A book series on Research and Application of Space-borne Microwave Remote Sensing， including 10 books， has been published by Science Press in 2019~2020. This book series summarizes the research progress of Chinese scientists during recent years on space-borne microwave remote sensing in both active and passive technologies. In active remote sensing of Synthetic Aperature Radar （SAR）， it includes the wide swath SAR， the imaging processing system for the SAR imagery， artificial intelligence for retrieval of SAR information，the knowledge-radar technology for space and aerial targets， and the target decomposition of polarimetric SAR technology. In passive remote sensing， it includes the research and application of Chinese Fen Yun meteorological and Hai Yang oceanic satellites. Also， the researches on planetary remote sensing， e.g Moon and Mars， and non-linear hyperspectral unmixing are presented. This article present a brief introduction and comment on this book series.
Accurate assessment of global carbon sequestration is the key step to accurately assess future CO2 concentrations and predict climate change. At present， the spatial and temporal uncertainties of global terrestrial and oceanic carbon sinks are very large. Although the model simulation method is widely used in global carbon source/ sink research， many studies have pointed out that the simulation results varied greatly. Besides the deficiencies in carbon cycle models， the lack of global observational data on fine spatiotemporal resolution is also a most important cause of global uncertainties in carbon cycle estimation. Therefore， the project aims to develop comprehensive， top-level， and long time-series Global Land & Ocean Carbon Cycle （GLOCC） datasets using multi-source earth observation data， and to explore new methods to directly estimate global carbon sequestration， such big data. The GLOCC datasets not only include products directly related to carbon sources and sinks， such as terrestrial ecosystem productivity， forest biomass， soil carbon pool and seawater carbon dioxide partial pressure， seawater particulate organic carbon， etc； it also includes key driven variables of carbon cycle process models for both terrestrial and ocean ecosystems. Over the past three years， we have collected and processed 28 domestic and foreign satellite data and 19 carbon-related global remote sensing products 1981 to 2019， and have developed a series of algorithms for processing of multi-source satellite data. We have also developed the inversion models for the 24 GLOCC products， in which the temporal resolution of some GLOCC parameters was improved from 8 days to 5 days by integrating domestic and foreign satellite remote sensing data. So far， 7 GLOCC products were freely available in multiple data centers. The project will benefit the global change research community by long-term global products with fine spatial and temporal resolution， and to provide new discoveries on global carbon sequestration.
Patch vegetation is a common landscape type in arid and semi-arid areas in the world. To detect patch vegetation using remotely sensed images is important for studying its pattern formation， function， and succession mechanisms， and understanding its impact on the ecohydrological processes in arid and semi-arid areas. This article reviews the current status of patch vegetation detection based on remote sensing technology， including remotely sensed data source such as aerial photographs and high-resolution satellite images， and application of detection approaches from pixel-based， object-based， and morphology-based methods， respectively. It is pointed out that the image quality， acquisition date of imagery， and the composition and structure of vegetation patch have an important influence on the classification of vegetation patch. For the overlapping patches， a better image segmentation algorithm is needed to be applied for improving detection accuracy. Finally， the research directions of remote sensing detection of vegetation patch are suggested in order to provide reference for monitoring patch vegetation patterns and dynamics in the future， including an application of high-spatial and spectral satellite remotely sensed imagery and unmanned aerial vehicle， and the development of more advanced image segmentation algorithms.
Land Surface Temperature （LST） is a crucial input parameter in the study of land surface processes. It’s effective to estimate the radiation balance and energy budget at local and global scales using remotely sensed data. Currently， the fast development of LST retrieval algorithm based on thermal infrared and microwave remote sensing has made a series of progress. Its accuracy can reach within 1K in the uniform area of flat surface coverage derived from thermal infrared remotely sensed data especially. However， it is still a great challenge for their application over complex surface area. This paper systematically summarizes the limitations of LST retrieval in complex topographic areas， including pathological absence of inversion model， terrain complexity， data loss caused by thick vapor cloud， and uncertainty of authenticity test. Furthermore， we present suggestions for the future research to improve the accuracy of LST retrieval over complex surface.
