EARTH OBSERVATION FOR MONITORING WATER RESOURCES AND IRRIGATION DEMAND

Transcription

1 EARTH OBSERVATION FOR MONITORING WATER RESOURCES AND IRRIGATION DEMAND Guido D Urso University of Naples Federico II & ARIESPACE srl Academic Spin-off

2 Key-words: Sustainable use of natural resources Planning and management of land and water resources Which tools?

3 During the last three decades, we have assisted two main developments: a) a detailed knowledge of the land surface processes through their mathematical description based on measurable parameters b) the availability of new generations of sensors, with enhanced spectral and spatial resolutions GIS + Earth Observation + Models u z ln k z0m * u z R T 0 m T A RS R X T C S q(z,t) v(x,y,t)

4 Agro-hydrological models and E.O. techniques for improving water management in agriculture 1. derivation of spatially distributed data concerning land surface attributes, i.e. surface albedo, fractional vegetation cover and Leaf Area Index VISIBLE-NEAR INFRARED wavelengths 2. estimation of instantaneous values of water balance terms, i.e. actual evapotranspiration and soil moisture - THERMAL IR wavelengths

5 We do not talk now about satellite sensors in the thermal infrared (TIR): temporal and spatial resolutions unsuitable for applications at farm scale (in most cases) MODIS (1000 m) Landsat (100 m)

6 Observations in the VIS-NIR

7 Observations in the VIS-NIR

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9 Satellite sensors in the VIS-NIR

10 Sensors capabilities improvements Landsat 7 ETM+ => 30 m SPOT5 => 10 m Quick Bird => 2,8 m

11 Landsat 8 image availability from Oct. To Apr. South. URUGUAY : 6 Path 224 Row 84

12 Landsat 8 image availability from Apr. to Sept. South. ITALY : 6 Path 189 Row 32

13 Major recent breakthroughs in satellite Earth Observation: RapidEye (2008): Constellation of 5 satellites Daily coverage for any location 6.5 m spatial resolution First sensor with imagery in the red-edge spectral region (important for vegetation)

14 Major recent breakthroughs in satellite Earth Observation: WorldView-2 (2009): Constellation of 5 satellites Daily coverage for any location 0.5 m spatial resolution First sensor with 8 bands from visible to near infrared

15 Sentinel-2 Launch: Sentinel-2A in 11 June 2015 Sentinel-2B in spectral bands spatial resolutions of m Potential applications for hyperspectral remote sensing in precision agriculture: Revisit time: 5 days with 2 satellites Crop N stress detection Chlorophyll content Weed mapping Pest & disease mapping Bare soil imaging for management zones delineation

16 EO data provider EO image processing center Delivery to final user The entire processing chain (including delivery to final user) can be completed within few hours from the satellite acquisition Map product

17 Which answers satellite Earth Observation can provide to farmers questions: How is the crop growing? Is growth uniform over my plot? How much irrigation apply? There are weeds or diseases spreading? Walnut in France: medium price /kg soil cover, leaf area, biomass, water stress, nutrient stress Crop vigour maps Evapotranspiration and irrigation requirement maps NNI (Nitrogen Nutrition Gross Index) income /ha from obtained remotely sensed data. Variable rate N fertilization map (kg N ha 1) on the basis of the Nstatus value in each pixel. The suggested rate is shown only for pixels belonging to N deficient areas FATIMA EU PROJECT JUST STARTED ON THIS ISSUE

18 Research development Huge knowledge (more than 30 years) on applications of optical remote sensing for crop conditions assessment Multispectral reflectance and temperature of crop canopies relates to two basic physiological processes: photosynthesis and evapotranspiration. In both processes Leaf Area Index (LAI) is the fundamental canopy parameter. (Moran et al., Remote Sensing Environm., 1997)

19 LAI (LICOR LAI-2000) b b b bbb b bb bb b b b b bbbb b bb b b b b b b b b b b bb b b bbb b b b b b b b b b b b b b b b b b b b b b bbb b bbb bbb b b b b b b b bb b b b b b b b CLAIR model cal. 1 WDVI LAI = - ln ( 1 - ) WDVI LAI measurements SPARC 2003 campaign Garlic Alfalfa Onion Papaver Onion Onion Alfalfa Onion Corn Alfalfa Alfalfa Sugarbeet Corn Corn Corn Sugarbeet Corn Potatoes The final value of was taken in correspondence of the minimum error between observed and estimated LAI

20 LAI (CLAIR model) 14/07 - LAI map from CLAIR model (WDVI, Clevers, 1989) 7,000 6,000 5,000 4,000 3,000 2,000 1,000 y=0,89x R^2=0,80 Alfalfa Corn Onion Garlic Potato SugarBeet 1:1 0,000 0,000 1,000 2,000 3,000 4,000 5,000 6,000 7,000 LICOR LAI Ground measurements RMSE=0.59 The empirical relationship has been verified by using 40 independent field measurements.

21 Piana del Sele: Mappa del LAI derivata da immagini SPOT4 (risoluzione 20 m)

22 LAI maps for canopy and yield management Irrigated vineyards, Sella e Mosca, Sardinia

23 P-M and vegetation parameters: ET p under standard conditions (FAO 56) This is the evapotranspiration from disease-free, well-fertilized crops, grown in large fields, under optimum soil water conditions and achieving full production under the given climatic conditions. By multiplying ET o by the crop coefficient, ET p is determined. Two calculation approaches are outlined: the single and the dual crop coefficient approach. This value can be used to define the maximum amount of irrigation water to be applied.

