The soil phase-transition temperature (PTT) is critical for accurately determining the soil freeze–thaw (FT) state, which is essential for developing surface FT state identification algorithms based on remote sensing technology and reliably validating FT products. In this study, we develop an innovative approach for determining soil PTT in the region of the dense soil moisture and soil temperature monitoring network in the Qinghai Lake Basin (QLB-NET). We employ four advanced models—polynomial regression (PR), random forest (RF), feedforward neural network (FNN), and transformer (XFMR) models—to establish relationships between soil PTT and various environmental factors for daily and site-specific soil PTT estimation. Soil PTT values derived from asigmoidcurve fitting (SCF) method that combines physical theory with empirical modeling are used in the models as the target data considering soil properties and topographic features as predictors. Additionally, we systematically evaluate the models’ performance and compare the results against those of the fixed 0°C threshold methods. The results indicate that most soil freezing temperatures range between -1°C and 0°C, and thawing temperatures range between -0.5°C and 0.5°C, with occasional freezing temperatures above 0°C. Model-derived soil PTT significantly outperforms the traditional 0°C threshold in terms of soil FT state classification accuracy, particularly during the frozen and FT transitional seasons. The XFMR model achieves superior performance in an internal comparative analysis, highlighting its particular effectiveness for soil PTT determination. These results provide valuable insights for improving remote sensing-based FT product validation and permafrost monitoring.

Uncrewed aerial vehicle (UAV) thermal infrared (TIR) remote sensing is playing an increasingly important role in diverse applications such as agriculture, forestry, hydrology, and ecological monitoring. Moreover, UAV-based remote sensing significantly contributes to the understanding of fundamental remote sensing science issues, such as scale variation and its impacts on multisource data collaboration. To cover extensive areas, UAVs often need to fly multiple strips to ensure complete coverage, leading to significant intervals between the start and end of missions. This can cause notable changes in brightness temperature (BT) due to the different observation times, introducing considerable uncertainty into the final BT mosaic, which, in turn, directly impacts subsequent applications, such as the calculation of land surface temperature (LST) and other temperature-related analyses. This study presents a temporal effect removal of LST (TERL) method to effectively correct for differences due to observation time and thereby enhance the temporal consistency of BT data. The core of TERL involves three key processes: 1) modeling the temporal information of TIR mosaic pixels; 2) deriving the temporal dynamics related to specific surface features by classifying image sequences; and 3) capturing the temporal variation of BT differences and temperature compensation. Validation results indicate that TERL significantly improves both the temporal comparability of pixels and the consistency between UAV temperature data and ground observations. Specifically, the root-mean-square error (RMSE) of the corrected data is 22.50%–77.14% smaller than that of the uncorrected data, with an impressive average reduction of 51.09%. Compared to the digital number probability density function fitting and radiative transfer simulation-based (DRAT) method, which primarily addresses temperature drift, TERL achieves an average RMSE reduction of 29.65%, showcasing its better performance. Moreover, the corrected data better reflect the temperature variation trends of surface features and show strong correlations with ground observation data, with most correlation coefficients exceeding 0.5. Thus, TERL facilitates more accurate comparisons and analyses of UAV TIR data, ultimately enhancing not only the reliability and effectiveness of quantitative remote sensing research with UAVs but also advancing the understanding of fundamental issues like scale variation in remote sensing science.

