MULTI-SOURCE SPECTRAL APPROACH FOR EARLY WATER-STRESS DETECTION IN ACTUAL FIELD IRRIGATED CROPS

Transcription

1 Department of Geography and Environmental Studies MULTI-SOURCE SPECTRAL APPROACH FOR EARLY WATER-STRESS DETECTION IN ACTUAL FIELD IRRIGATED CROPS Maria Polinova 1, Thomas Jarmer 2, Anna Brook 1 1 Spectroscopy & Remote Sensing Laboratory Center for Spatial Analysis Research (UHCSISR), Department of Geography and Environmental Studies, University of Haifa, ISRAEL - polinovamaria88@gmail.com, abrook@geo.haifa.ac.il 2 Remote Sensing Group, Institute of Computer Science, University of Osnabrueck, Germany - thomas.jarmer@uos.de

2 2 Optimal irrigation planning Inputs Crop Soil properties Weather Type of irrigation etc. Analysis Irrigation schedule Grant # Funded by Israeli Water Authority

3 3 Field feedback Canopy scale Leaf scale Crop growth rate: height, LAI, green biomass, etc. Photosynthesis rate, light use efficiency, chlorophyll and carotenoid level, etc.

4 4 Vegetation features by spectrum Elowitz, M. R. (213). What is Imaging Spectroscopy (Hyperspectral Imaging)?. Retrieved November, 27, 213.

5 5 Satellite spectral imagery Benefits Free or Mid-cost Wide spectral range Big area cover Disadvantages Low resolution (spatial, spectral and temporal) Weather conditions No survey managing

6 6 Spectral camera (airborne, field) Benefits Big area cover High spatial and spectral resolution VIS-NIR-SWIR range Survey manage Disadvantages Expensive Weather conditions

7 7 Field spectrometer Benefits Low-cost High spectral resolution VIS-NIR-SWIR range Direct sample measures Autonomous surveys Laboratory measures Disadvantages Point-by-point measurement Weather conditions

8 8 Vegetation Indices (VIs) VI Development technologies Type of sensitivity Formula Application in agriculture Reference Normalized Difference Vegetation Index (NDVI) Satellite Green biomass w88 w68 w88+w68 Water content, physical parameters Rouse et al., (height, LAI, biomass, dry matter), 1973 predicting yield Green w8 + (w55 1.7(w45 w67)) Crop coefficient, chlorophyll level, Gitelson, Atmospherically Resistant Index (GARI) Satellite Chlorophyll LAI, soil salinity, predicting yield Kaufman and Merzylak, 1996 Modified Triangular Vegetation Index (MTVI) Airborne LAI 2(w8 w55) 2.5(w67 w Crop coefficient, chlorophyll level, LAI, yield predicting Haboudane et al., 24 Leaf area index for cotton Field spectroscopy LAI exp(4.78ln w9 w68 w9+w68 Crop coefficient, green LAI, ) predicting yield Gutierrez, 212 Pigment-specific simple ratio algorithms Field spectroscopy Chlorophyll w81 w w81 w676 Nitrogen and water concentration, chlorophyll level, pests detecting Blackburn, 1998

9 Study Area 9

10 1 RapidEye Satellite Sensor (5m) Chickpea Tomato & Cotton 5 pixels

11 11 Landsat 8 OLI8 (3m) Chickpea Tomato & Cotton 1 pixels

12 12 OceanOptics USB4-VIS-NIR (1cm) 1 measures

13 13 Dynamics of VIs temporal behavior 1. Calculate average means 2. Estimate variability of VIs by coefficient of variability Cv = s / x where Cv is a coefficient of variation, s is the standard deviation, and x is the average mean

14 14 Tomato May/215 Jul/215 Jun/215 Plant water concentration 1% 9% 8% 7% 6% 5% 4% 3% 2% 1% % 5/16/215 6/5/215 6/25/215 7/15/215 8/4/ Weekly irrigation amount Total water amount=56mm /15/215 4/15/215 5/15/215 6/15/215 7/15/215

