The vegetation monitoring in LSA SAF: overview and potential applications

Transcription

1 The vegetation monitoring in LSA SAF: overview and potential applications F. J. García-Haro, F. Camacho, B. Martínez, M.A. Gilabert, A. Moreno, J. Meliá 5th LSA SAF User Workshop June 2013, Karlsruhe INDEX n VEGA products: n Product description & Expert knowledge n n n Quality monitoring Development of new products: EPS VEGA, CWC, GPP n Algorithm prototyping (SENTINEL-2 like, MODIS, ) Potential applications n Real time monitoring of drought related disturbances

2 PRODUCT CONTENT nseviri VEGA : daily products FVC Euro LAI FAPAR NAfr SAme SAfr Example: 15 April EUMETCAST 3 PRODUCT DESCRIPTION Percentage of gaps over 1 year of data Compliance with the URD Optimal: Err(FVC)<0.10 Medium: 0.10<Err(FVC)<0.15 Low: 0.15<Err(FVC)<0.20 Unusable Err(FVC)>0.20 n Usability of VEGA products is only limited for high view zenith angles 4

3 Product validation & Expert knowledge July 2006 MSG MERIS MODIS n Algorithms robust, validated against common literature methods n Products include pixel-level information (error estimate, QF) and fit well with the existing satellite and ground truth within the error bars n FVC: LAI: n FAPAR: (MSG), 0.20 (other products) ntake care about a possible over-estimation of FVC for low values (semi-arid areas). n FAPAR profiles present some noise introduced for the k2 BRDF parameter. nchanges in the algorithm could introduce changes in products No back-processing (so far) Quality monitoring Consistency LSASAF and Copernicus Global Land

4 Quality monitoring Consistency LSASAF and Copernicus Global Land Quality monitoring Consistency LSASAF and Copernicus Global Land

5 Quality monitoring Consistency LSASAF and Copernicus Global Land

6 Quality monitoring Consistency LSASAF and Copernicus Global Land Quality monitoring Consistency LSASAF and Copernicus Global Land

7 Quality monitoring Consistency LSASAF and Copernicus Global Land Quality monitoring Consistency LSASAF and Copernicus Global Land

8 Quality monitoring Consistency LSASAF and Copernicus Global Land Quality monitoring Consistency LSASAF and Copernicus Global Land

9 Quality monitoring Consistency LSASAF and Copernicus Global Land Quality monitoring Consistency LSASAF and Copernicus Global Land

10 Quality monitoring Temporal consistency SudOuest LAI MSG MOD15A2 LAI BIOPAR IN SITU Mongu LAI MSG MOD15A2 LAI BIOPAR IN SITU LA AI 3 LA AI DOY DOY PRODUCT DESCRIPTION Input (BRDF K0) Identified artefacts LAI Artefacts during late winter 2011 n n at some specific locations in the four SEVIRI regions. Caused by a sharp decrease in the BRDF bands, unadressed by quality flag Documented in Updated Validation Report 20

11 PRODUCT DESCRIPTION Identified artefacts Iberian Peninsula Madagascar Strips in FVC and LAI at some periods of year 2010 n Assigned an erroneous value of 0 n Unaddressed by quality flag Documented in Updated Validation Report 21 New products 10-day VEGA (MTVEGA) Delivery of 10-day VEGA (MTVEGA) based on AL3 (time scale of 30-day) 22

12 Algorithm develpmement (EPS) n n Joint retrieval of VEGA+CWC by inverting data generated from RTMs Powerful non parametric nonlinear retrieval methods Prior distributions N, C ab, C w, C m, C bp LAI, ALA, HOT, B s, FVC Database of RTM simulations Leaf Optical Properties Model PROSPECT l ( ) l ( ) Canopy Reflectance Model SAIL Reflectance, Absortance R b ( ) Background Optical Properties Observation Geometry s ; v ; ) Input: SEVIRI / EPS BRDF (K0) K0 in RED, NIR, SWIR Prior Knowldege Machine learning algorithms GPR, KRR, NN, SVR Outputs: FVC, LAI, FAPAR, CWC Algorithm Prototyping VALSE2: intercomparison of algorithms for S2 Example: SEN3EXP: Sentinel2 simulated from AHS LSA SAF NNET (Baret et al.)

13 Algorithm Prototyping LSA SAF VEGA and NN algorithm over SEVIRI LSA SAF PROSAIL NN A.Verger, F. Baret, F.J. García-Haro, F.Camacho and J. Meliá. Consistency of vegetation estimates from SEVIRI/MSG observations and operational algorithms. Proceedings RAQRSS-III. Torrent, Spain, 27 Sept-1 Oct. 2010, pp: Algorithm Prototyping Global estimates from MODIS (MCD43) Example: LAI, 10 June 2011 n Retrieval method: Gaussian Processes Regression (GPR)

14 Algorithm Prototyping Global estimates from MODIS (MCD43) Example: FVC, 10 June 2011 n Retrieval method: Gaussian Processes Regression (GPR) New products Prototyping of water content n n Canopy Water Content (kg/m 2 ), Fuel Moisture Content (%) Inversion of PROSAIL Prototyped on CHRIS/PROBA, SEVIRI, and MODIS Different retrieval Algorithms: GPR, NN, Example: 7 June 2011

