Real-time river velocity estimation using close-range sensing is crucial for hydrological research and engineering, especially during emergencies like flood monitoring where traditional contact-based measurements face challenges. This study proposes a novel drone-based image velocimetry system for real-time, non-contact sensing of velocity distributions along river cross-sections. Specifically, a drone-mounted camera terminal is developed for rapid and stable data collection and result estimation. Additionally, the Internet of Remote Things (IoRT) is introduced to enable real-time, reliable data transmission, providing high-precision velocity measurements in emergency scenarios. We demonstrated the system’s performance through various applications and field experiments. In the case of river surface with water splashes, comparisons with a propeller flow meter showed an average relative error of 11.33% and an absolute error of 0.071 m/s. Under low flow conditions with artificial particles on the surface, validation against surface velocity radar measurements yielded higher accuracy, with relative and absolute errors of 7.76% and 0.063 m/s, respectively. Additionally, the reliability of the system was evaluated, showing that transmitting images averaging 4 MB each took an average of 1.133 seconds per frame with a 0.44% packet loss rate. The transmission of optical flow estimation results, averaging 65 MB, required an average of 4.55 seconds. The response time for operational commands was below 0.013 seconds. Moreover, the impact of flight altitudes, sampling length, sampling intervals, and pyramid levels on system flow velocity estimation was analyzed. The breakthroughs in non-contact velocity estimation enable real-time river monitoring and offer a new approach to flood measurement in inaccessible areas.

Sufficient textural features are essential for river surface velocity measurements using image velocimetry. However, natural rivers often present a major challenge for accurate image velocimetry due to the scarcity of natural tracers. Developing methods to quickly and efficiently enhance flow features on river surfaces to boost flow velocity measurement precision is of vital important. In this study, we model river surface feature enhancement as a Markov decision process (MDP), and propose a specialized deep reinforcement learning (DRL)-based algorithm for particle dynamic compensation. In order to recognize and eliminate the position deviations in particle compensation resulting from wind interference and the system’s own errors while simultaneously enhancing flow features, we implement dual-frame differencing techniques before and after agent actions as state inputs, and optimize the reward function. We present a novel control framework for particle compensation unmanned aerial vehicles (PCUAV) that uses particle dynamic compensation system (PDCS) to improve river surface feature detection. The framework combines IoT technology with DRL algorithms to adjust particle compensation methods in real time based on particle seeding distribution index (SDI) measurements taken across the river surface. The proposed algorithm is employed in newly developed PDCS that is mounted on the UAV, allowing for quick particle compensation in areas lacking sufficient features. Experimental results indicate that the proposed algorithm can quickly reduce the SDI values within the targeted regions of interest to the range of [0.75, 1], while successfully identifying and counteracting environmental disturbances. Field tests validate the system’s performance and reliability, offering an innovative approach to estimating river surface velocities in environments with insufficient natural flow features.

Passive microwave remote sensing has witnessed unprecedented progress in soil moisture (SM) estimation over the decades. However, it is challenging to estimate SM accurately due to the insufficient understanding of microwave emission mechanisms. A ground-based radiometry experiment is undertaken over an agricultural field, toward the reexamination and improvement of the microwave emission models and retrieval algorithms of SM and vegetation water content (VWC). This article reports the preliminary analysis of the experimental data and the calibration of the$\tau $–$\omega $model against the collected measurements. First, the collected multifrequency dual-polarized brightness temperature (TB) reflects the temporal variation of surface SM and a significantly negative correlation between them is observed, with the coefficient of determination ($R^{2}$) and slope (S) of a linear fitting line ranging from 0.036 to 0.367 and from −16.7 to −81.5, respectively. Surface roughness and VWC impact the relationship between TB and SM, with variable$R^{2}$and S observed. Second, the calibrated parameters have improved the model performance, with$R^{2}$greater than 0.65 and root mean standard error (RMSE) less than 4.7 K at all frequencies and polarizations. The parameter values are frequency- and polarization-dependent, and the best performance of the model simulation is observed at V-polarization of L- and Ku-bands, with$R^{2} =0.80$and RMSE =4.69 K at the L-band and$R^{2} =0.74$and RMSE =2.9 K at the Ku-band. Overall, the experiment has provided valuable datasets for calibrating forward models and the calibrated model will facilitate the improvement of surface parameters (e.g., SM and VWC) retrieval.

The effective use of observational constraints to mitigate the impacts of surface roughness and vegetation cover and the quantitative estimation of soil moisture from nonstationary microwave signals present critical challenges. Integrating the temporal constraints imposed by observations can provide additional information for achieving improved soil moisture estimation accuracy. Considering the time-dimensional autocorrelation of time-series passive microwave brightness temperatures, blind source separation (BSS) was introduced for soil moisture estimation. The single-channel BSS was used to decompose the brightness temperature into several intrinsic mode functions (IMFs). The Akaike information criterion and average silhouette width were used to recognize the number of blind sources based on the IMFs for multidimensional signal reconstruction. The multichannel BSS was carried out to decompose the reconstructed multidimensional signals to determine the trends of their blind sources to estimate soil moisture. An experiment conducted in Naqu demonstrated that the BSS-based method effectively estimated soil moisture, with an RMSE of 0.027 cm3/cm3. Through 1000 experiments, an error-bound analysis indicated that the method is robust, maintaining an average soil moisture estimation error of 0.032 cm3/cm3[range 0–0.074 cm3/cm3]. Additionally, decomposing the SMAP brightness temperature for soil moisture estimation in Tianjun, Maqu, and Pali yielded good performance, with RMSEs of 0.037 cm3/cm3, 0.047 cm3/cm3, and 0.041 cm3/cm3, representing a significant improvement over the traditional inversion algorithms. Moreover, the proposed method bypasses the challenges of remote sensing-based soil moisture estimation, which is affected by variables such as surface roughness and vegetation cover. This changes the limitations of traditional soil moisture estimation methods that rely on microwave models.

Ecosystem monitoring involves fully understanding the complex interactions of ecosystem progress and providing massive scientific data to answer research questions. Fetching data in extreme areas without a public network largely relies on human efforts. Along with the ongoing advances of the Internet of Things (IoT), the Internet of Remote Things (IoRT), as a new paradigm, is being pursued. In this article, we improve the previous drone-enabled IoRT system and innovatively integrate the system with ecological monitoring devices for wildlife, phenology and environmental monitoring, and the monitored data are remotely retrieved by drones. The experimental results indicate that the data transmission rate between the drone relay and the terrestrial terminal reaches up to 10–15 MB/s. Experiments further demonstrate that in terms of wildlife monitoring, the required time to retrieve an image cached on a terrestrial terminal is approximately 0.35 s, the time required to transfer a set of phenology data is 0.29 and 0.34 s, and in regard to environmental monitoring data, the average time consumption reaches approximately 0.0302 s. Moreover, we introduce a signal strength-based priority strategy that can reduce the time consumption of data transmission between the drone relay and the terrestrial terminal. The demonstrated applications reveal that monitoring devices deployed in remote extreme regions can be connected in the IoRT network, and the way to retrieve data from these deployed devices is being revolutionized by the drone-enabled IoRT network.

