Remote sensing

1. By:
Iqura
Malik
17WM60R02
Evapotranspiration (ET)
Estimation with Remote
Sensing
SCHOOL OF WATER RESOURCES
INDIAN INSTITUTE OF TECHNOLOGY, KHARAGPUR

2. Contents
 Introduction
 Remote sensing approaches for ET
estimates
 Methods for deriving ET from remote
sensing
 Different remote sensing satellites.
 Case study
 Conclusion
 References

3. Introduction
 Evapotranspiration (ET) is a combined process of
evaporation and transpiration.
 More than 90 % of annual rainfall is lost in ET (Glenn
et al., 2007).
 ET is the largest outgoing water flux from the Earth’s
surface.
 It constitutes the link between the hydrological and
energetic equilibrium at the soil-vegetation-
atmosphere interface. (Khaldi et al., 2011).

4. Factors affecting ET
ET
Soil
Vegetation
surface Atmosphere
• Moisture
• Physical &
chemical
properties
• Tilth condition
• Soil cover
•Vegetation type
•Species
•Canopy cover
•Microclimate
•Water availability
•Irrigation water
•Irrigation condition
•Meteorological
condition
(Burt et al., 2005)

5. Importance of Quantifying
ET
 Useful for:
– Determining agricultural water consumption
– Assessing drought conditions
– Developing water budgets
– Monitoring aquifer depletion
– Monitoring crops.

6. Challenges in Measuring ET
 ET depends on many variables:
– solar radiation at the surface
– land and air temperatures
– humidity
– surface winds
– soil conditions
– vegetation cover and types
 Highly variable in space and time

7. Need of Remote sensing
Application for ET estimation
 ET estimation using hydrological methods, micro-meteorological
methods can only be considered as point measurements.
 Extrapolation of ET rates from a point to a large area decreases
the accuracy of the estimation .
 Satellite or airborne images using remote sensing techniques is a
practical method for developing the spatial variation of ET at a
regional scale. (Vinukollu et al., 2011).
 Combination of field and satellites-based measurements gives
more precise results for ET (Wang and Dickinson, 2012).

8. Objective
To provide an overview of different remote sensing
methods for estimation of Evapotranspiration

9. Methodology to Estimate
Evapotranspiration
Empirical Method
Residual Method
Interference
Method
Deterministic
Method
ET24 = Rn24 +A−B(Ts
−Ta)
λET= Rn - G – H
ETPlant = Kplant * Etref
(SVAT) model
Source: Couralt et al.,

10. Remote Sensing ET estimation
approaches modification
LANDSAT MODIS SENTINEL

11. LANDSAT Satellite program
overview

12. LANDSAT-7 LANDSAT-8
Enhanced Thematic Mapper
(ETM+)
Spatial Coverage – Global
Spatial Resolution:15m, 30m,
60m
Swath width: 185km
Temporal Coverage and
Resolution:
– April 15, 1999-
present
– 16-day revisit time
Spectral bands- 8
Operational Land Imager
(OLI)
And Thermal Infrared
Sensor (TIRS)
Spatial Coverage– Global
Spatial Resolution:15m, 30m
Swath width: 185km
Temporal Coverage and
Resolution:
– Feb 11, 2013 – present
– 16-day revisit time
Spectral bands- 9

13. Figure1 .Flowchart of the daily ET remote sensing estimation methodology (Yin etal,201

14.  Spatial and temporal resolution of Landsat thermal-band
imagery can be improved by fusing information from
other wavebands and satellites. (Anderson et al., 2012)
 Landsat imagery has the potential to provide valuable
early warning regarding soil moisture deficits and
canopy stress for operational management.
 Landsat 8 gives greater NDVI values than Landsat 7 in
mid-season stages.( González et al,2017)
Monthly drought conditions for April-September during 2002 recorded in the
US Drought Monitor (USDM) and mapped with the thermal-based GOES ESI
at 10-km resolution

15. MODerate Resolution Imaging
Spectroradiometer (MODIS)
 On-board Terra and Aqua.
 Terra passes from north to south across the equator in
the morning, while Aqua passes south to north over the
equator in the afternoon.
 Designed for land, atmosphere, ocean, and cryosphere
observations.
 The Surface Energy Balance Algorithm for Land (SEBAL)
was used to derive ET maps from MODIS images.
 It is based on Penman Monteith approach.
 Lee and Kim (2016) used SEBAL model alongwith
MODIS satellite images in South Korea and found that
ET of lowland areas was higher than that of highland

16.  Spatial Coverage and Resolution:
– Global, Swath: 2,330km
– Spatial Resolution Varies: 250m, 500m,1km
 Temporal Coverage and Resolution:
– 2000-present, 2 times per day
 Spectral Bands
 36 bands (red, blue, Infrared(IR), Near-IR, Mid-IR)
– Bands 1-2: 250m
– Bands 3-7: 500m
– Bands 8-36: 1000m