Unmanned aerial vehicle (UAV)-based fine-scale evapotranspiration (ET) estimation is becoming increasingly critical in precision agricultural water management. However, existing UAV-based ET estimation studies often directly transfer satellite-based ET models and parameterization schemes to fine-scale UAV data, which hampers accurate fine-scale ET estimation. Here, we use machine learning (ML)-based alternative estimation schemes to estimate key parameters of aerodynamic roughness length (z0m) and excess resistance (kB-1) in the surface energy balance system (SEBS) ET model. In addition, we use a computational fluid dynamics (CFD) model to provide downscaled meteorological data for the SEBS model. Compared to physical parameterization schemes, ML-based estimates ofz0mandkB-1show improved accuracy, reducing the mean root mean square error (RMSE) forz0mfrom 0.07 m to 0.04 m, and forkB-1from 4.58 to 2.41. Validation against eddy covariance (EC) systems with a source area of hundreds of meters shows that ML-based estimates of latent heat flux (LE) have an RMSE of 39.94 W/m2, which is superior to the RMSE of 77.44 W/m2achieved by physical parameterization schemes. ML-based LE estimates also show comparable accuracy with an RMSE of 41.94 W/m2when using CFD-based meteorological data. A comparison with an optical-microwave scintillometer (OMS) system with a source area spanning kilometers confirmed the importance of CFD-based meteorological data and reduced the mean relative error (MRE) for LE from 26.53% (using site-observed meteorological data) to 22.28%. Our proposed robust framework improves the accuracy of UAV-based ET estimates, thus helping to bridge the scale gap between satellite remote sensing and site-based observations.

The uncertainty of ground truth values at the pixel scale obtained via upscaling directly affects the credibility of remote sensing product validation. This article provides an in-depth analysis of the sources of uncertainty in ground truth evapotranspiration (ET) at the pixel scale. This uncertainty is quantitatively evaluated, and the methods for its control are discussed. The results indicate that the uncertainty from upscaling methods is highest, followed by that from auxiliary data, with that from instrument measurements being the smallest. The relative accuracy of the ground truth ET at the pixel scale for the LAS1–LAS4 (LAS5–LAS7) regions is 89.15%–90.16% (81.56%–82.58%). The accuracy for homogeneous surfaces is relatively high at approximately 90%–93%, whereas for moderately and highly heterogeneous surfaces, it is lower, varying from approximately 81% to 92%. To control uncertainty, precise instrument calibration, strategic positioning, the use of diverse constraints, and robust modeling are recommended to increase measurement accuracy and prediction reliability. This uncertainty study includes the analyses of different sources of uncertainty and quantitatively evaluates the uncertainty of ground truth values over different heterogeneous underlying surfaces. The results can be used to objectively evaluate the accuracy of remote sensing products, thus advancing studies of the uncertainty of ground truth values at the pixel scale and enhancing the scientific, reliable, and systematic validation of remote sensing products. This approach can greatly promote the validation of remote sensing ET products over heterogeneous surfaces.

Quantifying and monitoring land surface evapotranspiration (ET) is an essential task for understanding the earth’s water, energy, and carbon cycles. ET, specifically plant transpiration ($T$), is closely linked to the photosynthesis, which is coupled through stomatal function. However, the mechanistic links between sun-induced chlorophyll fluorescence (SIF) information indicating canopy photosynthetic activity and$T$are complex and difficult to derive empirically. An empirical SIF-$T$relationship at ecosystem scale was developed and coupled to the two-source energy balance model (TSEB-SIF) to estimate the ET and its components,$T$and soil evaporation,$E$. By comparing model predictions with observations from an irrigated cropland site located in a semiarid region, the TSEB-SIF model shows a slightly better performance to the TSEB model in estimating ET, especially under water deficit conditions. Moreover, the TSEB-SIF model more reliably partitioned the$T$from ET, while the TSEB model tended to overestimate the contribution of$T$to ET.

Unmanned aerial vehicle (UAV) thermal infrared (TIR) remote sensing is an important way to obtain land surface temperature (LST) with high spatial and temporal resolutions. Due to wide spectral response function (SRF) ranges of UAV thermal imagers, currently available LST retrieval methods suitable for satellite sensors may induce significant uncertainty when applied to UAV sensors. Despite that some methods have been proposed to retrieve LST from UAV remote sensing, studies considering the adverse effect caused by the SRF ranges are still rare. Here, we present a so-called Temperature Retrieval for UAV Broadband thermal imager data (TRUB) method to retrieve LST from UAV broadband thermal imager data. TRUB’s core includes two parts: 1) a simple lookup table (LUT) algorithm for reducing the uncertainty induced by the wide SRF ranges; and 2) models suitable for UAV remote sensing for estimating the atmospheric parameters. Validation from the Heihe River Basin shows that the LST retrieved by TRUB, of which the root mean square error (RMSE) and mean bias error (MBE) is 1.71 and −0.02 K, respectively, is highly consistent with thein situLST. TRUB is helpful to reduce the uncertainty caused by the wide SRF ranges of UAV thermal imagers and quantify the influence of atmosphere, thus can obtain UAV remote-sensing LST with better accuracy in large-area operating missions.

