In this paper, we present long term observations of atmospheric nitrogen dioxide (NO₂) and formaldehyde (HCHO) in Nanjing using a Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instrument. Ground based MAX-DOAS measurements were performed from April 2013 to February 2017. The MAX-DOAS measurements of NO₂ and HCHO vertical column densities (VCDs) are used to validate OMI satellite observations over Nanjing. The comparison shows that the OMI observations of NO₂ correlate well with the MAX-DOAS data with Pearson correlation coefficient (R) of 0.91. The comparison result of MAX-DOAS and OMI observations of HCHO VCD shows a good agreement with R of 0.75 and the slope of the regression line is 0.99. The age weighted backward propagation approach is applied to the MAX-DOAS measurements of NO₂ and HCHO to reconstruct the spatial distribution of NO₂ and HCHO over the Yangtze River Delta during summer and winter time. The reconstructed NO₂ fields show a distinct agreement with OMI satellite observations. However, due to the short atmospheric lifetime of HCHO, the backward propagated HCHO data does not show a strong spatial correlation with the OMI HCHO observations. The result shows the MAX-DOAS measurements are sensitive to the air pollution transportation in the Yangtze River Delta, indicating the air quality in Nanjing is significantly influenced by regional transportation of air pollutants.

The atmospheric carbon dioxide (CO₂) concentration has increased to more than 405 parts per million (ppm. 1ppm=10^-6m/s²) in 2017 due to human activities such as deforestation, land-use change and burning of fossil fuels. Although there is broad scientific consensus on the damaging consequences of the change in climate associated with increasing concentrations of greenhouse gases, fossil CO₂ emissions have continued to increase in recent years mainly from rapidly developing economies and China is now the largest emitter of CO₂ generating about 30% of all emissions globally. To allow more reliable forecast of the future state of the carbon cycle and to support the efforts for mitigation greenhouse gas emissions, a better understanding of the global and regional carbon budget is needed. Space-based measurements of CO₂ can provide the necessary observations with dense coverage and sampling to provide improved constrains on of carbon fluxes and emissions. The Chinese Global Carbon Dioxide Monitoring Scientific Experimental Satellite (TanSat) was established by the National High Technology Research and Development Program of China with the main objective of monitoring atmospheric CO₂ and CO₂ fluxes at the regional and global scale. TanSat has been successfully launched in December 2016 and as part of the Dragon programme of ESA and the Ministry of Science and Technology (MOST), a team of researchers from Europe (UK and Finland) and China has evaluated early TanSat data and contrast it against data from the GOSAT mission and models. In this manuscript, we report on retrieval intercomparisons of TanSat data using two different retrieval algorithms, on validation efforts for the Eastern Asia region using GOSAT CO₂ data and first assessments of TanSat and GOSAT CO₂ data against model calculations using the GEOS-Chem model.

Shipborne observations obtained with the coherent Doppler lidar (CDL) and radiosonde during 2014 campaign were used to study the structure of marine boundary layer in the Yellow Sea. Vertical wind profiles corrected for ship motion was used to derive higher-order statistics, showing that motion correction is required and significant for turbulence analysis. During a day with weak mesoscale activity, a complexed three-layer structure system was observed. The lowest layer showed a typical stable boundary layer structure feature. An aerosol layer with abrupt variation in wind speed and relative humidity always appeared at the middle layer, the formation of which may be due to Kelvin-Helmholz instability. The top layer encountered a dramatic change in wind direction, which may result from the warm advection from the Eurasian continent on the basis of backward trajectory analysis. Furthermore, the MABL height in stable regime was derived from potential temperature, CDL signal-to-noise ratio (SNR) and CDL vertical velocity variance, respectively. The stable boundary layer (SBL) height in SBL can be derived from the inversion layer of potential temperature profile, and the mixing height in SBL can be retrieved from the vertical velocity variance gradient method. Neither the SBL height nor the mixing height is in agreement with the height retrieved from CDL SNR gradient method because of different definition and criterion. One of the limitations of SNR gradient method for MABL retrieval is that it is easier to be affected by the lofted decoupled aerosol layer, where the retrieved result is less suitable. Finally, the higher-order vertical velocity statistics within the marine stable boundary layer were investigated and compared with the previous studies, and different turbulence mechanisms have an important effect on the statistics deviation.

