Assessment of instrument STability and Retrieval Algorithms for SMOS data (ASTRA)

Transcription

1 Assessment of instrument STability and Retrieval Algorithms for SMOS data (ASTRA) S.Paloscia IFAC-CNR MRSG - Microwave Remote Sensing Group Florence (Italy) Microwave Remote Sensing Group

2 II - Test of algorithms for the retrieval of soil moisture and woody biomass using L-band data Simonetta Paloscia, Paolo Pampaloni, Emanuele Santi, Simone Pettinato CNR-IFAC Florence (Italy) Microwave Remote Sensing Group

3 Research plan 1. Development of algorithms for the retrieval of soil moisture and woody biomass in forests and test at local scale on some Italian test areas 2. Comparison of SMOS data taken in forests with other satellite data working at the same or different frequencies (AMSR-E, SMAP) and model simulations.. 3. Validation on a global scale by using MW satellite data. 4. Use of active sensors (e.g. SMC and vegetation maps from ENVISAT/ASAR and ALOS-PALSAR data) as a reference for improving the ground resolution. 5. Effects of within-pixel variability

4 Preliminary Investigations Multi-frequency forest transmissivity Sensitivity to forest biomass Sensitivity to soil moisture Retrieval algorithms

5 The Experiments Sensitivity to SMC - Woody biomass Forest Transmissivty Satellite Airborne Ground based

6 Sensitivity to SMC and forest woody volume Airborne measurements

7 Forest characteristics

8 The sensitivity to forest biomass At low values of biomass, the difference between Tn in summer and winter is appreciable and probably due to the SMC effect, whereas at high values of biomass the difference in Tn is negligible L-band TnV Winter Summer TnV=0.03Ln(WV) R 2 = WV (m 3 /ha)

9 Sensitivity to SMC Tn L-band TnV TnH For forests with biomass 200m3/ha, the direct relationship between Tn at L-band and SMC is evident (Airborne data were collected in V pol. only) SMC(%) TnV=-0.002SMC R 2 = 0.88 TnH=-0.003SMC R 2 = 0.95

10 AMSR-E data 1. Russian forest (Evergreen) 2. Jagedaqi forest (China) 3. Foreste Casentinesi (Italy) EcoClimap (% forest cover) 1 3 2

11 PI(Ku)= LAI (R 2 =0.59) FI(Ku-Ka)= (R 2 =0.6) Winter data (snow) were not considered Jagedaqui (China) PI(Ku) & FI(Ku-Ka) vs. LAI Tn(TbhC/TbvKa) vs. Rainfall PI y = x R 2 = y = x R 2 = PIKu TbhC/TbvKa FI FI(Ku-Ka) LAI 3 y = x R 2 = LAI LAI Rainfall (cm) Tn= R (R 2 =0.79)

12 Russia PI(Ku) vs. LAI (Ecoclimap) Tn(TbhC/TbvKa) vs. Rainfall PI PIKu Russia y = x R 2 = LAI PI(Ku)= LAI (R 2 =0.56) Winter data (snow) were not considered TbhC/TbvKa y = x R 2 = Tn= R (R 2 =0.57) Winter data (snow) were not considered Rainfall (cm)

13 Casentino (Italy) PI(Ku) & FI(Ku-Ka) vs. LAI PI PIKu y = x R 2 = LAI PI(Ku)= LAI (R 2 =0.4) FI(Ku-Ka)= (R 2 =0.65) LAI FI FI(Ku-Ka) y = x R 2 = LAI

14 The Ground Based Experiment ) downward observations 2) downward observations under crown 3) upward observations under tree 4) sky observations 3 Frequency Pol. Incid. Angle 1.4 GHz V, H 6.8 GHz V,H 10 Ghz V,H 19 GHz V,H, ±45 37 GHz V,H, ±45 Accuracy 10 o -60 o ± 0.5 K

15 Pine Tree density (m -2 ) Tree Height (m) Trunk Diameter (cm) Poplar Woody Volume (m 3 /ha) Pine (April) SMC % range 35/40 (March) <30 (May) 10 (July) 30 (October)

16 The spectra (Downward) 0.99 Theta= Theta= TnH March May July October April (Pine) L C X Ku Ka TnH L C X Ku Ka Theta=30 Theta= TnV March May July TnV October April (Pine) L C X Ku Ka L C X Ku Ka

17 Angular trends (Downward) 0.98 March July Incidence Angle TnH (K) TbH (K) May Incidence Angle October Incidence angle TnH (K) TnH (K) TbV (K) Incidence Angle L-band C-band X-band Ku-band Ka-band