Here, four normalized difference water index (NDWI) variants, i.e., NDWI(860,970), NDWI(860,1240), NDWI(860,1640), and NDWI(1240,1640)are generated from the corn-oriented PROSAIL radiative transfer model. It is found that, instead of the linear relationship derived in previous studies, corn canopy water content (CWC) is best approximated as an exponential function of NDWI. Following the analysis of the PROSAIL-generated results, a newly optimized NDWI-based scheme is proposed for estimating corn CWC according to variations in the performance of the four NDWI variants under different CWC conditions. Validation results based on independent field data from the SMEX02, HiWATER2012, and Baoding2018 field experiments verify that this optimized NDWI-based corn CWC estimating scheme has a higher accuracy ($R = 0.87\,\,\pm \,\,0.03$, RMSE = 0.2068 ± 0.0145 kg/m2) than existing NDWI-based strategies for corn CWC retrieval. The feasibility of retrieving corn vegetation water content (VWC) based on the optimized NDWI-based scheme is also investigated, and the superiority of the optimized NDWI-based scheme for retrieving corn VWC is assessed. By comparing with four other NDWI-based corn VWC estimating methods, as well as the corn VWC parameterization scheme applied in the SMAP soil moisture algorithm, it is shown that our optimized NDWI-based scheme has the best VWC estimation accuracy, with the highest$R$of 0.89 ± 0.02 and the lowest RMSE of 0.7179 ± 0.0555 kg/m2.

Mitotic count is an important indicator for assessing the invasiveness of breast cancers. Currently, the number of mitoses is manually counted by pathologists, which is both tedious and time-consuming. To address this situation, we propose a fast and accurate method to automatically detect mitosis from the histopathological images. The proposed method can automatically identify mitotic candidates from histological sections for mitosis screening. Specifically, our method exploits deep convolutional neural networks to extract high-level features of mitosis to detect mitotic candidates. Then, we use spatial attention modules to re-encode mitotic features, which allows the model to learn more efficient features. Finally, we use multi-branch classification subnets to screen the mitosis. Compared to existing related methods in literature, our method obtains the best detection results on the dataset of the International Pattern Recognition Conference (ICPR) 2012 Mitosis Detection Competition. Code has been made available at: https://github.com/liushaomin/MitosisDetection.

The accurate estimation of the terrestrial latent heat flux (LE) from satellite observations at high spatial and temporal scales plays an important role in the assessment of the water and heat exchange between the earth's surface and the atmosphere. Although a variety of data fusion methods have been proposed to merge different LE products for more reliable estimates, most of them have ignored the spatiotemporal consistency of LE products across different resolutions. In this paper, we apply the multiresolution tree (MRT) method to improve the accuracy and reduce the inconsistency between the Moderate Resolution Imaging Spectroradiometer (MODIS) LE (MOD16) product and the Landsat-based LE product at different resolutions. Eddy covariance (EC) ground measurements at five sites, MODIS and Landsat images from January 2005 to December 2005 in the north central USA, are used to evaluate the performance of the MRT method. The results show that the MRT method can improve the accuracy of the original LE products (MOD16 and Landsat), and it has the potential to significantly reduce the uncertainty and inconsistency of these products. The bias decreased by 38.3% on average, and the root-mean-square error (RMSE) decreased by approximately 49.2% after the MRT was applied at each scale. Further studies are still required to make the MRT method more universal on a variety of land cover types for long-time periods.