17. MODIS Products
Product name Land Parameter
MOD 09 Surface reflectance
MOD 11 Land surface temperature and emissivity
MOD 12 Land cover/change
MOD 13 Vegetation indices/ NDVI
MOD 43 Albedo
MOD 15 Leaf area index (LAI)
MOD 16 Evapotranspiration (ET)

18. MOD16 Algorithm Evolution
(Nishida
etal,2007)
(Claugh.
et
al,2007)
(Mu
etal,2007a)
(Mu
etal,2011)

19. MOD16 Algorithm
 Dual-source model developed by Nishida et al.
(2003a,b)
 A pixel is characterized as a mixture of bare soil and
vegetation in MOD16.
 Evaporation fraction (EF) (Shuttleworth et al., 1989) is
defined as ET divided by available energy Q.
EF = ET/Q= ET/Rn-G
 MOD16 simplifies a landscape to a mixture of
vegetation and bare soil.

20.  The proportion of vegetation is denoted by the
fractional vegetation cover fveg whose value is
between 0 and 1.
ET = Q x EF

21. Claugh et al,2007
Developed ET
model using a
Penman–
Monteith
approach
Calculated FC using
NDVI
Ignored soil
evaporation
compared to
transpiration
Used NDVI and LAI
to calculate surface
resistance
Mu et al,
2007a
Modified Cleugh et al.'s
model to estimate the
global ET (RS-ET).
Uses MODIS land cover,
albedo, leaf area index (LAI),
and Enhanced Vegetation
Index (EVI) and a daily
meteorological data
Neglected the evaporation from the
intercepted precipitation from
plant canopy and night time ET

22. Figure 3.MODIS ET Algorithm for calculating daily MODIS ET. (Mu et al, 2007)

23. Improvements on the MODIS ET
algorithm
Includes
evaporation from
rain water
intercepted by the
canopy
Includes night time
evapotranspiration
Instead of
vegetation cover
fraction it uses the
Fraction of
Absorbed
Photosynthetically
Active
Radiation(FPAR).
• Tnight = 2 ×
Tavg−Tday

24. (Mu et al ,2011)
Figure 4. Flowchart of the improved MODIS Evapotranspiration (ET) algorithm

25. Improved MOD16 algorithm
 Based on the Penman–Monteith combination method (Monteith,
1965)
Where, λE is the latent heat flux (W m− 2)
λ is the latent heat of evaporation (J kg− 1);
ρ is the air density (kg m− 3)
Cp is the specific heat of air (J kg− 1 K− 1)
e is the actual water vapor pressure (Pa)
γ is the psychrometric constant (Pa K− 1)
s is d(esat)/dT (Pa K− 1), the slope of the curve relating
saturated water vapor pressure (esat, Pa) to
temperature (T, K)
A is available radiative energy partitioned between
− 2

26. Research at a Glance
 SEBAL as an effective tool for estimation of actual ET
especially for forest covers in dry seasons of year.(
Junior et al. ,2013)
 MODIS ET estimates were limited by satellite optical-
infrared remote sensing constraints over cloudy regions (
Jang et al. ,2013)
 For daily calculations, G might be ignored (Gavilána et
al.,2007).
 Mu etal, (2011) and Ghilain etal, (2011) compared
MOD16 Flux EC and found good correlation between
these two.

27. EUMETSAT LSA-SAF MSG ET
•EUMETSAT Satellite Application Facility on Land Surface
Analysis (LSA-SAF) and Meteosat Second
Generation(MSG) derived Evapotranspiration.
•Available Products: 30 Minutes ET, 1 day-LST, 15 min
Albedo,
Longwave and shortwave radiation
•Applicable at regional to global scales
•Spatial resolution : 3–5 km
•Introduced in 2012
Radiative data
derived from
(MSG)
geostationary
satellites
+
land-cover
information (from
ECOCLIMAP land
cover database +
LSA-SAF VEGA
products)
SEBAL
Instantenou
s and Daily
ET
https://landsaf.ipma.pt/en/products/evapotranspiration/met/

28. LSA-SAF MSG ETa algorithm
 Includes relationships classically used in Soil–
Vegetation–Atmosphere Transfer (SVAT) models.
where, Tsk is the modeled surface “skin” temperature
at 30- minute interval (K),
Ta is the air temperature (K),
qa is the air specific humidity (kg kg− 1)
qsat is the surface specific humidity at
saturation(kg kg− 1)
rs is the surface resistance (s m− 1)
ra is the aerodynamic resistance (s m− 1)
Hu et al,2015

