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In view of the limitations of the existing methods for personal massive photos management and browsing, a method of using geodatabase to manage and browse massive personal photos is proposed. Firstly, vector geodatabase is created and defined based on geographic information technology. Then, attribute information including the file name, photo date and time, photo point longitude, photo point latitude, location name, photo storage path and so on of each photo in the file system are extracted. Finally, based on the longitude and latitude of the photo point, the geospatial location data of each photo is generated, and written into the vector geodatabase together with the attribute information. Based on the storage path of photos in the file system, the links between the geospatial database and photos are established. Furtherly realize the attribute information and geographical space location management of massive photos, as well as the browsing and viewing of photos. Take personal photos as an example to introduce the data processing and verify the practicability of the method. The result shows that this method uses the idea of geodatabase to manage massive personal photos, which is conducive to reflect the geographical spatial distribution and density characteristics of the photos, and can use the time axis to connect the track of personal history, which is convenient for browsing and analyzing the experience.

Modeling and predicting changes in the function and structure of the terrestrial biosphere and its feedbacks to climate change strongly depends on our ability to accurately represent interactions of the carbon and water cycles and energy exchange. However, carbon fluxes, hydrological status, and energy exchange simulated by process-based terrestrial ecosystem models are subject to significant uncertainties, largely due to the poorly calibrated parameters. In this work, an adjoint-based data assimilation system (Nanjing University Carbon Assimilation System; NUCAS v1.0) was developed, which is capable of assimilating multiple observations to optimize process parameters of a satellite-data-driven ecosystem model – the Biosphere–atmosphere Exchange Process Simulator (BEPS). Data assimilation experiments were conducted to investigate the robustness of NUCAS and to test the feasibility and applicability of assimilating carbonyl sulfide (COS) fluxes from seven sites to enhance our understanding of stomatal conductance and photosynthesis. Results showed that NUCAS is able to achieve a consistent fit to COS observations across various ecosystems, including evergreen needleleaf forest, deciduous broadleaf forest, C3 grass, and C3 crop. Comparing model simulations with validation datasets, we found that assimilating COS fluxes notably improves the model performance in gross primary productivity and evapotranspiration, with average root-mean-square error (RMSE) reductions of 23.54 % and 16.96 %, respectively. We also showed that NUCAS is capable of constraining parameters through assimilating observations from two sites simultaneously and achieving a good consistency with single-site assimilation. Our results demonstrate that COS can provide constraints on parameters relevant to water, energy, and carbon processes with the data assimilation system and opens new perspectives for better understanding of the ecosystem carbon, water, and energy exchanges.

Accurately modeling gross primary productivity (GPP) is of great importance for diagnosing terrestrial carbon–climate feedbacks. Process-based terrestrial ecosystem models are often subject to substantial uncertainties, primarily attributed to inadequately calibrated parameters. Recent research has identified carbonyl sulfide (COS) as a promising proxy of GPP due to the close linkage between leaf exchange of COS and carbon dioxide (CO2) through their shared pathway of stomatal diffusion. However, most of the current modeling approaches for COS and CO2 do not explicitly consider the vegetation structural impacts, i.e., the differences between the sunlit and shaded leaves in COS uptake. This study used ecosystem COS fluxes from seven sites to optimize GPP estimation across various ecosystems with the Biosphere-atmosphere Exchange Process Simulator (BEPS), which was further developed to simulate the canopy COS uptake under its state-of-the-art two-leaf framework. Our results demonstrated substantial improvement in GPP simulation across various ecosystems through the data assimilation of COS flux into the two-leaf model, with the ensemble mean of the root mean square error (RMSE) for simulated GPP reduced by 20.16 % to 64.12 %. Notably, we also shed light on the remarkable identifiability of key parameters within the BEPS model, including the maximum carboxylation rate of RuBisCO at 25 °C (Vcmax25), minimum stomatal conductance (bH2O), and leaf nitrogen content (Nleaf), despite intricate interactions among COS-related parameters. Furthermore, our global sensitivity analysis delineated both shared and disparate sensitivities of COS and GPP to model parameters and suggested the unique treatment of parameters for each site in COS and GPP modeling. In summary, our study deepened insights into the sensitivity, identifiability, and interactions of parameters related to COS and showcased the efficacy of COS in reducing uncertainty in GPP simulations.