15 New products Prototyping of primary production n n NPP Net Primary Production, GPP Gross Primary Production A Monteith type parametric (C- Fix) model through the assimilation of LSA SAF products (DSSF, FAPAR, LST). Prototyped with MODIS n VALIDATION n n flux towers (eddy-covariance) Intercomparison with MOD17 Example: GPP, 21 June 2011 A. Moreno, Gilabert, et al (2012), Assessment of MODIS imagery to track light-use efficiency in a water-limited Mediterranean pine forest. Remote Sensing of Environment, 123, M.A. Gilabert, A. Moreno, F. Maselli, M. Chiesi, B. Martínez, F.J. García-Haro, A. Pérez- Hoyos, A. Carrara, J. Meliá (2013), Estimación de la producción primaria bruta en España empleando datos meteorológicos e imágenes MODIS y MSG, Proc. XV Congr. AET, Madrid Octubre Reconstruction of seasonal curves Potential applications False second-season False second-season False second-season Start of growing season 2007 Ja Fe Ma Ap Ma Jun Jul Au Se n Africa is a consolidated region: no gaps, temporal continuity, improving the capabilities of polar orbiters to characterise the phenology

16 Deriving phenologic attributes Potential applications 31 Guan, K., by E. F. Isabel Wood, D. Monteiro Medvigy, K. (EUMETRAIN) K. Caylor, S. Li and S. J. Jeong, 2013, Derive vegetation phenological time and structure information over Africa using SEVIRI daily LAI, IEEE transactions on Geoscience and Remote Sensing, in press. FEEDBACKS CLIMATE-VEGETATION n Impact of drought in Spain FVC shows large deviation for certains classes/periods in water-limited areas Shrub, herbaceous and broadleaved are very sensitive to drought SPI-12 (August 04 July 05) Extremely dry Severely dry Moderately dry Slightly dry Slightly wet Moderately wet Very wet Extremely wet FVC Broadleaved Forest Day of Year FVC Herbaceous Day of Year

17 Automated monitoring of vegetation disturbance Modeling the dynamics of vegetation n Season-trend parametric model: Model Residual Y t Trend t Stational t Random t Example: Time series of SEVIRI FVC 3 A k sin kwt FVC ( t ) a0 a1 t sin kwt k 1 k Example: Disturbance measure std _ resid( t) Y t Yˆ y n n APPLICATIONS: Real time detection (drought, fire): Retrospectively monitoring the extent of disturbances Automated monitoring of vegetation disturbance Example: Drought in Somalia (2011) n FVC/MSG time series n n n Savanna, 2 growing seasons Fully automated: Probability of disturbance maps (typically (yp y significant for std_resid<-1) Period of recovery (e.g. std_resid pass from -1 to0) SEVIRI Capabilities (VEGA) n High frequency and continuity it VEGA is specially suited for very rapid detection (e.g. < 1 month) 34

18 Potential applications Severe drought in Horn of Africa (2011): n Shortage of rain in the two rainy seasons (autumn and spring) 35 Drought monitoring Horn of Africa (2011) 36

19 Horn of Africa (2011) 37 Drought monitoring Horn of Africa (2011) 38

20 Horn of Africa (2011) 39 Drought monitoring Horn of Africa (2011) 40

21 Horn of Africa (2011) 41 Drought monitoring Horn of Africa (2011) 42

22 Horn of Africa (2011) 43 Drought monitoring Horn of Africa (2011) 44

23 Horn of Africa (2011) 45 Drought monitoring Horn of Africa (2011) 46

24 Horn of Africa (2011) 47 Drought monitoring Horn of Africa (2011) 48

25 Horn of Africa (2011) 49 Drought monitoring Horn of Africa (2011) 50

26 Horn of Africa (2011) 51 Drought monitoring Horn of Africa (2011) 52

27 Horn of Africa (2011) 53 Drought monitoring Horn of Africa (2011) 54

28 Horn of Africa (2011) 55 Drought monitoring Horn of Africa (2011) 56

29 57 Drought monitoring 58

30 Central Europe (May, 2006) 59 Drought monitoring 60

31 Potential applications 61 Drought monitoring 62

32 63 Drought monitoring 64

33 65 Drought monitoring Potential applications 66

34 67 Drought monitoring Eastern Europe (summer, 2007) 68

35 Eastern Europe (summer, 2007) 69 Drought monitoring 70

36 71 Drought monitoring 72

37 73 Drought monitoring 74

38 75 Drought monitoring 76

39 Iberian Peninsula (spring, 2009) 77 Drought monitoring Iberian Peninsula (spring, 2009) 78

40 79 Drought monitoring 80

41 81 Drought monitoring 82

42 83 Drought monitoring 84

43 85 Drought monitoring 86

44 87 Drought monitoring 88

45 89 Drought monitoring 90

46 91 Drought monitoring France (summer, 2011) 92

47 France (summer, 2011) 93 Drought monitoring Potential applications France (summer, 2011) 94

48 Potential applications 95 Drought monitoring Potential applications 96

49 Potential applications 97 Drought monitoring 98

50 Mediterranean countries (spring, 2012) 99 Drought monitoring Iberian Peninsula Drougth (2012) 100

51 Mediterranean countries (spring, 2012) 101 Drought monitoring Mediterranean countries (spring, 2012) 102

52 103 Drought monitoring Potential applications 104

53 Potential applications 105 Drought monitoring Potential applications 106

54 107 Drought monitoring 108

55 109 Drought monitoring Potential applications 110

56 111 The vegetation monitoring in LSA SAF: overview and potential applications F. J. García-Haro, F. Camacho, B. Martínez, M.A. Gilabert, A. Moreno, J. Meliá 5th LSA SAF User Workshop June 2013, Karlsruhe