Yuan Rong

A Remote Sensing Method for Estimating Surface Air Temperature and Surface Vapor Pressure on a Regional Scale

This paper presents a method of estimating regional distributions of surface air temperature (Ta) and surface vapor pressure (ea), which uses remotely-sensed data and meteorological data as its inputs. The method takes into account the effects of both local driving force and horizontal advection on Ta and ea. Good correlation coefficients (R2) and root mean square error (RMSE) between the measurements of Ta/ea at weather stations and Ta/ea estimates were obtained; with R2 of 0.77, 0.82 and 0.80 and RMSE of 0.42K, 0.35K and 0.20K for Ta and with R2 of 0.85, 0.88, 0.88 and RMSE of 0.24hpa, 0.35hpa and 0.16hpa for ea, respectively, for the three-day results. This result is much better than that estimated from the inverse distance weighted method (IDW). The performance of Ta/ea estimates at Dongping Lake illustrated that the method proposed in the paper also has good accuracy for a heterogeneous surface. The absolute biases of Ta and ea estimates at Dongping Lake from the proposed method are less than 0.5Kand 0.7hpa, respectively, while the absolute biases of them from the IDW method are more than 2K and 3hpa, respectively. Sensitivity analysis suggests that the Ta estimation method presented in the paper is most sensitive to surface temperature and that the ea estimation method is most sensitive to available energy.

Estimation of soil heat flux by apparent thermal inertia

By analyzing the relationship between midday soil heat flux (Gm) and apparent thermal inertia (ATI) by time series observations from YuCheng agroecological station, CAS, a method of estimating midday G by ATI was presented in the paper. ATI method is shown to have the close agreement with the observations. In situ observations from 2003 to 2005 were used to determine the coefficients of the formulation of Gm and ATI. Data in 2006 and 2008 were used to evaluate the method. In addition, effects of soil moisture on soil heat flux were specially investigated by field experiment. Soil water content is the dominant factor influencing soil heat flux for saturated or near-saturated soil (45%). As soil dries, soil temperature plays a more significant role and on the initial drying stage (from 45% to 30%), relatively higher soil temperature combined with high soil thermal conductivity make soil heat flux of wet soil much higher than that of dry soil.

A new algorithm to automatically determine the boundary of the scatter plot in the triangle method for evapotranspiration retrieval

Three different algorithms were developed in this paper to automatically determine the boundary of the triangle shape in the scatter plots. Experiment shows that the Algorithm III has the best performance among them based on the evaluation criteria of their robustness and computation time. The new algorithm proved to be effective to maintain the self-consistency and avoid the subjectivity in the traditional triangle method.

A Physically Based Spatial Expansion Algorithm for Surface Air Temperature and Humidity

An algorithm was developed to expand the surface air temperature and air humidity to a larger spatial domain, based on the fact that the variation of surface air temperature and air humidity is controlled jointly by the local turbulence and the horizontal advection. This study proposed an algorithm which considers the advective driving force outside the thermal balance system and the turbulent driving force and radiant driving force inside the thermal balance system. The surface air temperature is determined by a combination of the surface observations and the regional land surface temperature observed from a satellite. The average absolute difference of the algorithm is 0.65 degree and 0.31 mb, respectively, for surface air temperature and humidity expansion, which provides a promising approach to downscale the two surface meteorological variables.

Effects of Climate Variability on Evaporation in Dongping Lake, China, during 2003-2010

Based on two long-term, hourly (10:30–11:30 and 13:10–14:10) meteorological over-lake observations and data from Shenxian meteorological station, nearby Dongping Lake, the Penman-Monteith equation and reference evaporation ratio algorithm were used to calculate lake evaporation in Dongping Lake, China, from 2003 to 2010. The variation trend of evaporation of Dongping Lake was analyzed, and the influences that caused changes in lake evaporation were also discussed. The results show that (1) the total annual evaporation in Dongping Lake increased at 18.24 mm/a during 2003–2010.The major climatic factors accounting for this increase are the rising net radiation and the rising air temperature; (2) the total annual evaporation in a particular hour (13:10–14:10) in Dongping Lake increased at 4.55 mm/a during 2003–2010—the major climate factors that accounted for this increase are rising net radiation, followed by air temperature, wind velocity, and air humidity; (3) against the background of global warming, the climate of Dongping Lake tended to be dry during 2003–2010; the largest contribution to this comes from air temperature, followed by wind velocity and relative humidity; and (4) the monthly evaporation in Dongping Lake has seasonal variability.

