Use of SAR in applications

Transcription

1 Use of SAR in applications Methodology development Thuy Le Toan Centre d'etudes Spatiales de la Biosphère (CESBIO), Toulouse, France

2 Use of SAR data in applications (1) SAR data are used in ocean applications for which the SAR is unrivalled, such as wind and wave ocean oil slick sea ice monitoring for navigation

3 Use of SAR data in applications (2) Oil slick detection, SIR-C image Arabic Sea, West of Bombay

4 Use of SAR data in applications (2) Radarsat image, Scansar Narrow A Saint-Laurent golf, Quebec (Canada) Surface : 140 km x 159 km, 21/01/96 Resolution : 50 m Incidence : Horizontal polarization Use of SAR for ship routing in the Arctic zone. Radarsat has been optimized to facilitate the water/ice discrimination

5 Use of SAR in land applications For land applications, the SAR data are unrivalled for: Displacement measurement (by interferometry) All weather change detection (flood, storm damage..) Mapping of geological features Other potential applications are based on the use of multitemporal intensity SAR data. For C-band SAR: Crop monitoring Wetland and water bodies mapping Forest mapping at regional scale Land use mapping

6 Flood monitoring using ASAR (1) Water response varies with wind conditions Temporal change detection is more robust

7 Flood monitoring using ASAR (2)

8 Flood monitoring using ASAR (3) soil water soil water Change in backscatter? At HH, VV, HV? In no wind condition? When water surface is affected by wind? water

9 Land cover using ASAR Detection of structure, features and surface type with low temporal change For surfaces with strong temporal change, a-priori knowledge and good choice of data (dates, polarisation, incidences) are required Tianjing

10 Land cover at large scale (1) Wide Swath image Interest: large area (400 km width) with a resolution of 75 m (MERIS resolution is 250 m, MODIS, m and 400km SPOT VGT 1km) 400km RUSSIA Hailar Hulun Nur Buir Nur CHINA MONGOLIA Russia, Mongolia, China border August (R), October (G), November (B) 2004

11 Land cover at large scale (2) Detail of WS colour composite image RUSSIA

12 Land cover at large scale (3) ASAR WSM forest map GLC 2000 forest map 65km Forest map using temporal change Land cover map using SPOT VGT

13 Crop monitoring Crop responses varies with crop type, growth stage, soil moisture and roughness. The same crop type may have various backscatter responses at a given time. For crop mapping, unsupervised classification with training samples, as applied to optical data, may not work. A-priori knowledge (types of crop in the region, approximate crop calendar, cultivation practices) required for - choice of appropriate acquisition dates - choice of polarisation, incidence angle

14 Rice monitoring To illustrate Steps from research to application Physical background Experiment Theoretical modelling and simulation Development of algorithms Testing in other conditions Improvement of the methods Integration in research or application programme

15 Scattering from a rice canopy At C band, HH and VV: the dominant scattering mechanism is the double bounce vegetation-water HH>VV because of the stronger attenuation of VV by vertical stems (and Fresnel reflection R H > R V ) HH and VV increases with the plant biomass.the increase is very important (up to 10 db during the growth season) (Le Toan et al., 1997). Water HH/VV is related to biomass

16 Experimental studies 140 LEGEND : Test site 110 Java Sea LEGEND : Test site Sapporo 6 Jakarta Japan Sea 8 Pekalongan JATISARI SEMARANG JAVA (INDONESIA) Surakarta Yogyakarta Niigata AKITA Sendai 500 km Indian Ocean Tropical rice W N S E Fukuoka Kyoto Osaka JAPON Yokohama Pacific Ocean N W E Short Cycle : days 500 km S Temperate rice Long Cycle:150 days

17 ERS data analysis Backscattering Coefficient (db) Sowing Stem elongation -18 Plant height (cm) Backscattering Coefficient (db) Backscattering Coefficient (db) Age (# days after sowing) Moist Biomass (g/m²) Semarang test site Akita test site ESA-MOST Dragon Programme Advanced training course in Land Remote Sensing, 1 000Beijing, October

18 Electromagnetic modelling The rice plants are planted by bunches, regularly placed on the field Within a rice bunch, the cylindric stems are randomly placed with uniform distribution inside a circle The stems have leaves of elliptical disc shape c b