Ground-based remote sensing observation of Atmospheric greenhouse gas （GHG） column concentration has been taken great effort to support and validate satellite data . The high-resolution IFS 120/125 HR owns outstanding capabilities in its precision， but it is expansive and poor in transportability. The portable low resolution （0.5 cm-1） Fourier infrared spectrometer EM27/SUN records the direct solar absorption spectra through its own solar tracker， which is a new method to provide GHG monitoring where TCCON stations are sparse. Based on the principle that GHG have obvious absorption lines in the short-wave infrared region， the non-linear least squares algorithm PROFFIT and GGG are widely used to retrieve the column gas average dry air mole fraction （Xgas） from the recorded spectrum. EM27/SUN data has high accuracy and stability， which is suitable for scientific application. The international applications of EM27/Sun are mainly summarized into three categories： remote sensing of the gaseous composition of plumes， satellite validation and gas emission estimation on the city scale. The unique advantages and innovative results of EM27/SUN compared with other observation methods are discussed. It is proposed that the protable EM27/SUN may help quantifying the gas components of plumes， validating satellite data from home and abroad， source distribution estimation in urban areas and quantizing station-to-station variability of different TCCON sites by using this travelling spectrometer.
With the increasing importance of the ocean in the context of country's politics， economy， and resources； the Land Use Land Cover （LULC） related research of island has received growing attention， which is significant to the bio-physical and socio-economic development for island. The Sulawesi island has been declared as globally important conservation area. Considering the problem of slow extraction and low efficiency of traditional large-scale land cover， this study took the Sulawesi as study area and carried out LULC change analysis based on the Google Earth Engine （GEE） platform. In this study Landsat TM/OLI images were used. After pre-processing all of the Landsat image， signature classes were selected based on the GEE platform. Finally， Random Forest （RF） algorithm was used to extract all of the land cover types of Sulawesi island from 2000 to 2018. In this study， change characteristics of land cover were analyzed from three aspects including structure and type change， spatio-temporal change， and dynamic change. This study observes the efficiency of GEE platform in regional scale data processing and surface cover extraction. The overall classification accuracy of the study in 2000， 2015 and 2018 are 91%， 88% and 85%， and Kappa coefficients are 0.89， 0.86 and 0.82， respectively. The LULC change analysis of this study shows that the main land cover types of Sulawesi Island are forest and cultivated land comprises more than 85% of the entire island. During the time period of 2000~2018， the forest land of Sulawesi was reduced first （reduced by 7 982.29 km2） and then increased （increased by 9 079.17 km2） due to the resettlement plan and the prohibiting of illegal logging while. The area of cultivated land was decreased （total reduced by 14 267.35 km2） converting to forest land， grassland， and artificial surface. Artificial surface has been increased significantly， mainly due to population migration and economic development in the island. The findings of this study will play an important role in guiding policy for regional development as well as resource management and environmental protection of Sulawesi island.
The eco-environment along the China-Pakistan Economic Corridor （CPEC） is complex， and the contradictions between the population growth and land resources is prominent. Although the construction of the CPEC has promoted regional economic development， it has inevitably affected the regional ecological environment. Based on remote sensing， geographic information and scenario simulation methods， a risk assessment model from three aspects including risk source danger， ecological environment vulnerability and receiver loss degree， was constructed. Moreover， combining the land use simulation of 2030 under Baseline Development （BD） scenario， Investment Priority Oriented （IPO） scenario， and the Harmonious Development （HD） scenario， the temporal and spatial variation of ecological risk in the CPEC from 2015 to 2030 were also analyzed. The results showed that the area with higher ecological risk is mainly distributed in the transition zone from North Highlands to the Indus Plain. Our results also indicated that the setting of the scenario directly affects ecological risk. Among them， the high-risk area in IPO scenario had the largest increase. Compared with 2015， the proportion of medium， high and extremely high-risk areas increased by 4.75%， 4.84% and 2.25%， respectively in 2030. In the BD scenario， the proportion of medium， high and extremely high-risk areas increased by 2.79%， 2.56% and 1.04%， respectively. Meanwhile， moderate and high-risk areas of HD scenario showed an increasing trend， and the extremely high-risk areas decreased slightly， with increases of 1.15%， 2.19% and -0.16%， respectively. This study suggested that considering economic development and environmental protection at the same time is the optimal choice for future planning and construction. The comprehensive evaluation method based on remote sensing and GIS technology can provide technical support for ecological risk evaluation of economic corridors.
