Use of multi-temporal PalSAR ScanSAR data for soil moisture retrieval

Transcription

1 Use of multi-temporal PalSAR ScanSAR data for soil moisture retrieval Francesco Mattia (1), Giuseppe Satalino (1), Anna Balenzano (1) and Michele Rinaldi () (1) Consiglio Nazionale delle Ricerche (CNR) Istituto di Studi sui Sistemi Intelligenti per l Automazione (ISSIA), Bari, Italy () Consiglio per la Ricerca e Sperimentazione in Agricoltura (CRA-SCA), Bari, Italy Acknowledgement PALSAR and ASAR data were supplied within the framework of JAXA, RA 13 and ESA AO 3597 This work was partly supported by the Italian Ministry of Agriculture, Food and Forestry Policies (under AQUATER contract nr. 9/733/5) and ESA - ESTEC under contract nr /6/NL/HE

2 Background An integrative use of remote sensing data (e.g. land cover/use, leaf area index, wet and dry biomass, soil moisture content etc) and land process models (e.g. SVAT models) is the most promising approach to improve the management of renewable resources SAR data hold a great potential for the monitoring of plant and soil water content at the high spatial (e.g. 1-5 m) resolution it is extremely difficult to identify general methods to decouple the effect of vegetation from the effect of soil (and viceversa) For cereal crops: HH & VV C-band SAR data are suitable for wheat mapping because their ratio is very sensitive to wheat biomass and relatively insensitive to soil contribution. HH L-band SAR data show almost no interaction with canopy parameters whereas are very sensitive to soil moisture content

3 Objective investigate a synergistic use of temporal series of PalSAR ScanSAR and ASAR AP images to map wheat crop areas and then retrieve soil moisture content using a priori information the potential of using the rationing of multi-temporal σ data to retrieve prior guess values of m v (change detection) Outline Experimental data: Foggia (Southern Italy) campaign 6-9 Cereal crop maps from HH & VV C-band ASAR AP image Sensitivity of HH L-band SAR data to soil moisture content & wheat biomass soil moisture retrieval from multi-temporal HH pol. PalSAR data & a priori information feasibility of deriving m v guess values by change detection Discussion and Conclusions

4 Foggia Test site Puglia Arable land Foggia site (Puglia region, Southern Italy), Capitanata plain: nd largest plain in Italy Objective: A decision support system to manage water resources at irrigation districts scale in Southern Italy integrating Remote Sensing and crop growth modelling (AQUATER), 6-9, funded by Italian Ministry of Agriculture and Forestry Policies under contract n. 9/7393/5 CNES(6) Distribution Spot Image S.A./OASIS program Main crops (% of cultivated area): Wheat (48%) Sugar beet (3%) Tomatao ( 7%) Vineyard (8%) Olives ( 5%) Image classified by using multitemporal SPOT-5 data 7

5 In situ data Ground measurements (March August every 1 days) over wheat, sugar beet and tomato fields soil moisture, LAI, agronomic & structural parameters ancillary data (land cover, DEM, meteo data, soil texture, yield analysis, evapotranspiration,...) continuous measurements: meteo data, soil moisture profiles, evapotranspiration soil moisture (%) Wheat fields soil moisture Fresh Biomass precipitation TDR-LAI devices Fresh Biomass (kg/m ) precipitation (mm/day) DoY

6 PalSAR ScanSAR WB1 SAR data DoY Date track Polarization Product 4 4/1/7 9 HH WB1 5 19//7 9 HH WB1 7 13/3/7 96 HH WB1 84 5/3/7 94 HH WB /4/7 95 HH WB /4/7 93 HH WB /4/7 96 HH WB1 13 1/5/7 94 HH WB1 ASAR fine resolution AP DoY Date Swath Polarization Product 63 4/3/6 I7 HH,VV APS 8 3/3/6 I6 HH,VV APS /4/6 I6 HH,VV APS /5/6 I7 HH,VV APS /5/6 I5 HH,VV APS /6/6 I6 HH,VV APS /6/6 I7 HH,VV APS + 1 1/4/7 I6 HH,VV APS /5/7 I6 HH,VV APS /6/7 I7 HH,VV APS 17 1/6/7 I6 HH,VV APS Pol. HH, DoY=118, 8 Apr. 7