19 Electromagnetic modelling Er ( ) e ikr Nt = t + t + t + t r vi svi vsi svsi t i= With t = index for stem or leaf v = volume s = surface 1 2 Water Water 3 4 Water Water

20 Model data comparison Effect of Polarisation Effet of Incidence angle Backscattering Coefficient (db) RADARSAT (HH) ERS (VV) Solid line: modelling result Moist Biomass (g/m²) Backscattering Coefficient (db) -4-5 C-HH C-HH Solid line: modelling result Age (# days after sowing) Strong increase from transplanting to grain maturity HH>VV Increase smaller at high incidence

21 Methodology development Simulation of non uniform crop calendar (Indonesia) Backscattering Coefficient (db) nov 12-nov Acquisition ERS-1 22-nov 02-eéc 12-dec 22-dec 01-jan 11-jan 23/01/ jan 10/02/ /02/1994 Date 31-jan 10-feb 20-feb 01-mar 06/03/ mar 21-mar 31-mar 10-apr 20-apr 30-apr Early Late Large variability of backscatter of rice field at a single date Method based of a «rice signature» not applicable 10-may 20-may 30-may

22 Error (%) Methodology development To detect rice/non rice based on temporal change Change >3 db: rice L= Bruniquel L= L=25 5 L=256L=169 L= σ (db) Change 3 db -->Number of looks required: 50 to 75 Field size -->Window size for spatial filtering (1, 3x3,5x5..) -->Number of images needed for filtering

23 date 1 Methodology development date M date M. Calibration Registration. Initial images 1 2. M Multi image filtering 1 2. M Spatial filtering Geocoding 1 2. M Analysis, Retrieval Classification Calibrated coregistered Filtered Filtered geocoded Example of Chain developed using: Gamma ASAR (Gamma RS) Multi-image filtering (Quegan et al., 2000) Temporal change (Le Toan et al., 1997)

24 Methodology development

25 Rice in Vietnam

26 Method validation Good classification : 93,35%

27 Rice mapping ERS map Blue : single crop Green : double crop Red : triple crop GIS 1999

28 Retrieval of rice biomass Backscattering coefficient (db) ERS Dry biomass (g/m²)

29 Use of radar in crop models Rice production model : ORYZA 1 (Kropff et al ) Climatic data T. Radiation. Humidity. Rainfall Sowing date Development rate CO2 assimilation Biomass Parti- -tioning Biomass Leaves Stems Development stage Panicles Roots YIELD Sowing date Biomass Radar Data (s vs. Age) Radar Data (s vs. Biomass)

30 Rice monitoring using ENVISAT Jiangsu Province

31 Rice monitoring using ENVISAT Improvement of the method ENVISAT Polarisation (2 instead of 1) Choice of incidence angle Use of Wide Swath mode for large regions or provinces Rice in China Narrow Field Change in practices (one or two rice crop per year) Mid season drainage Different varieties

32 Small field: powerful filtering Filtering using 20 images (2 polarisations, 10 dates)

33 Use of polarisation HH Hongze (Jiangsu) VV

34 Rice mapping at a single date Using HH/VV Magenta=HH, Green=VV 34km*38km yellow=rice, red=urban, black=other September 6th, 2004, Hongze area

35 Hybrid rice June

36 Japonica rice 15 June 2005

37 Mapping of varieties Hybrid rice Japonica rice

38 Mapping of rice varieties Hybrid rice

39 Mapping of rice varieties Hybrid rice Japonica Rice with different calendar

40 Retrieval of biomass Seasonal variations of HH/VV ratio and rice wet biomass measured in 2004 at a test field (Gaoyou, province of Jiangsu)

41 Regional rice mapping Shuyang Suqian Funing 210 km * km 75m magenta pixel : size Aug18th Green : October 27th HuaiAn Chuzhou ASAR WSM region North of Qingjiang, Jiangsu province Yancheng

42 Regional rice mapping

43 Regional rice mapping Lianyungang Jieyu River Huaimu xin River Qiangwei River Guboshanh ou River Guanyun Xinqi River Guannian Guang river

44 Regional rice mapping HengGe Reservoir Lulan River Donghai Huaimuxin River Fengshan Reservoir

45 Summary A number of applications using SAR data are outlined Rice monitoring is presented as illustration for the different steps towards application Knowledge of the SAR scattering physics and SAR data statistical properties help to develop methodology for using SAR data in applications Remark: Availability of SAR data for the appropriate dates is crucial