The Community Earth System Model (CESM) developed by the National Center for Atmospheric Research (NCAR) has been used worldwide for climate studies. This study extends the efforts of CESM development to include an online (i.e., in-core) ensemble coupled data assimilation system (CESM-ECDA) to enhance CESM's capability for climate predictability studies and prediction applications. The CESM-ECDA system consists of an online atmospheric data assimilation (ADA) component implemented in both the finite-volume and spectral-element dynamical cores and an online ocean data assimilation (ODA) component. In ADA, surface pressures (Ps) are assimilated, while in ODA, gridded sea surface temperature (SST) and ocean temperature and salinity profiles at real Argo locations are assimilated. The system has been evaluated within a perfect twin experiment framework, showing significantly reduced errors of the model atmosphere and ocean states through “observation” constraints by ADA and ODA. The weakly coupled data assimilation (CDA) in which both the online ADA and ODA are conducted during the coupled model integration shows smaller errors of air–sea fluxes than the single ADA and ODA, facilitating the future utilization of cross-covariance between the atmosphere and ocean at the air–sea interface. A 3-year CDA reanalysis experiment is also implemented by assimilating Ps, SST and ocean temperature and salinity profiles from the real world spanning the period 1978 to 1980 using 12 ensemble members. The success of the online CESM-ECDA system is the first step to implementing a high-resolution long-term climate reanalysis once the algorithm efficiency is much improved.

With the development of economics and society, the degradation of natural resources and environment is aggravated globally. The values of ecological environment and ecosystem service have been paid more and more attention popularly. However, the spatial-temporal changes in ecosystem service value (ESV) and their hotspots in China are not well understood. Chinese government has integrated the construction of ecological civilization into all aspects and processes of economic construction, political construction, culture construction and social construction, and clearly puts forward that we should establish the concept of natural value and natural capital, also natural ecosystem is valuable. During 2012 to 2021, an important project of China's First National Geographic Conditions Census and Continuously Monitoring had been completed by Chinese government. In this project, high resolution land cover product all over the country had been generated, and would be updated continuously every year. This achievement had received more and more attention, which brought the possibility of big data computing. A calculating method of land’s ecosystem service value was proposed by using this achievement. The characters of data type, data time phase, and data structure for all the remote sensing information was analyzed. Based on this, a revised ecosystem service value assessment model was used for calculating. Combining the standard of terrestrial ecosystem in China and the equivalent value factor per unit ecosystem area, the specific calculating algorithm based on the big data was designed. Wuhan city, Hubei province, China was selected as the study area for validating the calculating method. The results showed that the total ecosystem service value in Wuhan city in 2015 was 9.42×1010 CNY, and the per capita ecosystem service value was 1.15×104 CNY. Specially, water supplied the most ecosystem service value which accounted for 79.95%, followed by forest, grassland, farmland, and desert. The research shows that high-precision quantification and spatialization ecosystem service value can be calculated and obtained by using the high resolution land cover product obtained in the project of China's First National Geographic Conditions Census and Continuously Monitoring; the independent research and development software has solved the problem of software requirements, and the operational efficiency and performance can meet the calculating needs; the big data calculating algorithm has solved the problems of design and computation of structured and unstructured big data computing models; The results of this study will help to maintain the sustainable development of an area, and supporting more effective decision-making around the implementation of ecological conservation policies.

With the rapid development of the remote sensing platform and sensor technology, remote sensing image data presents the typical characteristics of the massive complex, multi-source heterogeneous, spatial-temporal intensive, which puts forward higher requirements for data storage management efficiency and real-time online service capability. Combined with the demand for remote sensing image data, the multi-tier migration strategy and approach based on the thermal evaluation model of remote sensing image data are proposed, considering the file size and data activity of remote sensing image data. The linkage between local storage cluster and cloud storage implements multi-tier migration and dynamic flow of remote sensing image data, improves the utilization of storage devices and the rationality of storage resource allocation, and enhances the capability of fast, dynamic, and real-time online service of remote sensing image data.

