Potential of Sentinel-2 Type Observation

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MULTISOURCE EO DATA FOR THE OPTIMAL AGRICULTURAL DRAINAGE WATER MANAGEMENT IN THE SEMI-ARID AREA OF DOUKKALA (WESTERN MOROCCO): Potential of Sentinel-2 Type Observation Kamal Labbassi 1 Nadia Akdim,

1 MULTISOURCE EO DATA FOR THE OPTIMAL AGRICULTURAL DRAINAGE WATER MANAGEMENT IN THE SEMI-ARID AREA OF DOUKKALA (WESTERN MOROCCO): Potential of Sentinel-2 Type Observation Kamal Labbassi 1 Nadia Akdim, Silvia Maria Alfieri 2,3 and Massimo Menenti 3 1-Chouaib Doukkali University, Morocco. 2-Delft University of Technology, The Netherlands. 3- Institute for Mediterranean Agricultural and Forest Systems, Italy.

2 OUTLINE Study area. Methodology. Data set used. Results & Discussions. Conclusion

STUDY AREA Surface Corn Irrigation Oum Er-rabia River Al Massira Dam Hydraulic Basin of Oum

Sprinkler areas Wheat in Morocco, Irrigation remarkable for its sand strategic importance for

The region's climate is typically semiarid with a large variability rainfall Localized

Ce High service of Irrigated perimeter Low service of Irrigated perimeter The resources

3 STUDY AREA Surface Corn Irrigation Oum Er-rabia River Al Massira Dam Hydraulic Basin of Oum Er-rabia Fourrages The irrigated area of Doukkala is among the largest and earliest developed Sprinkler areas Wheat in Morocco, Irrigation remarkable for its sand strategic importance for national production, specially sugar beet (38%) and commercialized milk (20%). The region's climate is typically semiarid with a large variability rainfall Localized Irrigation averaging 316 Sugar mm Beet / year. Ce High service of Irrigated perimeter Low service of Irrigated perimeter The resources mobilized for irrigation of Doukkala area come mainly from the dam Al Massira, a major water storage structure in the basin of Oum Rbia with a capacity of approximately 2760 Mm 3.

4 Background & Motivation Decrease of water availability Climate change: successive drought & extreme phenomenon Increase in water demand Irrigation water management Problems Inefficient Irrigation technique Irrational irrigation water Allocation Increase of water cost Aging Hydro-agricultural infrastructures Expensive water mobilization

Methodology: Irrigation performance indicators (IP s) Menenti et al. (1990) and Menenti (2000).

equity the marginal of water of water benefit allocation of applying Eto pk irrigated comparison irrigation area. water with crop by means water of requirements.

ij = Volume received at reference unit j, (mm); The calculation of IP3 requires a numerical model of water within higher order unit i (m 3 ); Ea flow in the k,w = Actual soil evapotranspiration

5 Methodology: Irrigation performance indicators (IP s) Menenti et al. (1990) and Menenti (2000). V ij ij IP1 ij V i / / A A i IP2 ij k n Ep k * Aijk * 10 k 1 V ij IP3 i j k n Eak, w Eak * Aijk * 10 k 1 V sim In which: V i = Volume supplied to unit i (m 3 ); IP2: IP1: IP3: describes adequacy equity the marginal of water of water benefit allocation of applying Eto pk irrigated comparison irrigation area. water with crop by means water of requirements. Vthe actual crop evaporation with and without irrigation. ij = Volume received at reference unit j, (mm); The calculation of IP3 requires a numerical model of water within higher order unit i (m 3 ); Ea flow in the k,w = Actual soil evapotranspiration vegetaion of crop k Aatmosphere i continuum in addition to multispectral image data to = Irrigated area in unit i (ha); determine land cover by crop type. A ij = Irrigated area in reference unit j, Ea k = Actual evapotranspiration that would A ijk within higher order unit i (ha); = Area of crop k in unit j (ha); n = Total number of crops k. V sim = Potential evapotranspiration of crop k following the application of an irrigation volume V ij (mm); occur without any irrigation (mm); = Volume of water supplied to unit j according to simulation model (m 3 ). The goal is to provide ORMVAD a few tools for optimal management of irrigation ALCANTARA Kick-Off ESRIN4/10/2012

6 Methodology: Workflow

7 Data Collection: Satellite Data (2012/2013) Landsat Distribution continuity Mission RapidEye TIRS SPOT4- HRVIR1 (Take5) Faregh District Sidibennour District Zemamra District

and Khmiss mettouh meteorological station.

