蒸散发(Evapotranspiration,ET)是地表水循环和能量循环的关键纽带,准确、定量地估算区域ET对于理解陆-气相互作用、全球气候变化等至关重要.MOD16模型基于Penman-Monteith(P-M)方程,是一种获取区域ET的重要遥感模型.然而,MOD16模型没有直接利用土壤水分信息,而是通过相对湿度(Relative Humidity,RH)、饱和水汽压差(Vapor Pressure Deficit,VPD)、叶面积指数(Leaf Area Index,LAI)等间接表达土壤水分信息的作用,这可能会给区域ET的估算带来一些不确定性.该研究将归一化水指数(Normalized Difference Water Index,NDWI)作为土壤水分信息的补充项,对MOD16模型的地表阻抗进行修正,以改进MOD16模型(改进后的模型为MOD16-sm),并将改进后的模型在中国西北干旱区绿洲进行验证和应用.模型验证包括模拟值与观测值的对比及误差分析.模拟值与观测值的对比分析结果表明,MOD16-sm模型获取的ET精度较高,决定系数(Coefficient of Determination,R2)为0.77,均方根误差(Root Mean Square Error,RMSE)为0.8 mm/d,平均绝对误差(Mean Absolute Deviation,MAE)为0.46 mm/d;误差分析结果显示,MOD16-sm模型估算结果的误差控制优于MOD16模型,结合模拟值与观测值的对比分析可知,MOD16-sm模型改善了MOD16模型的部分高估现象,MOD16-sm模型能更好地反映土壤水分对ET的影响.模型应用包括ET估算值的空间分布分析及ET估算值的频率分布统计.对MOD16-sm模型的估算结果进行空间分析发现,高植被覆盖区的ET值较高,低植被覆盖区的ET值较低,说明MOD16-sm模型的ET估算结果与土地利用类型密切相关;研究区ET估算值的频率分布结果表明,MOD16-sm模型能较好地反映和表达出不同植被覆盖区的ET通量异质性.因此,利用NDWI对MOD16模型进行改进是可行的和合理的,该研究可为提高区域ET的估算精度提供参考和思路.

Soil moisture (SM) is a crucial component for understanding, modeling, and forecasting terrestrial water cycles and energy budgets. However, estimating field-scale SM based on thermal infrared remote-sensing data is still a challenging task. In this study, an improved Flexible Spatiotemporal DAta Fusion (FSDAF) method based on land-surface Diurnal Temperature Cycle (DTC) model (DFSDAF) was proposed to fuse Moderate Resolution Imaging Spectroradiometer (MODIS) and Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) land-surface temperature (LST) data to generate ASTER-like LST during the night. The reconstructed diurnal LST data at a high spatial resolution (90 m) was then utilized to drive a two-source normalized soil thermal inertia model (TNSTI) for the vegetated surfaces to estimate field-scale SM. The results of the proposed methods were validated at different observation depths (2, 4, 10, 20, 40, 60, and 100 cm) over the Zhangye oasis in the middle region of the Heihe River basin in the northwest of China and were compared with the SM estimates from the TNSTI model and other SM products, including AMSR2/AMSR-E, GLDAS-Noah, and ERA5-land. The results showed the following: (1) The DFSDAF method increased the accuracy of LST prediction, with the determination coefficient (R2) increasing from 0.71 to 0.77, and root mean square error (RMSE) decreasing from 2.17 to 1.89 K. (2) the estimated SMs had the best correlation with the observations at the 10 cm depth (with R2 of 0.657; RMSE of 0.069 m3/m3), but the worst correlation with observations at the 40 cm depth (with R2 of 0.262; RMSE of 0.092 m3/m3); meanwhile, the modeled SMs were significantly underestimated above 40 cm (2, 4, 10, and 20 cm) and slightly overestimated below 40 cm (60 and 100 cm); in addition, the field-scale SM series at high spatial resolution (90 m) showed significant spatiotemporal variation. (3) The SM estimates based on the TNSTI for the vegetated surfaces are more capable of characterizing the SM status in the root zone (~80 cm) or even deeper, while the SMs from AMSR2/AMSR-E, GLDAS-Noah, or ERA5-land products are closer to the SM in the surface layer (the depth is less than 5 cm). The TNSTI provided favorable data supports for hydrological model simulations and showed potential advantages for agricultural refinement managements and smart agriculture.

