. 3) the MLC model coupled with meteorological data could improve the precision of PM_2.5 estimations. Therefore, we suggest that the developed MLC model is useful for the PM_2.5 estimations in northeastern China.

As global warming problem is becoming serious in recent decades, the global sea level is continuously rising. This will cause damages to the coastal deltas with the characteristics of low-lying land, dense population, and developed economy. Continuously reclamation costal intertidal and wetland areas are making Shanghai, the mega city of Yangtze River Delta, more vulnerable to sea level rise. In this paper, we investigate the land subsidence temporal evolution of patterns and processes on a stretch of muddy coast located between the Yangtze River Estuary and Hangzou Bay with differential synthetic aperture radar interferometry (DInSAR) analyses. By exploiting a set of 31 SAR images acquired by the ENVISAT/ASAR from February 2007 to May 2010 and a set of 48 SAR images acquired by the COSMO-SkyMed (CSK) sensors from December 2013 to March 2016, coherent point targets as long as land subsidence velocity maps and time series are identified by using the Small Baseline Subset (SBAS) algorithm. With the DInSAR constrained land subsidence model, we predict the land subsidence trend and the expected cumulative subsidence in 2020, 2025 and 2030. Meanwhile, we used altimetrydata and densely distributed in the coastal region are identified (EEMD) algorithm to obtain the average sea level rise rate in the East China Sea. With the land subsidence predictions, sea level rise predictions, and high-precision digital elevation model (DEM), we analyze the combined risk of land subsidence and sea level rise on the coastal areas of Shanghai. The potential inundated areas are mapped under different scenarios.

Peanut is one of the major edible vegetable oil crops in China, whose growth and yield are very sensitive to climate change. In addition, agriculture climate resources are expected to be redistributed under climate change, which will further influence the growth, development, cropping patterns, distribution and production of peanut. In this study, we used the DSSAT-Peanut model to examine the climate change impacts on peanut production, oil industry and oil food security in China. This model is first calibrated using site observations including 31 years’ (1981-2011) climate, soil and agronomy data. This calibrated model is then employed to simulate the future peanut yield based on 20 climate scenarios from 5 Global Circulation Models (GCMs) developed by the InterSectoral Impact Model Intercomparison Project (ISIMIP) driven by 4 Representative Concentration Pathways (RCPs). Results indicate that the irrigated peanut yield will decrease 2.6% under the RCP 2.6 scenario, 9.9% under the RCP 4.5 scenario and 29% under the RCP 8.5 scenario, respectively. Similarly, the rain-fed peanut yield will also decrease, with a 2.5% reduction under the RCP 2.6 scenario, 11.5% reduction under the RCP 4.5 scenario and 30% reduction under the RCP 8.5 scenario, respectively.

Shanghai Pudong International airport is one of the three major international airports in China. The airport is located at the Yangtze estuary which is a sensitive belt of sea and land interaction region. The majority of the buildings and facilities in the airport are built on ocean-reclaimed lands and silt tidal flat. Residual ground settlement could probably occur after the completion of the airport construction. The current status of the ground settlement of the airport and whether it is within a safe range are necessary to be investigated. In order to continuously monitor the ground settlement of the airport, two Synthetic Aperture Radar (SAR) time series, acquired by X-band TerraSAR-X (TSX) and TanDEM-X (TDX) sensors from December 2009 to December 2010 and from April 2013 to July 2015, were used for analyzing with SBAS technique. We firstly obtained ground deformation measurement of each SAR subset. Both of the measurements show that obvious ground subsidence phenomenon occurred at the airport, especially in the second runway, the second terminal, the sixth cargo plane and the eighth apron. The maximum vertical ground deformation rates of both SAR subset measurements were greater than -30 mm/year, while the cumulative ground deformations reached up to -30 mm and -35 mm respectively. After generation of SBAS-retrieved ground deformation for each SAR subset, we performed a joint analysis to combine time series of each common coherent point by applying a geotechnical model. The results show that three centralized areas of ground deformation existed in the airport, mainly distributed in the sixth cargo plane, the fifth apron and the fourth apron, The maximum vertical cumulative ground subsidence was more than -70 mm. In addition, by analyzing the combined time series of four selected points, we found that the ground deformation rates of the points located at the second runway, the third runway, and the second terminal, were progressively smaller as time goes by. It indicates that the stabilities of the foundation around these points were gradually enhanced.

Spartina alterniflora, one of most successful invasive species in the world, was firstly introduced to China in 1979 to accelerate sedimentation and land formation via so-called “ecological engineering”, and it is now widely distributed in coastal saltmarshes in China. A key question is how to retrieve chlorophyll content to reflect growth status, which has important implication of potential invasiveness. In this work, an estimation model of chlorophyll content of S. alterniflora was developed based on hyper-spectral data in the Dongtan Wetland, Yangtze Estuary, China. The spectral reflectance of S. alterniflora leaves and their corresponding chlorophyll contents were measured, and then the correlation analysis and regression (i.e., linear, logarithmic, quadratic, power and exponential regression) method were established. The spectral reflectance was transformed and the feature parameters (i.e., “san bian”, “lv feng” and “hong gu”) were extracted to retrieve the chlorophyll content of S. alterniflora . The results showed that these parameters had a large correlation coefficient with chlorophyll content. On the basis of the correlation coefficient, mathematical models were established, and the models of power and exponential based on SDb had the least RMSE and larger R² , which had a good performance regarding the inversion of chlorophyll content of S. alterniflora.