18 Spectra (Upward) Upward - 30 Upward - 50 TnH July October L C X Ku Ka TnH 0.95 July 0.85 October L C X Ku Ka TnV TnV 0.65 July 0.55 October L C X Ku Ka 0.25 L C X Ku Ka

19 Angular trends (Upward) July - Upward July - Upward TnH TnV 0.90 L-band 0.80 C-band 0.70 X-band 0.60 Ku-band Ka-band L, C, X, Ku, Ka bands Incidence angle Incidence angle October - Upward October - Upward TnH TnV C-band 0.60 L-band X-band 0.50 Ku-band 0.40 Ka-band Incidence Angle Incidence Angle

20 Spectra & Angular trends The frequency spectra of downward looking measurements show similar trends in all seasons with a fairly steep change from L to C-band, followed by an almost flat behaviour up to Ka bands. Pines show a higher emission with respect to Poplars Downward L-band trend is flat in July, when vegetation is at its maximum development and SMC is low. Differences in L-band emission are related to changes in biomass and soil conditions (soil moisture and understory)

21 Spectra & Angular trends The angular trends are almost flat when looking downward, and increasing with zenith angle when looking upward. Measurements carried out in upward directions showed a significant difference between observations of trees with and without leaves, with a most significant effect at the higher frequencies.

22 Data analysis TB TB TB ω - τ model ds u d = (1 Rp) Ts + (1 ω) Tv(1 e = (1 ω) Tv(1 e = (1 Rp) Ts e τ µ τ µ ) + TBsky e τ µ τ µ ) Rp + (1 ω) Tv(1 e τ µ ) + Rp(1 ω) Tv (1 e τ µ ) e τ µ 1) downward observations under crown 2) upward observations under tree 3) downward observations 4 ) 1 ) 2 ) 3

23 Data analysis (cont.) Assuming τ and ω independent variables and solving the system we find: ω = TBd TBsky + TBds TBu + ( TBd ( TBd TBds + TBsky TBds + TBsky TBu ) Tv TBu ) Tv ( 7 ) τ = µ Ln TBds ( TBd TBsky TBu ) ( 8) Rp = TBds 2 TBdTBsky + TBdsTBsky TBdsTBu + + TBdsTs TBdsTs TBskyTs TBskyTs ( 9) Afterwards, also the transmissivity was computed: t= e -τ/µ

24 Data analysis: Results L-band LH July ω (MV) (SD) (MV) (SD) τ (MV) (SD) (MV) (SD) Theta Rp LV t October τ 0.37 (MV) 0.02 (SD) 0.41 (MV) (SD) Theta t

25 Data analysis: Results C-band CH CV July ω (MV) (SD) (MV) (SD) τ (MV) (SD) (MV) (SD) Theta Rp t October τ 0.52 (MV) (SD) 0.53 (MV) (SD) Theta t

26 Transmissivity at L, C, X & Ka bands in summer (leaves) and fall (defoliated) ω τ model 0,700 0,700 Transmissivity L-band 0,600 0,500 0,400 0,300 0,200 0,100 0,000 L Transmissivity C-band 0,600 0,500 0,400 0,300 0,200 0,100 0,000 C with leaves w/o leaves Incidence angle Incidence angle LH Leaves LV leaves LH defoliated LV defoliated CH leaves CV leaves CH defoliated CV defoliated Transmissivity X band X Transmissivity Ka band Ka with leaves w/o leaves Incidence angle Incidence angle XH leaves XV leaves Ka H defoliated Ka V defoliated XH defoliated XV defoliated Ka H leaves Ka V leaves

27 Summary In general, we observed that: Good sensitivity of L-band emission to woody biomass with a saturation at high values of woody volume For the observed poplar stand (WV < 200m 3 /ha), The detection of soil moisture under defoliated trees appears to be feasible both at L and C band Transmissivity (θ= 40, H pol) Leaves No leaves L-band C band

28 Satellite data Tb extraction The SMC Algorithm Selection of pixels collected on lands (Land/Ocean flag) Sea emissivity model and SSM/I data Check for data calibration FI and PI indexes computation Snow cover from FI Deserted areas from PI at C band Lat/Lon of the test areas Optical depth from highfrequency measurements Surface temperature from Tb at Ka band V pol ω τ model + ANN Inversion algorithm Data or maps Inversion algorithm SMC on test areas Daily SMC map