Biomass retrieval algorithm based on P-band BioSAR experiments of boreal forest

1. Biomass retrieval algorithm based on P-band BioSAR experiments of boreal forest Lars Ulander1,2, Gustaf Sandberg2, Maciej Soja2 1Swedish Defence Research Agency (FOI), Sweden 2Chalmers University of Technology, Göteborg, Sweden

2. Outline Background Test sites and data collections Temporal stability of backscatter Backscatter vs biomass Regression modeling Validation

3. Background BIOMASS is a P-band polarimetric SAR mission Frequency band 432-438 MHz Repeat pass PolInSAR and - during initial orbit - tomography BIOMASS is in phase A for ESA:s 7th Earth Explorer Three candidate missions are competing The other candidates are CoreH20 and Premiere Final selection of mission with be in 2013 This presentation reports on development & evaluation of biomass estimation algorithms for BIOMASS

4. P-band SAR experiments over boreal forest ESA has funded multiple SAR campaigns in support of satellite P-band SAR BIOMASS candidate mission BioSAR-1 (2007), Remningstorp 3 dates (Mar, Apr, May), 2 headings Stand-level biomass < 290 ton/ha Flat topography BioSAR-2 (2008), Krycklan 1 date (oct), 4 headings Stand-level biomass < 180 tons/ha Hilly topography Krycklan Remningstorp

5. BioSAR-1: Remningstorp, southern Sweden E-SAR coverage In situ and lidar data Biomass range: 10-290 tons/ha Low topography: 120–145 m asl Species Spruce Pine Birch

6. BioSAR-2: Krycklan, northern Sweden SAR images (blue); forest stands (red) Species Spruce Pine MIxed Biomass range: 20-180 tons/ha; Hilly topography: 135–350 m asl

7. BioSAR-1: Short-term backscatter stability Each data point corresponds to a forest stand and is defined by go [dB] backscatter from 2 tracks on 1 day Max diff is 0.6 dB, which is similar to the radiometric stability.

8. BioSAR-1: Long-term backscatter stability HV HH VV Mar vs May Mar vs May Mar vs May HV HH HH VV VV Apr vs May Apr vs May Apr vs May Observed backscatter reduction most likely due to decreasing moisture

9. BioSAR-1/-2: PHV backscatter vs Biomass Data from all stands, tracks, test sites • Re = Remningstorp • Kr = Krycklan • In general, backscatter spread is due to: • Moisture variations • Ground topography • Forest Structure Site Date Heading

10. …and the same for HH and VV Site Date Heading

11. General observations HV and HH shows good sensitivity to biomass across the entire range of biomass (10-290 tons/ha) HH gives largest dynamic range but also largest spread VV shows little dependency on biomass Krycklan results are, in general, a few dB lower than Remningstorp Observed backscatter variability implies that algorithm based only on single polarisation will perform poorly. Need for both multi-pol and topographic corrections

12. BioSAR-1/-2: Polarisation ratio • Potential to correct for soil moisture effects, since dependent on dielectric constant for a slightly rough surface. • Surface backscatter dominates for low biomass (VV > HH) • Sensitivity to biomass but large spread for low biomass

13. Algorithm development and evaluation Six regression models developed and evaluated Algorithm test designed to be challenging, i.e. Training using data from Krycklan (single date) Validation using all data from Remningstorp (three dates) All algorithms use a logarithmic transform of the biomass (W = above-ground dry biomass) to stabilise the variance

14. Six regression models tested  HV only, one-parameter model  HV only, two-parameter model  Model from Saatchi et al (TGRS 2007). Multiple polarisations, 7 parameters • Model from Saatchi et al (TGRS 2007). Multiple polarisations, 14 parameters. Requires separate crown and stem biomass; includes slope corrections. Recently proposed models:  HV + polarization ratio, 3 parameters  HV + polarization ratio, 6 parameters. Includes slope correction. NB. Backscatter in dB

15. Algorithm development and evaluation Models trained on 29 in-situ stands from Krycklan October 2008 Model validation using data from Remningstorp 2007 The validation results have been separated into different dates (Apr-May) and different test site data in Remningstorp, i.e. plot-level biomass where all individual trees within 80 m x 80 m have been measured and biomass error is a few percent stand-level biomass from helicopter lidar and field measurement with an error of 25 tons/ha

16. Residuals after model training; Krycklan data Training on all data and residuals separated for different headings. Algorithm 3 has smallest residuals, but all algorithms perform similar.

17. Independent validation; Remningstorp data Algorithm 6 (HV, pol-ratio, topo-corr) significantly better than the others

18. Biomass retrieval performance Algorithm 5 Algorithm 6 Algorithms trained with all data from Krycklan and evaluated with data from Remningstorp. Validation results from Remningstorp are shown in the plots above. T = stands V = plots 80 m x 80 m

19. Conclusions Single-pol P-band backscatter shows significant variability when data from different test sites, headings and dates are pooled Six different regression models have been trained and validated on BioSAR-1/2 data over boreal forests in Sweden Results show that multiple polarisations and topographic corrections significantly improve biomass retrieval Algorithm 5 and 6 perform significantly better Algorithm 6 includes HV, polarisation ratio and topographic corrections and gave the best results (RMSE 30-40 tons/ha) Algorithm 5 includes HV and polarisation ratio and gave the second best results (RMSE 50-70 tons/ha) The study shows importance of including polarisation ratio and topographic corrections besides HV-pol