With the rapid development of earth observation technology, the remote sensing data gradually becomes the significant data resources for spatiotemporal data analysis. However, to realize the efficient management and quick real-time service of the multi-source, multi-scale and multi-spectral is the key problem for the data management units. This paper brings up a novel technology system and resolution for online and real-time service of the massive remote sensing images by innovatively developing a data management model based on an extensible discrete global grid, inventing the grid-based search and dispatch method for remote sensing big data, firstly building a parameter-driven dynamic map service mode for multi -source, multi-scale and multi-spectral remote sensing big data. This method has greatly improved the efficiency for data management units, such as the Chinese government, corporations etc. and has produced a lot of social and economic benefits.

Accurate inversion of vegetation biochemicals using the PROSPECT model mostly depends on a proper inversion approach, including a suitable optimizing algorithm, appropriate dependent variables, and different properties from spectra of reflectance (R) and transmittance (T). In this paper, we propose a special inversion method using PROSPECT-5 and then explore its effectiveness in inverting chlorophyll, carotenoids, equivalent water thickness, and dry matter per area data from the ANGERS database. The inversion strategy includes (i) an optimal algorithm with constrained bounds (fminsearchbnd) to replace the common function fminsearch, (ii) and four parameters are considered together and separately as dependent variables of models, (iii) Using properties from the spectra of R, T and combined R&T to invert the above four biochemical parameters. The results show that fminsearchbnd can improve the model's R2 based on a field-measured database. Moreover, using the entire set of parameters together as the model inputs is more effective than using single parameters separately. T spectra are favoured for all parameter inversions in the model database while being inapplicable in the ANGERS database. These findings provide an appropriate inversion strategy for the PROSPECT-5 model in vegetation biochemical parameters analysis and suggest further research to develop an accurate inversion process for vegetation based on various physical models.

Typhoons are assumed to stimulate primary ocean production through the upward mixing of nutrients into the ocean surface. This assumption is based largely on observations of increased surface chlorophyll concentrations following the passage of typhoons. This surface chlorophyll enhancement, occasionally detected by satellites, is often undetected due to intense cloud coverage. Daily data from a BGC-Argo profiling float revealed the upper-ocean response to Typhoon Trami in the northwest Pacific Ocean. Temperature and chlorophyll changed rapidly, with a significant drop in sea surface temperature and a surge in surface chlorophyll associated with strong vertical mixing, which was only partially captured by satellite observations. However, no net increase in vertically integrated chlorophyll was observed during Typhoon Trami or in its wake. In contrast to the prevailing dogma, the result shows that typhoons likely have a limited effect on net primary ocean production. Observed surface chlorophyll enhancements during and immediately following typhoons in tropical and subtropical waters are more likely to be associated with surface entrainment of deep chlorophyll maxima. Moreover, the findings demonstrate that remote sensing data alone can overestimate the impact of storms on primary production in all oceans. Full understanding of the impact of storms on upper-ocean productivity can only be achieved with ocean-observing robots dedicated to high-resolution temporal sampling in the path of storms.

The stability control is one of the major technical difficulties in the field of highway subgrade construction engineering. Building deformation model is a crucial step for InSAR time series deformation monitoring. Most of the InSAR deformation models for deformation monitoring are pure empirical mathematical models, without considering the physical mechanism of the monitored object. In this study, we take rheology into consideration, inducing rheological parameters into traditional InSAR deformation models. To assess the feasibility and accuracy for our new model, both simulation and real deformation data over Lungui highway (a typical highway built on soft clay subgrade in Guangdong province, China) are investigated with TerraSAR-X satellite imagery. In order to solve the unknows of the non-linear rheological model, three algorithms: Gauss-Newton (GN), Levenberg-Marquarat (LM), and Genetic Algorithm (GA), are utilized and compared to estimate the unknown parameters. Considering both the calculation efficiency and accuracy, GA is chosen as the final choice for the new model in our case study. Preliminary real data experiment is conducted with use of 17 TerraSAR-X Stripmap images (with a 3-m resolution). With the new deformation model and GA aforementioned, the unknown rheological parameters over all the high coherence points are obtained and the LOS deformation (the low-pass component) sequences are generated.

