Topography impacts the fraction of radiation that reaches a target surface from the sun, sky, and surrounding terrains, which leads to distortions in the radiance levels that are observed by sensors. Although several mountain radiative transfer (RT) models have been developed, the surrounding topographic effects are rarely considered, which can cause non-negligible uncertainty in forward radiance modeling. In this study, we first extended the canopy reflectance model to be suitable for both continuous and discontinuous canopies over sloping terrain (BOST) to rugged terrain, and used it to investigate the impact of the surrounding topography on the reflected radiance and evaluate the contributions of diffuse irradiance from the sky and adjacent terrains. Discrete anisotropic RT (DART) simulations and remote sensing (RS) observations in a real mountain environment were used to evaluate the physical mechanism and model performance. The results suggested that the extended BOST model can capture terrain-induced variations in direct and diffuse radiation, which can be successfully used to simulate the reflected radiance in a real mountainous region. The model shows significant improvement compared with that before extension (i.e., root-mean-square error (RMSE) decreases ($R^{2}$increases) of 1.252 (0.025) and 2.035 (0.054) in the red and near-infrared (NIR) bands, respectively). In addition, we discovered that the surrounding topographic effects on the diffuse sky and terrain irradiance are significantly influenced by the wavelength, solar direction, and visible area of the sky and terrains. The extended BOST model serves as an effective tool for improving simulations of the observed radiance and facilitates the development of RT models for rugged terrains.

Multiple satellite data can provide rich information for time series leaf area index (LAI) inversion. Long Short-Term Memory (LSTM) has been widely applied to capture long time dependencies from sequential data and solve the problem of vanishing (or exploding) gradients. It's feasible to retrieve time series LAI based on the LSTM algorithm. In this study, an LSTM model was built based on MODIS reflectance and a fused LAI from three satellite LAI products, including GLASS, MODIS and GEOV2 LAI. Then, time series LAI was obtained from the LSTM model coupled with reflectance data. Finally, the retrieval results were validated against fine resolution LAI reference maps and compared with the three LAI products. The accuracy of the proposed LSTMfusion LAI was best (R2=0.85, RMSE=0.69) among all the LAI estimations. In addition, the comparison among the temporal profiles of LSTMfusion LAI and the three global LAI products illustrated that the proposed model was able to efficiently generate time series LAI, which was more continuous and smoother than MODIS and VIIRS LAI.

Time series leaf area index (LAI) is essential to studying vegetation dynamics and climate changes. The LAI at current status can be regarded as the accumulative consequence of the counterpart at prior times. Although the deep learning algorithm - Long short-term memory (LSTM) can capture long-time dependencies from sequential satellite data for time series LAI estimation, it only uses the information at prior statuses, and neglects the backward propagation of current vegetation change information. Thus, the LSTM-based LAI quality might be limited. In this letter, the bidirectional LSTM (Bi-LSTM) approach was proposed to integrate the information of multiple satellite products from both the past and future for temporal LAI retrieval. The fused values from GLASS, MODIS, and VIIRS LAI products, as well as MODIS reflectance in 2014-2015, serve as the output response and input for the Bi-LSTM training. Then, we compared the Bi-LSTM predictions with the counterparts from the LSTM, the fused LAI and three products using independent validation datasets in 2016. Results illustrated that our proposed Bi-LSTM method achieved better performance with higher accuracy (R2=0.84, RMSE=0.76) when compared to the LSTM estimation (R2=0.83, RMSE=0.82) and LAI products (R2<0.68, RMSE>1). Furthermore, our proposed method provided smoother and more continuous temporal profiles of LAI than other retrieval approaches.

