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In this paper, we present a comprehensive experiment, namely, an Integrated Microwave Radiometry Campaign for snow (IMCS), in Xinjiang, China, during the snow season of 2015–2016. The campaign hosted a dual-polarized microwave radiometer operating at L, K, and Ka bands to provide minutely passive microwave observations of snow cover at a fixed site, along with daily manual snow pit observations of snow physical parameters, automatic observations of 10 min four-component radiation and layered snow temperatures, and meteorological observations of hourly weather data and soil data. To the best of our knowledge, our dataset is unique in providing continuous daily snow pit data and coincident microwave brightness temperatures, radiation and meteorological data, at a fixed site over a full season, which can be straightforwardly used for evaluation and development of microwave radiative transfer models and snow process models, along with land surface process and hydrology models. The consolidated data are available at (https://doi.org/10.11888/Snow.tpdc.270886) (Dai, 2020).

Nitrogen dioxide (NO2) as one of the key pollutants in the atmosphere, is an important trace gas of traffic emissions or industrial activities. The coronavirus disease (COVID-19) epidemic preventions have significantly reduced the emissions of NO2, which provided an opportunity to disentangling the contributions to global emissions in major epidemic regions. Here, we quantified the influences of lockdown measures on the NO2 concentration at a global scale during the two waves of the COVID-19 epidemic, and disentangled the relative contributions of the major epidemic regions (i.e., China, Europe, and USA) on the global NO2 densities due to the lockdown measures. Our results revealed that there was an evident decrease of global NO2 in 2020, especially in China, Europe, and USA. The global mean NO2 densities decreased approximately 2.1 and 3.9 μmol/m2 in the first and second waves of the epidemic. While in the Northern Hemisphere, NO2 densities decreased 6.6 μmol/m2 on average during the first stage of the epidemic, and a slight increase (i.e., 1.7 μmol/m2) in the second wave of the epidemic. The magnitudes and durations of the second wave were much smaller than the first wave of the coronavirus infections. The strict lockdown measures implemented in China significantly decreased the NO2 concentration, which therefore the largest contributor in the first wave decreases of global average NO2 densities. Intervention at an early stage would be beneficial to the preventions of epidemic situation. Our study could provide references for economic loss assessments and economic recovery strategies during the stages ofpost-pandemic.

Snow depth is a key parameter in climatic and hydrological systems. Passive microwave remote sensing, snow process model and data assimilation are the main methods to estimate snow depth in large scale. The estimation accuracies strongly depend on input of snow parameters or characteristics. Because the evolving processes of snow parameters vary spatiotemporally, and are difficult to accurately simulate or observe, large uncertainties and inconsistence exist among existing snow depth products. Therefore, a comprehensive experiment is needed to understand the involution processes of snow characteristics and their influence on microwave radiation of snowpack, evaluating and improving the snow depth and SWE retrieval and simulation methods. An Integrated Microwave Radiometry Campaign for snow (IMCS) was conducted at the Altay National Reference Meteorological station (ANRMS) in Xinjiang, China, during snow season of 2015/2016. The campaign hosted a dual polarized microwave radiometer operating at L, K and Ka bands to provide minutely passive microwave observations of snow cover at a fixed site, daily manual snow pit measurements, ten-minute automatic 4-component radiation and layering snow temperatures, covering a whole snow season of 2015/2016. The measurements of meteorological and underlying soil parameters were requested from the ANRMS. This study provides a summary of the obtained data, detailing measurement protocols for microwave radiometry, in situ sow pit and station observation data. A brief analysis of the microwave signatures against snow parameters is presented. A consolidated dataset of observations, comprising the ground passive microwave brightness temperatures, in situ snow characteristics, 4-component radiation and weather parameters, was achieved in the National Tibetan Plateau Data Center, China. The dataset is unique in providing continuously daily snow pits data over a snow season at a fixed site and matched microwave brightness temperatures, radiation and meteorological data. The dataset is expected to serve for the evaluation and development of the microwave and optical radiative transfer models and snow evolution process models. The consolidated data are available at https://data.tpdc.ac.cn/en/ (doi: 10.11888/Snow.tpdc.270886) (Dai, 2020).

