Estimates of surface soil moisture under wet grass using L-band radiometry:

Transcription

1 Estimates of surface soil moisture under wet grass using L-band radiometry: KAUZAR SALEH, JEAN-PIERRE WIGNERON, PATRICIA DE ROSNAY, JEAN-CHRISTOPHE CALVET, YANN KERR, MARIA J ESCORIHUELA, PHILIPPE WALDTEUFEL Acknowledgments: Mike Schwank (ETH-Zurich, Switzerland) and ARS-USDA 1

2 Questions 2

3 Soil moisture retrievals over grass? Questions 1 Characterise the emission of grass 2 When can soil moisture be estimated? 3 Which retrieval approach performs better? 3

4 Radiometry experiments over grass BARC ELBARA-ETH SMOSREX L-band radiometer June-Sep 82 HandV, 0to 70 deg. Sandy loam Seeded grass & alfalfa ELBARA L-band radiometer May-July 04 H and V, 45 to 70 deg. Loam Seeded clover grass LEWIS L-band radiometer Jan03-Dec05 HandV,20to 60 deg. Silt-loam to loam Grass with litter [Wang et al. 82] [Schwank et al. 05] [de Rosnay et al. 06] 4

5 L-MEB (L-band Microwave Emission of the Biosphere model) [Wigneron et al. 05] TB=(1-ω p )(1-exp -τ/cos(θ) )T veg (1+Γ soil exp τ/cos(θ) )+(1-Γ soil )T eff exp -τ/cos(θ) exp -τatm SKY ATM VEGETATION OPTICAL DEPTH τ= τ n (sin 2 (θ)+tt p cos 2 (θ)) ; [Rtt=tt V tt H ] τ n, tt p VEG SINGLE SCATTERING ALBEDO ω p SOIL ROUGHNESS PARAMETERS Γ s = Γ s *exp(-h s cos Np (θ) ) h S, N P 5

6 1 Characterise the emission of grass ROUGHNESS PARAMETERS h S, N P RMSE TBH [K] RMSE TBV [K] Data set Cover Soil parameters Vegetation parameters h s N H N V tth Rtt b b ω H ω V Orchardgras BARC-g s * [0.91] 3.2 [0.62] BARC-a Alfalfa [0.87] 1.8 [0.68] ELBARA-A SMOSREX-A Seeded clover grass Natural grass Eq. (5) [0.56] - Eq. (7) [0.87] - roughness cannot be left as a completely free parameter in the retrieval process 6

7 1 Characterise the emission of grass VEGETATION PARAMETERS τ n, tt H,,Rtt Data set Cover Soil parameters Vegetation parameters h s N H N V tth Rtt b b ω H ω V RMSE TBH [K] RMSE TBV [K] BARC-g Orchardgrass [0.91] 3.2 [0.62] BARC-a Alfalfa [0.87] 1.8 [0.68] ELBARA-A Seeded clover grass Eq. (5) [0.56] - Eq. SMOSREX-A Natural grass (7) [0.87] - \ Vegetation: isotropic and non-scattering at H pol TB difficult to model at V polarisation 8

8 1 Characterise emission of grass from field experiments BARC-alfalfa τ n =b VWC+b ELBARA-ETH ELBARA R 2 =0.86 τ N /VWC ~ 0.14 τ N /VWC ~

9 1 Characterise emission of grass from field experiments SMOSREX-0304 R 2 =0.59 No rain interception + Soil moisture <15% + Afternoon measurements τ N /VWC ~

10 2 When can soil moisture be estimated? If the attenuation of the soil emission is not very high Standing vegetation does not attenuate soil emission very strongly (usually a rain interception problem) The litter layer, if present, is not very dense & wet I.e.: The soil signature features are maintained (TB=f(θ,pol)) 11

11 2 When can soil moisture be estimated? PR 50 = (TB 50,V -TB 50,H ) / (TB 50,V +TB 50,H ) SMOSREX [Saleh et al. 06] Rain 12h: 85% 12

12 2 When can soil moisture be estimated? PR 50 = (TB 50,V -TB 50,H ) / (TB 50,V +TB 50,H ) ELBARA-ETH Vegetation growth PR 50 <

13 2 When can soil moisture be estimated? If the attenuation is not very high BARC-grass BARC-alfalfa RMSE=1.3% in vol/vol SS=0.91 RMSE=2.5% in vol/vol SS=

14 2 When can soil moisture be estimated? attenuation often high or very high ELBARA-ETH SMOSREX-0405 RMSE(ε )=2 SS=0.56 RMSE=5.3% in vol/vol SS=0.78 [results filtered for PR 50 >0.031 [Saleh et al. 06] ] 15

15 2 When can soil moisture be estimated? ELBARA-ETH SMOSREX-0405 PR 50 < 0.02 RMSE(ε )=10 SS=-25 PR 50 < 0.02 RMSE=10% in vol/vol SS=

16 3 Which retrieval approach performs better? Data set Soil ( ) Vegetation pol. RMSE w s or e h s σ(hs) w s or ε or σ(ε ) τ n σ(τn) tth σ(tth) Rtt σ(rtt) BARC-g E H BARC-a E H ELBARA-A Eq. (5) E H 1.72 SMOSREX-A Eq. (7) H SMOSREX-B Eq. (7) H High constrain on roughness Soil moisture and optical depth free H or HV 16

17 1) Fours grass sites: LITTER/ NO LITTER Conclusion and future studies 2) Characterisation of grass parameters in L-MEB H pol: vegetation isotropic and non-scattering V pol: under investigation 3) Preferred approach to estimate soil moisture 2P (τ,soil moisture) with «roughness function» + PR 50 >0.02 (+) When litter is present «roughness» is associated to litter and soil 17

18 Conclusion and future studies 4) Unique data set over different types of grass from CoSMOS- Australia-NAFE: more types of crops/grasses and change of scale Thank you 18