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Energy distribution of land surface in China based on Remote Sensing and GIS

Tian Guoliang and Xu Xingkui
Institute of Remote Sensing Applications,
Chinese Academy of Sciences
P.O. Box 9718, Beijing 100101,
China,
E-mail: [email protected]

Abstract
A land surface feature model (LSFM) and energy exchange model based on remote sensing and GIS have been developed, including databases of land cover type, soil texture, elevation, climate planning, phenology and NOAA-AVHRR Images etc.. Monthly mean roughness length of land surface and albedo was calculated on basis of the databases applying statistic model and BRDF model. Land surface temperature is derived using NOAA-AVHRR data and split window models aiming to different climate region, then distribution of energy was calculated. Complementary relationship model, Penman-Monteith model and other statistic models were employed to calculate monthly mean evapotranspiration for whole China

The temporal and spactial distribution of land surface feature and energy was studies and discussed. The results shown that different climate and land cover intensity were main factors that affected physical parameters of land surface, and effect of snow was more stands out. The land cover type was the most important factor that affectted the distribution of land surface feature and energy exchange. Meanwhile, Distribution of cold and heat sources in 1997 for was studied. There was larege cold regions in Northeast, Xinjiang and Qingzang plateau. The factors that affect temporal-spatial distribution of albedo and latent heat were analyzed.

Introduction
Earth is a complicated great system. Energy source of his movements and living process comes from Sun directly or indirectly, but the energy distribution is distinct inhomogeneous at temporal and spatial scales due to Earth’s movement and different latitude, so that the importance of this effect excesses the effect of solar activity on the Earth’s system.

Temporal and spatial distribution of physical characteristics of land surface changes easy. Dynamic and thermodynamic actions of land surface with atmosphere due to the diversity of land surface feature are great difference. Every type of land surface has distinguished way of energy distribution and mass exchange. Change of land surface feature impacts on the balance of energy, momentum and mass between land and atmosphere, thereby affects local, regional even global climate changes. Therefore research on temporal and spatial distribution of energy of land surface is important significance for the research on interaction of land with atmosphere, global climate change and global change.

It is necessary to build data bases of land surface type in order to provide priori knowledge for global climate model (GCM) and to analyze land surface energy distribution due to differences of its physical characteristics and its role in exchanges of energy, momentum and mass. This is also prerequisite condition for inversion of albedo, land surface temperature and land surface roughness. Albedo and temperature of land surface reflect information of structure in vegetation and energy distribution, roughness describes intensity of turbulence exchange, and finely determine energy distribution in land – atmosphere system. China as a large country has very complicated land type and cross several climate zone. Methods of conventional measurements are not meet practical requirement in simultaneous and representative nature. Remote sensing and GIS provide power tools for study on land surface feature and temporal and spatial distribution of its energy.

Methods
Remote sensing can provide temporally land information with local, national and global scales. Data bases and spatial analysis models based on GIS can realize extraction of land surface feature, calculation and analysis of energy distribution of land surface.

Pre-processing of remotely sensed data
Pre-processing of remote sensing data must be conducted using NOAA-AVHRR data to inverse monthly parameters, albedo and temperature of land surface, including rectification, mosaic of different strap and projection change, reducing clouds and discrimination of cloud and snow because we need cloudfree data and also retaining snow information. A progressive approach method was used to distinguish cloud and snow as following:

  • Reflectance of channel 1 Rch1 >W1 ;
  • Normalized difference vegetation index NDVI£W2 ;
  • Brightness temperature of channel 3 and channel 4 Tch3 – Tch4 •W3 ;
  • Brightness temperature of channel 4 Tch4 >W4 ;
  • Reflectance of channel 3 Rch3< W5 .

Where Wn (n=1, 2, 3, 4, 5) is threshold. Corresponding threshold was calculated for different climate zone and month because different climate zone affects the thresholds. Atmosphere effect on NOAA-AVHRR data was corrected for calculation and comparison of monthly parameters of land surface ( Qin and Tian, 1994).