Canopy reflectance models over sloping terrain are critical and reliable tools for vegetation biophysical parameter retrieval. However, the applicability of existing models is strictly limited by individual canopy structure, such as continuous or discontinuous canopies, and hybrid vegetation structure is seldom considered in current models. Therefore, this defect greatly limits the application of canopy reflectance models in rugged terrains. To overcome this problem, we developed a canopy reflectance model suitable for both continuous and discontinuous canopies over sloping terrains (BOST). The scattering contributions of different canopies can be estimated by incorporating clumping indices; thus, we designed an algorithm to convert the clumping indices from flat terrain to sloping terrains, which explicitly considers the terrain effect on radiative transfer within canopies. In model validations, multiple schemes were used to obtain the objective evaluation results, including analyzing the terrain induced clumping index variations, comparing with the computer simulation model, typical canopy reflectance models, and the image reflectance in a real scene. The results suggested that the BOST model can capture the pattern of terrain-induced scattering components and reflectance distortions, and be successfully used in simulating the reflectance in a real mountainous region [RMSE (R2) values of 0.0016 (0.889), 0.0183 (0.932), and 0.0023 (0.903) in the red, NIR, and green bands, respectively]. BOST performed better in terms of accuracy and efficiency than the typical models. Further testing of BOST in topographic normalization and biophysical parameter retrieval showed its practical usage potential. These validations confirmed the good performance of BOST in both continuous and discontinuous vegetation scenes and indicated it can provide a promising tool to retrieve biophysical parameters in mountainous areas.

The nighttime light images acquired by the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) and the Suomi National Polar-orbiting Partnership satellite's Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) can reflect the comprehensive information of human activities. However, there are radiometric inconsistent problems between the existing DMSP/OLS nighttime lighting data such as lack of on-board radiation corrections, pixel saturation, and timescale discontinuities. The radiometric inconsistency between DMSP/OLS and VIIRS also limit the long-term night time light data-based studies. This paper proposes a new method for the inter-calibration of nighttime light data between DMSP/OLS and NPP/VIIRS in the economic corridors of Belt and Road Initiative. An automatic invariant pixel selection method was developed for the fitting of the inter-calibration models. Results revealed that combined with the NPP/VIIRS nighttime light data, the calibrated DMSP/OLS images at NPP/VIIRS radiometric level can well reflect the long-term socio-economic development trends along the economic corridors.

The China-Pakistan Economic Corridor (CPEC) is the flagship project of the Belt and Road initiative. However, many interconnection projects of CPEC pass through mountainous area, cold area and arid area, where has diverse climate, frequent extreme weather, vulnerable ecosystem and natural disasters. The vulnerable eco-environment in this region not only restrict the development of local economy but also the smooth implementation of corridor construction. To get a deep understanding of the eco-environment pattern, quality, ecological function, vulnerability and the potential risks in the corridor, this study developed the monitoring and integrated assessing technologies for the Ecoenvironment of CPEC. Results demonstrated that the ecosystem types of CPEC was mainly cropland, grassland and sparse vegetation. The corridor has undergoing a rapid development since 1990s. However, 78.89% of the land in the study area has different degrees of ecological vulnerability, with mainly the slight vulnerability area.

To solve the limitation of coarse spatial resolution of passive microwave surface soil moisture (SSM) product, many spatial downscaling methods has been proposed under the theoretic basis of the land surface temperature (LST)/vegetation index triangle space. However, in most studies, the topographic influences on the downscaling results is rarely analyzed but the impacts should be significant due to the strong effect of topographic changes on LST. To effectively solve this issue, a downscaling approach was developed in this study to disaggregate the Soil Moisture Active and Passive (SMAP) SSM product with the use of the Optical/Thermal infrared (TIR) observations from the Moderate-Resolution Imaging Spectro-radiometer (MODIS) onboard the Terra satellite for a typical mountain region located in the west of U.S. Two steps are included in the approach: (1) normalizing the terrain effect on LST and (2) downscaling passive SSM product based on a machine learning approach. The SNOTEL soil moisture observation network located in the study area was used to validate the downscaled SSM.