Mean Solar Exo-atmospheric Irradiances (ESUN) is an important parameter to calculate the apparent reflectance based on the satellite sensor measured DN values. GF-4 was launched in 2015, the ESUN of this satellite has not been officially reported, however. To determine which solar spectrum curve is best fitted to GF-4, this study calculated the ESUN of GF-1 at first, by using six distinct solar spectrum curves and spectral response curves of GF-1. Next, the results were validated by comparing with the operational released values. It indicates that the World Radiation Center (WRC) solar spectrum is the most accurate and reliable solar spectrum curve for GF-1, with a total error less than 0.1% for 4 bands. Finally, the ESUN of GF-4 was calculated by making use of the WRC solar spectrum curve.

Based on the SPOT/ NDVI data and meteorological data of Jianghuai watershed area, the temporal and spatial variation characteristics of NDVI and their correlation with climate factors (temperature and precipitation) are analyzed from 1998 to 2013 by utilizing the maximum value composite and linear regression method. The results showed that the vegetation growth has changed year by year with an overall trend in Jianghuai watershed region, and the number of pixels in the growing area accounts for 85.8% of the total. From the space point of view, expect for some regions in Hefei, Chuzhou and Luan are obviously decreasing, most of the other regions showing a growth trend. Vegetation was not positively correlated with temperature and precipitation, and the correlation between NDVI and temperature was higher than that of precipitation. Due to the differences of topography, geography and human activities, the correlation in different regions is different. In addition, human activities are also the influencing factors of vegetation change.

The 3D model is an important part of simulated remote sensing for earth observation. Regarding the small-scale spatial extent of DART software, both the details of the model itself and the number of models of the distribution have an important impact on the scene canopy Normalized Difference Vegetation Index (NDVI).Taking the phragmitesaustralis in the Yangtze Estuary as an example, this paper studied the effect of the P.australias model on the canopy NDVI, based on the previous studies of the model precision, mainly from the cell dimension of the DART software and the density distribution of the P.australias model in the scene, As well as the choice of the density of the P.australiass model under the cost of computer running time in the actual simulation. The DART Cell dimensions and the density of the scene model were set by using the optimal precision model from the existing research results. The simulation results of NDVI with different model densities under different cell dimensions were analyzed by error analysis. By studying the relationship between relative error, absolute error and time costs, we have mastered the density selection method of P.australias model in the simulation of small-scale spatial scale scene. Experiments showed that the number of P.australias in the simulated scene need not be the same as those in the real environment due to the difference between the 3D model and the real scenarios. The best simulation results could be obtained by keeping the density ratio of about 40 trees per square meter, simultaneously, of the visual effects.

The accuracy of the temperature and humidity profiles from the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU) is evaluated using three month of collocated datasets over East China. The AIRS/AMSU retrievals, radiosonde data (RAOB), and the ERA-Interim data from European Center for medium Range Forecast (ECMWF) are used in this validation. This study also compares the AIRS/AMSU retrieved profiles with it only retrieved by AIRS. Results of the entire intercomparison reveal that the RMSE of temperature profiles are in very good agreement with all cases, whilst the relative humidity RMSE show larger difference. Compared with RAOB for the AIRS/AMSU retrievals and ERA-Interim data, it is found that the ERA-Interim temperature and humidity profiles are superior to AIRS retrievals except the humidity in upper troposphere. The accuracy of AIRS/AMSU retrievals is a little bit better than only AIRS retrieved profile product.

The Noah and MYJ scheme are used to simulate the analysis of Huangshan valley wind in mesoscale numerical model. The valley wind evolution, formation mechanism and the influence are analyzed. The results of model simulation reveals that the wind direction changes with the alternation of day and night in Mount Huangshan area.The valley wind circulation plays an important role in the balance of heat in mountain areas. Therefore, according to the law of wind transformation and related features of the valley wind, the emission of pollutants can be controlled to reduce the pollution of the atmospheric environment.

The North China Plain is a major food producing region in China, and climate change could pose a threat to food production in the region. Based on China Meteorological Forcing Dataset, simulating the growth of summer maize in North China Plain from 1979 to 2015 with the regional implementation of crop growth model WOFOST. The results showed that the model can reflect the potential yield and water-limited yield of Summer Maize in North China Plain through the calibration and validation of WOFOST model. After the regional implementation of model, combined with the reanalysis data, the model can better reproduce the regional history of summer maize yield in the North China Plain. The yield gap in Southeastern Beijing, southern Tianjin, southern Hebei province, Northwestern Shandong province is significant, these means the water condition is the main factor to summer maize yield in these regions.