Building databases
It needs the data of climate, land cover, soil, phenology and topography for development of land surface feature model and calculation of land surface energy distribution. The following data bases have been built:

  • data base of climate planning in China;
  • data base of monthly land cover type in China;
  • data base of soil texture in China;
  • data base of phenological distribution in China;
  • data base of land elevation in China;
  • data base of climate in China.

Extraction of land surface feature in China
Exchange of land surface energy, momentum, mass is sensitive to land cover. Yearly mean type of land cover is not representative to monthly type of land cover because of difference of large area and climate change. Daily NOAA-AVHRR data were used to extract monthly land cover and build data base of monthly type of land cover based on data base of national resources at the scale of 1:4,000,000. The land cover was divided into 20 types, which included land cover type of IGBP required.

Inversion of land surface albedo in China
The semi-spherical albedo of land surface, as a part of energy balance model, decide s energy distribution in energy exchange between land and atmosphere. Climate model of computation albedo of land surface is not meet the requirement in spatial resolution (Dicknson, 1986). Calculation of albedo by using remote sensing has much more advantage. There are two models: direct inversion and indirect inversion models.

The direct inversion model is based on distribution weight of value in solar band observed by satellite to establish statistical model aiming different land cover (Brest, 1987). The most representative model of indirect inversion of albedo is kernels-driven model. The basic consideration of this model is to extract feature quality – “kernel” which closely relates vegetation type. The linear correlation between kernel and BRDF of land cover has been established (Walthall, et al, 1985, Wolfgang, et al, 1995). In this paper, a combination of the two models was employed to calculate monthly albedo of land surface in China.

Inversion of land surface temperature in China
Land surface temperature (LST) is important value for applications in the field of agrometeorology, climatology and environment. It is also an important parameter for energy balance model. There are many models to inverse LST using NOAA-AVHRR data, including theoretical model, semi-theoretical and semi-empirical model and statistical model etc.. The statistical model based on split window algorithm is one of practical methods. This model has been applied to inverse LST (Parata, 1993, Becker et al 1994, Ottle, 1992, Price, 1984, Sobrino et al, 1994 and Li, 1993). Greater error may be come into being if the model was used to inverse LST in large area because the split window algorithms were developed at special climate condition. For practice, it is necessary to build a set of data bases of a priori knowledge in order to assure accuracy of the models. The model used in this paper was fitted to similar climate condition and land cover according to climate planning and land cover type. The different models in winter and summer for some land type were also used.

Calculation of monthly roughness length of land surface in China
The exchange intensity of energy, momentum and mass between land surface and atmosphere relates to land cover type, season, vegetation height, vegetation canopy density etc.. So roughness of land surface is an important parameter for determining exchange intensity of energy which describes turbulence exchange intensity between land and atmosphere.

It is different to determine monthly roughness length with conventional climate models. Development and practice of remote sensing, especially advantage in temporal and spatial observation in large area become possible to build database of land surface feature and to calculate roughness length. The theoretical and statistical models for computation of roughness can be used (Monteith, 1973 and Jarvis, 1976). For practical and application, the statistical model was applied to calculate monthly roughness length of land surface type, which supported by a series of databases.

Calculation of evapotranspiration (ET) of land surface in China
Energy balance model is main model for calculating ET (moteith,1973), for example Bartholic model, Bowan ratio-energy balance (BREB), Brown-Rosenberg model (Brown, 1973), measurement technique of vegetation physiology, improved Verma – Rosenberg model (verma, 1976), 3D model of energy balance (Martsolf, 1975), complementary relationship model (Morton, 1983 and Xu, 1999) and Penmen-Monteith model etc.. Considering possibility of data acquisition and practice the different models were selected according to the regional features in China. Evapotranspiration in paddy field and marsh swamp area was calculated by potential evaporation models (Dickinson, 1986). C a 6 o empirical model was used for calculating latent flux of snow surface. The evaporation in lake area, river basin, desertification and desert was calculated with help of complementary relationship model. The Penman- Monteith model was employed to calculate ET in dry land, soil, forest area.