Fine spatial details of vegetation growth are usually lost in leaf area index (LAI) products obtained from coarse spatial resolution satellite sensors. This may bring uncertainties in ecosystem process models, which usually require LAI products with fine spatiotemporal resolutions. Successful downscaling of LAI dynamics to fine spatial resolution is very important for meeting the demands of these models. Hence, a multiscale multisensor approach using the ensemble Kalman smoother (EnKS) technique is proposed in this paper. The LAI dynamics at a coarser spatial resolution are incorporated as prior information into the remotely sensed observations for time series LAI estimation at a finer spatial resolution. Downscaled LAI dynamics are evaluated based on spatial distribution and temporal trajectory. The results indicate the assimilated LAI to be in good agreement with the reference values at the different spatial scales. For example, the coefficient of determination (R2) between the reference values and fine-resolution LAI results retrieved by the proposed approach is 0.71 with a root-mean-square-error (RMSE) value of 0.65 on Julian day 185 at the Agro site. The method has proved to be effective for downscaling LAI dynamics, which improves the spatiotemporal patterns of fine-resolution LAI retrievals with respect to earlier methods.

The implementation of the Belt and Road (B&R) initiative is of great significance to promoting economic development and regional cooperation in China and other countries along the corridors. Remote sensing technology can provide strong supports for ecological environment monitoring in the process of Belt and Road construction. However, due to frequent cloud contamination, the time series remote sensing images are not continuous in both spatial and temporal domain. This study proposed a new self-adaptive compositing approach (SACA) to produce the clear-sky VIIRS surface reflectance composites for the B&R regions. An evaluation was made for the SACA method by comparing it to the maximum NDVI (MaxNDVI), minimum Red (MinRed) and maximum ratio (MaxRatio) compositing schemes. Results showed that the SACA approach outperformed all other three methods. The results highlighted that the SACA method was feasible and effective at compositing continental VIIRS data, which has great potential for the B&R eco-environment studies.

The gross primary productivity (GPP) is an essential parameter of terrestrial carbon cycle, and simulation of GPP through terrestrial ecosystem process model usually needs a specification of model parameter. However, estimating model parameters in situ field or laboratory is a laborious and tedious work, causing a general lack of data. In this study, a reliable variational data assimilation scheme integrating Boreal Ecosystem Productivity Simulator (BEPS) with eddy covariance fluxes, was proposed to account for the seasonal variations of model parameters and improve temporally continuous GPP estimation. Results suggested that the proposed variational assimilation scheme in our study could effectively track the seasonal variations of model parameters. With optimal temporally continuous values of parameters, BEPS model had a better performance and potential ability for the GPP estimation.

We proposed a framework to generate high spatio-temporal resolution leaf area index (LAI) and uncertainty maps based on the integration of LAINet observation system, Consistent Adjustment of the Climatology to Actual Observations (CACAO) method and Gaussian process regression (GPR). LAINet, which is a wireless sensor network based automatic LAI observation instrument, was used to provide temporally continuous field measurements; CACAO, a data blending method, was used to blend the high and low spatial resolution remote sensing observations to obtain high spatio-temporal resolution remote sensing observations synchronous with the field measurements. GPR, a machine learning regression algorithm, was used to upscale the spatially discrete field measurements to spatially explicit LAI maps, and get the concomitant uncertainty maps. The performance of the proposed method was evaluated over a crop site, where seven LAI maps and their accompanying uncertainty maps all with 30 m and 8 days resolutions were generated. Results show that the framework can provide accurate LAI retrievals. In addition, the concomitant uncertainty maps provide insight into the reliability of the LAI retrievals. This paper contributes to precision agriculture and validation activities for coarse resolution LAI products.

In mountainous areas, land surface temperature (LST) shows significant terrain effect, which can be directly reflected by the spatial distribution associated with the change of topographic factors (elevation, slope, and aspect). By the way, the terrain effect diminishes the impacts from the differences in surface water and heat fluxes, and influences their comparison or estimation over complex terrain. In this study, a practical way to reduce the terrain effect is proposed based on the random forest method with datasets from MODIS products, which is used to build a LST prediction model instead of the previous model developed based on some numerical model or empirical method. The results indicates that the constructed LST model shows a good performance in predicting LST with the R2 of 0.93 and the RMSE lower than 2.0 K for four selected days. Corrected LST maps are compared with the original LST map, which presents a preliminary correction results with an obvious correction on pixels with significant terrain effect.