8 Data Collection : Agro-meteorological Data Daily meteorological (2012/2013) were collected at the same dates of the satellite observations from the regional office of agricultural development of Doukkala (ORMVAD) at Zemamra and Khmiss mettouh meteorological station. Air Temperature Relative Humidity Rs Wind Rainfall ETo * Tmax( C) Tmin( C) Tmoy( C) HRmax(%) HRmin(%) HRmoy(%) (MJ/m2) (m/s) (mm) (mm/j) Crop Calendar: Water Flow: Irrigation water volumes

Data Collection: Field Campaigns (2012/2013) SPOT4 (HRVIR1) Field Work 31/01/2013 Casier Faregh 18-19 - 20

Casier Faregh 23-24 Mai 2013 02/03/2013 Casier Faregh 20 Juillet 2013 17/03/2013 Casier Faregh 22/03/2013

10/12/2012 Sidi Bennour & Zemamra 08/02/2013 Sidi Bennour & Zemamra Landsat8 (OLI) 19/04/2013 Partie du

9 Data Collection: Field Campaigns (2012/2013) SPOT4 (HRVIR1) Field Work 31/01/2013 Casier Faregh Décembre /02/2013 Casier Faregh Fevrier /02/2013 Casier Faregh 3-4 Avril /02/2013 Casier Faregh Mai /03/2013 Casier Faregh 20 Juillet /03/2013 Casier Faregh 22/03/2013 Casier Faregh 06/04/2013 Casier Faregh 16/04/2013 Casier Faregh 21/04/2013 Casier Faregh RapidEye (REIS) 10/12/2012 Sidi Bennour & Zemamra 08/02/2013 Sidi Bennour & Zemamra Landsat8 (OLI) 19/04/2013 Partie du périmétre 26/04/2013 Périmétre irrigué 13/06/2013 Périmétre irrigué 29/06/2013 Périmétre irrigué 15/07/2013 Périmétre irrigué 9

10 Application : Sidi Benour Pilot Area FR AO SS ZM SB TN Multispectrale image of Doukkala Irrigation scheme: SB: Sidi Bennour; SS: Sidi Smail; FR: Faregh; AO: Aounates; Zr: Zemamra; TG: Tnine Gharbia 10

11 Results & Discussion: Kc-NDVI method Considering that these canopy variables influence the spectral response of vegetated surfaces, a direct correspondence between Kc and reflectance measurements can be established. Kc-NDVI : Generic equation An operational relationship Kc- NDVI is derived by considering a linear relationship between the maximum NDVI (set as 0.8) and the maximum Kc (1.2 at effective full cover) and the minimum (bare soil) NDVI (0.16) and bare soil Kc (0.4), respectively. Example of Kc map of Sidi Bennour district (08/02/2013) K 1.25* NDVI 0.2 c

12 Results & Discussion: NDVI Time Series (2012/2013) 10/12/ /02/ /04/ /04/ /06/ /06/ /07/2013

Results & Discussion: K c Time Series (2012/2013) 10/12/2012

13 Results & Discussion: K c Time Series (2012/2013) 10/12/ /02/ /04/ /04/ /06/ /06/ /07/2013

14 Results & Discussion: LAI Time Series (2012/2013) 10/12/ /02/ /04/ /04/ /06/ /06/ /07/2013

Results & Discussion: Albedo Time Series (2012/2013) 10/12/2012 08/02/2013

15 Results & Discussion: Albedo Time Series (2012/2013) 10/12/ /02/ /12/ /04/ /07/ /04/ /06/ /06/ /07/2013

16 Results & Discussion: Crop height Time Series (2012/2013) 10/12/ /02/ /04/ /04/ /06/ /06/ /07/2013

Results & Discussion: How consistent are our product using multicaptor data?? Surface Albedo (r ) r w (Menenti, 1984) RMSE=0.