Results and Discussion

Evapotranspiration distribution of land surface in China
Many factors affect ET of land surface, including climate factor, geographical factor, physical characteristics of land cover etc.. The most important factor is the climate element. The actual ET distribution in China was studied with climate region as the basic unit. The monthly ET in different climate region and yearly amount was calculated (table 1). Figure 1 indicated change chard of monthly ET in 9 climate regions. The monthly ET in different climate regions changed greatly, difference between maximum and minimum of monthly ET was about 100 mm in a year, and maximum difference of yearly ET extended more than 1000 mm, the minimum was about tens mm. The discrepancy of climate made their regular distribution and amount of monthly ET.
Table 1 monthly evapotranspiration in different climate regions (mm)

Figure 1. Monthly change of evapotranspiration in 9 climate regions
The temporal and spatial distribution of ET in different climate region in China possess the following features:

  • Monthly ET in summer was greater than that in winter for seasonal distribution. ET in east part of China was greater than that in west part and ET in high latitude area was less than that in low latitude area for spatial distribution.
  • The discrepancy between Eastern and Western in winter was not great due to dry climate influenced by northwest cold air mass, but ET increased with air temperature going up and enough rainfall after spring. It reached maximum in summer (June – August), the ET in Western was less because the west part located inland or plateau which related effect of southeast and southwest monsoon on it.
  • ET in high latitude area in winter was less than that in low latitude area. The discrepancy of ET in summer was not significant.
  • Change range of ET in coastal area of China was greater than that in central part of the country due to water budget in winter and summer. The less change of ET in whole year in Western and Northwestern was found.

Distribution of cold and heat sources in China
If energy from solar radiation and energy exchange between land surface and atmosphere was greater than the energy surrounding area the heat was transferred into the surrounding area through soil heat flux, sensible heat flux and latent heat flux. On the contrary, the heat in surrounding area would be transferred into this area. This area is called as heat or cold source.

Temporal and spatial change of cold and heat sources relates the discrepancy of heat actions of land surface directly. Their distribution is very important to atmospheric movement, atmospheric circulation form and change. The cold and heat sources can be calculated through amount calculation of energy balance. The distribution of cold and heat sources can be obtained when the net radiation and soil heat flux or sensible and latent heat fluxes are known. The equation of energy balance is
Rn = LE + H + G
Where Rn: net radiation; LE: latent heat flux; H: sensible heat flux and G: soil heat flux.
When Rn – G = LE + H > 0, as heat source

Rn – G = LE + H < 0, as cold source
The monthly distribution of cold and heat sources was derived. The cold source distributed in January and February, November and December and the distributions in other months were heat source. The distribution of cold area in January and December spreaded in large area from the distribution map of cold source. The area in February and November taken second place that mainly distributed in Northeastern, Northern of Xingjiang and Qingzang plateau. The cold area located in the region where yearly mean air temperature was less than 0°C and in the region of high albedo.

Conclusions

  • The data bases of land cover type in China was built and energy balance of land surface was calculated using remote sensing and GIS so that made reasonable coupling between energy transformation process and land cover type.
  • The data base of climate planning in China was introduced into the energy balance model so that the energy distribution could more reflect regional feature of climate. The results of radiation calculation more indicated the regional effect.
  • the model of actual evapotranspiration of land surface in China has been developed based on land cover type and land surface feature model. The results indicated that evapotranspiration distribution of land surface in China in summer was greater than that in winter. The ET in high latitude area was less than that in low latitude area, and ET in east part of China was greater than the ET in west part. Climate property, land cover type, precipitation distribution and human being activity are the main factors that influenced regional ET.
  • Monthly distribution of cold and heat sources has been calculated. The heat source distributed from March to October in 1997. There were large areas of cold source in November-December and January-February in Northeast, North part of Xingjiang, Qingzang Plateau. The yearly mean temperature was lower than zero degree in these cold areas, the land surface was covered by snow and yearly mean albedo was much more higher in the regions.
  • It is necessary to establish data base of priori knowledge for computation of LSE, and also feasible. It provides a useful mean for study on Energy balance of land surface in regions.
  • It is very important for improving accuracy of LSE calculation using combination of GIS with conventional methods and their comprehensive applications.