As the flagship of the “Belt and Road”, the development and construction of the “China-Pakistan Economic Corridor (CPEC)” are related to the common interests of the country along the line. Gwadar port, which lies at the end of the China-Pakistan Economic Corridor, is extremely important for the CPEC project. The impervious surface percentage (ISP) is an important indicator of urban development assessment and urban ecological environment evaluation. In this study, the ISP of the Gwadar city is estimated by using the Chinese high spatial resolution GF-1/2 images and fine spatial resolution HJ-1A/B images from 2009 to 2017. The growth magnitude of the ISP in Gwadar is then analyzed. Results reveal that the estimated ISP are highly reliable for detecting and characterizing change trends. The R2(Correlation coefficient-squared) of imitating and predicting by using Random Forest method are 0.96 and 0.76 respectively. The root mean square error (RMSE) of the estimating results are approximately 12.52%. The results indicate that there are two stages in Gwadar's impervious surface growth. First stage is that there is a period of rapid growth from 2009 to 2014. The construction of residences and roads cause the fast increase at a rate of 2.3% per year. The other stage begins with the development of the China-Pakistan Economic Corridor. The greatest growth is in the port area from 2014 to 2017, and the growth magnitude is 0.06% per year.

Together with the continuous development of passive microwave surface soil moisture (SSM) products from newly launched satellites, it is necessary to perform reliable validations to assess their accuracy. With this aim, a new “bottom-up” validation approach is proposed based on the synergistic use of in situ SSM measurements from the soil moisture measurement station network (REMEDHUS) of the University of Salamanca, Salamanca, Spain, and optical/thermal infrared observations over 18 cloud-free days from Landsat-8. An SSM estimation method using the boundary information from the land surface temperature and normalized difference vegetation index triangle space was developed for regional SSM mapping. The retrieved SSM reached a relatively good performance (mean R2and rootmean-squared error of 0.64 and 0.033 m3/m3, respectively). Then, the regional SSM was aggregated into the grid-cell scale of the Advanced Microwave Scanning Radiometer 2 (AMSR2) L3 high-resolution (0.1° /10 km) soil moisture product to validate it both at network and grid-cell levels. At the network level, the derived regional SSM showed a good agreement with the averaged in situ measurements over the network (R = 0.731). However, at the grid-cell level, small variations were observed for the cells over the network between the estimates and the AMSR2 product, with negative biases (-0.044 to -0.090 m3/m3) and positive correlations (R > 0.25) for most cells. In addition, it is shown that the descending product has a slightly better performance than does the ascending one. The preliminary assessments suggested that the proposed method provides new insights into the validation of passive microwave soil moisture products, while avoiding the common issue related to the big disparity in spatial scales between satellite observation and in situ measurements.

GF-4 is the first Chinese high resolution geostationary orbit satellite. It has great application potential in many earth-related studies. Given the low geometric accuracy of GF-4 images in mountain area, in this paper, a new operational and practical automatic orthorectification approach was proposed to improve the orthorectification accuracy of GF-4 images. The new approach adopted a two-level area-based algorithm to automatically search tie points between GF-4 and the base Landsat images. Then the images was further orthorectifyed using the improved rational polynomial coefficients model optimized by tie points. Results demonstrated that the new orthorectification approach could significantly improve the orthorectification accuracy. It is also suitable for orthorectification of GF-4 images with different clouds coverage.

Ecosystems is survived in the suitable environment which provide appropriately abiotic resources for organism and IUCN have applied abiotic degradation as Criterion C to assess the risk of ecosystems. However, the origin and collapse status of the criterion is vague for assessors, and the results are inconsistent as the response of ecosystems to environment are different. Therefore, the ecological amplitude of ecosystem to environment stress was introduced in the ecosystems risk assessment with the relationship between criterion and ecological amplitude. To put this concept into practice, remote sensing was applied to acquire the optimum and tolerance of each ecosystem. The natural forest in xishuangbanna was assessed by this proposed method to identify the stress of temperature. The result show that the status of natural forest in the past is least concern (LC), and vulnerable (VU) in the feature. With temperature in the future significantly increasing, natural forest may be suffered with heat stress. The proposed method describe the risk derived from degradation of environment in mechanism greatly improved the consistency and feasibility of Criterion C in the ecosystems risk assessment.