Land surface temperature (LST) is a key variable at the land-atmosphere boundary. For many research projects and applications an all-weather LST product at moderate spatial resolution (e.g., 1 km) would be highly useful, especially in frequently cloudy areas. Merging thermal infrared (TIR) and microwave (MW) observations is able to overcome shortcomings of single-source remote sensing to derive such an LST. However, in current merging methods, models adopted for downscaling MW LST fail to quantify the effect of temporal variation of LST. Thus, accuracy of the merged LST can be deteriorated and therefore remain a major impediment for these methods to be generalized over large areas. In this context, we propose a new practical method to merge TIR and MW observations from a perspective of decomposition of LST in temporal dimension. The physical basis of the method is decomposing LST into three temporal components: annual temperature cycle component, diurnal temperature cycle component prescribed by solar geometry, and weather temperature component driven by weather change. The method was applied to MODIS and AMSR-E/AMSR2 data to generate an 11-year record of 1-km all-weather LST over Northeast China: the resulting merged LST has an accuracy of 1.29-1.71 K when validated against in situ LST; besides, no obvious differences in accuracy of the merged LST were found between clear-sky and unclear-sky conditions. Furthermore, the proposed method outperforms the previous method in both accuracy and image quality, indicating its good capability to generate daily 1-km all-weather LST, which will benefit continuous monitoring of earth's surface temperature.

The matrix doubling (MD) model is a numerical solution to the radiative transfer equation. It can achieve better accuracy in simulating microwave signals from vegetated terrain by considering multiple-scattering effects. However, it is difficult to apply the MD model to retrieving work due to its high complexity. This letter presents a case study performed on corn to demonstrate a multiangular (5°-65°), multiband (1.4/6.925/10.65 GHz) microwave emission model considering multiple-scattering effects by parameterizing the MD model. The simulated emissivity differences between the theoretical model and parameterized model are small. The mean absolute percent errors are all less than 1%, and the root mean square errors (RMSEs) are all within the range of 10-3. Validations using airborne polarimetric L-band microwave radiometer data and ground-based trunk-mounted multifrequency microwave radiometer data indicate that the parameterized model achieves good accuracy with overall RMSEs within 8K at all three bands.

Satellite passive microwave (MW) remote sensing has a better ability to observe land surface temperature (LST) in cloudy conditions than thermal infrared (TIR) remote sensing. Due to the much greater thermal sampling depth (TSD) of MW, currently available MW LST do not represent the thermodynamic temperature of the land surface and, therefore, yield systematic differences from TIR LST. The TSD effect is particularly prominent over barren land and sparsely vegetated surfaces. Here, we present a novel TSD correction (TSDC) method to estimate the MW LST over barren land. The core of this method is a new formulation of the passive MW radiation balance equation, which allows linking MW effective physical temperature to the soil temperature at a specific depth. The TSDC method is applied to the 6.9-GHz channel of AMSR-E in northwestern China-western Mongolia and western Namibia (WN). Evaluation shows that LST estimated by the TSDC method agrees well with the MODIS LST. Validation based on in situ LSTs measured at the Gobabeb site in WN demonstrates the high accuracy of the TSDC method: it yields a root mean squared error of about 2-3 K and slight systematic error. In contrast, other methods without TSDC yield lower accuracies and significantly underestimate LST. Therefore, the TSDC method has the potential to generate MW LST with the same physical meaning and similar accuracy as TIR LST. This study provides implications for developing practical and accurate methods to estimate MW LST over other land surface types and at the global scale.

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.

Remotely sensed evapotranspiration (RS_ET) products have been applied from regional to global. However, the validation of remote sensing products over heterogeneous land surfaces has been hindered due to the challenges in the theory and methods in recent decades, especially in estimation of “ground-truth” at the satellite pixel scale. In this study, an innovative validation framework including quantification of the spatial heterogeneity, optimization of the ground sampling strategy, multi-scale measurement, upscaling theory, uncertainty analyses, and validation method (direct validation, indirect validation and cross validation), was proposed to validate RS_ET products at different scales. Here, the framework was applied in Haihe and Heihe basin in China. The results showed the proposed validation framework of RS_ET product was reasonable and feasible.

Remotely sensed land surface temperature (LST) is influenced by the viewing angles and ground structure. By selecting a sparsely vegetated surface as the study area, the effects of structural parameters of land surface on remotely sensed LST are analyzed in this paper. Results demonstrate that both the density of canopy and size of canopy have significant influences on the remote sensing observations of LST. The difference between the LSTs obtained at nadir and off-nadir views also varies according to the acquisition season. This work is expected to be beneficial for quality control of remotely sensed LST.