The MODIS land surface temperature (LST) product is one of the most widely used data sources to study the climate and energy-water cycle at a global scale. However, the large number of invalid values caused by cloud cover limits the wide application of the MODIS LST. In this study, a two-step improved similar pixels (TISP) method was proposed for cloudy sky LST reconstruction. The TISP method was validated using a temperature-based method over various land cover types. The ground measurements were collected at fifteen stations from 2013 to 2018 during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) field campaign in China. The estimated theoretical clear-sky temperature indicates that clouds cool the land surface during the daytime and warm it at nighttime. For bare land, the surface temperature shows a clear seasonal trend and very similar across stations, with a cooling amplitude of 4.14 K in the daytime and a warming amplitude of 3.99 K at nighttime, as a yearly average. The validation result showed that the reconstructed LST is highly consistent with in situ measurements and comparable with MODIS LST validation accuracy, with a mean bias of 0.15 K at night (−0.43 K in the day), mean RMSE of 2.91 K at night (4.41 K in the day), and mean R2 of 0.93 at night (0.90 in the day). The developed method maximizes the potential of obtaining quality MODIS LST retrievals, ancillary data, and in situ observations, and the results show high accuracy for most land cover types.

Land surface temperature (LST) is a critical state variable of land surface energy equilibrium and a key indicator of environmental change such as climate change, urban heat island, and freezing-thawing hazard. The high spatial and temporal resolution datasets are urgently needed for a variety of environmental change studies, especially in remote areas with few LST observation stations. MODIS and Landsat satellites have complementary characteristics in terms of spatial and temporal resolution for LST retrieval. To make full use of their respective advantages, this paper developed a pixel-based multi-spatial resolution adaptive fusion modeling framework (called pMSRAFM). As an instance of this framework, the data fusion model for joint retrieval of LST from Landsat-8 and MODIS data was implemented to generate the synthetic LST with Landsat-like spatial resolution and MODIS temporal information. The performance of pMSRAFM was tested and validated in the Heihe River Basin located in China. The results of six experiments showed that the fused LST was high similarity to the direct Landsat-derived LST with structural similarity index (SSIM) of 0.83 and the index of agreement (d) of 0.84. The range of SSIM was 0.65&ndash, 0.88, the root mean square error (RMSE) yielded a range of 1.6&ndash, 3.4 &deg, C, and the averaged bias was 0.6 &deg, C. Furthermore, the temporal information of MODIS LST was retained and optimized in the synthetic LST. The RMSE ranged from 0.7 &deg, C to 1.5 &deg, C with an average value of 1.1 &deg, C. When compared with in situ LST observations, the mean absolute error and bias were reduced after fusion with the mean absolute bias of 1.3 &deg, C. The validation results that fused LST possesses the spatial pattern of Landsat-derived LSTs and inherits most of the temporal properties of MODIS LSTs at the same time, so it can provide more accurate and credible information. Consequently, pMSRAFM can be served as a promising and practical fusion framework to prepare a high-quality LST spatiotemporal dataset for various applications in environment studies.