Improving the accuracy of remotely sensed soil moisture (SM) is a challenging and popular topic. Quantifying the uncertainty in the SM inversion process and enhancing the confidence of SM retrieval are promising ways to address this challenge but have received little attention. We present a Bayesian probabilistic inversion algorithm that can simultaneously retrieve SM, surface roughness, and vegetation optical depth and quantify the uncertainty in the inversion. The proposed algorithm is evaluated using airborne polarimetric L-band multibeam radiometer (PLMR) observations. We use three combinations, 3-angular observations at V-polarization (3CV), 3-angular observations at H-polarization (3CH), and 6-channel observations (6CA), to identify the optimal configuration for SM retrieval by taking advantage of PLMR’s dual-polarization and multiple angles. Uncertainties are quantified by introducing multiple uncertainty quantification metrics into Bayesian posterior distributions of SM retrievals. The estimates are validated against multiscale ground-based measurements, including manual measurements and wireless sensor network (WSN) measurements, and the spatial representativeness of the ground-based reference regarding the validation of pixel-scale SM retrievals is discussed. The 6CA attempt yields the best SM estimates (correlation coefficient (R)$\ge0.864$, root mean square error (RMSE)$\le0.04~\text{m}^{3}/\text{m}^{3}$, and unbiased RMSE (ubRMSE)$\le0.035~\text{m}^{3}/\text{m}^{3}$), while the 3CH attempt yields the lowest uncertainty. In addition, dense manual measurements are more representative than sparsely distributed WSN measurements. Overall, combining dual-polarized observations yields the best SM estimates but introduces additional uncertainty. This study highlights uncertainties quantification in SM inversion and thus provides confidence in SM inversion, facilitating improved SM retrieval algorithms.

Active and passive microwave characteristics of diurnal soil freeze-thaw transitions and their relationships are crucial for developing retrieval algorithms of the soil liquid water content ($\theta _{\mathrm {liq}}$) and freeze/thaw state, which, however, have been less explored. This study investigates these microwave characteristics and relationships via analysis of ground-based measurements of brightness temperature ($T_{B}$) and backscattering coefficients ($\sigma ^{0}$) in combination with simulations performed with the Tor Vergata discrete radiative transfer model. Both an L-band (1.4 GHz) radiometer ELBARA-III and a wide-band (1–10 GHz) scatterometer are installed in a seasonally frozen Tibetan meadow ecosystem to measure diurnal variations of$T_{B}$and copolarized$\sigma ^{0}$at both hh ($\sigma _{\mathrm {hh}}^{0}$) and vv ($\sigma _{\mathrm {vv}}^{0}$) polarizations. Analysis of measurements collected between December 2017 and March 2018 shows that 1) diurnal cycles are observed in both$T_{B}$and$\sigma ^{0}$due to the change in surface$\theta _{\mathrm {liq}}$caused by diurnal soil freeze-thaw transitions; 2) a negatively linear relationship is found between$e$and$\sigma ^{0}$regardless of frequency, polarization combinations, and observation angles; 3) slopes ($\beta$) of linearly fit equations between$e^{H}$and$\sigma _{\mathrm {hh}}^{0}$decrease with increasing observation angles of ELBARA-III, while the ones between$e^{V}$and$\sigma _{\mathrm {vv}}^{\mathrm {0 {}}}$increase with increasing observation angles; and 4) correlations between$e$and$\sigma ^{0}$increase with decreasing microwave frequency of$\sigma ^{0}$measurements and ELBARA-III observation angles, and magnitudes of diurnal$\sigma ^{0}$cycles also increase with decreasing microwave frequency. Moreover, the calibrated Tor Vergata model shows capability to reproduce both diurnal$e$and$\sigma ^{\mathrm {0 {}}}$variations as well as to quantify their relationships at different frequencies and observation angles.

Downward longwave radiation (DLR) at the surface is a key variable of interest in fields, such as hydrology and climate research. However, existing DLR estimation methods and DLR products are still problematic in terms of both accuracy and spatiotemporal resolution. In this article, we propose a deep convolutional neural network (DCNN)-based method to estimate hourly DLR at 5-km spatial resolution from top of atmosphere (TOA) brightness temperature (BT) of the Himawari-8/Advanced Himawari Imager (AHI) thermal channels, combined with near-surface air temperature and dew point temperature of ERA5 and elevation data. Validation results show that the DCNN-based method outperforms popular random forest and multilayer perceptron-based methods and that our proposed scheme integrating multisource data outperforms that only using remote sensing TOA observations or surface meteorological data. Compared with state-of-the-art CERES-SYN and ERA5-land DLR products, the estimated DLR by our proposed DCNN-based method with physical multisource inputs has higher spatiotemporal resolution and accuracy, with correlation coefficient (CC) of 0.95, root-mean-square error (RMSE) of 17.2 W/m2, and mean bias error (MBE) of −0.8 W/m2in the testing period on the Tibetan Plateau.

Despite efforts made to model and predict COVID-19 transmission, large predictive uncertainty remains. Failure to understand the dynamics of the nonlinear pandemic prediction model is an important reason. To this end, local and multiple global sensitivity analysis approaches are synthetically applied to analyze the sensitivities of parameters and initial state variables and community size (N) in susceptible-infected-recovered (SIR) and its variant susceptible-exposed-infected-recovered (SEIR) models and basic reproduction number (R0), aiming to provide prior information for parameter estimation and suggestions for COVID-19 prevention and control measures. We found that N influences both the maximum number of actively infected cases and the date on which the maximum number of actively infected cases is reached. The high effect of N on maximum actively infected cases and peak date suggests the necessity of isolating the infected cases in a small community. The protection rate and average quarantined time are most sensitive to the infected populations, with a summation of their first-order sensitivity indices greater than 0.585, and their interactions are also substantial, being 0.389 and 0.334, respectively. The high sensitivities and interaction between the protection rate and average quarantined time suggest that protection and isolation measures should always be implemented in conjunction and started as early as possible. These findings provide insights into the predictability of the pandemic models by estimating influential parameters and suggest how to effectively prevent and control epidemic transmission.