24 P-M and vegetation parameters: ET p under standard conditions ET E p p 1 ( R R G) D / r (1 r / r ) ns nl E a c a R ns (1 r) S r c t R t 0.5LAI r a z 2 2 U h ln 3 c zt h ln 3 c 0.123hc hc 0.168U Based on the definition of crop water requirements of F.A.O. Paper 56 (1-step approach - Penman-Monteith equation):

25 P-M and vegetation parameters: ET p under standard conditions Experimental values of crop coefficients K c have been proposed by Doorenbos and Pruitt (1977). Due to its simplicity, the crop coefficient approach is still widely used in irrigation scheduling (FAO, 1998). In reality, the value of K c is related to the actual development of the canopy and to the environmental conditions. By combining ET p with ET 0, K c can be analytically defined as: K f ( K, T, RH, U ; r, LAI, h ) c a c The K c concept was introduced because of the difficulties related with the measurements of vegetation parameters. Earth Observation is the solution to this issue.

26 Estimation of potential evapotranspiration (upper boundary) MULTISPECTRAL SATELLITE DATA ALBEDO r MAP LAI MAP AGROMETEO DATA K, T a, RH, U Kilometers IMAGE PROCESSING h crop MAP ET ( K, T, RH, U ; p a r, LAI, h ) c ETp MAP ETo mm/d non irriguo <

27 Methodological background (in brief) Vuolo, D Urso et al., Agric. Water Manag., 147: Within 48 hours from each image acquisition: a) Pre-processing of EO images; b) EO-based crop development products; c) Calculation of CWR and suggested irrigation depth (pixelscale and plot scale); d) Delivery of information to final users.

28 Validation of Penman-Monteith EO based on irrigated crops (NO STRESS) ETactual from Eddy Cov. Chicory from Burba & Anderson Maize Alfalfa Vineyard

29 ETa ETact ET reale Irrigated vineyards, Sardinia y = 0.56x R 2 = 0.58 from doy 226 to doy Vigna ETact= 0.56 ETp from 7:00 to 11:00 from 12:00 to 16: from 17:00 to 19: ETp ET potenziale ETo * Kc (ASD) ETp

30 D E M E T E R Project co-funded by the European Commission Satellite-Assisted Irrigation Advisory Service e-saias

31 SIRIUS: EO for river-basin governance (end 2014) Space-assisted services for Sustainable Irrigation: Tools & instruments for implementation of WFD & Sustainable Development Strategy (SDS): * water use monitoring, * water saving, * enabling true participation/collaboration SPIDER webgis + ppgis + + multi-sensor constellation + + water footprint: Service to water managers at farm, irrigation scheme, aquifer, river-basin slide 31

32 Definition of Users requirement for satellite-based information service for crop management Spatial resolution: between 1 and 20 m depending on farm extension and management Temporal resolution: 3-8 days Delivery of product to final users: within hours from satellite acquisition (larger time lag depending on applications) Product accuracy: ± 10%

33 Technological implementation = find a balance for the following issues i. availability of ancillary input data, with no or minimal contribution from end-users ii. iii. elaboration and processing time, with minimum possible time lag between E.O. acquisition date and information delivery to final users accuracy of algorithms for deriving crop water requirements, with minimum possible parameterisation.

34 the NEW FARMER HOUSE: tools for enhancing the traditional background experience with scientific knowledge and new technologies advancements in farm management technologies (INTERNET - GPS) development of Geographical Information Systems new imaging systems from space Soil and crop variability, meteo data scientific knowledge of crop growth processes

35 An example of.

36 How users access the data? 1. Farmer access 2. Admin access

37 Which data are given to the farmers? 1 Mapping of the effective crop vigour

38 Which data are given to the farmers? 2 Acquiring local meteorological data and now also weather forecast ET crop Easy integration with other data at farmlevel (i.e. from soil moisture sensors)

39 Which data are given to the farmers? 3 Irrigation advices: maximum irrigation amount calculated by considering the ACTUAL CROP DEVELOPMENT

40 EXAMPLE OF SATELLITE-BASED IRRIGATION ADVISORY SERVICE IN ITALY and South AUSTRALIA

41 ETP, water supply. (mm/day) Cumulative (cubic meter / ha). The irrisat farmer START Farm: **** crop: Maize ETP and water supply supply ETP /6 8/6 15/6 22/6 29/6 6/7 13/7 20/7 27/7 days 100 supply cumulative ETP cumulative 0 satellite acquisition

42 Another comparison

43 Consorzio di Bonifica Destra Sele

44 Volumi irrigui specifici (in mm) e totali per i distretti irrigui della piana in Destra Sele (Campania), per la stagione irrigua 2005 (Progetto DEMETER)

45 Volumi irrigui misurati e stimati con dati di Osservazione della Terra nel distretto irriguo Boscariello, Consorzio di Bonifica Destra Sele, nel 2012

46 Lessons learned. Farmers want to know how their crops are growing (am I doing well?) Integrate EO data with the standard management procedures of farmes Need of regular contacts with farmers for training in using new technologies and HOW handling the information they get

47 Some conclusions E.O. is now entered in the real world of agricultural applications thanks to improved spatial and temporal resolution of VIS-NIR data More and more data available (cost tending to zero!), more knowledge of processes and data interpretation, more computation power, ICTs, spin-off and so on Farmers finally on the way to accept and implement innovations in their current practices.

48 - durso@unina.it