Accurate measurements of the three-dimensional structure characteristics of urban buildings and their greenhouse effect are important for evaluating the impact of urbanization on the radiation energy budget and research on the urban heat island (UHI) effect. The decrease in evapotranspiration or the increase in sensible heat caused by urbanization is considered to be the main cause of the UHI effect, but little is known about the influence of the main factor “net radiant flux” of the urban surface heat balance. In this study, experimental observation and quantitative model simulation were used to find that with the increase of building surface area after urbanization, the direct solar radiation flux and net radiation flux on building surface areas changed significantly. In order to accurately quantify the relationship between the positive and negative effects, this study puts forward the equivalent calculation principle of “aggregation element”, which is composed of a building’s sunny face and its shadow face, and the algorithm of the contribution of the area to thermal effect. This research clarifies the greenhouse effect of a building with walls of glass windows. Research shows that when the difference between absorption rates of a concrete wall and grass is −0.21, the cooling effect is shown. In the case of concrete walls with glass windows, the difference between absorption rates of a building wall and grass is −0.11, which is also a cooling effect. The greenhouse effect value of a building with glass windows reduces the cooling effect value to 56% of the effect of a building with concrete walls. The simulation of changes in net radiant flux and flux density shows that the greenhouse effect of a 5-story building with windows yields 15.5% less cooling effect than one with concrete walls, and a 30-story building with windows reduces the cooling effect by 23.0%. The simulation results confirmed that the difference in the equivalent absorption rate of the aggregation element is the “director” of cooling and heating effects, and the area of the aggregation element is the “amplifier” of cooling and heating effects. At the same time, the simulation results prove the greenhouse effect of glass windows, which significantly reduces the cold effect of concrete wall buildings. The model reveals the real contribution of optimized urban design to mitigating UHI and building a comfortable environment where there is no atmospheric circulation.

城市建筑立体结构特征和楼房温室效应的精准量测是评估城市化对辐射能量收支影响以及城市热岛效应研究的重要内容之一.城市化导致蒸散发减少或显热增加被认为是形成城市热岛效应的主因,而目前对于城市下垫面热量平衡主因子"净辐射通量"的影响了解甚少.文章应用实验观测和定量模型模拟,发现随着城市化后楼房表面积增加,楼房建筑表面积上太阳直接辐射通量和净辐射通量发生了明显的变化.为了准确地计算这种正负效应的定量关系,本研究提出了楼房朝阳面与其阴影面构成一体的"聚合元"等价计算原理以及面积对热效应贡献的算法,阐明了楼房玻璃窗墙壁的温室效应.研究表明,当水泥墙与草地的吸收率差值为-0.21时,体现了冷效应.在嵌有玻璃窗的水泥墙壁另一情形下,楼房墙与草地的等效吸收率差值为-0.11,也为冷效应,玻璃窗楼房的温室效应值相当于减缓到56％的水泥墙楼房冷效应值.对于净辐射通量和通量密度变化的模拟表明:5层楼温室效应相当于减少水泥墙冷效应15.5％,30层楼温室效应相当于减少水泥墙冷效应23.0％.模拟结果证实了聚合面的等效吸收率差值为冷热效应的"定向器",聚合元面积为冷热效应的"放大器".同时,模拟结果证实了玻璃窗的温室效应,明显减缓了水泥墙壁楼房的冷效应.模型能揭示在没有大气环流影响下城市的设计对减缓城市热岛现象和构建舒适环境的真实贡献.