Reference

  • Becker,F. and Z. L. Li, 1990, Toward split window method over land surface, JNT. Re. Sens., Vol. 11, No. 3.
  • Brest, C. L. And Samuel n. Groward, 1987, Deriving surface albedo measurement from narrow band satellite data, INT. Re. Sens., Vol. 8, No. 3.
  • Brown, K. W. and N. J. Rosenber, 1973, A resistance model to predict evapotanspiration and its applications to a sugar beet field, Argon. J., 65.
  • Dickinson, R. E., et al, 1986, Biosphere-atmosphere transfer scheme (BATS) for the NCAR community climate model, Atmosphere Analysis and Prediction Division, National Center for Atmosphere Research Boulder, Colorado.
  • Javis P. G., 1976, the interpretation of the variation in leaf water potential and stomatal conductance found in canopies in the field, Phi. Trans. R. Soc. Lond. B, 273.
  • Li, Z. L., 1993, Feasibility of land surface temperature and emissivity determination from AVHRR data, Remote Sensing Environ., 43.
  • Martsolf, J. D. and H. A. Panofsky, 1975, A box model approach to forest protection research, Hortscience, 10.
  • Monteith, J. L., 1973, Principle of environment physics, Arnold London.
  • Morton, F. L., 1983, Operational estimation of Arial evapotranspiration and their significance to the science and practice of hydrology, Journal of Hydrology, 66.
  • Ottle, C. And D. Vidal-Madjar, 1992, Estimation of land surface temperature with NOAA 9 data, Re. Sens. Environ., 40.
  • Parata, A. J., 1993, Land surface temperature derived from the Advanced Very High Resolution Radiometer and the Along-Track Scanning Radiometer, 1. Theory, J. of Geographical Research, Vol. 98, No. D9.
  • Price, J. C., 1984, Land surface temperature measurement from the split window channels of the NOAA 7 Advanced very high resolution radiometer, Journal of Geographical Research, Vol.89, No. D5.
  • Qin Yi and Tian Guoliang, 1994, A research on the method and computer program of correction of atmosphere effects on NOAA-AVHRR image, part one: principle and model, Remote Sensing of Environ. in China, Vol. 9, No. 1.
  • Sobrino, J. A., 1994, Improvements in the split-window technique for land surface temperature determination, IEEE Trans. On Geoscience and Remote Sensing, Vol. 32, No. 12.
  • Thom, A. S., 1975, Momentum, mass and heat exchange of plant communities, In J. L. Monteith, Vegetation and the atmosphere, Vol. 1, Principle, Academic Press, London.
  • Verma, S. B. and N. J. Resenberg, 1976, Resistance-energy balance method for predicting evapotranspiration: determination of boundary layer resistance and evaluation of error effects, Agron. J., 68.
  • Walthall, C. L., et al, 1985, Simple equation to approximate the bidirectional reflectance from vegetation canopies and bare soil surface, Appl. Opt., Vol. 24, No. 3.
  • Wolfgang, W. et al, 1995, On the derivation of kernels for kernel-driven models of bidirectional reflectance, J. Geographical Research.
  • Xu, X. K., et al, 1999, Application of complementary relationship model for satellite remote sensing, Chinese J. Remote Sensing, Vol. 3, No. 1.
  • Xu, X. K., 1999, Analysis of land surface energy characteristics in China based on remote sensing and GIS, PHD thesis, Institute of Remote Sensing Applications, Chinese Academy of Sciences.
  • Zhou Xiuji, 1995, Atmosphere prediction, dynamic GIS and Remote Sensing, Annual Report, LARSIS, IRSA, CAS.