To effectively test the scene heterogeneity for polarimetric synthetic aperture radar (PolSAR) data, in this paper, the distance measure is introduced by utilizing the similarity between the sample and pixels. Moreover, given the influence of the distribution and modeling texture, the K distance measure is deduced according to the Wishart distance measure. Specifically, the average of the pixels in the local window replaces the class center coherency or covariance matrix. The Wishart and K distance measure are calculated between the average matrix and the pixels. Then, the ratio of the standard deviation to the mean is established for the Wishart and K distance measure, and the two features are defined and applied to reflect the complexity of the scene. The proposed heterogeneity measure is proceeded by integrating the two features using the Pauli basis. The experiments conducted on the single–look and multilook PolSAR data demonstrate the effectiveness of the proposed method for the detection of the scene heterogeneity.

Oil and natural gas are important for energy supply around the world. The exploring, drilling, transportation and processing in oil and gas regions can release a lot of volatile organic compounds (VOCs). To understand the VOC levels, compositions and sources in such regions, an oil and gas station in northwest China was chosen as the research site and 57 VOCs designated as the photochemical precursors were continuously measured for an entire year (September 2014–August 2015) using an online monitoring system. The average concentration of total VOCs was 297 ± 372 ppbv and the main contributor was alkanes, accounting for 87.5 % of the total VOCs. According to the propylene-equivalent concentration and maximum incremental reactivity methods, alkanes were identified as the most important VOC groups for the ozone formation potential. Positive matrix factorization (PMF) analysis showed that the annual average contributions from natural gas, fuel evaporation, combustion sources, oil refining processes and asphalt (anthropogenic and natural sources) to the total VOCs were 62.6 ± 3.04, 21.5 ± .99, 10.9 ± 1.57, 3.8 ± 0.50 and 1.3 ± 0.69 %, respectively. The five identified VOC sources exhibited various diurnal patterns due to their different emission patterns and the impact of meteorological parameters. Potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) models based on backward trajectory analysis indicated that the five identified sources had similar geographic origins. Raster analysis based on CWT analysis indicated that the local emissions contributed 48.4–74.6 % to the total VOCs. Based on the high-resolution observation data, this study clearly described and analyzed the temporal variation in VOC emission characteristics at a typical oil and gas field, which exhibited different VOC levels, compositions and origins compared with those in urban and industrial areas.

Since 2012, an array of 105 Biogeochemical-Argo (BGC-Argo) floats has been deployed across the world's oceans to assist in filling observational gaps that are required for characterizing open-ocean environments. Profiles of biogeochemical (chlorophyll and dissolved organic matter) and optical (single-wavelength particulate optical backscattering, downward irradiance at three wavelengths, and photosynthetically available radiation) variables are collected in the upper 1000 m every 1 to 10 days. The database of 9837 vertical profiles collected up to January 2016 is presented and its spatial and temporal coverage is discussed. Each variable is quality controlled with specifically developed procedures and its time series is quality-assessed to identify issues related to biofouling and/or instrument drift. A second database of 5748 profile-derived products within the first optical depth (i.e., the layer of interest for satellite remote sensing) is also presented and its spatiotemporal distribution discussed. This database, devoted to field and remote ocean color applications, includes diffuse attenuation coefficients for downward irradiance at three narrow wavebands and one broad waveband (photosynthetically available radiation), calibrated chlorophyll and fluorescent dissolved organic matter concentrations, and single-wavelength particulate optical backscattering. To demonstrate the applicability of these databases, data within the first optical depth are compared with previously established bio-optical models and used to validate remotely derived bio-optical products. The quality-controlled databases are publicly available from the SEANOE (SEA scieNtific Open data Edition) publisher at https://doi.org/10.17882/49388 and https://doi.org/10.17882/47142 for vertical profiles and products within the first optical depth, respectively.