The remarkable feature of urbanization is that the natural surface is constantly replaced by the impervious surface with large heat capacity， which leads to the Urban Heat Island （UHI） and other serious urban ecological problems. The Bangladesh-China-India-Myanmar （BCIM） Economic Corridor is an important road section of the ancient Southern Silk Road and a strategic passage for the construction of the “Belt and Road” Initiative （BRI）. Kolkata， located at the strategic position of the BCIM Economic Corridor， is extremely important for the BCIM project. So it is of great significance to research the development of Kolkata and its correlation to Land Surface Temperature （LST） for the construction of the Indian section in the BCIM Economic Corridor. Traditional researches on the relationship between LST and Impervious Surface Percentage （ISP） are mainly based on years， however， few researchers have paid attention to seasonal variations. Based on Landsat images of dry season， rainy season and cool season， this paper retrieved LST and ISP in order to explore and analyze the seasonal variation in the relationship between LST and the ISP. The results indicated that： ① For this study， the distribution of low temperature and high temperature was concentrated， and the high temperature was concentrated in the built-up area， but the low temperature distributed mainly over water body and the area with relatively high vegetation cover； ② The overall UHI effect had been decreasing from dry season to rainy season and then to cool season， and that was strongest in dry season and weakest in cold season； ③ In each season， the LST was positively correlated with ISP. With the increase of the ISP， the LST increased rapidly at first， then slowly， and finally sharply. And when the ISP increased by 0.1， the LST increased by 0.53 ℃ in dry season， 0.35 ℃ in rainy season and 0.26 ℃ in cool season. In summary， the study of thermal environment in Kolkata will be of positive significance to cognition of thermal environment background and ecological effects in India section of BCIM Economic Corridor.
Lakes in the Tibetan Plateau are important indicators of climate change. Monitoring the water level changes of the plateau lakes is essential for an accurate evaluation of regional climate change and its impact on the surrounding hydrologic environment. Because of the remoteness and poor accessibility of these alpine lakes， it is difficult to conduct long-term and continuous in-situ observation of lakes， yet the development of remote sensing technology has partly solve the difficulty. The use of multi-source altimetry satellite data can effectively monitor the continuous variation of lake water level and improve the understanding of climate change response characteristics of lakes in the Tibetan Plateau. Based on multi-source altimetry water level assimilation data accessed at Hydroweb， lake inundation area mapping results， combined with temperature and precipitation ground observation data， water level change characteristics of Qinghai Lake and Siling Co are analyzed from a comparative perspective on different time scales such as multi-decadal and interannual， seasonal， extreme dry and wet years. Based on this， the spatio-temporal difference of lake changes in the Tibetan Plateau and the response characteristics of lakes in different climate sub-regions are discussed. The results show that the water level difference between Qinghai Lake and Siling Co is quite obvious. The water level of Qinghai Lake had decreased year by year from 1998 to 2004. As rainfall increases， the water level began to rise from 2005， and the cumulative increase has been 2.95m until 2018. The water level of Siling Co has been growing since 1998， except for 2015 and 2016. The water level rose more rapidly from 2000 to 2010， with an annual growth rate of about 0.8 m/a. Finally， we have found and discussed the response of the water level changes of Qinghai Lake and Siling Co to different climatic characteristics， and provided the basis for further research on the driving mechanism of lake change in combination with remote sensing observation and hydrological model.
