Recent improvements in the L-MEB model - Impact on the accuracy of the soil moisture retrievals

Transcription

1 Recent improvements in the L-MEB model - Impact on the accuracy of the soil moisture retrievals J-P Wigneron, Y. Kerr, P. Ferrazzoli, M. Schwank, E. Lopez Baeza, M. Parrens, R. Fernandez-Moran, S. Wang, A. Al-Yaari, P. Richaume, S. Bircher, A. Mialon, A. Al Bitar, A. Mahmoodi, S. Delwart, S. Mecklenburg INRA ISPA; CESBIO; University Tor Vergata, Roma; Swiss Federal Research; Institute WSL & Gamma Remote Sensing AG; University of Valencia; Beijin Normal Univ.; ESA/ESRIN

2 Outline L-MEB = forward model used in the Level-2 (ESA) and level-3 (CNES) SMOS algorithms regular improvements based on cal/val studies based on in situ sites (ELBARA VAS site, etc.) and SMOS observations TODAY: focus on the soil roughness parameterization

3 L-MEB soil modeling is based on 4 parameters: Hr, Qr, Nrv, Nrh ( HQN modeling): Γ soil-p = (Qr.Γ soil-p + (1-Qr). Γ soil-q) e-hr cosnrp(θ) - Q r ~ at L-band -Nr: -2, -1, 0, 1, or 2 are the most common values (note : for SMAP, Nr is only a scaling factor for Hr) Hr = f(stdh): STDh= STD of height (geometric roughness) Choudhury Valid over AMSR-E range (1.4-90GHz)! Montpetit et al. (RSE 2015) L-MEB [Wigneron et al., 2011]

4 Lawrence model (an update) Zs=SD 2 /LC best parameter for roughness modelling for - radiometric signatures (Lawrence et al, 2013) - radar (Zribi et al., 2002, etc) HQN model parameterization: H R SM=30% SM=10% Q R Lawrence et al., 2013, IEEE TGRS 1 STD 2 /L c 2 H R N RV N RH Validation (PORTOS -93 data) H R H R

5 Retrieved SMOS optical depth : Decoupling vegetation and roughness effects? [Patton and Hornbuckle, 2013 Fernandez Moran, 2015, VAS, etc.]

6 Scientific Questions We are confident in relationships Hr = f(std) or Hr = f( STD 2 /Lc); Hr ~ 0-1.2, Qr ~ (from field experiment and EM modelling) However, many questions remain: calibrating Hr at the scale of the SMOS observations (meaning of STD or LC?)? -Hr also account for the effects of topography (Wang et al., 2015), litter in forest and grassland (Grant et al., 2007, 2008), etc. No consensus in the literature about the values of Nr: Nr: -2, -1, 0, 1, or 2 are the most common values What about the effects of topography, SM spatial heterogeneities over large and heterogeneous pixels on roughness? Decoupling varying vegetation and soil roughness effects? Currently: Hr=0.1, Nrh=2, Nrv=0 in L-MEB (global & time invariant default parameters!) Systematic studies were carried out at L-band

7 First Step: Simplifying the 0-order RT model (tau-omega) Assuming effective values of the scattering albedo (Kurum et al., 2013) ω ~ 0 Assuming values of the roughness parameters (Lawrence et al., 2013): Q R ~ 0 N RH = N RV Vegetation and roughness effects can be grouped: 2

8 if N rp = -1, equations can be simplified further as: where: - both vegetation and roughness effects are gathered in one single parameter (TR) - 2-P retrievals of (SM, TR) can be made, vs retrieving (SM, TAU) = SRP method SRP: no need to calibrate HR : it is retrieved within TR no need to decouple vegetation/roughness effects time changes in both vegetation/roughness can be accounted for 3

9 if N rp = -1, equations can be written as: TB(p, θ) = T [ 1 r G(p,θ) exp (- 2 TR / cos(θ) ) ] where TR = τ NAD + Hr/2 if Hr = 0, equations can be written as: TB(p, θ) = T [ 1 r G(p,θ) exp (- 2 TAU / cos(θ) ) ] Both equations (1) and (2) are similar: we can go to one another by a change of variable TR TAU! So, in 2-P retrievals using Nr = -1 or Hr = 0 leads to the same SM retrievals! the difference is only conceptual : -using Nr= -1, TR (roughness and vegetation effects) is retrieved -using Hr =0, TAU (vegetation effects) is retrieved BUT the same values of SM are retrieved! 3

10 Systematic studies were carried out at L-band based on long term -ELBARA observations (VAS site) Fernandez-Moran et al. (2014) - SMOS (+ AMSRE): SCAN sites, Parrens et al. (2014), Fernandez-Moran et al. (2015) Hr varying from 0, 0.1, 0.2 to 1 Nrv= Nrh = -1, 0, 1 or 2 Q = SRP method (Hr=0 or Nr=-1)

11 ROUGHNESS ANALYSIS OVER THE VAS (2015) Roberto Fernández Morán, J-P Wigneron, E. Lopez Baeza, Y. Kerr et al., RSE, sub.