An unexplored aspect of L-band microwave emission is the impact of soil moisture and soil temperature (SMST) profile dynamics on diurnal brightness temperature ( TB) signatures of frozen soil. This study investigates this effect by comparing the TBsimulations of layered ( TB,l) and uniform ( TB,u) soils using a newly developed integrated land emission model. The multilayer Wilheit model and the single-layer Fresnel model are adopted to compute the smooth soil reflectivity for the layered and uniform soils, respectively. A four-phase dielectric mixing model is used to calculate the soil permittivity ( εs). A data set of concurrent ELBARA-III TBand SMST profile measurements performed in a seasonally frozen Tibetan meadow ecosystem is used for the analysis. The simulated TB,lconsidering SMST profile information captures well the ELBARA-III measurements with low biases (≤6 K) and high correlations ( R2≥ 0.88). TB,uproduced based on the Fresnel model using the soil moisture of 2.5 cm is more consistent with the TB,l. The sensitivity test of averaging SMST profile below 2.5 cm leads to maximum differences of 2 K in TB,lsimulations, indicating that the TBvariations are primary dominated by the SMST dynamics at the surface layer. A sensitivity test of the Wilheit model to different εsparameterizations shows that the dielectric model of Zhang et al. is comparable to the four-phase dielectric model in simulating TB,l, while the Mironov et al. 's model demonstrates larger biases for frozen soil with, on average, 2.2% clay content, 49.7% sand content, and a bulk density of 1 g·cm-3.

Advancements in the Internet of Things (IoT) have led to the revolutionary potential to achieve intelligent environmental monitoring. However, extreme conditions across hard-to-reach areas, where public ground networks do not provide sufficient coverage, have resulted in the difficult backhaul of monitoring data from remote areas of interest. Because of the urgent demand for low-cost data collection in such areas, this article proposes a novel drone-enabled IoT relay system to provide high-speed data collection to support remote environmental monitoring. The high-speed ability of 5-GHz communication technology was exploited to reduce the time required for data transmission between ground monitoring devices and drone. Meanwhile, the long-range (LoRa) technology, as a low-power and long-distance wireless communication technology, is adopted as a wake-up strategy for waking up the high-power 5-GHz module. Based on 5 GHz and LoRa technology, a drone-based onboard relay and ground intelligent terminal are designed. An application is used to demonstrate the feasibility of the designed system. Numerous real-world experiments validate the effectiveness of the designed drone-enabled IoT relay system and show its capability for high-speed data collection. The field experiments demonstrate that the system collects cached data with a stable 3.5-MB/s throughput at an altitude of 140 m. The breakthroughs in the drone-enabled IoT relay facilitate intelligent environmental monitoring in remote areas without public networks and provide a new perspective on data backhaul over areas of interest.

Photosynthetically active radiation (PAR) incident at the surface is crucial for understanding and modeling the Earth's climate and ecosystems. In this article, a general parameterization scheme suitable for PAR estimation under cloudy skies is proposed based on an elaboration on the PAR radiative transfer (RT) processes above, in, and beneath cloud layers. Its most important novel property is that all RT processes in cloudy atmospheres are explicitly explained, including ozone absorption, Rayleigh scattering, cloud single scattering, cloud multiple scattering, aerosol scattering, and cloud reflection. Theoretical accuracy evaluations show over 95% of errors (against rigorous RT calculations) lie within ±20 W/m2, and an operational application with multisource satellite products as inputs shows the root-mean-square error (RMSE) of ≤42 W/m2on the hourly timescale. Therefore, the parameterization scheme is accurate both in theory and actual applications.

A clear bias exists in the widely used gridded precipitation products (GPPs) that result from factors about topography, climate, and retrieval algorithms. Many existing optimization works have a deficiency in validation and domain sizes, which makes the evaluation and corrections of the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) product still challenging. In this letter, we propose a bias-correction approach that combines coarser-resolution gauge-based precipitation with a probability distribution function (PDF) to improve the accuracy of CHIRPS. The data from 27 local precipitation gauges in Shanghai are utilized to testify the performance of our method. Results explain that daily corrected CHIRPS (Cor-CHIRPS) product has higher accuracy than CHIRPS compared with ground truths (GrTs) in terms of both error statistics and detection capability, particularly in spring, autumn, and winter. Moreover, Cor-CHIRPS better captures the frequencies of precipitation events and well depicts the spatial characteristics of the annual precipitation.

Microwave backscattering models play key roles in surface scattering modeling and soil moisture inversion in active microwave remote sensing. However, numerous evaluations indicate that significant discrepancies between the model simulations and radar observations remain, and these discrepancies are regarded to be attributed to inaccuracies in the models. What do such discrepancies originate from is unclear and has not been comprehensively analyzed. To this end, this paper presents an uncertainty analysis to explore the intrinsic reason for the discrepancies between the backscattering model simulations and radar observations. The probability distribution function and the corresponding statistical characteristics are introduced to describe the uncertainty in the model outputs. We find that the scale dependence of the key model inputs leads to significant uncertainties in the model inputs, and the uncertainties are transferred into the model outputs. Thus, the discrepancies between the model simulations and radar observations are intrinsically caused by the spatial scaling and related uncertainties of key model inputs. In short, the scale mismatch between the model inputs and remote sensing pixels is an intrinsic factor that causes the discrepancies between the model simulations and radar observations. This finding suggests that the scaling effect of model inputs should be carefully considered when using the backscattering models at the pixel scale, and equivalent inputs matched at the corresponding scales should be developed for remote sensing applications. Thus, this analysis insights into the scale dependence of inputs for backscattering models and suggests to provide scale-matched inputs where the models are applied at different scales.

This letter summarizes a special stream of the IEEE Geoscience and Remote Sensing Letters devoted to understanding the heterogeneity in soil moisture, evapotranspiration, and other related ecohydrological variables based on multiscale observations from satellite-based and airborne remote sensors, a flux observation matrix, and an ecohydrological wireless sensor network in the Heihe Watershed Allied Telemetry Experimental Research project. Scaling and uncertainty are the key issues in the remote-sensing research community, especially regarding the heterogeneous land surface. However, a lack of understanding and an inadequate theoretical basis impede the development and innovation of forward radiative transfer models, as well as the quantitative retrieval and validation of remote-sensing products. We summarize the prior considerations regarding surface heterogeneity research and report the main outcomes and contributions of this special stream. The highlights of this stream are related to spatial sampling, upscaling, uncertainty analysis, the validation of remote-sensing products, and accounting for heterogeneity in remote-sensing models.