Research on land surface processes at the catchment scale has drawn much attention over the past few decades, and a number of watershed observatories have been established worldwide. The Heihe River Basin (HRB), which contains the second largest inland river in China, is an ideal natural field experimental area for investigation of land surface processes involving diverse landscapes and the coexistence of cold and arid regions. The Heihe Integrated Observatory Network was established in 2007. For long-term observations, a hydrometeorological observatory, ecohydrological wireless sensor network, and satellite remote sensing are now in operation. In 2012, a multiscale observation experiment on evapotranspiration over heterogeneous land surfaces was conducted in the midstream region of the HRB, which included a flux observation matrix, wireless sensor network, airborne remote sensing, and synchronized ground measurements. Under an open data policy, the datasets have been publicly released following careful data processing and quality control. The outcomes highlight the integrated research on land surface processes in the HRB and include observed trends, scaling methods, high spatiotemporal resolution remote sensing products, and model–data integration in the HRB, all of which are helpful to other endorheic basins in the “Silk Road Economic Belt.” Henceforth, the goal of the Heihe Integrated Observatory Network is to develop an intelligent monitoring system that incorporates ground-based observatory networks, unmanned aerial vehicles, and multi-source satellites through the Internet of Things technology. Furthermore, biogeochemical processes observation will be improved, and the study of integrating ground observations, remote sensing, and large-scale models will be promoted further.

Land surface temperature (LST) retrieved from satellite remote sensing data has become a key parameter in research on global environmental change; therefore, the acquisition of accurate satellite-derived LST information is crucial for the diagnosis and analysis of global change. However, it is relatively difficult to obtain the true value of a pixel due to the scale mismatch between in situ measurements and satellite-based observations, especially for commonly heterogeneous and nonisothermal land areas, which greatly increases the difficulty in estimating pixel-representative LST values from in situ measurements for validation of satellite-based LST products. In this study, a supplemented radiance-based (SR-based) validation method was developed to evaluate the latest moderate resolution imaging spectroradiometer (MODIS) Collection 6 Level 2 daily LST/land surface emissivity (LSE) products over a heterogeneous and nonisothermal region of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project, West China. In the SR-based framework, pixel-representative LST values are simulated by the MODTRAN model from the corresponding in situ measurements, such as LSE and atmospheric profile measurements, to evaluate the MODIS LST products. The validation results show that the MODIS daytime LST products from the Aqua satellite (MYD11_L2) have a greater accuracy than those from the Terra satellite (MOD11_L2). Analyses of the effect factors indicate a strong correlation between the errors in the MOD11_L2 LST product and the corresponding difference in the MODIS brightness temperature between bands 31 and 32. Although the requirement of synchronous or quasisynchronous in situ measurements for the validated LST products may limit the applicability of the SR-based method, it is still an effective and simple method for validating satellite-derived LST products over mixed pixels. Our method is an indispensable supplement for the validation methods of satellite-derived LST products, and it can be applied in West China and other areas with heterogeneous land surfaces.

Dryland regions cover >40% of the Earth's land surface, making these ecosystems the largest biome in the world. Ecosystems in these areas play an important role in determining the interannual variability of the global terrestrial carbon sink. Examining carbon fluxes of various types of dryland ecosystems and their responses to climatic variability is essential for improving projections of the carbon cycle in these regions. In this study, we made use of observations from a regional flux tower observation network in a typical arid endorheic basin, the Heihe river basin (HRB). As a representative area of both the arid region of China and the entire region of central Asia, the HRB includes the main ecosystems in arid regions. We compared the spatial variations of carbon fluxes of five terrestrial ecosystems (i.e., grassland, cropland, desert, wetland, and forest ecosystems) and explored the responses of ecosystem carbon fluxes to climatic factors across different ecosystems. We found that our region exhibits a carbon sink ranging from 85.9 to 508.7 gC/m2/yr for different ecosystems, and the water availability is critical to the spatial variability of carbon fluxes in arid regions. Carbon fluxes across all sites exhibited weak correlations with temperature and precipitation. Marked differences in precipitation effects were observed between the sites within oases and those outside of oases. Irrigation and groundwater recharge were of great importance to the variations in carbon fluxes for the sites within oases. Evapotranspiration (ET) exhibited strong relationships with carbon fluxes, indicating that ET was a better metric of soil water availability than was precipitation in driving the spatial variability of carbon fluxes in arid regions. This study has implications for better understanding the carbon budget of terrestrial ecosystems and informing ecological management in dryland regions.