Remote sensing-based estimation of soil moisture is crucial in many aspects including basin scale water resource management, irrigation scheduling, regional scale drought monitoring and crop yield forecasting. In this study, we evaluate the potential of visible/thermal-infrared remote sensing in soil moisture estimation, by assessing the TVDI-based method and three categories of methods based on evaporative fraction/potential evaporation ratio (EFM1, EFM2 and EFM3). In combination with ASTER data set, soil moisture in middle reach of the Heihe River Basin is predicted by the above-mentioned four methods and validated by the ground-based measurements from eco-hydrological wireless sensor network and hydro meteorological observation network in the middle reach of Heihe river basin. Results indicate that uncertainties arise from the empiricism of the TVDI-based method in the process of determining dry and wet edges. On the other hand, the evaporation fraction/potential evaporation ratio methods can to some degree reduce the uncertainties, and among the three methods, EFM1 and EFM3 outperform EFM2. In addition, the thermal-infrared based methods require accurate soil parameters to reproduce the variation of soil moisture.

Evaporation (E) and transpiration (T) information is crucial for precise water resources planning and management in arid and semiarid areas. Two-source energy balance (TSEB) methods based on remotely-sensed land surface temperature provide an important modeling approach for estimating evapotranspiration (ET) and its components of E and T. Approaches for accurate decomposition of the component temperature and E/T partitioning from ET based on TSEB requires careful investigation. In this study, three TSEB models are used: (i) the TSEB model with the Priestley-Taylor equation, i.e., TSEB-PT; (ii) the TSEB model using the Penman-Monteith equation, i.e., TSEB-PM, and (iii) the TSEB using component temperatures derived from vegetation fractional cover and land surface temperature (VFC/LST) space, i.e., TSEB-TC-TS. These models are employed to investigate the impact of component temperature decomposition on E/T partitioning accuracy. Validation was conducted in the large-scale campaign of Heihe Watershed Allied Telemetry Experimental Research-Multi-Scale Observation Experiment on Evapotranspiration (HiWATER-MUSOEXE) in the northwest of China, and results showed that root mean square errors (RMSEs) of latent and sensible heat fluxes were respectively lower than 76 W/m2 and 50 W/m2 for all three approaches. Based on the measurements from the stable oxygen and hydrogen isotopes system at the Daman superstation, it was found that all three models slightly overestimated the ratio of E/ET. In addition, discrepancies in E/T partitioning among the three models were observed in the kernel experimental area of MUSOEXE. Further intercomparison indicated that different temperature decomposition methods were responsible for the observed discrepancies in E/T partitioning. The iterative procedure adopted by TSEB-PT and TSEB-PM produced higher LEC and lower TC when compared to TSEB-TC-TS. Overall, this work provides valuable insights into understanding the performances of TSEB models with different temperature decomposition mechanisms over semiarid regions.

A trapezoid interpolation thermal disaggregation (TI_DisTrad) model was proposed in this study. This model can disaggregate coarse resolution land surface temperature (LST) to fine resolution LST based on fractional vegetation cover (FVC) versus LST space. The proposed model assumes that the quantitative relationships among the Bowen ratio, FVC and LST can work for the pixels inside the FVC-LST space at both coarser and finer resolutions. Pixels that were outside the FVC-LST space were addressed with a support vector machine regression. We evaluated the TI_DisTrad model over an agricultural region in central Iowa (USA) and an urban region in Beijing (China). The performance of the TI_DisTrad model was assessed by comparing results against those of five other popular benchmark models. The results show that the TI_DisTrad model was slightly superior to three of the benchmark models over the agricultural regions and achieved more accurate LST compared to two of the benchmark models over the urban region. When using two surface energy balance models (the one-source model and the two-source model), the estimated evapotranspiration (ET) from the TI_DisTrad disaggregated LST data was more accurate than the estimated ET from the disaggregated LST obtained using the other benchmark approaches, corresponding to an increase in average accuracy of the TI_DisTrad model.