As part of the Dragon 4 project, the water extents of Wuchang and Shengjin lakes have been extracted from Sentinel-2 time series, using all exploitable images since the beginning of the acquisitions in 2015. The aim of the study is to assess the capability of the Sentinel-2 constellation and Landsat 8 over the Anhui region, especially the high temporal resolution. A total of 32 dates have been used and 10 Landsat 8 images (Libra) have been added to try to reduce the temporal gaps in the Sentinel-2 acquisitions caused by cloudy conditions. Extractions were done using a SERTIT-ICube automatized routine based on a supervised Maximum Likelihood Classification. These extractions allow to recreate the dynamic of the two lakes and show the drought and wet periods. During the 3 years interval, the surface peaks in July 2016 for both lakes. The lowest level appears at two different dates for each lake; in January 2018 for Wuchang, in February 2017 for Shengjin. Wuchang Lake surface area appears to be more variable than Shengjin Lake, with many local maximum and minimum between the end of 2017 and April 2018. In the case of Wuchang Lake, floating vegetation is a problem for automatic water surface area extraction. The lake is covered by vegetation during long periods of time and the water below can’t be detected by automatic radiometric means. Nevertheless, Sentinel-2 stays a pertinent and powerful tool for hydrological monitoring of lakes confirming the expectation from the remote sensing wetland community before launch. The presence of NIR and SWIR bands induces a strong discrimination between water and other classes, and the systematic acquisitions create dense time series, making analysis more consistent. It makes possible to sensor events occurring over short periods of time. Thanks to this a link can be done between endangered bird species, such as the Siberian Crane and the Lesser White-Fronted Goose and periodically flooded areas. These midterm results illustrated the pertinence and powerful of multi-source optical satellite data for environmental analysis.

Wetlands are among the most productive and essential ecosystems on earth, but they are also highly sensitive and vulnerable to climate change and human disturbance. One of the current scientific challenges is to integrate high-resolution remote sensing data of wetlands with wildlife movements, a task we achieve here for dynamic waterbird movements. We demonstrate that the White-naped cranes Antigone vipio wintering at Poyang Lake wetlands, southeast of China, mainly used the habitats created by the dramatic hydrological variations, i.e. seasonal water level fluctuation. Our data suggest that White-naped cranes tend to follow the water level recession process, keeping close to the boundary of water patches at most of the time. We also highlight the benefits of interdisciplinary approaches to gain a better understanding of wetland ecosystem complexity.

Satellite geodesy is capable of observing glacier height changes and most recent studies focus on the decadal scale due to limitations of data acquisition and precision. Glaciers at the Mt. Everest (Qomolangma), locating at the central Himalaya, have been studied from the 1970s to 2015. Here we obtained TerraSAR-X/TanDEM-X images observed in two epochs, a group around 2013 and another in 2017. Together with SRTM observed in 2000, we derived geodetic glacier mass balance between 2000 and 2013 and 2013 and 2017. We proposed two InSAR procedures for deriving the second period, which yields with basically identical results of geodetic glacier mass balance. The differencing between DEMs derived by TerraSAR-X/TanDEM-X shows better precision than that between TerraSAR-X/TanDEM-X formed DEM and SRTM, and it can capable of providing geodetic glacier mass balance at a sub-decadal scale. Glaciers at the Mt. Everest (Qomolangma) and its surroundings present obvious speeding up in mass loss rates before and after 2013 for both the Chinese and the Nepalese sides. The previous obtained spatial heterogeneous pattern for glacier downwasting between 2000 and 2013 generally kept the same after 2013. Glaciers with lacustrine terminus present the most rapid lost rates.