Biomass burning injects many different gases and aerosols into the atmosphere that could have a harmful effect on air quality, climate, and human health. In this study, a comprehensive biomass burning emission inventory including domestic and in-field straw burning, firewood burning, livestock excrement burning, and forest and grassland fires is presented, which was developed for mainland China in 2012 based on county-level activity data, satellite data, and updated source-specific emission factors (EFs). The emission inventory within a 1 × 1 km2 grid was generated using geographical information system (GIS) technology according to source-based spatial surrogates. A range of key information related to emission estimation (e.g. province-specific proportion of domestic and in-field straw burning, detailed firewood burning quantities, uneven temporal distribution coefficient) was obtained from field investigation, systematic combing of the latest research, and regression analysis of statistical data. The established emission inventory includes the major precursors of complex pollution, greenhouse gases, and heavy metal released from biomass burning. The results show that the emissions of SO2, NOx, PM10, PM2.5, NMVOC, NH3, CO, EC, OC, CO2, CH4, and Hg in 2012 are 336.8 Gg, 990.7 Gg, 3728.3 Gg, 3526.7 Gg, 3474.2 Gg, 401.2 Gg, 34 380.4 Gg, 369.7 Gg, 1189.5 Gg, 675 299.0 Gg, 2092.4 Gg, and 4.12 Mg, respectively. Domestic straw burning, in-field straw burning, and firewood burning are identified as the dominant biomass burning sources. The largest contributing source is different for various pollutants. Domestic straw burning is the largest source of biomass burning emissions for all the pollutants considered, except for NH3, EC (firewood), and NOx (in-field straw). Corn, rice, and wheat represent the major crop straws. The combined emission of these three straw types accounts for 80 % of the total straw-burned emissions for each specific pollutant mentioned in this study. As for the straw burning emission of various crops, corn straw burning has the largest contribution to all of the pollutants considered, except for CH4; rice straw burning has highest contribution to CH4 and the second largest contribution to other pollutants, except for SO2, OC, and Hg; wheat straw burning is the second largest contributor to SO2, OC, and Hg and the third largest contributor to other pollutants. Heilongjiang, Shandong, and Henan provinces located in the north-eastern and central-southern regions of China have higher emissions compared to other provinces in China. Gridded emissions, which were obtained through spatial allocation based on the gridded rural population and fire point data from emission inventories at county resolution, could better represent the actual situation. High biomass burning emissions are concentrated in the areas with more agricultural and rural activity. The months of April, May, June, and October account for 65 % of emissions from in-field crop residue burning, while, regarding EC, the emissions in January, February, October, November, and December are relatively higher than other months due to biomass domestic burning in heating season. There are regional differences in the monthly variations of emissions due to the diversity of main planted crops and climatic conditions. Furthermore, PM2.5 component results showed that OC, Cl−, EC, K+, NH4+, elemental K, and SO42− are the main PM2.5 species, accounting for 80 % of the total emissions. The species with relatively high contribution to NMVOC emission include ethylene, propylene, toluene, mp-xylene, and ethyl benzene, which are key species for the formation of secondary air pollution. The detailed biomass burning emission inventory developed by this study could provide useful information for air-quality modelling and could support the development of appropriate pollution-control strategies.

The objective of this study was to determine whether nutrient digestibility and growth performance of mink were affected by different dietary copper levels. Ninety-six 60-day-old healthy female minks were randomly assigned to 8 treatment groups with 12 animals in each group and fed a diet supplemented with either 0, 4, 8, 16, 32, 64, 128 or 256 mg/kg copper as CuSO4 × 5H2O, respectively. Our data showed that body weight and average daily gain increased (linear and quadratic, P < 0.05) as Cu increased in the diet, the highest body weight and average daily gain were seen in the Cu32 group. Feed : gain ratio responded in a linear (P = 0.0025) fashion with increasing level of Cu, the lowest feed : gain ratio was seen in the Cu64 group. Digestibility of ether extract responded in a linear (P = 0.0190) fashion with increasing level of Cu. There were no differences in apparent digestibility of dry matter, CP, and gross energy among groups (P > 0.05). N retention linearly (P = 0.0363) responded to increasing levels of Cu. Glutamic-oxalacetic transaminase and glutamic-pyruvic transaminase increased (linear and quadratic, P < 0.05) as Cu increased in the diet; the highest glutamic-oxalacetic transaminase and glutamic-pyruvic transaminase were seen in the Cu256 group. Total protein of the minks was similar among the treatments, however, albumin in serum responded in a linear (P = 0.0370) and quadratic (P = 0.0049) fashion with increasing level of Cu. The activity of ceruloplasmin responded in a linear (P = 0.0001) and quadratic (P = 0.0203) fashion with increasing level of Cu. The activity of Cu-Zn superoxide dismutase responded in a linear (P = 0.0010) fashion with increasing level of Cu. Our results indicate that supplemental Cu plays an important role in the growth performance of mink, helping young female mink digest and efficiently utilize added dietary fat.

