Do we need ET from remote sensing?

Transcription

1 ICID, Montpellier, Sep Do we need ET from remote sensing? Luis S. Pereira President elect, CIGR Vice-President Hon., ICID Professor, Agricultural Engineering Research Center, Institute of Agronomy, Technical University of Lisbon

2 ET RS for Hydrology Areal ET Water balance Groundwater discharge and recharge Agriculture Early warning of droughts Yield estimation Irrigation Irrigation performance Systems management Irrigation advising

3 RS may be useful for estimating many irrigation management parameters Bastiaanssen et al., 2000

4 Accuracy of RS estimates for rainfed and irrigated agriculture Report Ede-Wageningen Expert Consultation Meeting, May 2001 However these results may be too much optimistic or a great care is required to achieve them

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6 Potential users and Land SAF products Internal evaluation Agriculture Forestry Natural Hazard and and Risk Risk Management Land Land Surface Temperature Soil Soil Moisture Evapotranspiration Biophysical Parameters Albedo Land Land Surface Temperature Evapotranspiration Biophysical Parameters Albedo Land Land Surface Temperature Soil Soil Moisture Evapotranspiration Snow Cover Biophysical Parameters Terrestrial Transport Safety Land Land Surface Temperature Snow Cover Passanha, 2003

7 Hydrology and climatology P ET + = S + Sav Runoff Recharge

8 Regional daily ET ICID Int. Workshop on Remote Sensing of ET for Large Regions, Montpellier, France, Sept., 17, Source: 2003 ETYP, Greece, 2003

9 FAO-Penman-Monteith Carlson-Buffum Granger FAO_PM was considered best Source: ETYP, 2003

10 Regional daily ET Texas: Rn from RS vs. Rn from ground Texas: ETo from RS vs. ETo from ground

11 Source: Narasimhan & Srinivasan, 2002 ET from ground based observations didn't match well with the ET from NOAA-AVHRR satellites: 1. FAO-PM was used to derive ET from ground but all the parameters needed for the estimation of ET using FAO-PM could not be derived from AVHRR satellite to compute ET - RS. 2. A constant wind velocity of 2 m/s was used for ET-RS, while actual wind velocity was used for FAO-PM 3. The ET derived from ground stations is obtained from point observations and ET from AVHRR is using parameters measured over 1 x 1 km.

12 Regional ET from RS in Northwest China Source: Yunhao et al., 2003

13 Caselles et al., 1992 FAO_PM method RS-ET vs. ground ET, Khan catchment (savana vegetation), Ivory Coast, using the EVA tool Terfai et al., 2003

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15 Relating grownig season areal evapotranspiration computed from ground observations and ET computed from NDVI for North Dakota, Szilagyi, 2002.

16 Annual ET to estimate groundwater discharge in Death Valley, Nevada and California, combining satellite and ground observations, USGS, 2001

17 Regional ET from RS applied to Rio Grande Basin, New Mexico, to estimate soil moisture Source: Sung-ho Hong et al., New Mexico Tech

18 Agricultural applications Dry matter production in Harran plain, Turkey. Source:

19 Remote sensed weather parameters and ET for early warning on drought in the Horn of Africa (Oroda 2003)

20 Irrigation One vision

21 A different vision

22 Performance indicators Source: Bastiaanssen & Bos, 1999 The question is not how accurate are the estimates of ET or the indicators but how they are useful and used

23 Irrigation performances computed from RS-ET at Nilo Coelho, Brasil (Bastiaanssen et al., 2001) These indicators may be useful for project management but require further detail at farmer and crop level

24 These results are aggregated at sytem and month levels. A step further is to analyse where farmers or crops more attention is required

25 Drought for 1996 is well identified Source: LISSE, 2002

26 However, we may innovate about performance indicators that overcome common misunderstandings such as efficiency

27 Where the water goes? To irrigation only? To nature? To other uses? How ET estimates helps to understand performances? ICID Int. Workshop on Remote Sensing of ET for Large Regions, Montpellier, France, Tank Sept., irrigation 17, 2003 in Sigirya, Sri Lanka

28 Water use at basin scale Courtesy by D. Molden, IWMI

29 WATER USE CONSUMED FRACTION Beneficial Preserved Quality REUSABLE Degraded Quality NON- CONSUMED FRACTION NON- REUSABLE Nonbeneficial LOSSES

30 IRRIGATION ADVISORY SERVICES Current IAS are based on traditional (non-remote-sensing) data sources, although the use of land-use maps derived from remote sensing has become rather common. But the day-to-day estimates of crop water requirements are still performed on the basis of field agrometeorological stations and field data. RS has not been used operationally in that area for three main reasons and limitations (Jochum et al., 2003): the lack of adequate time-space resolution of satellite imagery for IAS needs; the lack of mature EO/RS-derived products that match directly the IAS operations, aspects related to the adequate and easy-to-use information for the farmer, which requires also to improve the farm ICID irrigation Int. Workshop Remote systems. Sensing of ET for Large Regions, Montpellier, France, Sept., 17, 2003

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34 IAS diagram from Land Irrigation Support Service, LISSE, 2002

35 Schematic structure of space-assisted IAS, LISSE, 2002

36 From land use mapping to Kc maps From LISSE, 2002, with data from Albacete

37 LAI from WDVI (Landsat TM) Map of LAI, July 94 derived from LANDSAT TM image, Gromola LAI predicted 5 Artichokes Scale 1 0 Km 4 Forages Maize Fruit-trees Vegetables 3 R 2 = LAI values < LAI measured WDVI predicted LAI vs. observed in Gromola, Sele Plain (South Italy) D Urso, >5.0

38 Map of Kc derived from RS, Gromola, Italy D Urso, 2003 Knowing the Kc for pristine crop conditions the advice may include agronomic and irrigation management isuues for improving yields and water productivity

39 Comparing potential ET using diffrent interpolation techniques between two satellite passes (interpolation of r and LAI, of Kc and from 10-day averaged Kc) E p LAI = 2 r = 0.18 K c = 0.94 LAI = 4 r = 0.15 K c = 1.17 direct calc. interp. aver.10d kc kc LAI = 1.2 r = 0.12 K c = day 1994 D Urso, 2003

40 Validation of SEBAL The thin line represents the five-day average Kc for lysimeter and the thick line represents the assumption used in SEBAL to extrapolate between images From Allen et al., 2002.

41 ETrF ETrF = Kcb for alfalfa reference crop W.Grain-Agrimet W.Grain /1/00 3/31/00 4/30/00 5/30/00 6/29/00 7/29/00 8/28/00 9/27/00 10/27/00 Agrimet curve is for Twin Falls, Idaho for 2000 Other curve is from SEBAL Allen, 2003

42 Differences between RS-ET from SEBAL and other information sources may be explained (Allen, 2003): SEBAL integrates ET during entire period (beyond planting - harvest) SEBAL detects impacts of actual planting date and growth rate SEBAL detects impacts variety on ET SEBAL detects impacts of actual water and cultural management However, these impacts refer exactly the aspects that make it different to just compute ET without following a crop or to estimate ET in real time using crop observations. The largest errors in ET estimates don t come from Kc but from crop dates and not adjust ET to the actual management practices

43 ICID, Montpellier, Sep Do we need ET from remote sensing? Yes, we do. But it will not solve the problems that we have with ET from ground observations when wisdom will not be there in using ET data