Retrieval of crop characteristics from high resolution airborne scanner data

Transcription

1 Retrieval of crop characteristics from high resolution airborne scanner data K. Richter 1, F. Vuolo 2, G. D Urso 1, G. Fernandez 3 1 DIIAT, Facoltà di Agraria, Università degli studi di Napoli Federico II, Italy 2 ARIESPACE srl,italy 3 Department of Thermodynamics Faculty of Physics,University of Valencia, Spain

2 Introduction / Background Increase of E.O. data in last years availability of high spectral, spatial and temporal resolution imagery Development of methodologies for canopy parameter estimation Empirical-statistical methods (vegetation indices) Physical approaches (RT model inversion) Operational applications: opportunity to estimate canopy parameters in a cost-effective way and on a largescale Well-timed information, for direct application of irrigation/fertilization (precision farming) Systematic monitoring of the land parameters - trace of plant growth trend: adjusting of agricultural practices for a sustainable use of the natural resources in the future

3 Objectives of the study (a) Evaluation of some of the methods and models developed for the estimation of Leaf Area Index (LAI) from E.O. data (b) Test of spectral sampling proposed for the Sentinel-2 multi-spectral mission using these optimal algorithms

4 ESA AgriSAR 2006 campaign: Imagery and in situ data acquisition Compact Airborne Spectrographic Imager (CASI 1500, ITRES Research Limited) overpass: 05. July 2006, 10:00 UTC 1.5 m spatial resolution, 288 spectral channels (ranging from 368 to 1042 nm) (Spectral calibration and atmospheric correction carried out by Univ.Valencia (Guanter et al., 2006), using an optimized version of the MODTRAN4 radiative transfer code) Collection of ground data of LAI: LI-COR LAI-2000

5 Maize 222 ( 102 ha) Sugar beet 102 ( 18 ha) Study area Goermin farm (northeastern part of DEMMIN)

6 Sugar beet (102), 06/07/2006 Maize (222), 05/07/2006

7 Methodology: Models and inversion techniques PROSAILH model - PROSPECT [N, Cab, Cw, Cm] (Jacqemoud et al., 1990) - SAILH [Prospect, LAI, ALA, ALFA-soil, HOT] (Verhoef 1984) 1-dimensional, turbid medium model

8 L e a f l e v e l Leaf variables: - N - Chl a+b - C w - C m PROSPECT Leaf reflectance & trans mittance C a n o p y l e v e l canopy structure: - LAI - HOT - ALA SAILH CANOPY REFLECTANCE Illumination & View geometry Soil reflectance nm

9 Model inversion Iterative optimization algorithm: Sequential Quadratic Programming (SQP) - drawbacks: local minimum, high time and computation demand Look up table (LUT): cases of canopy parameter combinations R i meas Ref measured RMSE = 1 n λ n λ ( i i R ) mea s RLUT i= 1 2 R i LUT Ref simulated

10 Range of input variables (lower and upper bounds (LB, UP) for PROSAILH model inversion using SQP algorithm, and classes/ distributions of input variables for establishment of the LUT. Variables Min (LB) Max (UP) Number of classes (LUT) Distribution of variables Leaf parameters: (PROSPECT) N Uniform C ab [µg/cm²] Gaussian C m [g/cm²] Uniform C w [g/cm²] Uniform Canopy structure variables (SAIL) LAI [m² m -2 ] Gaussian ALA [ ] Gaussian HotS 0.25(field 222), 0.33 (field 102) ALFA Uniform

11 Spectral samplings: B1 chosen according to the results of a study from Thenkabail et al. (2004): Optimal bands characterizing vegetation and crops due to their sensitivity to chlorophyll, soil background, biomass, LAI, plant moisture and vegetation stress.. VIS: (in [nm], band center, spectral width: 10 nm) Red edge: NIR: 885

12 Spectral samplings: B2 Spectral bands proposed for the upcoming E.O. multi-spectral satellite Sentinel-2 developed by ESA in the framework of GMES (Global Monitoring for Environment and Security) VIS: (in [nm], band center (spectral width)) 490 (65) 560 (35) 665 (30) Red edge: 705 (15) 740 (15) NIR: 775 (20), 842 (115) 865 (20)

13 Spatial resolution pixel size: 1.50 m pixel size: 20 m

14 Spatial resolution pixel size: 1.50 m pixel size: 20 m

15 The CLAIR model (Clevers, 1989) 1 WDVI LAI ln 1 * α WDVI = WDVI = ρ ρ i r ρ ρ si sr * : extinction coefficient, expressing the increase of LAI for a unit of WDVI ( here: set to 0.8) ρ i ; ρ r : reflectance of the observed canopy in red and infrared bands ρ si ; ρ sr corresponding values for bare soil conditions Red band: 676 nm NIR band: 885 nm

16 Results: Evaluation using ground data of LAI, field 102: Map of with-in field variability of LAI for sugar beet field (102) using LUT and band configuration B1

17 Results: Evaluation using ground data of LAI, field 222: Map of with-in field variability of LAI for maize field (222) using LUT and band configuration B1

18 B1: Optimal bands B2: Sentinel-2 bands Measured and estimated LAI values for both fields achieved by LUT approach for the two different band configurations B1 (left) and B2 for SENTINEL-2 (right).

19 Conclusions Higher accuracy in LAI estimation by using physical model inversion than a statistical-empirical approach Higher suitability of the LUT approach in operational applications than SQP Choice of the Sentinel-2 proposed spectral bands revealed a quite high LAI accuracy compared to best spectral sampling However: results are limited to only one sensor and two different crop types, small LAI-range (max ) Conclusively, the results of the study may represent the basis for developing an operational physical based model for the retrieval of LAI from the Sentinel-2 2 sensor data

20 Thank you for your attention!