Evapotranspiration（ET） is one of the most important term of land surface， which is an indicator of agriculture growth conditions and yield. Remote sensing technology has advantages in monitoring evapotranspiration， however， remote sensing evapotranspiration products develop slower than other remote sensing products. This paper compared seven widely used ET products and two ET systems， including MODIS-MOD16， SSEBop， ET_PT-JPL， ET_ALEXI， LSA-SAF， GLEAM， BESS， ETWatch and ETMonitor. We introduced the details of these ET products. In order to propose a framework of estimating high accuracy and widely used remote sensing ET products， and to better understand the complexity and uncertainty in ET estimation， we focused on comparing the design ideas and processing procedure of these ET products through summarizing algorithms of estimating actual ET， resistances parameterizations， available energy calculating methods， temporal upscaling methods， estimating procedures and input data sources， respectively. In addition， the current problems of ET calculating from remote sensing are proposed， including complex algorithms with multi parameters， inconsistent spatial and temporal resolutions， and difficult to be validated. Finally， we discussed possible directions of the future remote sensing ET products.
Accurate estimation of terrestrial evapotranspiration （ET） is challenged substantially in studies on the Earth system and global change. Remotely sensed ET products have greatly fostered researches on related areas. However， there are various ET products retrieved based on different theories involving hydrology and quantitative Remote Sensing （RS）. These theories are probably too esoteric to understand for students and young scientists （new users） alike. As such， ignoring the physical meaning of the products may hinder their proper application， lead to unreasonable analysis and affect an in-depth study of scientific problems. Therefore， we compared and analyzed six RS-based ET products， including MODIS and GLEAM （1980~2018）， over the Loess Plateau， northwest China， aiming to discuss critical problems when applying these products. The results show that： ① there is a low correlation between RS-based ET and water balance ET （R2<0.4） because significant differences exist at yearly scale （ANOVA， P<0.01）， with mean absolute percent errors ranging from 17% to 30%； ②spatially， although ET values show a similar distribution in general， substantial difference exists at certain regions， with a value up to 400 mm/y； ③when analyzing change trends of ET over the Loess Plateau based on these ET products， inconsistent trend or even opposite change trends will be observed. It concluded that before applying RS products， one should perform validations and master their advantages and limitations before application， including algorithm， model input and possible error propagation. This study can provide guidelines for selecting proper ET products for related researches in arid and semi-arid regions.
As an active microwave sensor， the Synthetic Aperture Radar（SAR） is widely employed in earth observation. However， the traditional radar scattering model is based on geometrical optical approximation， which ignores the interaction among the ground objects， i.e.， the multiple scattering effects. To fully exploit and utilize the information of target electromagnetic scattering characteristics in millimeter-wave SAR images， it is imperative to simulate and verify the multiple scattering effects of typical targets. In this paper， the correspondence between the equivalent current distribution of the spherical surface and the dihedral angle and the segmentation imaging results was analyzed based on the Method Of Moments （MOM）， and the electromagnetic simulation imaging was performed by the Back-Projection （BP） algorithm. The effect of scattering on the single/bistatic mode radar imaging was summarized. The results show that the target surface equivalent current distribution and the segmental equivalent synthetic aperture angle change with the incident angle， and the segmented equivalent synthetic aperture angle affects the azimuth resolution. Moreover， it is found that the bistatic radar images include more abundant electromagnetic scattering information. These findings can provide reference for design and verification of SAR system， echo characteristic data collection of typical targets and identification of targets based on high-resolution SAR images.