Although many challenges exist, validation of the satellite land surface temperature (LST) product over heterogeneous surface can provide new and in-depth understandings of the product. Lessons learned from the validation are important to improve the satellite LST product. In order to better understand the relationship between LSTs measured through different approaches and instruments and test the possibility to upscale the ground measured LST over heterogeneous surface, a MUlti-Scale Observation Experiment on land Surface temperature (MUSOES) was designed and conducted in an extremely arid region in Northwest China. The experiment was concentrated at two typical sites (i.e. HHL - sparsely forest, and SDQ - open shrubland). It began from July 2014 and have run normally for two years. First results of this experiment have been presented here. MUSOES provides a basis to examine the upscaling of the ground measured LST to the LST at the satellite pixel scale over the heterogeneous surface.

Airborne TIR remote sensing can obtain land surface temperature (LST) with high spatial resolution. However, the swath width of airborne stripes is usually limited. Therefore, it is necessary to generate the LSTs for a large area through temporal normalization of LSTs derived from different stripes. By selecting an agricultural oasis as the study area, this study compares the diurnal temperature cycle (DTC) model and polynomial regression (PR) technique in the temporal normalization of the LSTs derived from the Thermal Airborne Spectrographic Imager (TASI) data. The results show that the DTC model has better accuracy in normalizing the LSTs. However, the PR technique is simple and requires less ancillary data. The DTC method can normalize the LST to any specific time and generate temporally continuous LSTs, while the PR method can only do relative normalization. This study is helpful to reduce the temperature differences of different airborne stripes and obtain airborne LSTs with both high spatial and temporal resolutions.

Thermal cameras are helpful instruments for measuring surface temperatures in field experiments. However, previous studies haven't detailed the method of deriving component temperatures of vegetation and soil over heterogeneous surfaces. In addition, the sources contributing to uncertainties in the derived component temperatures require further investigation. We present a study wherein the component temperatures of a dynamically developing maize field were derived from thermal images. The sources influencing the derived component temperatures have been investigated and different parameterization schemes for atmospheric downwelling radiation have been compared. The results demonstrate that the thermal cameras provide a feasible method of deriving the component temperatures. If the thermal camera is mounted at approximately 30 m above the target and then the atmospheric upwelling radiation and transmittance is ignored, a 1.0-2.0 K error for the component temperatures may occur.

To validate satellite-based surface fluxes by ground measurements properly, several numerical simulations were carried out at a homogeneous alpine meadow site and mixed cropland site, considering various atmospheric conditions and different land cover distribution types. By comparing various pixel selection methods, the results showed that footprint was significant in insuring a consistent spatial scale between ground measurements and satellite-based surface fluxes, particularly for heterogeneous surface and high-resolution remote sensing data. Because large aperture scintillometer measurements cover larger areas than eddy covariance (EC) system measurements, the spatial heterogeneity at a subpixel scale in complicated surface should be further considered in validating coarse satellite data. Thus, more accurate validation data and scaling methods must be developed, such as measuring surface fluxes at the satellite pixel scale by a flux measurement matrix or airborne EC measurements.

Surface sensible heat flux (SHF) is a critical indicator for understanding heat exchange at the land-atmosphere interface. A common method for estimating regional SHF is to use ground observations with approaches such as eddy correlation (EC) or the use of a large aperture scintillometer (LAS). However, data observed by these different methods might have an issue with different spatial supports for cross-validation and comparison. This letter utilizes a geostatistical method called area-to-area regression kriging (ATARK) to solve this problem. The approach is illustrated by upscaling SHF from EC to LAS supports in the Heihe River basin, China. To construct a point support variogram, a likelihood function of four parameters (nugget, sill, range, and shape parameters) conditioned by EC observations is used. The results testify to the applicability of ATARK as a solution for upscaling SHF from EC support to LAS support.