Observation and modeling of the coupled energy and water balance is the key to understand hydrospheric and cryospheric processes at high elevation. The paper summarizes the progress to address this aspect in relation with different earth system elements, from glaciers to wetlands. The energy budget of two glaciers, i.e. Xiao Dongkemadi and Parlung No.4, was studied by means of extended field measurements and a distributed model of the coupled energy and mass balance was developed and evaluated. The need for accurate characterization of surface albedo was further documented for the entire Qinghai Tibet Plateau by numerical experiments with Weather Research and Forecast (WRF) on the sensitivity of the atmospheric boundary layer to the parameterization of land surface processes. A new approach to the calibration of a coupled distributed watershed model of the energy and water balance was demonstrated by a case study on the Heihe River Basin in northwestern China. The assimilation of land surface temperature did lead to the retrieval of critical soil and vegetation properties as the soil permeability and the canopy resistance to the exchange of vapour and carbon dioxide. The retrievals of actual Evapo-Transpiration (ET) were generated by the ETMonitor system and evaluated against eddy covariance measurements at sites spread throughout Asia. As regards glacier response to climate variability, the combined findings based on satellite data and model experiments showed that the spatial variability of surface albedo and temperature is significant and controls both glacier mass balance and flow. Experiments with both atmospheric and hydrosphere-cryosphere models documented the need and advantages of using accurate retrievals of land surface albedo to capture lan-atmosphere interactions at high elevation.

This paper is devoted to the development and testing of the optimal procedures for retrieving biophysical crop variables by exploiting the spectral information of current multispectral optical satellite Sentinel-2 and Venus and in view of the advent of the new Sino-EU hyperspectral satellite (e.g., PRISMA, EnMAP, and GF-5). Two different methodologies devoted to the estimation of biophysical crop variables Leaf area index (LAI) and Leaf chlorophyll content (Cab) were evaluated: non-kernel-based and kernel-based Machine Learning Regression Algorithms (MLRA); Sentinel-2 and Venus data comparison for the analysis of the durum wheat-growing season. Results show that for Sentinel-2 data, Gaussian Process Regression (GPR) was the best performing algorithm for both LAI (R²=0.89 and RMSE=0.59) and Cab (R²=0.70 and RMSE=8.31). Whereas, for PRISMA simulated data the Kernel Ridge Regression (KRR) was the best performing algorithm among all the other MLRA (R²=0.91 and RMSE=0.51) for LAI and (R²=0.83 and RMSE=6.09) for Cab, respectively. Results of Sentinel-2 and Venus data for durum wheat-growing season were consistent with ground truth data and confirm also that SWIR bands, which are used as tie-points in the PROSAIL inversion, are extremely useful for an accurate retrieving of crop biophysical parameters.

The aim of this paper is to offer a statistically sound method to make a precise account of the speed of land degradation and regeneration processes. Most common analyses of land degradation focus instead on the extent of degraded areas, rather than on the intensity of degradation processes. The study was implemented for the Potential Extent of Desertification in China (PEDC), composed by arid, semi-arid, and dry sub-humid regions and refers to the period 2002 to 2012. The metrics were standard partial regression coefficients from stepwise regressions, fitted using Net Primary Productivity as the dependent variable, and year number and aridity as predictors. The results indicate that: ① the extension of degrading lands (292896km² or 9.12% of PEDC) overcomes the area that is recovering (194560km² or 6.06% of PEDC); and ② the intensity of degrading trends is lower than that of increasing trends in three land cover types (grassland, desert, and crops) and in two aridity levels (semi-arid and dry sub-humid). Such an outcome might pinpoint restoration policies by the Chinese government, and document a possible case of hysteresis.

The AMEOS (Assimilating Multi-source Earth Observation Satellite data for crop pests and diseases monitoring and forecasting) project aims to bring together cutting edge research to provide pest and disease monitoring and forecast information, integrating multi-source information (Earth Observation, meteorological, entomological and plant pathological, etc.) to support decision making in the sustainable management of insect pests and diseases in agriculture. The main objective of the project, that is, improving crop diseases and pests monitoring and forecasting, will be achieved by utilizing EO data, developing new algorithms, and combining new and existing data from multi-source EO sensors to produce high spatial and temporal land surface information. The project foresees the assessment of the possibility of using available satellite images datasets to assess the evolution of diseases on permanent (olive groves, vineyards), or row crops (wheat) in Italy and China. The paper describes the results of the research activity which focused on: ① improving the classification of the agricultural areas devoted to winter wheat and olive trees, starting from what has been made available from the Corine Land Cover initiative; ② developing an approach suitable to be automated for estimating trees by using Sentinel 2 images; ③ developing a new index, REDSI (consisting of Red, Re₁, and Re₃ bands), for detecting and monitoring yellow rust infection of winter wheat at the canopy and regional scale. The research activity covers the: Province of Lecce, that is the Italian area strongly affected, since 2015, by the Xylella fastidiosa disease which causes a rapid decline in olive plantations. Province of Anyang, Neihuang county, which was affected by the yellow rust disease in the spring 2017.