15 15 Tomato NDVI /17/215 5/6/215 6/25/215 8/14/215 RapidEye OLI8 Field_spec GARI % % 48% 3/17/215 5/6/215 6/25/215 8/14/215 RapidEye OLI8 Field_spec MTVI /17/215 5/6/215 6/25/215 8/14/215 RapidEye OLI8 Field_spec % 19% 25% LAIc 25% 3/17/215 5/6/215 6/25/215 8/14/215 RapidEye OLI8 Field_spec PSSR % /17/215 5/6/215 6/25/215 8/14/215 54% 3% 68% RapidEye OLI8 Field_spec

16 16 Cotton May/215 Jun/215 Jul/215 Aug/215 1% 9% 8% 7% 6% 5% Plant water concentration Stressed (dry leave) 4% 3% 2% 1% % 4/26/215 5/26/215 6/25/215 7/25/215 8/24/215 9/23/ Weekly irrigation amount Total water amount=495mm /22/215 6/22/215 7/22/215 8/22/215

17 17 Cotton NDVI % /27/215 5/16/215 7/5/215 8/24/215 RapidEye OLI8 Field_spec Field_stress GARI % /27/215 5/16/215 7/5/215 8/24/215 RapidEye OLI8 Field_spec Field_stress MTVI % %.4.2 3/27/215 5/16/215 7/5/215 8/24/215 RapidEye OLI8 Field_spec Field_stress 3 LAIc 25 PSSR % % 17% 35% 124%.5 16% 24% 24% 21% 3/27/215 5/16/215 7/5/215 8/24/215 RapidEye OLI8 Field_spec Field_stress % 25% 21% 44% 18% 42% 12% 3/27/215 5/16/215 7/5/215 8/24/215 RapidEye OLI8 Field_spec Field_stress

18 mm 18 Chickpea Jan/215 Mar/215 Apr/215 May/215 Jun/215 Plant water concentration 1% 9% 8% 7% 6% 5% 4% 3% 2% 1% % 1/16/215 2/15/215 3/17/215 4/16/215 5/16/215 6/15/ Daily irrigation amount 5/18/215 5/25/215 6/1/215 Total water amount=24mm

19 19 Chickpea NDVI % /6/215 3/7/215 5/6/215 7/5/215 RapidEye OLI8 Field_spec 3% GARI 35% 8% 19% 3% 3% 1/6/215 3/7/215 5/6/215 7/5/215 RapidEye OLI8 Field_spec MTVI % 23% % 1/6/215 3/7/215 5/6/215 7/5/215 RapidEye OLI8 Field_spec LAIc % 25% 1 58% 88% 37%.5 15% 18% 2% 34% 1/6/215 3/7/215 5/6/215 7/5/215 RapidEye OLI8 Field_spec % 5% 4 PSSR 47% 26% 38% 43% 75% 2 1/6/215 3/7/215 5/6/215 7/5/215 RapidEye OLI8 Field_spec

20 2 Assessment of variability level Test datasets Tomato Cotton Chickpea Stressed cotton Chickpea on samples (flowering) (flowering/fr uit formation) (ripening) 4 samples (vegetative) (vegetative) (flowering) (fruit formation) 3 samples (emergence) (vegetative) (flowering) 18 samples (vegetative, with stress signs) 1 samples (flowering/ fruit formation)

21 21 Variability in health and stressed crops NDVI GARI MTVI 1% 1% 1% 8% 8% 8% 6% 6% 6% 4% 4% 4% 2% 2% 2% % % % 1% 8% 6% 4% LAIc 1% 8% 6% 4% PSSR Tomato Cotton Chickpea Stressed cotton 2% % 2% % Chickpea on

22 22 Water-stress detection Satellite imagery Field spectroscopy Estimation growth rate (LAI, green biomass, crop coefficient) Compression reference values with Calculation VIs (photosynthesis rate, pigmentation) and their variability Assessment absolute values the

23 23 Irrigation optimization by spectral data Detected stress by field spectroscopy Inputs Crop Soil properties Weather Type of irrigation Analysis Irrigation schedule Field feedback by spectral data etc. Water requirement by spectral image

24 Thank you for your attention! Maria Polinova Spectroscopy & Remote Sensing Laboratory Center for Spatial Analysis Research (UHCSISR) Department of Geography and Environmental Studies University of Haifa ISRAEL +972 () (Office) +972 () (Fax) polinovamaria88@gmail.com