17 Results & Discussion: How consistent are our product using multicaptor data?? Surface Albedo (r ) r w (Menenti, 1984) RMSE= Scatter plot of the estimated (r) by SPOT4 (HRVIR1) vs Landsat8 (OLI) in Faregh district, 26th April 2013 Comparison between Mean and standard deviation values of r estimated by SPOT4 (HRVIR1) and Landsat8 (OLI) on 26th April 2013 within 10 samples of (20X20). t-test: t= no difference significant (-1.54; df=17; p=0.14 )

18 Results & Discussion: How consistent are our product using multicaptor data?? RMSE=O.0328 RMSE=O.O469 RMSE=O.O2 RMSE=O.0343 RMSE=O.O288 RMSE=O.O06

19 Results & Discussion: Assessment of Irrigation performance (IP2) Temporal variability of CWR, IWR mean values and water allocation (mm/month) IP2: Head-Tail End pattern Spatial distribution of IP2 per CGR for both SidiBennour and Zemamra districts

Discussion: Application in irrigation _water management In practice, the information provided by remote sensing could be used for irrigation water management in two ways: RapidEye (REIS): 08/02/2013

The primary users of this information are farmers and the operators of the tertiary canals.

20 Discussion: Application in irrigation _water management In practice, the information provided by remote sensing could be used for irrigation water management in two ways: RapidEye (REIS): 08/02/2013 Pixel-wise CWR : The Pixel-wise CWR data provide a reference for better precision in quasi-real time scheduling of water gifts. The primary users of this information are farmers and the operators of the tertiary canals. Precise irrigation scheduling depends on the spatial variability of CWR at the plot scale and the RMSE value of CWR estimates. The CWR data aggregated by CGR and district: provide a reference to adjust water allocation. The primary user of this information is the water management body in our case ORMVAD at the different management levels involved in planning and operation of water distribution. In general, its necessary to take into account the difference between CWR, irrigation water requirement (IWR), and net irrigation water requirement (NIWR) in order to determine water allocation.

21 Conclusion (Semi-) empirical algorithms to assess phonology (K c -NDVI method) and retrieving canopy biophysical parameters (Analytical approach), have been used by constructing a time series of multi-spectral satellite image data with different spatial, temporal and spectral resolution (SPOT4, Landsat8 and RapidEye ). The spatial distribution of K c, r, LAI and h c was used in conjunction with groundbased meteorological data for mapping maximum crop evapo-transpiration (ET c ). These methods are fast, robust and easily applicable to large data sets and thus suitable for operational purposes. The appraisal of irrigation performance in terms of adequacy between CWR and water allocation at both the district and CGR level documented a significant mismatch of requirements and allocations. Taking rainfall into account, the difference between requirements and water supply becomes acceptable in winter, but the irrigation water deficit increases in summer. In general the adequacy of water allocation to requirements (both spatially and temporally) could be improved by judicious management of irrigation, i,e by reducing the water excess in some CGR/date and use it in others CGR in deficit

Future. The approach will be extended for other spatial and temporal scale taking into account the type of crop by the introduction of new types of data! (more detail in the spectral and the temporal.

22 Future. The approach will be extended for other spatial and temporal scale taking into account the type of crop by the introduction of new types of data! (more detail in the spectral and the temporal..sentinel2) To meet the needs of agricultural operators to monitor the growing season for the points related to: Monitoring of plant cover crops on schedule. Monitoring the illegal use of the irrigation water along and around the main channel and unserved parcels with a weekly rate (distribution takes place every 15 days) Pixel wise management of irrigation: precision irrigation Estimation of crop area and yields.

23 THANK YOU

WATER RESOURCES IN MOROCCO: AN OVERVIEW. Kamal LABBASSI

WATER RESOURCES IN MOROCCO: AN OVERVIEW Kamal LABBASSI MoroccanAssociation of Remote Sensingof the Environment MARSE Aware of the strategic issues related to water sector, Morroco has implemented a strategy