FY-3 meteorological satellite is the second generation of polar orbit operational meteorological satellite in China. It has global， all-weather and multispectral detection capabilities， and is loaded with two microwave atmospheric sounding payloads of MWTS and MWHS. Since the first satellite launched in 2008， FY-3 microwave atmospheric sounders have played an active role in disaster prevention and numerical weather prediction. The radiometric calibration of FY-3 meteorological satellite is a data processing process of obtaining the target brightness temperature from the original observation data， which includes four technical links： pre-launch calibration， on orbit calibration， comprehensive radiometric calibration and historical data re-calibration. Accurate radiometric calibration is the basis of quantitative application of satellite passive microwave radiometer remote sensing data. This paper summarizes the comprehensive radiometric calibration technology of the microwave atmospheric sounders of FY-3 meteorological satellite， expounds the basic principle and technical status of the comprehensive radiometric calibration technology of the microwave atmospheric sounding load of FY-3 meteorological satellite， and looks forward to the future development of the comprehensive radiometric calibration technology of the microwave atmospheric sounding load of FY-3 meteorological satellite.
Analyzing the dynamic changes of lakes is of great significance to revealing the environment evolution features. The changes in surface water area and groundwater storage of the Hulun Lake during the past 3 decades have been studied by processing all available Landsat images from 1986 to 2018 using Google Earth Engine. The time-series of surface water area changes， which is closely related to the water level change observed by satellite altimetry， is divided into four stages， including a steady increase （before 2000）， a dramatic decline （2000 to 2012）， a rapid recovery （2013 to 2015） and a level period （after 2016）. A comprehensive analysis of the reasons for these changes has been done using multi-source models such as precipitation， evaporation and river flow. The role of each factors playing in the above mentioned four stages has been identified. Finally， according to the water balance formula， we estimated the groundwater changes， which is shown to be highly decreased.
The images provided by an individual satellite are difficult to meet the requirement for a long time series observation due to the factors such as satellite operation duration， satellite passage time， atmosphere condition and others. Therefore， it is necessary to quantitatively study the relationship between different satellite sensor data for their collaborative use for a long time series earth observation. In this paper， the quantitative relationship between Sentinel-2A MSI and Landsat-8 OLI sensors’ data was studied. Based on three synchronous， cloud-free image pairs of the two sensors， the corresponding bands of the two sensors were compared band-by-band by using the Region Of Interest （ROI） method and the whole test area method to explore the quantitative relationship between them. The results show that the Top Of Atmospheric （TOA） reflectance data of Sentinel-2A MSI and Landsat-8 OLI are generally consistent， and the mean R2 values obtained via the two methods are 0.89 （whole test area method） and 0.99 （ROI method）， respectively. However， the differences between the two sensors’ data have also been revealed. The total TOA reflectance of Sentinel-2A MSI is generally about 5% higher than that of Landsat-8 OLI， which， however， is different in different bands. The analysis shows that this difference is due to the two sensors’ differences in spectral response function and spectral range， as well as the difference in land cover type of the test areas. By regression analysis， the data conversion equations of each band between the two sensors are obtained. The validation results show that the conversion equations can significantly improve the consistency of Sentinel-2A MSI and Landsat-8 OLI data， providing a feasible method for the collaborative use of the two sensors’ data.
Salinization is one of the main forms of land degradation which leads to fragile ecological environment and low efficiency of agricultural production. Remote sensing combined with machine learning algorithm is one the most popular methods in salinization monitoring. In terms of machine learning algorithm， the model feature subset and parameters is vital to modeling accuracy. Therefore， accurate identification and optimization of model parameters and feature subset is crucial for machine learning based inversion and prediction of Soil Salt Content （SSC）. Based on Sentinel-1 SAR， Landsat 8 OLI images and DEM data， a total of 40 environmental factors of 8 categories were extracted. In conjunction with Pearson correlation analysis， the Candidate Feature Variables （CFVs） were initially selected. The CFVs were introduced into the Grid Search （GS） algorithm， Genetic Algorithm （GA） and the Particle Swarm Optimization （PSO） to simultaneously identify the model parameter and feature subset of Support Vector Regression （SVR）. Salinization monitoring models （GS-SVR， GA-SVR， PSO-SVR） were established， respectively. The optimal model was applied into the SSC prediction of Manasi Irrigation District in growing season， 2016. The results show that the extracted environmental factors showed good correlations with SSC， and the vegetation indices and feature spaces were more sensitive to salinization than other types of environmental factors. Compared with GS-SVR， the GA-SVR and PSO-SVR methods improved the accuracy of the salinization monitoring while reducing the number of feature subset， and the fitness value increased by 53.87% and 69.96%， respectively. During the growing season， salt accumulates in spring and autumn and fades in summer. The trend of average SSC of the whole study area and the central part and the southern part was decreasing-increasing， while the northern part was increasing-decreasing-increasing. According to the SSC violin plots in the growing season， it was found that the trend of SSC range of the whole study area and the central part and the northern part was expansion-contraction-expansion， while it was expansion-contraction-stability in southern part of study area. This study provided the technical support for accurate salinization monitoring and dynamic change of SSC in growing season.