We developed a 1-D model (the PLC-P model) for modeling canopy reflectance over sloping terrain. The effects of sloping terrain on single-order and diffuse scattering are accounted for by path length correction (PLC) and the P theory, respectively. Currently, we have developed the prototype of PLC-P model, in which only the sloping effects on the single-order are accounted for through path length correction. This first version of PLC-P model is called PLC model. The PLC model was validated via Monte Carlo simulations. The comparison with the Monte Carlo simulation revealed that the PLC model can capture the pattern of slope-induced reflectance distortion with high accuracy. The PLC-P model can provide a promising tool to improve the simulation of canopy reflectance and the retrieval of biophysical variables over mountainous regions.

Aiming to solve the limitation of coarse spatial resolution of passive microwave soil moisture product, a soil moisture relationship model based on random forest method was constructed with land surface temperature (LST), normalized difference vegetation index (NDVI), and surface albedo (ALB) from MODIS products and surface soil moisture (SSM) from AMSR-E soil moisture product in the study area at the east edge of the Tibetan Plateau. The results show better performance of the proposed compared with the commonly used purely-empirical method, with the R2values above 0.88 and the RMSE values lower than 0.05m3/m3, respectively. It suggested that the proposed soil moisture relationship is able to capture the spatio-temporal variation of surface moisture well. There should be great potential to improve the downscaling soil moisture accuracy when the model is used in the passive microwave soil moisture downscaling scheme.

In mountainous areas, surface solar radiation (SSR) exhibits high spatiotemporal variation at different slopes and aspects due to its great topographic relief. To get mountain SSR spatial distribution, remote sensing-based methods have been popularly used, which separate SSR into direct solar radiation, diffuse sky radiation, and adjacent terrain radiation. However, the methods are highly depended on the atmospheric and angular information derived from different data sources. To clearly address the uncertainties associated with the estimation, this letter conducted a preliminary comparison study by using different atmospheric transmittance models and digital elevation model (DEM) data to retrieve SSR in the Mt. Gongga region. The comparison results indicated that the uncertainty of the atmospheric constituent data greatly limited the performance of the physical atmospheric transmittance models. The resolution of DEM data also played an important role in SSR determination because of the determination of surface angular information. High-resolution (30-m) DEM data showed better performance than low one (90 m). In addition, the systematic underestimation of SSR estimation with the empirical model was significantly improved by using the averaging method with nearby pixel values. It indicated that the geometric errors of satellite image and DEM data should be considered in the estimation.

Sloping terrain induces distortion of canopy reflectance (CR), and the retrieval of biophysical variables from remote sensing data needs to account for topographic effects. We developed a 1-D model (the path length correction (PLC)based model) for simulating CR over sloping terrain. The effects of sloping terrain on single-order and diffuse scatterings are accounted for by PLC and modification of the fraction of incoming diffuse irradiance, respectively. The PLC model was validated via both Monte Carlo and remote sensing image simulations. The comparison with the Monte Carlo simulation revealed that the PLC model can capture the pattern of slopeinduced reflectance distortion with high accuracy (red band: R2= 0.88; root-mean-square error (RMSE) = 0.0045; relative RMSE (RRMSE) = 15%; near infrared response (NIR) band: R2= 0.79; RMSE = 0.041; RRMSE = 16%). The comparison of the PLC-simulated results with remote sensing observations acquired by the Landsat8-OLI sensor revealed an accuracy similar to that with the Monte Carlo simulation (red band: R2= 0.83; RMSE = 0.0053; RRMSE = 13%; NIR band: R2= 0.77; RMSE = 0.023; RRMSE = 8%). To further validate the PLC model, we used it to implement topographic normalization; the results showed a large reduction in topographic effects after normalization, which implied that the PLC model captures reflectance variations caused by terrain. The PLC model provides a promising tool to improve the simulation of CR and the retrieval of biophysical variables over mountainous regions.

