rainfall
rainfall
day of year
sensible heat flux
H (W/m2)
May 2005, 中国寿县
rainfall
rainfall
day of year
2.2 基于非参理论的遥感反演与误差分析
latent heat flux 误差分析
∆ δ (LE) = δ ( (Rn −G)) −δ (4σεTs3 (Ts −Ta )) ∆ +γ
理论基础：energy balance, nonequilibrium thermodynamics,
equilibrium evaporation
sensible heat flux latent heat flux
Rn net radiation G soil heat flux γ psychometric constant
∆ slope of saturated vapor pressure at Ta
2.1 非参数化蒸散理论与例证：site-1
中国长武观测站 (35.2N,107.7E)
Flux and Radiation Observation System (FROS) measures radiation components & turbulent fluxes in the
1.2 主流遥感蒸散反演原理与问题
Conventional retrieval approach
LE = φmax Tmax −Ts ∆ (Rn − G) Tmax − Tmin ∆ +γ
G: soil heat flux
No-ET
(Jiang & Islam GRL 1999)
Rn: net radiation
时间尺度转换：
instantaneous observation daily totals
evaporative flux ratio (EFR): EF = LE /(Rn − G) or ES = LE / S ↓
(Brutsaert & Sugita 1992 Crago 1996 Lhomme & Elguero 1999)
中国地理学会2008年度年会
2008年7月15日于长春国际会展中心
关于遥感反演地表蒸散动态变化的 若干问题与探索
刘元波 中国科学院南京地理与湖泊研究所
主要内容
1 若干关键科学问题 1.1 经典蒸散理论与问题 1.2 主流遥感反演方法与问题 1.3 遥感数据不确定性问题 2 可能解决方法和途径 2.1 非参数化蒸散理论与例证 2.2 基于非参理论的遥感反演与误差分析 2.3 遥感数据不确定性分析与量化 3 研究展望
signal-to-noise ratio: 2.91
(Liu & Hiyama, Water Resour Res 2007)
2.3 遥感数据不确定性分析与量化―2
空间尺度转换：
pixel-wise observation heterogeneous surface
scaling functions for evaporation
2 Spatial resolution (n-km)
over the heterogeneous surface?
3 Temporal influences (t)
satellite data quality & subsequent retrieval?
可能解决方法和途径： 相关问题探索
2.1 非参数化蒸散理论与例证
Ts ~ reflectance (地面温度与反射率三角分布) SEBAL (Bastiaanssen et al 1998) SEBS (Su 2002) Ts ~ vegetation index (地面温度与植被指数三角分布) Nemani & Running 1989 Moran et al 1994 Carlson et al 1995 Norman et al 1995 Jiang & Islam 1999 Nishida et al 2003
γ: psychometric constant ∆: slope of saturated vapor pressure Ts: surface temperature Tmax: maximum surface temperature Φmax=1.26 (the Priestly-Taylor’s parameter)
遥感反演误差：±10 W/m2 ±10 W/m2 (Ts±1K)
±20 W/m2 (Ts±2K)
总体误差：±30 W/m2
LE estimates using ASTER data
α obs. est. Diff(%). 0.163 0.249 52.8
Ts(K)
311.3 311.8 0.2
Rn(W/m )
H=
γ
∆ +γ
(Rn − G) + 4εσTs3 (Ts − Ta )
∆ LE = (Rn − G) − 4εσTs3 (Ts − Ta ) ∆ +γ
ε surface emissivity Ta surface air temperature Ts surface temperature σ S-B constant
(angular & adjacent effects)
uncertainty sources account for difference (1.0±1.79K)
surface heterogeneity 0.4±0.44K retrieval approach 0.6±1.76K
Two rectification approaches to reduce the inconsistency
atmospheric boundary layer
eddy covariance techniques
2 Oct. 2004
latent heat flux
1-month period in May 2005, 中国长武观测站
rainfall
day of year
sensible heat flux
ET-only
误差→100W/m2(Verstraeten et al RSE 2005) 边界确定具主观性(Carlson Sensors 2007)
1.3 卫星遥感数据不确定性问题
1 Temporal resolution (snapshot)
the daily totals from the snapshot?
atmospheric boundary layer
eddy covariance techniques
photo at http://satellite.hyarc.nagoya-u.ac.jp/LAPS/index.htm
latent heat flux
LE (W/m2)
May 2005, 中国寿县
遥感反演蒸散动态变化：
若干关键科学
问题
1.1 经典蒸散理论与问题―发展简史
traced back to 18th BC (Brutsaert 1982) major progress in 19th • Howard L Penman 1948 Proc R Soc London A193 120-145 Natural evaporation from open water, bare soil & grass
关于地表温度的不确定性检验：
ASTER LST(90m) scaling functions scaled LST (1km) comparative analysis MODIS LST(1km)
4 ∑εiTi secφi secγ i π L n T =( − (1− ))1/ 4 ε ∑secφi 2ε ∑secφi
improved agreement: -0.2±1.57K, 0.1±1.33K
(Liu et al Rem Sens Environ 2006 Liu et al Sensors 2007)
2.3 遥感数据不确定性分析与量化―3
时相诸要素的定量表达：
参照目标 DN0：PIF物体,无大气,天顶日照,天顶观测,标准日地距离 对象目标 DN : PIF物体, 时相要素 (DNPIF, G, B, L, d,Θ, Tg, Ts, Tv, ρa)
energetic and atmospheric diffusive controls on surface heat fluxes
[ combination theory ]
• contributions and reviews
现代气象学范式
Slatyer & McIlory (1961) Monteith (1973) Thom (1975) Monteith (1981) Brutsaert (1982) Monteith & Unsworth (1990) McNaughton and Jarvis (1991) Parlange et al. (1995) Raupach (2001) Shuttleworth (2007)
620.1 515.7 -16.8
2
LE(W/m )
340.0 284.4 -16.4
2
LE =
∆ (Rn − G) ∆ +γ
− 4εσTs3 (Ts − Ta )
G = 0.05Rn Ta = 298.5(K)
Changwu observation station
100 500 W/m2
2.3 遥感数据不确定性分析与量化―1