The determination of the spatial heterogeneity of the regional evapotranspiration over a complex underlying surface in an oasis-desert region is crucial for water resource management in a river basin and aiding in irrigation decisions. The surface energy balance system (SEBS) model has been widely used to estimate surface energy fluxes. However, the parameterization of surface roughness length for momentum transfer (z0m) and heat transfer (z0h) did not perform well for a complex underlying surface. Moreover, it is difficult to estimate surface soil heat flux, i.e., G0, accurately at the regional scale. In this letter, the parameterization schemes of z0m, z0h, and G0were optimized. Measurements from 21 sets of eddy covariance systems were used to validate the model performance. The results show that the revised SEBS model root-mean-square errors (RMSEs) of the satellite-based sensible and latent heat fluxes (H and LE) decreased from 97.2 W · m-2to 56.9 W · m-2and from 102.9 W · m-2to 74.8 W · m-2, respectively, at the footprint scale. At the pixel scale, the RMSEs of the revised model estimates of the H and LE were 40.9 W · m-2and 57.5 W · m-2, respectively. The improved agreements between the estimates and the measurements indicate that the revised SEBS model is appropriate for estimating regional energy fluxes over heterogeneous oasis-desert surfaces. Furthermore, the spatial and temporal patterns of the LE in the middle reaches of the Heihe River were investigated.

The two-source energy balance (TSEB) model was successfully applied to estimate evaporation (E), transpiration (T), and evapotranspiration (ET) for land covered with vegetation, which has significantly important applications for the terrestrial water cycle and water resource management. However, the current composite temperature separation approaches are limited in their effectiveness in arid regions. Moreover, E and T are difficult to measure on the ground. In this letter, the ground-measured soil and canopy component temperatures were used to estimate E, T, and ET, which were better validated with observed ratios of E (E/ET%) and T (T/ET%) using the stable oxygen and hydrogen isotopes, and the ET measurements using an eddy covariance (EC) system. Our results indicated that even under the strongly advective conditions, the TSEB model produced reliable estimates of the E/ET% and T/ET% ratios and of ET. The mean bias and root-mean-square error (RMSE) of E/ET% were 1% and 2%, respectively, and the mean bias and RMSE of T/ET% were -1% and 2%, respectively. In addition, the model exhibited relatively reliable estimates in the latent heat flux, with mean bias and RMSE values of 31 and 61 W · m-2, respectively, compared with the measurements from the EC system. These results demonstrated that a robust soil and vegetation component temperature calculation was crucial for estimating E, T, and ET. Moreover, the separate validation of E/ET% and T/ET% provides a good prospect for TSEB model improvements.

To study the multiscale characteristics of ecohydrological processes in the Heihe River Basin, an intensive flux observation matrix was established, which consisted of mainly 17 eddy covariance (EC) flux stations in a 5.5 km × 5.5 km area of the Zhangye oasis. Formal observations began in June and continued through September 2012. Before the main campaign, an intercomparison for all instruments (including 20 EC sets) was conducted in the Gobi desert. All the data provided a rare opportunity to assess the flux uncertainties of EC measurements. Three methods were chosen in this assessment. For the Gobi intercomparison, a simple method based on elementary error analysis could provide the systematic errors and random uncertainties for each EC; uncertainties for sensible heat flux were generally less than 10% in this area. For flux matrix observations, by using mainly the method of Mann and Lenschow (1994), the uncertainties estimated for sensible heat, latent heat, and CO2fluxes were approximately 18%, 16%, and 21%, respectively, for the selected period. These were comparatively high because of the inherent heterogeneities of the oasis. The flux uncertainty quantification, including its probability distribution and the nonconstant variance characteristics shown for these data sets, is essential for flux data interpretation and applications, particularly the validation of relevant remote sensing models.

A comprehensive validation method of remotely sensed evapotranspiration (ET) was proposed in this paper. Among this method, an observation system was constituted of an eddy covariance system, a large aperture scintillometer and an automatic weather station, and then an observation network was established at Miyun, Guantao, Daxing, Xiaotangshan, and Haidian Park sites in Hai River Basin, which were set up from 2004 for ET and correlative parameters measurements at different satellite pixel scales. On this basis, rigorous data process and quality control were executed to ensure the high quality of observations. Meanwhile, a validation procedure of remotely sensed ET based on ground measurements was presented, and the method of selecting validation pixels and evaluation index were investigated intensively. According to this method, validation of remote sensing ET was performed in Beijing area. The results demonstrated the proposed validation method was feasible.