7 Cereal crop maps from ASAR APS images HH/VV estimated over training fields HH/VV (db) Map accuracies range between 75% and 78% (up to approximately 9% if a spatial averaging at field scale is applied). DoY Year Δγ (db) Satalino et al., TGRS, vol. 47 (), Feb. 9 P(HH/VV crop) P(crop) HH/VV (lin) 7

8 L-band & HH pol. σ sensitivity to m v & biomass/lai wet dry Model approach: Tsang et al., 1985 σ tot = τ σ s + σ veg + σ db1 + σ db + σ m z h

9 Retrieval algorithm: constrained minimization backscattering direct model (IEM) ( ˆ ) F (,, p ) σ = θλ + ε pˆ j i i j= 1,... M i (i=1,, N) a priori estimates of surface parameters (s, l & m v ) Maximum likelihood solution for p j ( σ) F ( θ λ = 1.. ) 1,, p 1 p pˆ i C = N + i M p i j M j j j ( Δσ ) ( Δ ) i j Hydrology and Earth System Sciences, vol. 13, n.3, 9 ( Δ σ ) = ε i i total rms error error on a priori estimates

10 Cereal crop maps (from ASAR APS data) A priori information: m v & s In situ data / change detection Further Options: hydrologic modelling; spaceborne radiometers; 3rd ALOS Joint PI Symposium, Kona, Hawai i, November 9-13, 9 Flow chart (N) multi-temporal PALSAR WB1 images Preprocessing (SAR images and incidence angle registration, temporal filtering, masking) Inversion: constrained minimization (N) m v maps Input: Trade off: N-large & Τ short (i.e. roughness remains almost const.) In this study: N=3 (images) were used N.B. Correlation length: no a priori information fitting parameter

11 Example of m v map over wheat fields & consistency check Prior m v constant: derived from in situ measurements DoY=113 6% 5% m v 4% db

12 ())()))(())3rd ALOS Joint PI Symposium, Kona, Hawai i, November 9-13, 9 Rationing of multi-temporal σ data σ [db] HσHDoY1vDoY1fbYσ HDoYmDoYvf(( R=.7 mdoy (Do1YbDom v ratio fb ratio L HH Wheat field, AgriSAR () () σ α HH ( εs, ϑ) = D (1) σ α ( ε, ϑ) α HH α D Wagner and Scipal, TGRS, vol. 38 n.4, ; Wickel et al., IJRS vol. n.8, 1; Yang et al., IJRS vol. 7 n.19, 6 ( ε, ϑ) s (1) HH s = (cosϑ + α = i ji j ε 1 s ε sin s 1 θ ) Linear System of M equations in N unknowns (generally under determined, i.e. M<N)

13 Example of m v map over wheat fields & consistency check Prior m v derived on a pixel basis using change detection approach DoY=113 5% m v 4%

14 Conclusions ASAR HH/VV backscatter ratio can be used to map cereal crops with accuracies ranging between 75% and 78% up to approximately 9% if a spatial averaging at field scale is applied. Multi-temporal soil moisture maps underlying cereal crops can be retrieved from PALSAR ScanSAR images at HH polarization & a priori information on soil moisture content. The assessment of their accuracy is still in progress. Rationing of multi-temporal HH L-band σ data can provide valuable prior guess values of m v Future work Assess the feasibility of applying the same method to other crops (e.g. sugar beet, winter rape, tomato) Apply the algorithm to other sites (e.g. North Europe) Further Investigate the potential of using the rationing of multi-temporal σ data to retrieve m v