Agricultural monitoring is essential for adequate management of food production and distribution. Crop land and crop type classification, using remote sensing time series, form an important tool to capture the agricultural production information. The recently launched Sentinel-2 satellites provide unprecedented monitoring capacities in terms of spatial resolution, swath width, and revisit frequency. The Sentinel-2 for Agriculture (Sen2-Agri) system has been developed to fully exploit those capacities, by providing four relevant earth observation products for agricultural monitoring. Under the Dragon 4 Program, the crop mapping with various satellite images and a specific focus on the Yellow River irrigated agricultural area in the Ningxia Hui Autonomous Region in China was carried out with the Sentinel-2 for Agriculture system (Sent2Agri). 9 types of crops were classified and the crop type map in 2017 was produced based on 35 scenes Sentinel 2A/B images. The overall accuracy computed from the error confusion matrix is 88%, which includes the cropped and uncropped types. After the removal of the uncropped area, the overall accuracy for a cropped decrease to 73%. In order to further improve the crop classification accuracy, the training dataset should be further improved and tuned.

Approximately half of the world’s population is at the risk of at least one vector-borne parasitic disease. The survival of intermediate hosts of vector-borne parasitic diseases is governed by various environmental factors, and remote sensing can be used to characterize and monitor environmental factors related to intermediate host breeding and reproduction, and become a powerful means to monitor the vector-borne parasitic diseases. Schistosomiasis is a parasitic disease that menaces human health. Oncomelaniahupensis (snail) is the unique intermediate host of Schistosoma, so monitoring and controlling the number of snail is key to reduce the risk of schistosomiasis transmission. In this paper, Landsat 8 OLI and Sentinel 2 MSI data had been used to obtain the environmental factors (vegetation, soil, temperature, terrain et al.), which are related to the multiplying and transmission of intermediate host. Then this study used T-S (Takagi-Sugeno) Fuzzy RS model to establish a new suitable index membership function due to the different RS data, and a long time series monitoring of snail distribution in Dongting Lake from 2014 to 2018 was achieved. A comparative analysis was performed to validate the predicted results against the field survey data. The results demonstrated the accuracy of the developed model in predicting the distribution of snails.

In recent years, Polarization SAR (PolSAR) has been widely used in the filed of crop biomass estimation. However, high dimensional features extracted from PolSAR data will lead to information redundancy which will result in low accuracy and poor transfer ability of the estimation model. Aiming at this problem, we proposed a estimation method of crop biomass based on automatic feature selection method using genetic algorithm (GA). Firstly, the backscattering coefficient, the polarization parameters and texture features were extracted from PolSAR data. Then, these features were automatically pre-selected by GA to obtain the optimal feature subset. Finally, based on this subset, a support vector regression machine (SVR) model was applied to estimate crop biomass. The proposed method was validated using the GaoFen-3 (GF-3) QPSΙ (C-band, quad-polarization) SAR data. Based on wheat and rape biomass samples acquired from a synchronous field measurement campaign, the proposed method achieve relative high validation accuracy (over 80%) in both crop types. For further analyzing the improvement of proposed method, validation accuracies of biomass estimation models based on several different feature selection methods were compared. Compared with feature selection based on linear correlation, GA method has increased by 5.77% in wheat biomass estimation and 11.84% in rape biomass estimation. Compared with the method of recursive feature elimination (RFE) selection, the proposed method has improved crops biomass estimation accuracy by 3.90% and 5.21%, respectively.