The alpine region is important in riverine and watershed ecosystems as a contributor of freshwater, providing and stimulating specific habitats for biodiversity. In parallel, recent climate change, human activities and other perturbations may disturb hydrological processes and eco-functions, creating the need for next-generation observational and modeling approaches to advance a predictive understanding of such processes in the alpine region. However, several formidable challenges, including the cold and harsh climate, high altitude and complex topography, inhibit complete and consistent data collection where/when needed, which hinders the development of remote sensing technologies and alpine hydrological models. The current study presents a suite of datasets consisting of long-term hydrometeorological, snow cover and frozen ground data for investigating watershed science and functions from an integrated, distributed and multiscale observation network in the upper reaches of the Heihe River Basin (HRB) in China. Gap-free meteorological and hydrological data were monitored from an observation network connecting a group of automatic meteorological stations (AMSs). In addition, to capture snow accumulation and ablation processes, snow cover properties were collected from a snow observation superstation using state-of-the-art techniques and instruments. High-resolution soil physics datasets were also obtained to capture the freeze-thaw processes from a frozen ground observation superstation. The updated datasets were released to scientists with multidisciplinary backgrounds (i.e., cryosphere science, hydrology, and meteorology), and they are expected to serve as a testing platform to provide accurate forcing data and validate and evaluate remote sensing products and hydrological models for a broader community. The datasets are available from the Cold and Arid Regions Science Data Center at Lanzhou https://doi.org/10.3972/hiwater.001.2019.db.

With advantages of multispatial resolutions, a high retrieval accuracy, and a high temporal resolution, the satellite-derived land surface temperature (LST) products are very important LST sources. However, the greatest barrier to their wide application is the invalid values produced by large quantities of cloudy pixels, especially for regions frequently swathed in clouds. In this study, an effective method based on the land energy balance theory and similar pixels (SP) method was developed to reconstruct the LSTs over cloudy pixels for the widely used MODIS LST (MOD11A1). The southwest region of China was selected as the study area, where extreme drought has frequently occurred in recent years in the context of global climate change and which commonly exhibits cloudy and foggy weather. The validation results compared with in situ LSTs showed that the reconstructed LSTs have an average error < 1.00 K (0.57 K at night and -0.14 K during the day) and an RMSE < 3.20 K (1.90 K at night and 3.16 K in the daytime). The experiment testing the SP interpolation indicated that the spatial structure of the LST has a greater effect on the SP performance than the size of the data-missing area, which benefits the LST reconstruction in the area frequently covered by large clouds.

Land surface temperature (LST) products derived from the moderate resolution imaging spectroradiometer (MODIS) sensor are one of the most important data sources used to research land surface energy and water balance at regional and global scales. However, MODIS data are severely contaminated by cloud cover, which limits the applications of LST products. In this paper, based on the spatio-temporal autocorrelation of land surface variables, a reconstruction algorithm depending on the correlations between spatial pixels in multiple time phases from available MODIS LST data is developed to reconstruct clear-sky LST values for missing pixels. Considering the impacts of correlation and bias between predictors and reconstructed data on the modeling error, the known data in the reconstructed time phase are combined with the data temporally nearest to them as predictor variables to establish their temporal relationships with the reconstructed data. The reconstructed results are validated by a series of evaluation indices. The average correlation coefficient between the reconstructed results and ground-based observations is 0.87, showing high temporal change accuracy. The difference in Moran’s I, representing spatial structure characteristics between the known and reconstructed data, is 0.03 on average, indicating a slight loss of spatial accuracy. The average reconstruction rate is approximately 87.0%. The modeling error, as part of the reconstruction error, is only 1.40 K on average and accounts for 5.0% of the total error. If the product and modeling errors are removed, the residual error represents approximately 3.5 K and 5.6 K of the annual mean difference between the cloudy and cloudless LST at night and during the day, respectively. In addition, different reconstruction cases are demonstrated using various predictor data, including many combinations of multi-temporal MODIS LST data, the microwave brightness temperature, and the combination of the normalized difference vegetation index and terrain data. Comparisons among cases show that the known MODIS LST data are more reliable as predictor variables and that the data combination advocated in this paper is optimal.