Surface soil moisture (SSM) is an important parameter at the land-atmosphere interface. In past decades, passive microwave remote sensing offers a good opportunity for obtaining SSM on a global scale, and many downscaling methods have been proposed using the triangle-based empirical soil moisture relationship models to overcome the limitation of coarse spatial resolution of its SSM products for regional applications. This paper aimed to examine and compare the effectiveness of five typical triangle-based empirical soil moisture relationship models for estimating SSM with Landsat-5 data and in situ measurements from the Maqu network on the northeastern part of the Tibetan Plateau for nine cloud-free days. The results showed that the model that treats the SSM as a second-order polynomial with land surface temperature, vegetation indices (VIs), and surface albedo as inputs exhibited the best performance compared with the results of other models. The VI comparison indicated that the use of the normalized difference VI or the fractional vegetation cover in this model outperformed other VIs, with the root-mean-square deviation of approximately 0.055 m3/m3and the coefficient of determination ($\text{R}^{2}$ ) above 0.78 at the nine-day average level. In addition, a significant spatial scale effect of the model was also found through analyzing the model fitting results at different window sizes. The study provides important insight into the best empirical relationship models for capturing soil moisture dynamics. These models can support the passive microwave soil moisture data spatial downscaling and validation applications in future studies.

Surface soil moisture (SSM) is a key state variable in controlling land surface energy balance and hydrological process. Based on the mechanism behind land surface temperature (LST)-vegetation index (VI) triangle space, an empirical relationship model has been proposed for SSM estimation with LST, NDVI, and surface albedo, and it has been applied in downscaling the coarse resolution microwave soil moisture product. In this paper, three soil moisture observation networks (REMEDHUS, MAQU, and MURRUMBIDGEE) were selected to evaluate the performance of this model at different climate and land cover conditions with in situ soil moisture measurements and Landsat satellite observations. According to the estimation results from different days for each network, it was found that the model was able to capture SSM variation with a satisfied accuracy [overall root-mean-squared error (RMSE) ranging from 0.025 to 0.055 m3/m3], and the R2can reach 0.9 on some individual days. However, the performance has high daily variability with some poor ones. The reason is partly attributed to the high sensitivity of the coefficients of the model to the variation degrees of the input LST, normalized difference vegetation index (NDVI), and SSM. Meanwhile, the spatial scale differences between the point measurement and satellite footprint observation are another important issue. To improve the model performance, a new relationship model was proposed by introducing the modified normalized difference water index, and the estimation results had a pronounced improvement (overall RMSE ranging from 0.021 to 0.049 m3/m3) compared with the previous model. The application effect of the proposed model showed that the model coefficient calibration accuracy greatly determined the uncertainty level of the estimation results.

Fractional vegetation cover (FVC) is one of the most critical indicators for herbaceous wetland vegetation status, degradation and desertification process simulations. The dense in time series and high spatial resolution of FVC is required for wetland ecosystem monitoring because of its heterogeneous landscapes and rapid spatio-temporal variations. However, due to the tradeoff in satellite sensor designs, it is hard to acquire both high temporal and spatial satellite images for FVC estimation for wetland ecosystem. In this paper, the dense in time series HJ composites at 30-m spatial resolution and UAV platform were used for the estimation of time series FVC in Zoige wetland area. Considering the spatial variability of soil backgrounds for peat wetland area, an improved adaptive endmember selection linear spectral mixture (LSMM) model was proposed in this paper. The results revealed that the proposed method can provide a higher estimation accuracy than the background invariant LSMM model, and the time series FVC estimation result is helpful to reflect both the spatial pattern and temporal variation characteristics of heterogeneous wetland regions.