In situ observation of the marine environment has traditionally relied on ship-based platforms. The obvious consequence is that physical and biogeochemical properties have been dramatically undersampled, especially in the remote Southern Ocean (SO). The difficulty in obtaining in situ data represents the major limitations to our understanding, and interpretation of the coupling between physical forcing and the biogeochemical response. Southern elephant seals (Mirounga leonina) equipped with a new generation of oceanographic sensors can measure ocean structure in regions and seasons rarely observed with traditional oceanographic platforms. Over the last few years, seals have allowed for a considerable increase in temperature and salinity profiles from the SO, but we were still lacking information on the spatiotemporal variation of phytoplankton concentration. This information is critical to assess how the biological productivity of the SO, with direct consequences on the amount of CO2 "fixed'' by the biological pump, will respond to global warming. In this research programme, we use an innovative sampling fluorescence approach to quantify phytoplankton concentration at sea. For the first time, a low energy consumption fluorometer was added to Argos CTD-SRDL tags, and these novel instruments were deployed on 27 southern elephant seals between 25 December 2007 and the 4 February 2011. As many as 3388 fluorescence profiles associated with temperature and salinity measurements were thereby collected from a vast sector of the Southern Indian Ocean. This paper addresses the calibration issue of the fluorometer before being deployed on elephant seals and presents the first results obtained for the Indian sector of the Southern Ocean. This in situ system is implemented in synergy with satellite ocean colour radiometry. Satellite-derived data is limited to the surface layer and is restricted over the SO by extensive cloud cover. However, with the addition of these new tags, we are able to assess the 3-dimension distribution of phytoplankton concentration by foraging southern elephant seals. This approach reveals that for the Indian sector of the SO, the surface chlorophyll a (chl a) concentrations provided by MODIS were underestimated by a factor 2 compared to chl a concentrations estimated from HPLC corrected in situ fluorescence measurements. The scientific outcomes of this programme include an improved understanding of both the present state and variability in ocean biology, and the accompanying biogeochemistry, as well as the delivery of real-time and open-access data to scientists (doi:10.7491/MEMO.1).

In vivo fluorescence of Chlorophyll-a (Chl-a) is a potentially useful property to study the vertical distribution of phytoplankton biomass. However the technique is presently not fully exploited as it should be, essentially because of the difficulties in converting the fluorescence signal into an accurate Chl-a concentration. These difficulties arise noticeably from natural variations in the Chl-a fluorescence relationship, which is under the control of community composition as well as of their nutrient and light status. As a consequence, although vertical profiles of fluorescence are likely the most recorded biological property in the open ocean, the corresponding large databases are underexploited. Here with the aim to convert a fluorescence profile into a Chl-a concentration profile, we test the hypothesis that the Chl-a concentration can be gathered from the sole knowledge of the shape of the fluorescence profile. We analyze a large dataset from 18 oceanographic cruises conducted in case-1 waters from the highly stratified hyperoligotrophic waters (surface Chl-a = 0.02 mg m−3) of the South Pacific Gyre to the eutrophic waters of the Benguela upwelling (surface Chl-a = 32 mg m−3) and including the very deep mixed waters in the North Atlantic (Mixed Layer Depth = 690 m). This dataset encompasses more than 700 vertical profiles of Chl-a fluorescence as well as accurate estimations of Chl-a by High Performance Liquid Chromatography (HPLC). Two typical fluorescence profiles are identified, the uniform profile, characterized by a homogeneous layer roughly corresponding to the mixed layer, and the non-uniform profile, characterized by the presence of a Deep Chlorophyll Maximum. Using appropriate mathematical parameterizations, a fluorescence profile is subsequently represented by 3 or 5 shape parameters for uniform or non-uniform profiles, respectively. For both situations, an empirical model is developed to predict the "true" Chl-a concentration from these shape parameters. This model is then used to calibrate a fluorescence profile in Chl-a units. The validation of the approach provides satisfactory results with a median absolute percent deviation of 33 % when comparing the HPLC Chl-a profiles to the Chl-a-calibrated fluorescence. The proposed approach thus opens the possibility to produce Chl-a climatologies from uncalibrated fluorescence profile databases that have been acquired in the past and to which numerous new profiles will be added, thanks to the recent availability of autonomous platforms (profiling floats, gliders and animals) instrumented with miniature fluorometers.