Block kriging (BK) is a common method of predicting the true value at the pixel scale when validating remote sensing retrieval products. However, measurement errors (MEs) increase the prediction uncertainty. In this letter, an extended interpolation technique - BK with MEs (BKMEs) - is developed. The properties of BKME are proven through derivation and demonstrated in a case study of soil moisture (SM) upscaling. Three prediction scenarios - one without MEs (BK), BK with homogeneous MEs (BKHOME), and BK with heterogeneous MEs (BKHEME) - are considered for the upscaling of SM data observed by a distributed wireless sensor network, and the results are compared. Both BK and BKHOME yield the same upscaling results, which differ from those of BKHEME, and the prediction results of BKHEME show less bias than those of the other scenarios. Because both BKHOME and BKHEME consider MEs, their prediction results show smaller kriging variances than do the BK results. Three primary conclusions are drawn. The first is that the optimal kriging coefficients assigned to the observations are affected not only by spatial distance but also by the MEs when the MEs of the samples are unequal. The second is that when the MEs are equal, it may not be necessary to consider the MEs to predict the value for an unobserved location. The third is that although the prediction uncertainty can be reduced by considering MEs, it is more meaningful to consider unequal MEs than equal MEs in the prediction process. BKME is an advanced upscaling method that achieves improved prediction accuracy by considering MEs.

Soil moisture (SM) inversions based on active microwave remote sensing have shown promising progress but do not easily meet expected application requirements because a number of inversion algorithms can only produce point estimates of SM and cannot quantify the uncertainty of SM inversions. Although previous studies have reported Bayesian maximum posterior estimations that are capable of retrieving SM within a probabilistic framework, they have primarily focused on the optimal estimators of SM and have typically ignored the uncertainty of SM inversions. This paper presents an SM probabilistic inversion (PI) algorithm based on Bayes' theorem and the Markov Chain Monte Carlo technique and capable of revealing the uncertainty of SM inversions and obtaining highly accurate SM estimates via maximum likelihood estimations (MLEs). The algorithm is implemented based on the advanced integral equation model, water cloud model simulations, and dual-polarized TerraSAR-X observations. The ground SM and vegetation water content (VWC) measurements from the Heihe watershed allied telemetry experimental research experiments are applied for validation. The results show that: 1) uncertainties in SM inversions, defined with respect to the measures of dispersion of SM posterior probability distribution, are approximately 0.1-0.12 m3/m3and 2) an acceptable inversion accuracy is obtained via MLEs, which present an SM Root Mean Square Error (RMSE) of 0.045 and 0.047 m3/m3for bare and vegetated soils, respectively, and a VWC RMSE of 0.45 kg/m2. The presented PI can quantify the uncertainty in SM inversions; therefore, it should be useful for improving active microwave remote sensing estimations of SM.

Passive microwave remote sensing has experienced significant success for soil moisture (SM) inversion. However, quantifying the uncertainties caused by soil parameter sensitivities has not attracted sufficient attention. Although local sensitivity analysis (SA) has been used to describe parameter sensitivity in the past, it fails to quantify parameter sensitivities, especially interactions, for nonlinear microwave emission models. This paper presents a comprehensive evaluation that combines physically based emission models and various global SA algorithms to evaluate parameter sensitivity. All the algorithms exhibit highly consistent sensitivity measures, which means a reliable SA result is obtained. The results indicate that the sums of the main sensitivity indices of SM and surface roughness parameters-root-mean-square height (RMSH) and correlation length-are greater than 0.92 and 0.95 for emissivity and brightness temperature (TB), respectively. Furthermore, we find that: 1) the parameter probability distributions have little effect on the sensitivity measures; 2) the SM sensitivity decreases and the RMSH sensitivity increases as the frequency increases and the incidence angle decreases; and 3) the SM is more sensitive on V-polarized than on H-polarized emissivity and TB, while the RMSH is much more sensitive on the polarization index. The presented global SA quantitatively explains the optimal frequency, incidence angle, and polarization for SM inversion and extends the parameter SA for microwave emission models to a more general framework, as well as provides an implication for bare soil emission modeling and SM inversion.

Scaling is a fundamental research issue in the geosciences and plays an essential role in the comparison and integration of datasets and in the calibration and validation of environmental models. Much environmental research suffers from a scale discrepancy between different data sources and models[1]. In the validation of remote sensing products, for instance, soil moisture is typically measured in situ at the scale of several dm3, while satellites measure soil moisture for grid cells at least several km2in size[2]. Different spatial scales (supports) in the input and output cause the issue of scale transformation[3]. Various methods have been proposed to transfer the spatial scale of land surface parameters but most are appropriate only for homogeneity situations. In most real-world situations, heterogeneity is inevitably present. This article focuses on scaling methods for land surface parameters under spatial heterogeneity and develops a methodological framework for `scale transformation'. This framework is composed of two types of methods to handling the issue of scale transformation: (1) from multiple in situ observations at point support to obtain satellite footprint-scale estimates (area support), named as MOPTA; (2) from multiple in situ observations at footprint scale (area support) to obtain another footprint-scale estimate (area support), named as MOATA. Land surface parameters considered are soil moisture and evapotranspiration (ET). As to MOPTA, we investigates two cases of upscaling in situ soil-moisture observations to satellite footprint-scale estimates. The in situ observations are acquired by three types of ecohydrological wireless sensor network (WSNs) deployed in 5 cm depth, varying measurement precision. WSNs covers approximately 16 (4 × 4) MODIS 1-km spatial resolution pixels. In the first case, A block kriging (BK) upscaling strategy is used to scale up soil moisture to MODIS pixel averages with in situ observations of unequal precision[4]. Furthermore, when measurement times of ground-based and satellite-based observations are not the same, temporal variation in soil moisture must be taken into account. At this case, a spatio-temporal regression block kriging (STRBK) is used to upscale in situ soil moisture observations collected as time series at multiple locations to pixel-scale estimates for validating the Polarimetric L-band Multi-beam Radiometer (PLMR) retrieved soil moisture product in the Heihe watershed[5]. As to MOATA, two types of in situ observations are involved: eddy correlation (EC) which measurements are normally a few to hundreds of meters[6][7] and large aperture scintillometer (LAS) which measurements are integrated over a long transect of approximately 500-5000 m from the same or different underlying surfaces[6][7]. For the purpose of cross-validation and comparison, the scale transformation between EC observations and LAS observations needs to be carried out. A area-to-area regression kriging is used to handle the problem of the nonstationarity of a random function and the issue of scale transformation[8]. This framework will be further developed to handling more cases. When land surface parameter show high spatio(-temporal) heterogeneity within the footprint, the upscaling strategy will take this into account through modelling the mean and variance as non-constant values that depend on high-resolution covariates. We will do this both in the spatial and spatio-temporal setting, in the case of the latter making use of space-time geostatistics and the stochastic partial differential equation (PDE) approach.