Mapping and modeling the complex ecosystems and their changes over time are key issues in spatial ecology, biogeography, ecosystem ecology and biodiversity researches. This paper attempts to propose a simple, practical and automatic method to produce the ecosystem map in mountainous areas by fusing multi-source data and the related knowledge. The multi-source data included the 30m-resolution land cover map and the vegetation map of China (1:1 000 000). Three fusion strategies were contained in the proposed approach: hard matching, buffer matching and merged categories matching. Meanwhile, the related spatial distribution knowledge and the law of spatial distance decay were used to determine the optimal vegetation type, when more than two vegetation types are matched simultaneously. Taking the Southwestern China as study area, a new 30m-resolution ecosystem map with 144 ecosystem types was generated by the proposed method, which was used to establish the red list of ecosystems and evaluate the condition of biodiversity in the White Paper: China's Biodiversity.

Land cover change is one of the most important and easily detectable indicator for various changes happened on the Earth's surface, and is also closely related to global climate, biodiversity, food and fiber demand, and other critical environmental and ecosystem services. Supported by the Carbon Special Program and the Ecological Decade Program, the spatial patterns of land cover changes and its driving forces were investigated in Southwestern China from 1990 to 2010 in this paper. The residential land expansion, croplands lost, plateau lake extension and shrinkage, forests recovery and increasing rubber and orchard plantations are the general characteristics of land cover changes in Southwestern China. National macro-policies (such as Western Development Strategy and Ecological Protection Projects), urbanization, rural labors transfer, hydropower development, climate change and natural disasters are the main driving forces for land cover changes in this region over the last two decades.

The world's ecosystems and biodiversity have suffered a great loss as the result of human activities, which considerably attract urgent for the assessment of the biodiversity at ecosystem-level. Thus, the IUCN Council recently adopted a new Red List criteria as a global standard for ecosystem risk assessment, aims to establish a global Red List of ecosystem. Remote sensing is one of the most powerful technology for earth observation at macro-scale, which have great potential to impetus the establishment of a global Red List of ecosystem. Here we operated a systematic assessment of ecosystems with this protocol in Southwestern China, to present the procedure of assessment for biodiversity at ecosystem level based on remote sensing data and test the applicability of IUCN Red List criteria in high heterogeneous region. The result show that the red lists of this area contain all the nationally protected types of Atlas of China's vegetation, which means the high robustness of the protocol. The prospects indicate that remote sensing may play a more important role in achieving the goal of global Red List of ecosystems.

How to accurately detect cloud and snow in the remote sensing imagery is an open problem for the remote sensing application. For only visible and near infrared band in HJ-1A/B CCD images, the cloud detection algorithm using the shortwave infrared and thermal infrared band is restricted by the band-lacking problem. Based on the multi-temporal information of the HJ-1A/B CCD images, a new algorithm is proposed in this paper. Using available images in one month, a cloud-free reference image was firstly composed. Then, the cloud and snow pixel are separated through the difference of the blue band between the reference and each date. Subsequently, the regional covariance matrix is computed further to eliminate the non-cloud pixels. The test result shows that, the overall accuracy is about 85.96% to 93%. It indicates that the proposed method can integrate the temporal and texture information to improve detection accuracy for the cloud and snow.

The leaf area index is one of key parameters for ecosystem monitoring, global carbon circulation and climate change. At present leaf area index is routinely available from Earth Observation (EO) instruments such as MODIS. However MODIS-derived estimates of LAI require validation before they are utilised by the ecosystem modelling community. The paper presents a validation of the MODIS LAI collection 5 product over forested terrain in Xishuangbanna, southwest China, based on field measurements which are upscaled using high resolution HJ image. Results suggest that the MODIS LAI product has an accuracy with R2of 0.35 and RMSE of 0.61 m2/m2, and overestimates values in the high LAI broadleaf forest (where LAI>3.5 m2/m2). Further validation efforts must be carried out over the study area for an assessment of the MODIS LAI in order to improve its accuracy.