In this paper, the evapotranspiration in Wushen County, located in the Mu Us Sandland, was estimated by Advection-Aridity Model with meteorological data and 1km resolution reflectance of NOAA/AVHRR from 1981 to 2000. Then the spatial and temporal distributions of the evapotranspiration in Wushen County were analyzed. The results show: (1) Advection-Aridity Model combined with remotely sensed data could estimate regional evapotranspiration better. (2) The annual mean evapotranspiration during 19812000 in Wushen County is 264mm increasing from northwest to southeast while the annual mean precipitation is 320mm. (3) The temporal variation of evapotranspiration is in agreement with that of precipitation. (4) The relative variance of annual evapotranspiration is between 8% and 16%, which is the smallest in the eastern headwaters, bigger in the middle and the biggest in the north and southwest. Keywordsevapotranspiration; Wushen County; AdvectionAridity Model;

The paper focuses on interpreting the different spatial relationships between NDVI and Ts, a triangular or a trapezoid, and analyzing transformation condition and the physical connotation and ecological meaning of the vegetation index-surface temperature feature space. Further, using the Temperature-Vegetation Dryness Index (TVDI), we explain the existent meaning of a triangular shape after NDVI arrives at saturated state (NDVI=1) by analyzing the relationship between NDVI, LAI and evapotranspiration. The specific relations between NDVI and Ts will help us validate and update land surface models well.

Large aperture scintillometers (LAS), Bowen ratio, eddy covariance measurements, soil moisture in various depths with measuring masses and routine weather observation such as visibility, cloud, wind speed and weather phenomenon with manual work were employed to study sensible heat flux over homogeneous bare soil surface from March 20th to April 20th, 2002, at XiaoTangshan area, Beijing. The diurnal variation of sensible heat flux from LAS is analyzed in this paper, and the relation between sensible heat flux and weather conditions is discussed. Moreover, the correlation coefficient between LAS based surface sensible heat fluxes and the influencing factors such as soil surface temperature and wind speed were analyzed. The analysis will help us to improve the accurate of LAS measurements. Further, the comparisons of the scintillometer flux measurements with the measurements of Bowen ratio and eddy correlation methods were done in order to make more research on scaling-up of surface sensible heat flux from point to area

The measurements of air temperature and air humidity were employed to analyze the diurnal variation of atmospheric emissivity over unirrigated bare soil, from March 20th to April 20th, 2002, at XiaoTangShan area, Beijing. Further, sky radiative temperature and soil radiative temperature with thermal infrared multi-angles were used to study the differences between surface radiative temperature over bare soil without being calibrated and calibrated with standard blackbody and the differences between surface "true" temperature, which were calculated with the downwelling longwave ambient radiation and soil emissivity, and surface radiative temperature, which were calibrated by standard blackbody. The results may be scientific reference to invert land surface temperature with remote sensing and to study land surface energy balance.

Based on the observational data, we validate these complementary relationship models such as advection-aridity model, CRAE model and Granger model on the point and region scale. It was showed that complementary relationship models perform well over winter wheat fields, but rather differently over grassland. With the exception of several extreme arid years, annual errors of simulated evapotranspirations by Advection-Aridity model, CRAE model and Granger model were less than 10% in the Yellow River Basin on a monthly base.

The SEBS model (the surface energy balance system) based on land surface energy balance equation is used to estimate sensible heat flux and latent heat flux using remotely sensed data. In This work, The SEBS model is validated with two sets of data collected in two field experiment on winter-wheat field in Shunyi county of Beijing (116/spl deg/ 26' E-117/spl deg/ E; 40/spl deg/ N-40/spl deg/ 21' N) and on bare soil in Changping county of Beijing (116/spl deg/ 26' E- 116/spl deg/ 28' E; 40/spl deg/ 10' N-40/spl deg/ 12' N),China. Sensible and latent heat flux measured by eddy correlation method are compared with these SEBS estimates. The results show: (1) diurnal variant of Sensible and latent heat flux estimated bv SEBS basically agreed with the measured by eddy correlation method both on winter-wheat field and on bare soil, but the performance on winter-wheat field is better than on bare soil, the performance of sensible flux is better than that of latent flux. (2) Both on winter-wheat field and on bare soil, the precision of sensible heat flux estimated by SEBS is higher than that of latent heat flux, while the SEBS model performs better on winter-wheat field than on bare soil. (3) The sensitivity of SEBS to even' parameter is different. The SEBS model is most sensitive to the available energy, up to 0.3, while it is more sensitive to surface-air temperature difference and aerodynamic resistance, up to 0.1 and 0.09 respectively.