This study proposes a method for improving the estimation of surface turbulent fluxes in surface energy balance system (SEBS) model under water stress conditions using MODIS data. The normalized difference water index (NDWI) as an indicator of water stress is integrated into SEBS. To investigate the feasibility of the new approach, the desert-oasis region in the middle reaches of the Heihe River Basin (HRB) is selected as the study area. The proposed model is calibrated with meteorological and flux data over 2008- 2011 at the Yingke station and is verified with data from 16 stations of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project in 2012. The results show that soil moisture significantly affects ET under water stress conditions in the study area. Adding the NDWI in SEBS can significantly improve the estimations of surface turbulent fluxes in water-limited regions especially for spare vegetation cover area. The daily ET maps generated by the new model also show improvements in drylands with low ET values. This study demonstrates that integrating the NDWI into SEBS as an indicator of water stress is an effective way to improve the assessment of the regional ET in semi-arid and arid regions.

Well design and coordinated implementation of validation activity is necessary to evaluate the accuracy of remote sensing product. However, validation is not a straightforward task and remain many challenges. A generally recognized difficult issue is the inconsistence between sparse observations and remote sensing pixels, strong spatial and temporal variations of surface variables, and the intrinsic heterogeneity of land surfaces. Thus, to develop, design and conduct reasonable validation schemes and activities to acquire ground truth at pixel scale over heterogeneous land surfaces is urgently needed. This contains, from the perspective of measurement, integrating various ground observations collected at multi-scale, in order to validate different types of RSPs from site to network, especially for those land surface variables with strong spatial-temporal variations. To this end, a dedicated validation initiative has been launched in China since 2011. The main scientific objectives and research contents are to develop mathematical approaches for spatial sampling optimization to acquire the ground truth at pixel scale over heterogeneous land surfaces, to form a series of recognized and practicable technical specifications to guide validation of various RSPs, and to establish a prototype of national validation network for long term operation. Specific validation activities, such as HiWATER, were conducted from site to network, through multi-scale observations collected from multi-platform and multi-source sensors, to experimentally examine those proposed methodologies and guidelines. Following the experience of these validation exercises, we are coordinating a Chinese validation network to use standardized and recognized technical specifications in implementing future validation attempts, aiming to extend validation exercises from point scale to regional scale and to national scale across different zones.

Development and validation of hydrological cycle elements derived from remote sensing observations are of utmost importance for the study of hydrology at different scales, especially at watershed scale. This paper presents the progress we have made in developing and validating watershed scale hydrological cycle products, mainly including precipitation, snow cover area (SCA), soil moisture (SM), evapotranspiration (ET) and groundwater variation. Corresponding high quality remote sensing products (RSPs) have been produced. In addition, to validate the RSPs of water cycle variables, we established several ground observation networks which can provide extensive and high quality validation dataset. Our efforts significantly improve our understanding in watershed water cycle variables, and the developed water cycle products and validation data products have been widely used in several research domains, providing supporting for several key research projects. Based on these efforts, the developed and validated RSPs having been merged into hydrological and land surface models with the aid of land data assimilation method, to allow us to close the water cycle at the basin scale, and further improve our knowledge on terrestrial water study.

The polarimetric synthetic aperture radar (PolSAR) has been demonstrated a huge application potential in soil moisture (SM) inversion under vegetation. However, this promising approach has seldom been reported applied in an oasis of the northwest arid regions of China. This paper presents an analysis of component decomposition based on Radarsat-2 images. The main purpose of the analysis is to provide a preliminary recognition of the scattering mechanisms. The results of the analysis show that the scattering mechanisms in the vegetated oasis are very complex, which implies that scattering mechanisms must be decomposed before SM inversion in a vegetated surface. The analysis provide basis for the SM inversion under vegetation in an oasis of arid regions.

Optimal selection of observation locations is an essential task in designing an effective ecohydrological process monitoring network, which provides information on ecohydrological variables by capturing their spatial variation and distribution. This article presents a geostatistical method for multivariate sampling design optimization, using a universal cokriging (UCK) model. The approach is illustrated by the design of a wireless sensor network (WSN) for monitoring three ecohydrological variables (land surface temperature, precipitation and soil moisture) in the Babao River basin of China. After removal of spatial trends in the target variables by multiple linear regression, variograms and cross-variograms of regression residuals are fit with the linear model of coregionalization. Using weighted mean UCK variance as the objective function, the optimal sampling design is obtained using a spatially simulated annealing algorithm. The results demonstrate that the UCK model-based sampling method can consider the relationship of target variables and environmental covariates, and spatial auto- and cross-correlation of regression residuals, to obtain the optimal design in geographic space and attribute space simultaneously. Compared with a sampling design without consideration of the multivariate (cross-)correlation and spatial trend, the proposed sampling method reduces prediction error variance. The optimized WSN design is efficient in capturing spatial variation of the target variables and for monitoring ecohydrological processes in the Babao River basin.

The ground truth estimated by in situ measurements is important for accurately evaluating retrieved remote sensing products, particularly over heterogeneous land surfaces. This letter analyzes the role of multisource remote sensing observations on the upscaling of soil moisture observed by a wireless sensor network at the pixel scale via the regression kriging (RK) method. Three types of auxiliary remote sensing information are employed, including Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Temperature Vegetation Dryness Index (TVDI; 90 m), Polarimetric L-band Multiband Radiometer brightness temperature (700 m), and Moderate Resolution Image Spectroradiometer TVDI (1000 m). Moreover, a comparison with the ordinary kriging method is analyzed. The spatial inferences show that the RK method is more accurate and that its spatial pattern is more consistent with the auxiliary data when the trend is successfully removed, particularly when spatial continuity is destroyed by irrigation. The ASTER TVDI has a higher resolution and stronger correlation with soil moisture and yields more accurate interpolation results than the other types of remote sensing information. Although medium-resolution data do not substantially contribute to capture the spatial patterns of soil moisture, such data may still improve the prediction accuracy.