Land cover products are the important background for scientific researches. There are several land cover data sets at regional or global scales. However, in the Southwestern China where are the typical mountainous regions, it is usually more difficult to map land cover products because of the high proportion of complex terrain area, conspicuous landscape heterogeneity and difficult image acquisition and preprocessing. Supported by Land Cover Monitoring Project (CLCP) funded by Chinese Academy of Sciences, land cover product of Southwestern China in 2010 was mapped through an object-oriented method combined with the decision tree rules, and the land cover products in 2000 and 2005 were obtained by the change detection. The validation shows that the overall accuracies of the primary and secondary classes reach 95.09% and 90.34% respectively. Taking Sichuan province and Tibet autonomous region as case, the CLCP product analysis indicates that a total of 14,580 km2and 8,174 km2area, accounting for 3.04% and 0.68% of the region respectively, had changed in the last 20 years. The main driving forces including climate change, human activities and natural disasters are also discussed.

Based on the HANTS and dynamic threshold method, the spatio-temporal changes of the alpine grassland phenology in the Zoigê plateau was analyzed by using MODIS EVI data from 2001 to 2013. The results were found as follows: (1) The spatial distribution of the average vegetation phenology from 2001 to 2013 is closely related to the water and heat conditions. Accompanying the deterioration in heat and water conditions from low altitude to high altitude and south to north, SOG(start of growing season) was delayed gradually, EOG(end of growing season) advanced slowly, and LOG(long of growing season) shortened gradually. Water played an important role in the regional differentiation of phenology (2) From 2001 to 2013, SOG came earlier by 0.6d/a, EOG was late by 0.2d/a, and LOG lengthened by 0.8d/a. The inter-annual phenology changes of the vegetation exhibited significant differences at different elevations and water condition. (3) Heat and moisture is the main ecological factor influencing the growth of plant. Temperature responses of phenology significantly became stronger with increasing cumulative preseason precipitation.

Evapotranspiration (ET) is an important part of land surface water cycle and plays a key role in water resource management. Because of economy development and climate change, water resource shortage has become a looming crisis threatening the countries' security and social stability. Therefore, understanding the spatial and temporal pattern of ET change in South Asia has a significant impact. In this study, time series daily ET was estimated for South Asia in 2008 by using MODIS data combined with station observations and GLDAS data with SEBS energy balance model. Monthly ET estimation was calculated based on daily ET. Spatial and temporal analysis was conducted to analyze the daily ET temporal change for different land cover types located at different part of South. The results showed that the temporal change of rainfall has large impart on ET temporal variation. Quantification of ET in South Asia also suggested that the water availability is the major limitation to the evaporation and transpiration processes.

The leaf area index is one of key parameters for ecosystem monitoring, global carbon circulation and climate change. The remote sensing data from different satellites have become the primary data source for estimating leaf area index from regional to global scale. In this study, we assess the potential use of Landsat TM, HJ-1 CCD, and SPOT HRVIR sensors for leaf area index estimation in a mountainous area. Results suggest that three sensors behave similarly for LAI inversion over complicated terrain. The maximum correlation coefficients are in the order of broadleaf forest (0.82) > shrub/grass (0.78) > all plots (0.61) > needleleaf forest (0.53), which are derived from the field LAI-SPOT SWVI, TM SR, SPOT SWVI, and SPOT RSR relationships for all plots, needleleaf forest, broadleaf forest, and shrub/grass, respectively.

Recent years, the human activity and climate change greatly threaten the water resource security in South Asia. As an important parameter in land surface, evapotranspiration (ET) is essential to understand water cycle, estimate surface runoff and groundwater, and manage water resource. Satellite based ET derivation has been applied widely in many studies and become a popular way to estimate ET. In this study, time series ET in South Asia was derived with SEBS model with MODIS/Terra satellite data and field site observation data for the period March 2008-June 2008. Due to the lack of field flux observation, the derived ET was cross-validated by MOD16 ET product. The analysis results suggested that the proposed method was able to capture the reasonable spatial and temporal variation of ET more effectively than MOD16 product.