Drought monitoring for apple is essential and rarely reported. Furthermore， most of studies on apple drought monitoring are based on station observations， which cannot adequately represent the spatial information. Remote sensing based drought indices can be used for spatial and temporal dynamic drought monitoring， but its applicability in apple remains to be researched. Based on the MODIS reflectance， land surface temperature and land cover data from 2014 to 2018， combined with soil moisture data and field survey， the consistency of the Temperature Vegetation Dryness Index（TVDI）， Normalized Difference Water Index（NDWI）， vegetation supply Water Index of apple with Soil Moisture（SM） at 10 cm were analyzed to explore the ability of remote sensing based drought indices in characterizing drought conditions. Then，the sensitive period of drought indices to SM was further researched. The results indicated that VSWI calculated by EVI （recorded as VSWI（EVI））had the best temporal and spatial consistency with SM， and its performance in drought monitoring in 2014 and 2017 were consistent with the actual drought.The correlation between VSWI（EVI），TVDI（EVI） and SM were higher than those of VSWI （NDVI） and TVDI （NDVI） respectively， which demonstrated that EVI can improve the characterization ability of VSWI and TVDI for drought. TVDI，NDWI，VSWI had a delay in response to SM. VSWI （EVI） with lag 24 days had the highest correlation with SM， while NDWI had timely response to SM. Therefore， combined with VSWI （EVI） and NDWI may be more conducive to monitoring drought of apple.In different growth stages， drought indices had different sensitivity to soil moisture， and VSWI has the most obvious sensitivity difference in different growth stages. At new Shoots 'vigorous Growing period （may to june），drought indices are more sensitive than that of budding and flowering， fruit expansion and maturity stages. The research results can provide a reference for monitoring drought of apple by remote sensing method.
Forecasting and identifying the clouds that may develop into Tropical Cyclones （TCs） from tropical clouds are essential for early warning disasters. In this study， spaceborne microwave brightness temperature observations are used to build a new model to predict TC genesis in the Northwest Pacific. A sample dataset consists of 326 developing TC samples， and 2 112 non-developing tropical disturbance samples is established. They are collected by DMSP satellite during the typhoon season （May to October） from 2005 to 2009. Based on this dataset， 35 brightness temperature characteristic parameters were calculated including 25 statistical parameters and 10 landscape pattern parameters. Landscape pattern parameters are included to describe the spatial distribution of brightness temperature near TC center. The dataset is then divided into a training set （2007~2009） and a test set （2005~2006）. For the training set， the model's overall accuracy is 85.27%， the hit rate and false alarm rate of tropical cyclones are 90.91% and 20%， respectively. For the test set， the above three parameters are 79.47%，80%， and 20.79%， respectively. Therefore， the tropical cyclone generation model based on microwave brightness temperature data can effectively predict tropical cyclones' development.