A profound and comprehensive understanding of the sensitivity of soil parameters related to backscattering coefficient is significant for the use of active microwave algorithms for soil moisture inversion. This paper presents a global sensitivity analysis (SA) based on the Advanced Integral Equation Model for soil moisture retrieval. The analysis involves diverse parameter ranges, sensor frequencies, incidence angles, surface correlation functions, and polarizations across various experiments. The primary objectives are to quantitatively and systematically evaluate the parameter sensitivities and their variations under various conditions, resulting in an improved understanding of microwave scattering and suggesting potential approaches to the improvement of soil moisture retrieval. The performance of this SA leads to the parameter sensitivities being quantified. Sensitive and insensitive parameters are distinguished. The existence of the former informs the direction of model calibration, implying that these parameters can be inverted with high confidence. Setting the latter as constants would be a step toward model simplification. Various conditions are observed to influence the parameter sensitivities, suggesting that it is possible to perform soil moisture or roughness inversions under the most sensitive conditions for the parameters. Finally, an SA of various combinations of dual-polarization, dual-frequency, and dual-incidence-angle backscatter is conducted. The results suggest that certain combinations enhance the sensitivities of certain parameters and allow for better estimation of their values. Ultimately, the presented global SA highlights the quantitative and systematic evaluation of parameter sensitivities, particularly their interactions, leading to a more profound understanding of scattering and an improvement in soil moisture estimation.

The Cosmic-ray Soil Moisture Observing System is a promising soil moisture measurement network. It can measure soil moisture at an intermediate spatial scale with a single sensor. In this letter, the measured cosmic-ray neutron counts during the Heihe Watershed Allied Telemetry Experimental Research were used to evaluate the capabilities of the cosmic-ray probe in soil moisture retrieval at a heterogeneous farmland. The Cosmic-ray Soil Moisture Interaction Code model was utilized to model the interaction between the measured neutron counts and the soil water content. Soil moisture at the footprint scale of the cosmic-ray probe obtained using a wireless sensor network (SoilNET) was used as the calibration and validation data. The results show that the cosmic-ray probe is capable of monitoring the hourly heterogeneous soil moisture dynamics at the intermediate spatial scale in a noninvasive way. Moreover, the informative measurement depth of the cosmic-ray probe can also be derived and is consistent with the soil moisture results.

This letter introduces the ecohydrological wireless sensor network (EHWSN), which we have installed in the middle reach of the Heihe River Basin. The EHWSN has two primary objectives: the first objective is to capture the multiscale spatial variations and temporal dynamics of soil moisture, soil temperature, and land surface temperature in the heterogeneous farmland; and the second objective is to provide a remote-sensing ground-truth estimate with an approximate kilometer pixel scale using spatial upscaling. This ground truth can be used for validation and evaluation of remote-sensing products. The EHWSN integrates distributed observation nodes to achieve an automated, intelligent, and remote-controllable network that provides superior integrated, standardized, and automated observation capabilities for hydrological and ecological processes research at the basin scale.

Upscaling ground-based moisture observations to satellite footprint-scale estimates is an important problem in remote sensing soil-moisture product validation. The reliability of validation is sensitive to the quality of input observation data and the upscaling strategy. This letter proposes a model-based geostatistical approach to scale up soil moisture with observations of unequal precision. It incorporates unequal precision in the spatial covariance structure and uses Monte Carlo simulation in combination with a block kriging (BK) upscaling strategy. The approach is illustrated with a real-world application for upscaling soil moisture in the Heihe Watershed Allied Telemetry Experimental Research experiment. The results show that BK with unequal precision observations can consider both random ground-based measurement errors and upscaling model error to achieve more reliable estimates. We conclude that this approach is appropriate to quantify upscaling uncertainties and to investigate the error propagation process in soil-moisture upscaling.

A multi-sources data assimilation system has been developed for the catchment scale land water and energy cycle researches. The land surface model, microwave radiative transfer model and ensemble Kalman filter have been coupled in this system, the high performance computing was also considered. This system is being used in the data assimilation of soil moisture, soil temperature, microwave brightness temperature and snow water equivalent at catchment scale.

Land cover type is a crucial parameter that is required for various land surface models that simulate water and carbon cycles, ecosystem dynamics, and climate change. Many land use/land cover maps used in recent years have been derived from field investigations and remote-sensing observations. However, no land cover map that is derived from a single source (such as satellite observation) properly meets the needs of land surface simulation in China. This article presents a decision-fuse method to produce a higher-accuracy land cover map by combining multi-source local data based on the Dempster–Shafer (D–S) evidence theory. A practical evidence generation scheme was used to integrate multi-source land cover classification information. The basic probability values of the input data were obtained from literature reviews and expert knowledge. A Multi-source Integrated Chinese Land Cover (MICLCover) map was generated by combining multi-source land cover/land use classification maps including a 1:1,000,000 vegetation map, a 1:100,000 land use map for the year 2000, a 1:1,000,000 swamp-wetland map, a glacier map, and a Moderate-Resolution Imaging Spectroradiometer land cover map for China in 2001 (MODIS2001). The merit of this new map is that it uses a common classification system (the International Geosphere-Biosphere Programme (IGBP) land cover classification system), and it has a unified 1 km resolution. The accuracy of the new map was validated by a hybrid procedure. The validation results show great improvement in accuracy for the MICLCover map. The local-scale visual comparison validations for three regions show that the MICLCover map provides more spatial details on land cover at the local scale compared with other popular land cover products. The improvement in accuracy is true for all classes but particularly for cropland, urban, glacier, wetland, and water body classes. Validation by comparison with the China Forestry Scientific Data Center (CFSDC)–Forest Inventory Data (FID) data shows that overall forest accuracies in five provinces increased to between 42.19% and 88.65% for our MICLCover map, while those of the MODIS2001 map increased between 27.77% and 77.89%. The validation all over China shows that the overall accuracy of the MICLCover map is 71%, which is higher than the accuracies of other land cover maps. This map therefore can be used as an important input for land surface models of China. It has the potential to improve the modeling accuracy of land surface processes as well as to support other aspects of scientific land surface investigations in China.

Land surface heterogeneities are important for accurate estimation of land–atmosphere interactions and their feedbacks on water and energy budgets. To physically introduce existing land surface heterogeneities into a mesoscale model, a land data assimilation system was coupled with a mesoscale model (LDAS-A) to assimilate low-frequency satellite microwave observations for soil moisture and the combined system was applied in the Tibetan Plateau. Though the assimilated soil moisture distribution showed high correlation with Advanced Microwave Scanning Radiometer on the Earth Observing System soil moisture retrievals, the assimilated land surface conditions suffered substantial errors and drifts owing to predicted model forcings (i.e., solar radiation and rainfall). To overcome this operational pitfall, the Coupled Land and Atmosphere Data Assimilation System (CALDAS) was developed by coupling the LDAS-A with a cloud microphysics data assimilation. CALDAS assimilated lower frequency microwave data to improve representation of land surface conditions, and merged them with higher frequency microwave data to improve the representation of atmospheric conditions over land surfaces. The simulation results showed that CALDAS effectively assimilated atmospheric information contained in higher frequency microwave data and significantly improved correlation of cloud distribution compared with satellite observation. CALDAS also improved biases in cloud conditions and associated rainfall events, which contaminated land surface conditions in LDAS-A. Improvements in predicted clouds resulted in better land surface model forcings (i.e., solar radiation and rainfall), which maintained assimilated surface conditions in accordance with observed conditions during the model forecast. Improvements in both atmospheric forcings and land surface conditions enhanced land–atmosphere interactions in the CALDAS model, as confirmed by radiosonde observations.