In this paper, based on the measurements of soil elements content and infrared spectra of 26 soil samples collected in more than 10 places, the relationship between soil emissivity in mid-infrared bands and the content of 11 soil elements including organic matters such as NO(3)-N, P, K, Ca, Mg, Cu, Fe, Mn, Zn and pH are analyzed. The bands where the soil elements content are significantly correlated with emissivity are given. And soil elements content estimation method is established based on the soil emissivity spectra with the partial least squares regression model and multiple stepwise regression model. The results show that: (1) In 8~10 μm, the correlation coefficient (R(2)) between Ca and soil emissivity is the highest, followed by Mg, Mn and Fe, with the highest correlation coefficient of 0.85 and the lowest, 0.52. In the range of 6~8 μm, the correlations between the contents of K, Fe, NO(3)-N, Zn and emissivity decrease gradually, with the highest correlation coefficient of 0.75 and the lowest 0.48. In 10~14 μm, the correlation between soil elements contents and emissivity is the highest for Mn, followed successively by P and K. (2) The scatter plot of soil emissivity and pH value has a parabola relation basically. The emissivity is the highest when pH value is 7, while the emissivity decreases gradually with the gradual decrease of pH value. (3) The accuracy of the estimated soil elements content from the partial least squares regression method is higher than that from the multiple stepwise regression method. It is noted that R(2) between the measurements and the estimates for the elements of Cu, Fe and Ca from the partial least squares regression method are very high (larger than 0.9). Additionally, using the simulated emissivity spectrum in the ASTER thermal infrared bands, modeling R(2) and validation R(2) between the measurements and the estimates for the elements of Ca from the multiple stepwise regression method are high (0.774 and 0.892, respectively). Using the simulated emissivity spectrum in the MODIS infrared bands, modeling R(2) and validation R(2) for Ca and Fe are higher than 0.85, and modeling R(2) and validation R(2) for Mg, K are higher than 0.5. As a whole, the emissivity spectrum in ASTER band 10 and band 11 and MODIS bands 28, 29, 30 are more sensitive to soil elements content, and thus they are more suitable for the estimation of soil elements content.

Evapotranspiration (ET) is crucial to water resource management, and regional ET is usually estimated by remote sensing. However, ET estimates through remote sensing are instantaneous values that need to be converted into daily totals. The study proposed the Gaussian fitting method to convert instantaneous ET retrieved by remote sensing into daily ET in Northwest China. Results showed that the Gaussian fitting method performed well. The simulated daily ET had high accuracy and had good agreement with measurements from EC stations, with an R-2 (coefficient of determination) of 0.87, an RMSE (root mean square error) of 0.46 mm and a MAE (mean average error) of 0.41 mm on two study days. The study indicated that the Gaussian fitting method can be regarded as an effective approach to convert instantaneous ET into daily ET.

Surface air temperature is a basic meteorological variable to monitor the environment and assess climate change. Four remote sensing methods-the temperature-vegetation index (TVX), the univariate linear regression method, the multivariate linear regression method, and the advection-energy balance for surface air temperature (ADEBAT)-have been developed to acquire surface air temperature on a regional scale. To evaluate their utilities, they were applied to estimate the surface air temperature in northwestern China and were compared with each other through regressive analyses, t tests, estimation errors, and analyses on estimations of different underlying surfaces. Results can be summarized into three aspects: 1) The regressive analyses and t tests indicate that the multivariate linear regression method and the ADEBAT provide better accuracy than the other two methods. 2) Frequency histograms on estimation errors show that the multivariate linear regression method produces the minimum error range, and the univariate linear regression method produces the maximum error range. Errors of the multivariate linear regression method exhibit a nearly normal distribution and that of the ADEBAT exhibit a bimodal distribution, whereas the other two methods display negative skewness distributions. 3) Estimates on different underlying surfaces show that the TVX and the univariate linear regression method are significantly limited in regions with sparse vegetation cover. The multivariate linear regression method has estimation errors within 1 degrees C and without high levels of errors, and the ADEBAT also produces high estimation errors on bare ground.