A new physically based method for Air temperature downscaling

An international widespread concern about scaling is how to choose appropriate scale or resolution, and how to evaluate the impact of them[1]. Air temperature is an important input variable to estimate terrestrial evapotranspiration based on satellite remote sensing. The air temperature obtained by the observations from surface meteorological stations is limited in their spatial and temporal representation, while the validated GDAS (Global Data Assimilation System) has many advantages, it can provide the simulated temperature data every 3 hours, and it has great value in downscaling analysis. There are three major driving factors of the near surface air temperature: the surface long-wave radiative balance, land-air turbulent heat exchange, and advection. The fluctuation of the Air temperature (2m height level above ground) mainly depends on underlying surface feedback. Northern China was chosen as the study area. Using air temperature data from the GDAS forcing dataset as a data source, we proposed a new method for downscaling air temperature based on land surface temperature. In order to evaluate the performance of our methods, bilinear interpolation, spline interpolation were used in the comparison. To assess the performance of the downscaling approaches, the ground measurements were used to compare with the downscaling results. Experiments show that the effect of static feedback interpolation is the best based on the surface temperature. What we have got are as follows. First, In most plain areas, the air temperature(2m height level above ground) mainly depends on the temperature of the surface temperature. Second, during the process of downscaling, pure mathematic methods appear to be not sufficient. It is necessary that the effects of physical basis be taken into consideration.

Emissivity measurement for low emissivity objects by two blackbody tube methods

Emissivity is not only an essential parameter for land surface temperature retrieval in remote sensing, but also a characteristic parameter to measure the ability of targets thermal infrared radiation. Field measurement of emissivity is a challenging task, but one special feature is that it can obtain high accuracy of the emissivity. The Model 102 Portable Fourier Transform Infrared Spectrometer(Model 102FTIR spectrometer) [1] developed by Designs and Prototypes has less stray radiation, a wide spectral range, high resolution, however, it is very hard to measure the emissivity of objects with low emissivity such as aluminum. Two methods to measure the emissivity proposed by Zhang in 1985[2].In this paper, we use them based on the Model 102FTIR spectrometer and the blackbody tube to measure the low emissivity objects [2]. To evaluate the methods, marble, board, and aluminum are chosen as the samples which have respectively high, medium and low emissivity. Though the instant environment irradiance is changed, the real irradiance of the sample remains unchanged [3], the radiance temperature and emissivity separation algorithm are used to obtain the true emissivity curves of these samples. The emissivity curve of marble measured by the blackbody tube method matches very well with that measured by the instrument, which proves the feasibility of our methods. Meanwhile, low emissivity objects like aluminum meet the emission features in reality.

Downscaling of air humidity based on thermal inertia

Air humidity is an important input variable to estimate terrestrial evapotranspiration based on satellite remote sensing. The air humidity obtained by the observations from surface meteorological stations is limited in their spatial and temporal representation. The validated GDAS (Global Data Assimilation System) dataset can provide the simulated data every 3 hours [1]; and the FY-2C (the first operational geostationary meteorological satellite FengYun-2C from China) can provide the data of land surface temperature hourly. Moreover, they are of great value in downscaling the air humidity. Air humidity (2m height above the ground level) both from thermometer screen to high ground is driven by the advection, the turbulence and the radiation processes [2]. As Zent et al. [3] put it in 1993, thermal inertia can be used to study the changes of air humidity. North China was chosen as the study area. Using the GDAS forcing dataset, the MODIS, FY-2C and ground observations, a new method is proposed to downscale the near surface air humidity based on thermal inertia. Finally, in order to evaluate the performance of our method, Inverse Distance Weighted (IDW) interpolation, SPLINE interpolation, together with the ground measurements were used to compare with our method.

Estimation of evapotranspiration based on remote sensing in Heihe River Basin

Regional evapotranspiration (ET) is an important component in water cycle and energy balance. The research on regional evapotranspiration is very important to further our understanding of the global climate change, land surface atmosphere interaction, water cycle and ecological study. However, traditional site observation is only representative of the local site scale, which can not reflect the real regional conditions. Remote sensing provides a promising way to retrieve regional evapotranspiration. Heihe River Basin is the second largest inland river basin in northwest of China, water shortage has become a crucial problem to regional ecological, environmental, economic and other related areas. The research on regional evapotranspiration is in an urgent need in this area. SEBS model, a physically based model, was selected to retrieve regional evapotranspiration. Combined with MODIS products and site observations, evapotranspiration in typical days were estimated with SEBS model. The instantaneous results were validated with flux observation data from Yingke station. In most days SEBS model have good estimation of surface fluxes, the difference between observations and estimation is limited in 50W/m2. For longer time ET estimation, reference evapotranspiration fraction was selected to scale instantaneous measurements to daily and motherly evapotranspiration. Adjusted pan observations were used to validate the scaled daily results, the results indicate reference evapotranspiration fraction based method have good quality to scale daily ET. Moreover, clear spatial patterns of ET were found in Heihe River Basin. This spatial pattern is closely correlated with land use/land cover and water uses in Heihe River Basin.