Photosynthesis is a very important sub-process in the carbon cycle and is a crucial sub-modular function in carbon cycle models. In this study, two typical photosynthesis parameterization schemes were compared based on meteorological and eddy covariance (EC) observations at an alpine meadow site. The photosynthesis model parameters were estimated using the Markov Chain Monte Carlo (MCMC) method. The results indicated that the Farquhar-conductance coupled model better predicted the gross primary production (GPP) for the alpine meadow ecosystem at an hourly time scale than the light use efficiency (LUE) model even though the Farquhar-conductance coupled model has a lower computational efficiency than the LUE model. Compared to the Ball–Woodrow–Berry (BWB) stomatal conductance model, coupling the Farquhar model with the Leuning stomatal conductance model more accurately simulated GPP.

International audience; Continuous land-surface temperature (LST) observations from ground-based stations are an important reference dataset for validating remote-sensing LST products. However, a lack of evaluations of the representativeness of station observations limits the reliability of validation results. In this study, a new practical validation scheme is presented for validating remote-sensing LST products that includes a key step: assessing the spatial representativeness of ground-based LST measurements. Three indicators, namely, the dominant land-cover type (DLCT), relative bias (RB), and average structure scale (ASS), are established to quantify the representative levels of station observations based on the land-cover type (LCT) and LST reference maps with high spatial resolution. We validated MODIS LSTs using station observations from the Heihe River Basin (HRB) in China. The spatial representative evaluation steps show that the representativeness of observations greatly differs among stations and varies with different vegetation growth and other factors. Large differences in the validation results occur when using different representative level observations, which indicates a large potential for large error during the traditional T-based validation scheme. Comparisons show that the new validation scheme greatly improves the reliability of LST product validation through high-level representative observations.

This study presents preliminary results of the validation of the Moderate Resolution Imaging Spectroradiometer (MODIS) daily LST products (MOD/MYD11A1, Version 5) using longwave radiation ground measurements obtained at 12 stations in the North Arid and Semi-Arid Area Cooperative Experimental Observation Integrated Research program. In this evaluation process, the broadband emissivity at each station was obtained from the ASTER Spectral Library or estimated from the MODIS narrowband emissivity Collection 5. A comparison of the validation results based on those two methods shows that no significant differences occur in the short-term validation, and a sensitivity analysis of the broadband emissivity demonstrates that it has a limited effect on the evaluation results. In general, the results at the 12 stations indicate that the LST products have a lower accuracy in China’s arid and semi-arid areas than in other areas, with a mean absolute error of 2–3 K. Compared with the temporal mismatch, the spatial mismatch has a stronger effect on the validation results in this study, and the stations with homogeneous land cover have more comparable MODIS LST accuracies. Comparisons between the stations indicate that the spatial mismatch can increase the influence of the temporal mismatch.

Alpine meadow coversmost of the Qinghai-Tibet Plateau where frozen soil is widely distributed. In order to correctly simulate the carbon, water and energy flux of an alpine meadow site at Qinghai-Tibet Plateau, a widely used carbon cycle model Biome-BGC and a cold region land surface model SHAW were coupled. The outputs of the coupled model were validated with the observed carbon fluxes (Gross Primary Productivity, Net Ecosystem Exchange, Ecosystem Respiration), energy fluxes (Latent heat flux, Sensible heat flux), water flux (Evapotranspiration), soil moisture and soil temperature at A'rou site which is located on the east edge of Qinghai-Tibet Plateau. The results indicate that the coupled model can correctly predict the interactions between alpine meadow ecosystem and atmosphere.

