Initial Investigations into the Potential and Limitations of

Initial Investigations into the Potential and Limitations of Remote Sensed Data for Irrigation Scheduling in High Value Horticultural Crops

Outline • Background – irrigation system requirements into the future • Use of NDVI in irrigation scheduling • Thermal – the ultimate irrigation scheduling tool?

Background • Ongoing switch from flood/furrow irrigation to drip in perennial horticulture • Supported through the Integrated Horticulture Systems Project in the Murrumbidgee Irrigation Area • Aims to see majority of horticulture converted to pressurized irrigation systems by 2010

Drip and Flood Water Use

Yields

Managing High Tech Irrigation Systems 6 Soil probes for 6 ha paddock Assume each probe measured 1 m 2 So we know what is happening on: Can we get something better ? • Method lacks ability to ‘see’ what is happening over the whole vineyard • Only infer the plant stress based on the soil moisture, plants can also be stressed due to a number of other factors such as soil salinity,

Large Scale Low Cost Irrigation Scheduling - NDVI for Irrigation Scheduling/Management/Benchmarking

NDVI • NDVI = (RNIR – Rred) / (RNIR + Rred) NDVI = (Band 4 - Band 3) / (Band 4 + Band 3)

Irrigation Scheduling – FAO 56 Readily available from Weather stations/SILO ETc = ETo x Kc Relates actual water use of the crop to reference water use -Large variation and crop/management specific NDVI to Kc functional relationship

Canopy Cover and Light Interception Vs WU Williams and Ayars (2005) Mc. Clymont et al. ECC = 1. 2 NDVI – 0. 2 (extrapolated from Johnson and Scholasch, 2005)

Irrigation Scheduling from Remote Sensing indices Satellite, airborne or On-ground Spatial Measurements NDVI / EAS Images from Satellite or quad bike On Ground ETo from Weather Station Incorporates management/soil/water/salinity constraints Determination of Kc from NDVI / EAS Data Representing Individual Paddocks ETc = ETo X Kc Potential Evaporation based on Atmospheric Demand Actual crop evapotranspiration across regions

NDVI + ETo data Harvesting Daily delivery of tailored irrigation scheduling information direct to irrigator on SMS ETc = ETo x kc CRC IF Irrigateway server Initialisation data – system parameters Benchmarking and data mining

SMS Drip Scheduler • Uses simple SMS text messages for delivering irrigation scheduling information • Will be tested with 20 horticultural growers this coming season in MIA irri. GATEWAY Dripper run times (min) for Y’day: A-250, B-330, C-270. 2 days: A-510, B-620, C-545. 3 days: A-790, B-920, C-770.

NAFE • NAFE 06 NDVI data will be used for fine tuning of EAS/ECC relationships to NDVI • Investigation into scaling effects from high resolution NDVI (NAFE 06) data to Landsat NDVI in relation to providing irrigation scheduling information – sensitivity analysis

Thermal

Crop Water Stress Index (CWSI) What is CWSI? • Relates canopy temperature to an index between 0 and 1 indicating how stressed the plant is: • 0 = No stress • 1 = High stress (Tc-Ta)NTUBL (Tc-Ta)NWSBL Measured with IR temperature sensor or thermal camera (Tc-Ta)NWSBL = Non water stressed base line – equated fully open stomata and fully transpiring canopy (Tc-Ta)NTUBL = non-transpiring upper baseline –equated to temp. of nontranspiring canopy with stomata closed

Agrosense - Irriscan • • • Trials undertaken in MIA in 2002 Collaboration with MIGAL Galilee Technology Centre, Israel 0. 1 m 2 Resolution 1250 ha per day On-site calibration

Results

Canopy Temperature and Salinity Stress 1 3 2 4

Crop Water Stress Index (CWSI) – Jones et al. What is CWSI? • Relates canopy temperature to an index between 0 and 1 indicating how stressed the plant is: • 0 = No stress • 1 = High stress Measured with IR temperature sensor or thermal camera Tdry = upper bound for canopy temp. – equated to temp. of non-transpiring canopy with stomata closed Twet = non-stressed baseline – equated fully open stomata and fully transpiring canopy

Wet Reference Surfaces

Results Wet Reference Surfaces

NAFE • Assessment of alternative methods of determining baselines for CWSI • Comparison of PLMR data with high intensity on-ground gravimetric soil moisture content sensing

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