The validation is an important guarantee of quality,reliability and applicability of Remote Sensing Products (RSPs),and is also the foundation to improve the RSPs accuracy,extend the application domain and strength the application ability.This paper introduced the progresses and lessons learned from a project titled by ‘ key technology of remote sensing products validation and its experimental evaluation’ supported by Ministry of Science and Technology of China.The progresses included:①)Formulating a series of national standards composed of general methods for the validation of terrestrial quantative RSPs,field-site selection and instrumentation for land surface RSPs,and other 24 individual standards of remote sensing variables;②)Building integral technique process system of RSPs validation;(③)Developing some key methods from optimized spatial sampling,upscaling to validation strategy;(④)Obtaining the multi-scale satellite-airborne-ground synchronized observation and evaluating systematically the validation standard and techniques;(⑤)Setting up national validation network for RSPs,exploring multimode allied observation experiment and forming the prototype and operation mechanism for the validation network.

A "two times thermal irradiance and four times measuring method" was proposed to measure the surface emissivity of any object. Compared with the previous methods, the proposed method can completely eliminate the interference of the lens and cavity walls of the sensor to measure accurately the irradiance of the observed object, implying its ability to improve the accuracy of surface emissivity measurement and making the equipment of emissivity measurement portable. The designed 1 000 + w/m(2) strong heat radiation source considerably improved the signal to noise ratio of the equipment. To compensate the warming effect of the measured object under the strong heat radiation source, we proposed a universal expression to solve the emissivity in the non-isothermal system and a "Process Method" to reduce warming. The comparison of three measurement results showed that the proposed method outperformed the others.

In the inversion of land surface temperature (LST) from satellite data, obtaining the information on land surface emissivity is most challenging. How to solve both the emissivity and the LST from the underdetermined equations for thermal infrared radiation is a hot research topic related to quantitative thermal infrared remote sensing. The academic research and practical applications based on the temperature-emissivity retrieval algorithms show that directly measuring the emissivity of objects at a fixed thermal infrared waveband is an important way to close the underdetermined equations for thermal infrared radiation. Based on the prior research results of both the authors and others, this paper proposes a new approach of obtaining the spectral emissivity of the object at 8–14 µm with a single-band CO2 laser at 10.6 µm and a 102F FTIR spectrometer. Through experiments, the spectral emissivity of several key samples, including aluminum plate, iron plate, copper plate, marble plate, rubber sheet, and paper board, at 8–14 µm is obtained, and the measured data are basically consistent with the hemispherical emissivity measurement by a Nicolet iS10 FTIR spectrometer for the same objects. For the rough surface of materials, such as marble and rusty iron, the RMSE of emissivity is below 0.05. The differences in the field of view angle and in the measuring direction between the Nicolet FTIR method and the method proposed in the paper, and the heterogeneity in the degree of oxidation, polishing and composition of the samples, are the main reasons for the differences of the emissivities between the two methods.

Soil heat flux (G) is an important component of the surface energy balance and plays an important role in the partition of sensible and latent heat fluxes from the available energy. The instantaneous daytime soil heat flux can be estimated as a fraction of net radiation (Rn). Depending on vegetation cover, soil moisture, and on the time of observation, the ratio of G to Rn is different. In order to investigate the diurnal variation of G/Rn with soil moisture for bare soil , an experiment was conducted from September to November in Beijing in 2015. Based on the observations, the relationship of G/Rn in the morning and in the afternoon was formulated, respectively, by using Rn , the maximum and the minimum land surface temperatures in the morning and in the afternoon. The validation result shows that the bias between the estimated G/Rn and the observed G/Rn are 0.014 and -0.0235 in the morning and in the afternoon, respectively. The average RMSE between the estimated G/Rn and the observed G/Rn are 0.032 and 0.0375, respectively.