Historic freeze-up and break-up dates of lake can record the regional climate variability. Observations of lake ice from different regional or country's stations are inconsistent and their number was declined in past two decades, which makes climate change analysis difficult. This paper proposes a dynamic threshold method to derive the freeze- up and break-up dates of big lakes using passive microwave brightness temperature data. Lake ice information from 35 big lakes in Northern Hemisphere is retrieved in past three decades. These freeze-up and break-up dates were validated from 18 stations' observations. The linear correlation coefficients between observations and retrievals are 0.926 for freeze-up dates and 0.936, respectively.

The land-atmosphere interaction in the Tibetan Plateau plays an important role in the Asian summer monsoon and the global energy and water cycle. This study presents a method to improve the land surface water and energy fluxes simulation by using a land data assimilation system (LDAS), which merging microwave remote sensing data and GCM output into a land surface model. NCEP reanalysis data is used as the background field and also as the meteorological forcing for the land surface model. Two experiments were designed as by driving LDAS-UT with two sets of atmospheric forcing data, (1) with in situ observed forcing data and (2) with NCEP reanalysis data at Gaize and Naqu sites. Results show that LDAS is able to estimate land surface soil moisture and energy fluxes accurately. The RMSE of soil moisture simulation is around 0.03–0.05 and RMSE of net radiation simulation is around 30W/M2. This study reveals the potential for using satellite remote sensing data to improve land surface fluxes estimation.

As a new flux measuring instrument, Large Aperture Scintillometer (LAS) developed rapidly in recent years, which can measure sensible heat flux in large scales, from several hundred meters to several kilometers even to ten kilometers, In other words, LAS can match well with remote sensing scale.

The glacial lakes from 1977 to 2009 in the Nepal-China border region were investigated using satellite imagery. 247 ice-contact or ice-proximal glacial lakes with total area of 126.3km2in the study area were identified in 2009. The expansion rate of growing lakes are approximately2/year in area since 1977, respectively. The glacial lake growth rate keeps increase from 1977. The expansion rate of growing lake on southern side is greater than that on northern side.

Soil moisture is the central focus of land surface and atmospheric modeling because it controls surface water and energy fluxes and consequently affects land-atmosphere interactions. Although global or regional satellite-derived surface soil moisture data sets are readily available, knowledge about assimilating them into numerical weather prediction (NWP) models is limited. The methods of assimilating soil moisture products in NWP models have several limitations, and they cannot be applied in near-real-time applications. As a result, this paper focuses on the development of a system [a Land Data Assimilation System coupled with a mesoscale Atmospheric model (LDAS-A)] that couples satellite land data assimilation with a mesoscale model to physically introduce land surface heterogeneities into the mesoscale model. The LDAS-A consists of a sequential LDAS that directly assimilates the lower frequency passive microwave brightness temperatures, and therefore, its use is feasible for near-real-time NWP applications. The LDAS-A was validated for the Tibetan Plateau using surface, radiosonde, and satellite observations. The simulation results show that the LDAS-A effectively improved the land surface variables (i.e., surface soil moisture and skin temperature) compared with the no-assimilation case and that it has the potential to correct uncertainties resulting from model initialization, model-specific parameters, and model forcing on a wider scale. The improved land surface conditions in the LDAS-A improve the land-atmosphere feedback mechanism, and the assimilated results provide better prediction of atmospheric profiles (i.e., potential temperature and specific humidity) than the no-assimilation case when compared with radiosonde soundings. Improvements in solar radiation, in addition to soil moisture, are necessary to introduce realistic land-atmosphere interactions into a mesoscale model.

Radar remote sensing has shown its potential for retrieving soil moisture from soil surfaces. Since the backscattering process is also influenced by soil roughness, the characterization of this roughness is crucial for an accurate estimation. The algorithm proposed in this investigation aiming to obtain the roughness parameters for every SAR pixel which could facilitate the derivation of soil moisture in virtue of multi-angle ASAR images, and combined with a semi-empirical calibration for the correlation length. An application of the means was performed in the middle reaches of the Heihe river basin and achieved satisfactory results (RMSE less than 6 vol %).

Normalized difference vegetation index (NDVI) is the most widely used vegetation index due to its simplicity, ease of application, and wide-spread familiarity. Time-series NDVI products have been proven to be a powerful tool to learn from past events, monitor current natural-resource conditions, extract canopy biophysical parameters and forecast terrestrial ecosystems on different scales. However, the current NDVI product is still spatiotemporally discontinuous mainly due to cloud cover, seasonal snow and atmospheric variability. In this work, a simplified data assimilation method is proposed to reconstruct high-quality time-series MODIS NDVI data. Results indicate that the newly developed method is easy and effective in reconstructing high-quality MODIS NDVI time series.

Incorporating the spatial correlation information into the land data assimilation system provides the opportunities to improve the analysis quality of the assimilation system. In order to make use of the spatial autocorrelation information effectively in the land data assimilation system, we used the geostatistics to explore and describe the spatial variations of the microwave brightness temperature data. The corresponding experimental semivariogram model, covariance model, nugget effect, sill value and range were calculated. Then these results were applied to the localization of the microwave remote sensing observation error covariance successfully, and played an important role regarding the enhanced performance of the direct radiance assimilation system.

We have developed Chinese land data assimilation system (CLDAS). In this system, the Common Land Model (CoLM) is used to simulate land surface processes. The radiative transfer models of thawed and frozen soil, snow, and vegetation are used as observation operators to transfer model predictions into estimated brightness temperatures. The EnKF algorithm is implemented as data assimilation method to integrate modeling and observation. The system is capable of assimilating passive microwave remotely sensed data such as special sensor microwave/imager (SSM/I) and advanced microwave scanning radiometer enhanced for EOS (AMSR-E). In this study, we primarily compare the assimilation results of soil moisture with AMSR-E L3 surface soil moisture products and in situ observations from GAME-Tibet experimental fields. The results indicate that the relationship between the simulated and assimilated surface soil moisture with AMSR-E L3 surface soil moisture products is very low. In comparison with in situ observations from GAME-Tibet experimental fields, the assimilated results of soil moisture are better than the simulated results. Additionally, the assimilated results can describe the thawed-frozen cycle.