The management of artificial forest farms in the south of China is slightly large and the forest cover changes frequently. Therefore， accurately and quickly obtaining forest change information is of great significance for studying ecological environment changes and management. At present， the more commonly used forest cover change detection methods are the direct comparison analysis method and the post-classification comparison method. In order to explore the applicability and effectiveness of the two change detection methods， the direct comparison analysis method and the post-classification comparison method in the detection of forest cover change in southern China’s artificial forest farms with high management intensity and complex terrain. In this study， the Guangxi Gaofeng Forest Farm was used as the research area， and the GF-1 PMS images were selected as the data source. The Iterative Re-weighted Multiple Change Detection （IR-MAD） and the EnMAP-Box based random forest （ImageRF） post-classification comparison methods. After the two comparison methods of change method， the change detection of the two-stage image forest cover in the study area was carried out. The results show that the overall accuracy of the iterative weighted multivariate change detection result is 89.31%， and the Kappa coefficient reaches 0.80. The overall accuracy of the EnMAP-Box based random forest （ImageRF） post-classification comparison method is 86.02%， and the Kappa coefficient is 0.75. The former has better accuracy and extraction effect than the latter. It shows that this method can quickly and accurately grasp the change of forest cover in the study area， and provide technical support for studying the change of forest ecological environment and management of forest farms.
Exploring the distribution and change characteristics of atmospheric aerosols across the country and accurately understanding the optical characteristics of aerosols in China are very important for studying atmospheric environmental pollution and coping with global climate change. The applicability of the MODIS MAIAC aerosol optical thickness data from 2008 to 2016 in China was verified， and the Mann-Kendall method was used to analyze the spatiotemporal characteristics of AOD values in China from different spatiotemporal scales and aerosol types. The results show that： ①The verification shows that the C6 MAIAC inversion results perform well on the Chinese AERONET matching point， and the C6 MAIAC inversion AOD results are applicable to the Chinese region； ②From the perspective of seasonal scale， the seasonal change of AOD is characterized by high overall spring， high summer center， and low autumn and winter levels. The average AOD of each province and the average AOD of each province are similar with seasonal changes. ③Spatially， AOD is characterized by high southeast， low northwest， and high value centers. ④The change of AOD in China as a whole shows the characteristics of decreasing and agglomeration in the east and increasing and scattered in the west. It can further explore the distribution of different types of aerosols and the relationship between aerosols and typical atmospheric pollutants， with a view to providing better decision-making guidance for environmental pollution control in China.
With the rapid development of remote sensing technology in China， more and more domestic satellite data are applied to various industries. In the field of wetland remote sensing monitoring， remote sensing estimation of wetland biomass and carbon storage is a research area of great concern to researchers In particular， the use of our own developed GF series of satellites for wetland ecosystem resources monitoring to provide a new way and method. In this paper， GF-1 satellite-based method was developed to estimate the aboveground biomass and soil organic carbon density of Alpine marsh wetlands in Ruo'ergai By selecting and calculating 27 remote sensing factors such as single band information， vegetation index information， texture feature and terrain feature of GF-1 remote sensing data， the modeling factors are determined by stepwise regression method an estimation model of aboveground biomass and organic carbon density in zoige wetland was constructed. The results showed that the aboveground biomass of the whole zoige wetland was 1.09 million tons， the soil organic carbon density of 0~30 cm soil was 18.99 kg/m2.Through field investigation， the estimation accuracy of aboveground biomass was 86.44%， and the estimation accuracy of organic carbon density was 81.56%. Moreover， the aboveground biomass and soil organic carbon density are consistent with the spatial characteristics of the wetland vegetation distribution in the study area， which is mainly concentrated in the middle and northwest. The research results estimated by the model are of good reliability and rationality.
Urban nuclei play a crucial role in conducting urban function， polycentric urban structure have become an important urban spatial model. However， there have few studies about the identification of urban nuclei. Rapid and precise extraction of urban nuclei is significative for urban management and planning. Our research introduced a method for identifying urban nuclei based on point of interest big data. Study indicated that our method successfully identified the urban nuclei in the case city， provided their spatial range， and also showed a fine detection effect on the structure of urban nuclei. In addition， the rationality and reliability of the results were checked through related tests. Different from traditional studies， our method can identify the boundary of urban nuclei， which is also convenience and simple. The results indicated that this urban nuclei identification method based on POI big data can accurately locate the location of urban nuclei， which might provide valuable spatial location reference information for urban planning and precise management in the future.
