Fractional Vegetation Cover Estimation from PROBA/CHRIS Data: Methods, Analysis of Angular Effects and Application to the Land Surface Emissivity

Transcription

1 Fractional Vegetation Cover Estimation from PROBA/CHRIS Data: Methods, Analysis of Angular Effects and Application to the Land Surface Emissivity J.C. Jiménez-Muñoz 1, J.A. Sobrino 1, L. Guanter 2, J. Moreno 2, A. Plaza 3 & P. Martínez 3 1 Global Change Unit (University of Valencia) 2 Remote Sensing Unit Laboratory of Earth Observation (University of Valencia) 3 Neural Network and Signal Processing Group (University of Extremadura)

2 OVERVIEW 1. OBJECTIVES 2. FIELD MEASUREMENTS AND CHRIS IMAGES 3. METHODS FOR FVC ESTIMATION 4. ANGULAR EFFECTS 5. SURFACE EMISSIVITY RETRIEVAL FROM FVC VALUES 6. CONCLUSIONS

3 OBJECTIVES: Comparison between different methods for FVC retrieval Accuracy of the retrieved FVC Effect of the CHRIS view angle on the FVC Land surface emissivity retrieval from FVC values

4 FIELD MEASUREMENTS: Field measurements were carried out in the framework of the SPARC 2003 at the Barrax test site FVC was measured in situ using hemispherical photographs (Martínez et al., 2004) Fractional vegetation cover measured in situ over different samples using hemispherical photographs (FVC in situ ) and standard deviation values (σ). Sample Notation FVC in situ σ Garlic G Corn C Corn C Sugarbeet B Alfalfa A Alfalfa A Potatoes P Martínez, B., Baret, F., Camacho-de Coca, F., García-Haro, F. J., Verger, A., & Meliá, J. (2004). Validation of MSG vegetation products Part I. Field retrieval of LAI and FVC from hemispherical photographs, In Remote Sensing for Agriculture, Ecosystem and Hydrology, edited by M. Owe, G. D Urso, B.T.Gouweleeuw, A.M. Jochum, Vol. 5568, Bellingham, WA, pp

5 PROBA-CHRIS IMAGES: Image acquired on 14-July-2003 over the Barrax test site Operation mode-1: 62 spectral bands, spatial resolution of 34 m. The image used has been atmospherically corrected: Guanter et al. (2005)

6 METHODS FOR FVC ESTIMATION FROM CHRIS DATA: Vegetation indices and empirical approaches * NDVI NDVI0 Scaled NDVI: NDVI = (NDVI 0 : soil, NDVI : vegetation) NDVI NDVI 0 Gutman & Ignatov (1998): FVC = NDVI * Carlson & Ripley (1997): FVC = NDVI *2 Carlson, T. N., & Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index, Remote Sensing of Environment, 62, Gutman, G., & Ignatov, A. (1998). The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models, International Journal of Remote Sensing, 19(8),

7 METHODS FOR FVC ESTIMATION FROM CHRIS DATA: Vegetation indices and empirical approaches Values proposed by different authors: - Gutman & Ignatov (1998): NDVI 0 = 0.04, NDVI = Sobrino & Raissouni (2000): NDVI 0 =0.20, NDVI = Calculated from the ASTER spectral library: NDVI 0 =0.13, NDVI = Values found for the Barrax test site: NDVI 0 =0.08, NDVI =0.98 Expression obtained from the NDVI extracted from the CHRIS image and the FVC measured in situ: FVC = NDVI (standard error of estimation = 13%) Sobrino, J. A., & Raissouni, N. (2000). Toward remote sensing methods for land cover dynamic monitoring: application to Morocco, International Journal of Remote Sensing, 21(2),

8 METHODS FOR FVC ESTIMATION FROM CHRIS DATA: Vegetation indices and empirical approaches The NDVI shows problems of saturation for high vegetation covers In order to solve this problem Gitelson et al. (2002) propose a relation bewteen FVC and VARIgreen VARI green = ρ green ρ red ρ + ρ ρ green red blue (TOA reflectivities) Equation proposed by Gitelson et al. (2002): FVC % = VARI (σ < 10%) Equation found for the Barrax test site: FVC % = VARI (σ = 8%) Gitelson, A. A., Kaufman, Y. J., Stark, R., & Rundquist, D. (2002). Novel algorithms for remote sensing estimation of vegetation fraction, Remote Sensing of Environment, 80,

9 METHODS FOR FVC ESTIMATION FROM CHRIS DATA: Spectral Mixture Analysis (SMA) Linear Spectral Unmixing (LSU) SMA divides each ground resolution element into its constituent materials using endmembers (EMs), which represent the spectral characteristics of the cover types. General form of the LSU models (Sabol et al., 2002): Ne Ne em= 1 ρ = F ρ + E i em em, i i em= 1 F em = 1 ρ i : band reflectivity Ne: number of EMs F em : fraction of EM Ei: unmodeled residual Sabol, D. E., Gillespie, A. R., Adams, J. B., Smith, M. O., & Tucker, C. J. (2002). Structural stage in Pacific Northwest forests estimated using simple mixing models of multispectral images, Remote Sensing of Environment, 80, 1-16.