In the process of measurement, a number of factors will affect the quality of data. Therefore, data must be verified and assessed before their applications. This work discussed some methods for quality assessment of crop spectral data, which include methods of analysis of spectral characteristics, statistical test and spectral simulation. The method of spectral analysis compares measured spectra with the reference spectrum, and analyzes the location of wave crest, wave trough and the shape, intensity of spectral reflectance curves. The method of statistical test consists of shape similarity test and intensity test. The shape similarity test analyzes the correlation between measured spectral data and the reference spectral datum over the special wavelength range and assesses quality of the measured data by the correlation coefficients. The intensity test calculates the mean value and standard deviation of spectral data, and forms a spectral zone around the mean value. We consider it as the abnormal one if one spectral curve goes beyond the spectrum zone. The method of spectral simulation mainly compares measured spectra with simulated spectra by combined PROSPECT-SAIL model. Results show these methods of quality assessment are feasible.

Eddy covariance and Bowen ratio measurements were carried out over different surfaces, and used to characterize the daily variation of energy fluxes and energy balance under different weather condition. These will be used to identify the phenomena to be addressed in future modeling works. Results show that (1) The diurnal variation characteristics of the fluxes were different over different surfaces, especially the latent heat flux, (2) The energy imbalance persisted in different surfaces with an average about 20%; the energy balance closure was better in the afternoon than in the morning averagely, possibly suggesting the underestimation of storage terms, which are usually larger in the morning.

Large Aperture Scintillometers (LAS), Bowen ratio and eddy covariance measurements were employed to study sensible heat flux over homogeneous bare soil surface from March 20/sup th/ to April 20/sup th/, 2002, at XiaoTangshan area, Beijing. The diurnal variation of sensible heat flux from LAS is analyzed in this paper, and the relation between sensible heat flux and weather conditions is discussed. Further, test comparisons of the scintillometer flux measurements with the measurements of Bowen ratio and eddy correlation methods show good agreement; The correlation coefficients are over 0.8.

This paper describes an evaluation of the aerodynamic resistance models based on direct measurements over a wheat field and a bare soil surface. According to the performance analysis it is shown that (1) Over bare soil surface, the models of Choudhury-2, Verma-Rosenberg and Mahrt-Ek have relatively high precision, while on the winter wheat field, XieXianqun model, Choudhury-1 model and Brutsaert model performed better. (2) The aerodynamic resistance is inversely proportional to the wind speed until reaching a state of stabilization. (3) The model to estimate the aerodynamic resistance should be chosen according to the surface condition.

Roughness height for heat transfer is a crucial parameter in estimation of heat transfer between the land surface and the atmosphere, especially when radiometric measurements are used. Although many empirical formulations have been proposed, the uncertainties associated with these formulations are shown to be large, especially over sparse canopies. In this contribution, a simple physically based formula is derived for the estimation of the roughness height for heat transfer for bare soil surfaces. The new formula is validated with measurements collected in a filed campaign in Xiaotangshan, near Beijing, China in 2002. The present model in further shown to be able to explain the diurnal variation in the roughness height for heat transfer. The turbulent heat fluxes estimated using radiometric measurements as inputs are markedly improved when this new formula is used.

On the analysis of characteristics of current agriculture, we put forward the conception of digital agriculture, and construct its framework. Relationships between precision agriculture, digital Earth, information agriculture, virtual agriculture and digital agriculture are analyzed. The necessities to put forward the concept of digital agriculture, the feasibility to realize digital agriculture and the measures should be taken are also discussed in our paper.