12 SM WAS RETRIEVED WITH DIFFERENT METHODS AND COMPARED TO IN SITU DATA (2013) 2-P retrieval (SM, TAU) 3-P retrieval (SM, TAU, ttv) Varying H R = {0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 1} Q R = {0, 0.1} N RH, N RV = {(2, 2), (1, 1), (0, 0), (-1, -1), (2, 0), (1, -1)} *Retrievals were done at 6 am and 6 pm every day

13 SRP 2-P Correlation (R) values for varying Hr, Nrh and Nrv values Nrv=Nrh=-1 3-P

14 2-P ubrmse SRP for varying Hr, Nrh and Nrv values 3-P Nrv=Nrh=-1

15 2-P Bias SRP for varying Hr, Nrh and Nrv values 3-P Nrv=Nrh=-1

16 Main results (VAS site) for both 2-P and 3-P retrievals, best results (R and ubrmse) were obtained considering N RV = N RH = -1 (SRP method) considering all configurations (in terms of values of N RV, N RH and Q R ) the coefficient R decreased and the ubrmse increased, for increasing values of H R 3-P retrievals (ttv is retrieved, with SM and TAU): improved results in terms of correlation R and ubrmse vs 2-P retrievals (but larger bias). ttv accounts for specific structural characteristics of the vineyards

17 A systematic evaluation of the roughness parameters Nr and Hr over SCAN network(usa) retrievals considering homogeneous pixels Jan July 2013 Parrens et al., 2014b IEEE TGARS Best correlation values (R2) : SMOS retrievals vs in situ data SRP

18 A systematic evaluation of the roughness parameters Nr and Hr over SCAN (USA) Parrens et al., 2014b Lowest ubrmse: SMOS retrievals vs in situ data SRP

19 A similar evaluation over the SCAN network (USA), with the SMOS prototype processor (accounting for pixel heterogeneity) 48 o N SCAN (R) asc Correlation (R) Fernandez et al., 2015b 40 o N SM retrievals: 32 o N -actual L2 SMOS: 24 o N SMOS SRP SRP2 -SRP method: 120 o W 105 o W 90 o W 75 o W 60 o W SCAN (ubrmse) asc ubrmse 48 o N 40 o N 32 o N 24 o N SMOS SRP SRP2 120 o W 105 o W 90 o W 75 o W 60 o W SRP method leads to -best results in terms of correlation (R) & ubrmse -higher underestimation of SM 48 o N SCAN (bias) asc Bias Bias: what about the effects of varying sampling depths? 40 o N ~ 2-3 cm for SMOS (Escorihuela et al., 2010) 32 o N 24 o N SMOS SRP SRP2 ~ 5-10 cm for in situ SM probes 120 o W 105 o W 90 o W 75 o W 60 o W

20 Conclusions Future activities= more in depth evaluation of: roughness modeling: (1) combined roughness-vegetation retrievals? (2) using global maps of Hr in L2, L3? Cf results by Parrens et al. (poster) for SMOS, by Wang et al. (2015) for AMSRE vegetation structure effects: ttv and tth parameters (Cf Fernandez et al., Poster) accounting for pixel heterogeneity: simplifying the algorithm? (ECMWF SM is used over the forest fraction, when the nominal fraction is dominant)

21 First global maps of roughness (Hr) -SMOS, Parrens et al., 2015, to be sub. (Cf poster) -AMSR-E, Wang et al., 2015, Rem Sens 2-step Method: (1) Retrieving TR = Hr + TAU/2 (2) decoupling vegetation (TAU) and roughness (Hr) global map of Hr at C-band (AMSR-E) US: retrieved roughness parameter Hr (a) and slope classification (b)

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23 Comparing: -Nr=-1, -default SMOS configuration (Hr=0.1, Nrh=2, Nrv=0) -SMOS Level 3 data Considering homogeneous pixels Parrens et al., 2014b

24 main conclusions for both 2-P and 3-P retrievals, best results (R and ubrmse) were obtained considering N RV = N RH = -1. In both cases, the SM retrievals were either independent for 2-P retrievals (for SRP) or only slightly dependent for 3-P retrievals on the value of H R. 3-P retrievals (ttv is retrieved, with SM and TAU), generally led to improved results in terms of correlation R and ubrmse vs 2-P retrievals, (but larger bias). when NRV = NRH = -1, for 2-P, R = 0.68 (QR = 0) or R = 0.71 (QR = 0.1) For 3-P R = 0.77 (QR = 0) or R = 0.82 (QR = 0.1) the specific structural characteristics of the vineyards, with a preferential vertical orientation of the vine stems and stocks, could be accounted for in the 3-P retrievals (in the ttv parameter)? for all the other configurations (in terms of values of N RV, N RH and Q R ) the coefficient R decreased and the ubrmse increased, for increasing values of H R.