The ground-based sites are critical for assessing the uncertainties and evaluating the accuracy of the satellite-derived land surface temperature (LST) products by using the temperature-based validation (T-based) method. However, there is often a risk of the spatial mismatch between the observations of the satellite-sensor and the ground validation sites. Therefore the heterogeneity analysis of the ground-based sites, which can indicate the representativeness of the sites' observation, has been a basic and key stage in the whole process. The purpose of this paper is to analyses the heterogeneity of the ground sites based on the variogram function. In this study, the variograms of the eighteen sites in the Heihe River Basin (HRB) based on NDVI data were calculated to discuss the heterogeneities of these sites. Then according to the heterogeneity analysis results, these sites were leveled ground observation scale for validating satellite-derived LSTs.

Phenology controls the seasonal activities of vegetation on land surfaces and thus plays a fundamental role in regulating photosynthesis and other ecosystem processes. Therefore, accurately simulating phenology and soil processes is critical to ecosystem and climate modeling. In this study, we present an integrated ecosystem model of plant productivity, plant phenology, and the soil freeze–thaw process to (1) improve the quality of simulations of soil thermal regimes and (2) estimate the seasonal variability of plant phenology and its effects on plant productivity in high-altitude seasonal frozen regions. We tested different model configurations and parameterizations, including a refined soil stratification scheme that included unfrozen water in frozen soil, a remotely sensed diagnostic phenology scheme, and a modified prognostic phenology scheme, to describe the seasonal variation in vegetation. After refined soil layering resolution and the inclusion of unfrozen water in frozen soil, the results show that the model adequately reproduced the soil thermal regimes and their interactions observed at the site. The inclusion of unfrozen water in frozen soil was found to have a significant effect on soil moisture simulation during the spring but only a small effect on soil temperature simulation at this site. Moreover, the performance of improved phenology schemes was good. The phenology model accurately predicted the start and end of phenology, and its precise prediction of phenology variation allows an improved simulation of vegetation production.

Gross primary production GPP is an important variable in studies of the carbon cycle and climate change. The Moderate Resolution Imaging Spectroradiometer MODIS-GPP product MOD17 provides global GPP data for terrestrial ecosystems; however, it is not well validated in China. In this study, an eddy covariance EC system observed GPP at 10 sites in northern China and was used to validate MOD17. The results indicated that MOD17 presents a strong bias in the study region due to the meteorological data, MODIS FPAR fraction of absorbed photosynthetically active radiation MOD15, and the model parameters in the MODIS-GPP algorithm, Biome Parameters Look Up Table BPLUT. Maximum light-use efficiency ϵ0 had the strongest impact on the predicted GPP of the MODIS-GPP algorithm. After using the inputs observed in situ and improving parameters in the MODIS-GPP algorithm, the model could explain 85% of the EC-observed GPP of the sites, whereas the MODIS-GPP algorithm without in situ inputs and parameters only explained 26% of EC-observed GPP.

The accurate estimation of Gross Primary Production (GPP) is an important issue in carbon cycle studies. In this study, GPP dynamic of two typical vegetation in Heihe River Basin (alpine meadow in the upper stream and maize in middle stream) were estimated using Eddy Covariance (EC) and Vegetation Photosynthesis Model (VPM). Yearly GPP observed by EC at alpine meadow is 853 gC/m 2 /yr, which is only half of GPP of the value 1567 gC/m 2 /yr at maize field. This is mainly attributed to irrigation, fertilization and high light use efficiency at maize field. More than 80% carbon fixed by photosynthesis is release to atmosphere through ecosystem respiration at alpine meadow and at maize field 60%. The maximum light use efficiency of maize is 2.66 gC/MJ ARAR, while it is 1.6 gC/MJ ARAR at alpine meadow. VPM can correctly simulate seasonal dynamic and magnitude of GPP at both of the two sites. GPP values predicted by VPM were 872 gC/m 2 /yr at alpine meadow site and 1246 gC/m 2 /yr at maize site. The determination coefficients were as large as 0.9 at the alpine meadow site and 0.93 at the maize site. Average GPP value at each season predicted by VPM is very close to that observed by EC.