The estimation of evapotranspiration(ET) over heterogeneous land surface over arid and semi-arid region is quite complicated and significant. In this paper, NOAA/AVHRR remote sensing data, NCEP grid data and the meteorological stations data are used to estimate daily ET over the Heihe river basin by integrating Surface Energy Algorithm for Land model [1,2] and the Penman method[3]. As for clear days, Remote sensing model is used to retrieval the instantaneous ET to daily ET [4]. FAO-17 Penman method is also used to estimate the same day's reference crop ET by NCEP grid data and the meteorological stations data. The relationship between actual ET and the reference crop ET can be used to calculate the actual ET based on the results from FAO-17 Penman method on the cloudy days. These results are validated by observation data and other study in Heihe River Basin.

A simultaneous airborne, satellite and ground based remote sensing experiment which is aiming to improve the observability, understanding, and predictability of hydrological and related ecological processes at catchmental scale is implemented in a typical inland river basin of northwest China. The experiment is composed of the cold region, forest, and arid region hydrological experiments as well as a hydro/meteorological elements and Doppler radar precipitation observation experiment. Airborne microwave radiometers at L, K and Ka bands, hyperspectral imager, thermal imager, and lidar are used. Various satellite data are collected. Based on these observations, the remote sensing retrieval models and algorithms of water cycle variables can be developed or improved, and a catchment-scale land/hydrological data assimilation system is going to be developed.

In this paper, the Common Land Model (CLM) and the Monteith type carbon model-C-FIX were applied to estimate the water use efficiency (WUE) of western China in 2002. The input data mainly included NCAR and Meteo France meteorological data sets, 1 km USGS soil and land cover data, the global 0.25deg monthly MODIS LAI data and the 1 km VGT-S10 NDVI products. Some field measurements of WUE for different plants in the study area were used for validation. The total annual NPP and actual ET in western China in 2002 were estimated about 0.96PgC and 2098km3H2O. The mean annual WUE per square meter was about 0.32gC/mm. The spatial pattern of the mean annual WUE in Western China as well as the seasonal WUE profiles of different ecosystems were illustrated and descript in detail. In general, the mean annual WUE of the main ecosystems in western China were ranked as: mountain forest > desert shrub and woodland > irrigated farmland > alpine meadow > gobi >= cold desert. This study could provide quantitative and spatially distributed data to help water resource utilization and managements in arid and semi-arid regions of western China.

This paper introduces the application of the ensemble Kalman filter (EnKF) technique for the assimilation of passive microwave remote sensing observations into a landsurface model, to improve the snow depth (SD) predictability. A new landsurface model, currently developed at the Japan Meteorological Agency (JMA), which is based on the simple biosphere model (SiB), is used as a forward model to predict the change of the snow pack. The microwave emission model of layered snowpacks (MEMLS) is used as observation operator, to transfer the model prediction into the corresponding satellite brightness. The assimilation system was applied using data from the coordinated enhanced observation period (CEOP) Asia-Australia monsoon project (CAMP) Eastern Siberia Taiga region for the period from November 2002 to March 2003. The data sets includes JMA-GSM model output, which is used as forcing data, satellite brightness temperature observation from the advanced microwave scanning radiometer (AMSR-E) and in-situ snow depth (SD) observation and the current AMSR-E snow depth product for comparison. The assimilation results are in good agreement with the data from the snow depth observation sites in this region and improve the forecast of the land-surface model. Furthermore, comparison with the AMSR-E SD product showed, that the assimilation results are also in better agreement with the in-situ snow depth observation.

Sandy desertification of Minqin oasis was monitored by the combined use of remote sensing and GIS. After interactive interpretation of the landsat TM (1986) and ETM+ (2000) images, sandy desertification map and land-use map were compiled and analyzed by GIS softwares, Arc/View and ArcGIS. Results show that: (1) in 2000, the total sandy desertification land area was about 4087.5649 km/sup 2/, in which the area of the slightly sandy desertification land occupied 1.54%, the moderately land 6.60%, the severely land 14.78% and the very severely land 77.08%; (2) three sandy desertification types were identified: grassland desertification, farmland desertification and dune reactive or encroach which occupied about 1784.9233 km/sup 2/, 178.9948 km/sup 2/ and 2123.6468 km/sup 2/ in area respectively; (3) from 1986 to 2000, the most important change was the increase of farmland, mainly converted from grassland, salinized land, sand land and woodland. But, all these reclaiming actions were at the risk of sandy desertification, especially, the rapid development of sand land reclamation has badly damaged the periphery environment of the oasis and accelerated the sandy desertification. Some constructive countermeasures, for the ecological protection and the ecological refugee settlement of Minqin oasis, are proposed in the paper.

Net primary productivity (NPP) is a key component of the terrestrial carbon cycle. The accurate estimation of NPP on regional and global scale is crucial for the studies of global change. In this paper, the Monteith type parametric model-C-Fix, the 1 km SPOT4/VEGETATION data as well as the global meteorological data provided by Meteo France were used to estimate NPP of the terrestrial ecosystems in Western China (73/spl deg/-112/spl deg/E, 26/spl deg/-50/spl deg/N) for the year of 2002. The total yearly NPP of Western China was estimated at 0.96 P (=10/sup 15/)g C in 2002, but the total mean NPP was only equal to 168 g C/m/sup 2//year over the study area of 4.5 million km/sup 2/. The spatial pattern of the annual accumulated NPP as well as the monthly dynamics of NPP in Western China were illustrated and descript in detail. In addition, the NPP-values in the annual accumulated level and the mean level for the different ecosystems were evaluated by using the newest 1:1M land-use map of Western China. The results showed that the spatial and temporal patterns of NPP in Western China are attributable to the complex interaction between natural environment, various climates and human activities. Although Western China has a large area of land, the total mean NPP level is very low due to the hard natural conditions. Among them, the restricted water resource is the main element to control the NPP of Western China.

The influences of the vegetation in the detection of soil moisture can be significant and may limit the applicability of the satellite passive microwave sensors. Sensitivity analysis of the applied soil moisture algorithm using the ground based radiometer measurements can show where the problem will arise and how they may be circumvented. This paper investigates the method of retrieving one-dimensional soil moisture profile and surface and canopy temperatures, under the influence of vegetation, from passive microwave, visible and near infrared measurements, by using the novel application of data assimilation technique.

The Normalized Difference Vegetation Index (NDVI) data set of the Heihe River Basin (96.45/spl deg/-102.8/spl deg/E, 35.4/spl deg/43.5/spl deg/N) from April 1992 to September 1993 and from February 1995 to January 1996 was derived from the NOAA Pathfinder AVHRR data set. The principal component analysis was applied to the data set. The seasonal phenological characteristics of the Heihe River Basin was obtained by analyzing the first four principal components.