To estimate the surface air temperature by remote sensing, the advection-energy balance for the surface air temperature (ADEBAT) model is developed which assumes the surface air temperature is driven by the local driving force and the advective driving force. The local driving force produces a local surface air temperature whereas the advective driving force changes it by adding an exotic air temperature. An advection factor f is defined to measure the quantity of the exotic air brought by the advection. Since the f is determined by the advection, this paper improves it to a regional scale by using the Inverse Distance Weighting (IDW) method whereas the original ADEBAT model uses a constant of f for a block of area. Results retrieved by the improved ADEBAT (IADEBAT) model are evaluated and comparison was made with the in situ measurements, with an R2 (correlation coefficient) of 0.77, an RMSE (Root Mean Square Error) of 0.31 K, and a MAE (Mean Absolute Error) of 0.24 K. The evaluation shows that the IADEBAT model has higher accuracy than the original ADEBAT model. Evaluations together with a t-test of the MAD (Mean Absolute Deviation) reveal that the IADEBAT model has a significant improvement.

The estimation of soil thermal inertia (STI) (P) on the vegetated surface is a challenging task due to the difficulty in acquiring soil temperature under vegetation. In most cases, mixed surface temperature (T) is used to replace soil temperature (T s ) to estimate P. Inevitably, errors are introduced because of the effect of vegetation. In this paper, on the basis of a simplified STI concept and an operational algorithm of surface temperature separation, the differences of STI estimated from T s , vegetation temperature (Tv) and T were quantified. When there is large difference between T and T s (as much as 10 K), the mean absolute percentage difference (MAPD) between STI estimated from T s (STI S ) and STI estimated from T (STI M ) can reach 60%. A normalized STI (STI N ) to account for the vegetated surface was proposed in terms of the linear mixing theory, which can be used to estimate the relative soil water (SW) content. Under the condition that the wilting point of the soil moisture and the saturated soil moisture are known for an area, SW content can then be calculated from STI N . The comparisons of the relationships between soil moisture from the advanced microwave scanning radiometer-earth observing system and STI S , STI V , STI M , and soil moisture estimated from STI N show that STI N is the best indicator of soil moisture, with the highest correlation coefficient (R 2 ) of 0.64 and 0.75 for the two validation domains.

Regional surface air temperature is a significant indicator to research the climate and monitor the environment. In order to estimate surface air temperature in regional scale by remote sensing observations, the study based on the improved advection-energy balance for the surface air temperature (ADEBAT) model to acquire surface air temperature in North China. Results showed that the improved ADEBAT model performed well, the retrieved air temperature had good agreements with measurements from weather stations, with correlations coefficients (R 2 ) of 0.73, 0.78 and root mean square error (RMSE) of 0.6°C, 0.5°C, respectively, on two days.

比辐射率是影响地表温度遥感反演精度的主要因素,是定量热红外遥感领域研究的核心内容,其与土壤质地、土壤成分、土壤水分和土壤粗糙度关系密切.首先通过试验方法初步研究了土壤水分和土壤粗糙度对土壤比辐射率的影响.其次采用傅里叶变换红外光谱仪观测不同水分条件下土壤比辐射率波谱.最后采用基于改变两次环境观测原理的一种主被动漫射式实时比辐射率测定装置观测不同粗糙度条件下8-14 μm土壤比辐射率均值的变化.结果表明,土壤比辐射率随土壤水分的增加而增加,其中影响最显著波段范围是3.3-5.3 μm,这个波段范围内波段平均比辐射率干土与湿土差异大于0.2;影响最小的波段范围是11-15 μm,这个波段范围内波段平均比辐射率干土与湿土差异在0-0.015之间;在热红外波段,8-9.5 μm是土壤水分对比辐射率影响最大的波段,干土与湿土比辐射率差异大于0.05;土壤比辐射率随着粗糙度的增加略有增加,干土和湿土都有此规律.