10 METHODS FOR FVC ESTIMATION FROM CHRIS DATA: Spectral Mixture Analysis (SMA) Linear Spectral Unmixing (LSU) The reflectivity spectra for EM have been automatically extracted from the image using the AMEE (Automated Morphological Endmember Extraction) method (Plaza et al., 2002; Plaza et al., 2004) RMSE = 11.9 % Test using in situ measurements RMSE < 12 % FVC from LSU Garlic (G1) 0.4 Corn (C2) Corn (C1) Sugarbeet (B3) 0.2 Alfalfa (A10) Alfalfa (A1) Potatoes (P1) FVC in situ Plaza, A., Martínez, P., Pérez, R., & Plaza, J. (2002). Spatial/spectral endmember extraction by multidimensional morphological operations, IEEE Transactions on Geoscience and Remote Sensing, 40(9), Plaza, A., Martínez, P., Pérez, R., & Plaza, J. (2004). A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data, IEEE Transactions on Geoscience and Remote Sensing, 42(3),

11 ANGULAR EFFECTS Low variations with the CHRIS observation angle have been found on the NDVI & FVC Garlic (G1) Corn (C2) Corn (C1) Sugar Beet (B3) Alfalfa (A10) Alfalfa (A1) Potatoes (P1) NDVI View angle (º) FVC Garlic (G1) Corn (C2) Corn (C1) Sugar Beet (B3) Alfalfa (A10) Alfalfa (A1) Potatoes (P1) View angle (º)

12 LAND SURFACE EMISSIVITY (LSE) RETRIEVAL FROM FVC LSE can be obtained for a mixed surface composed by soil and vegetation using a very simplified approximation: ε = ε (1 P) + ε P s v V V ε s : soil emissivity ε v : vegetation emissivity P v : FVC LSE can be obtained for a certain THERMAL BAND from FVC values and by finding the band emissivities for soil and vegetation. These emissivities have been obtained from the spectra included in the ASTER spectral library.

13 LAND SURFACE EMISSIVITY (LSE) RETRIEVAL FROM FVC CIMEL CE (NOAA-AVHRR) 0.6 band filter function band 4 band 3 band 2 Expressions for the land surface emissivity retrieval from fractional vegetation cover (P v ) values and for each CIMEL band. INSTRUMENT BAND EXPRESSION (10.54 µm) ε = P v wavelength (µm) CIMEL 1 2 (11.96 µm) 3 (10.80 µm) ε = P v ε = P v CIMEL CE (TERRA-ASTER) 4 (8.82 µm) 1 (10.54 µm) ε = P v ε = P v 0.6 band 1 2 (11.29 µm) ε = P v filter function band 6 band 5 band 4 band 3 band 2 CIMEL 2 3 (10.57 µm) 4 (9.15 µm) 5 (8.69 µm) 6 (8.43 µm) ε = P v ε = P v ε = P v ε = P v wavelength (µm)

14 LAND SURFACE EMISSIVITY (LSE) RETRIEVAL FROM FVC Error on emissivity BAND 1 (8-13 um) BAND 2 ( um) BAND 3 ( um) BAND 4 ( um) (Error assumed on FVC = 10%) Error on emissivity Fractional vegetation cover BAND 1 (8-13 um) BAND 2 ( um) BAND 3 ( um) BAND 4 ( um) BAND 5 ( um) BAND 6 ( um) Fractional vegetation cover Error on emissivity < 0.01 in most cases

15 LAND SURFACE EMISSIVITY (LSE) RETRIEVAL FROM FVC For rough surfaces, the cavity effect should be taken into account: ε = ε (1 P) + ε P + C s v V V However, the retrieval of the cavity term (C) is not an easy task, and needs further research Therefore, for rough surfaces errors higher than those shown before will be expected MAIN IDEA: Land surface emissivity can be retrieved from sensors without thermal bands

16 LAND SURFACE EMISSIVITY (LSE) RETRIEVAL FROM FVC Example: Broadband (8-14 µm) emissivity map obtained from CHRIS data

17 CONCLUSIONS: - FVC can be obtained from vegetation indices and spectral mixture analysis with accuracies lower than 15%: FVC from NDVI: σ = 13% FVC from VARI green : σ = 8% FVC from LSU: rmse < 12% - Low angular variations have been noticed for the different CHRIS observation angles. - LSE can be obtained from FVC values (without using thermal bands!) with an accuracy lower than 0.01 for flat surfaces. For rough surfaces, higher errors are expected due to the cavity effect.