Land surface temperature (LST) is a key parameter in climatological and environmental studies [1]. The Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the NASA Terra and Aqua Earth Observing System satellites, can provide global temperature and narrowband emissivity data on a daily basis. However, when the surface is obscured by clouds, the variable cannot be measured directly by using satellite thermal infrared channels, which leads to many invalid value pixels in the MODIS LST products. Methods for calculating LST of the MODIS cloudy pixels are important, yet few studies have been done. The objective of this paper is to estimate the LST values of the cloudy-pixels using the neighboring-pixel approach (NP) and MODIS NSSR (net surface shortwave radiation) product. In this study, the Heihe River Basin was selected as a case study area. The estimation was validated using ground-measured data of Huazhaizi (HZZ) desert station which is covered by homogeneous desert steppe. The validation shows that the reconstruction values of MODIS LSTs can agree well with the ground-measured data, and the biggest absolute error is 2.6K.

Gross Primary Production (GPP) is the sum of carbon absorbed by plant canopy. It is a key measurement of carbon mass flux in carbon cycle studies. Remote sensing based light use efficiency model is a widely used method to estimate regional GPP. In this study, MODIS-PSN was used to estimate GPP in Heihe River Basin. In order to better the model accuracy, maximum light use efficiency ( e 0) in MODIS-PSN is estimated using local observed carbon flux data and meteorological data. After adjustment of parameter e 0, MODIS-PSN can correctly estimate GPP for major vegetation type in the Heihe River Basin. Then, yearly GPP over Heihe River Basin was estimated. The results indicated that about 1.4*10 13 g carbon enter terrestrial ecosystem through vegetation photosynthesis in the Heihe River Basin one year. In contrast, there is just 5.73*10 13 g carbon enter terrestrial ecosystem according to the standard MODIS GPP product, which is greatly underestimated GPP in the Heihe River Bain.

It is very necessary to validate MODIS land surface temperature (LST) for its application, especially in the arid and semi-arid regions. In this study, the Terra and Aqua MODIS 1km daily LST products (MOD/MYD11A1) are validated using ground based longwave radiation observation. The longwave radiation ground measurements during 2008 to 2009 were collected from four automatic weather stations in the Heihe River Basin. In this validation process, the land surface broadband emissivities of the validation stations were obtained from ASTER Spectral library. Then the ground-measured LSTs of validation stations were converted from surface longwave radiation based on Stefan-Boltzmann's law and thermal radioactive transfer theory. The validation results indicated that: except for DYKGT station, the mean bias was less then 1K and the mean absolute error (MAE) range was about 2-3K; MYD11A1 LSTs from Aqua have larger biases, MAEs, and RMSDs than that of MOD11A1 LSTs from Terra in most cases. The comparisons with ground measured LSTs show that the MAEs and RMSDs from daytime MOD/MYD11A1 comparisons are larger than that from nighttime MOD/MYD11A1 comparisons.

Leaf area index (LAI) and leaf chlorophyll content (Cab) are two significant parameters that control physical and physiological processes in vegetation canopies. The hyper spectral data, CHRIS/PROBA, were used in this study. Simultaneous ground experiments for CHRIS were carried out in July 1, 2008 in Zhangye during experiment of the Watershed Airborne Telemetry Experimental Research (WATER). In this paper, Soil-Leaf-Canopy (SLC) canopy reflectance model was used to simulate spectral of canopy and inverted for vegetation variables. For inversion of this coupled model, the very fast simulated re-annealing algorithm was used as an inversion strategy of retrieval of LAI and Cab. The retrieval results were validated with field measurements of LAI and Cab. The root-mean-square error (RMSE) of LAI and Cab were 1.2614 and 14.82µg/cm2 respectively. The verified result shows that the retrieval algorithm for vegetation parameters based on SLC and simulated annealing algorithm is credible.