29. Difference between MOD16 and LSA-SAF MSG
ET
MOD16 LSA-SAF MSG
Penman monteith equation Soil–Vegetation–Atmosphere Transfer
(SVAT) modelsbased
ETa is constrainted by atmospheric
vapor pressure difference ( VPD) and
relative humidity.
ETa is constrained by water availability.
Data inputs are land cover, leaf area
index (LAI) and albedo
Data inputs are LAI, fractional
vegetation cover, snow cover, albedo,
downwelling short-wave and long-wave
surface fluxes, land cover
Do not use thermal infrared remote
sensing of land surface temperature
(LST) as input data
Do not use thermal infrared remote
sensing of land surface temperature
(LST) as input data
8-day ET product 30 min ET product

30. Sentinel-2
 Sentinel-2 is an Earth observation mission developed
by European Space Agency in 2015 as part of
the Copernicus Programme to perform terrestrial observations
 Global coverage of the Earth : 10 days with one satellite and 5
days with 2 satellites
 Land observation: Vegetation, soil
and water cover.
 Spatial Resolution : 10 m, 20 m and 60 m
 Swath Width: 290 km
https://www.youtube.com/watch?v=jljN8_7Tz

31.  Sentinel-2 satellites carries 13
spectral channels in the
visible/near infrared (VNIR) and
short wave infrared spectral
range (SWIR).
 Instrument :MSI (Multispectral
instrument)
 It includes two new bands in
red edge (b5 and b6).
[ESA Earth online
Fig 6. Sentinel-2 NDVI over Midi-Pyrene
• Able to deliver more accurate green LAI and canopy chlorophy
(Delegido et al,2011)

32. SENTINEL-2
Images
Albedo Emissivity
Fraction of
vegetative
cover
NDVI
MODI
S LST SEBS Actual ET
Kyalo.D.K., 201
Flowchart to estimate Evapotranspiration from satellites

33. CASE STUDY:
Comparison of MOD16 and LSA-
SAF MSG Evapotranspiration
products over Europe for 2011
(Hu et al,2011)

34. Objective
 Compare the MOD16 and LSA-SAF MSG
Evapotranspiration estimates with in-situ
measurements of water and energy fluxes
 Compare the spatial patterns of MOD16 and LSA-
SAF MSG Evapotranspiration over Europe in
2011.

35. Data Collection
 MOD16 ET data:
 MOD16A2 (Latent heat flux) which is produced at 1 km
spatial resolution and 8-day compositing periods.
 The product is global in coverage and divided into 286
tiles.
 Study consist of 23 tiles covering most of Europe for each
of the 8-day intervals spanning 2009–2011.
 LSA-SAF MSG Eta:
 It includes two evapotranspiration products: the
instantaneous ETa estimates, with a time interval of 30-
minute (MET) and the daily ETa product (DMET)
 Daily LSA-SAF MSG ETa product of 2011 was used and
8-day composite was derived to coincide with the
MOD16A2 temporal scale.

36. Eddy covariance flux data:
 15 eddy covariance flux towers for 2011 collected
from the European Fluxes Database Cluster (EFDC,
available at http://www.europe-fluxdata.eu/)
 There are 2 cropland sites with winter wheat and
winter barley respectively, 2 grasslands, 2 open
shrublands, 1 savanna, and 8 forest sites.
Location of the 15 flux sites used overlying a land cove
IGBP Code Classification
1-5 Forest
5-16 Non-forests

37. Methodology
1. Evaluation against ground measurements-
-Time series of MOD16 and LSA-SAF MSG ETa estimates
were compared with EC observed ETa at 15 flux tower
sites
-Pixel resolution: 1 km for MOD16 and 5 km for LSA-SAF
MSG ETa.
- Correlation coefficient (Rest–obs), root mean square error
(RMSEest–obs), and annual mean bias (remote sensing
estimates minus EC observations) were computed for
each site.
2. Spatial intercomparison-
Spatial resolution of the MOD16 ETa product was
downgraded to the 5 km resolution of the LSA-SAF MSG
ETa product over Europe by spatially averaging the
original estimates.

38. Results
 Comparison
with observed
Eta:
LSA-SAF
MSG ETa is
closer to the
EC
observations
than MOD16
for most of the
ground sites

39. Spatial Intercomparison
• RMOD16–MSG over most
of Europe is higher
than 0.8 except for
semi-arid regions i.e.,
similar temporal
variations.
• The annual MOD16
ETa is larger than LSA-
SAF MSG ETa in
northwestern Spain,
France, and most
areas with latitude
higher than 50° N.
•MOD16 ETa is
Maps of the (a) correlation coefficient RMOD16–MSG,
(b) root mean square error RMSEMOD16–MSGa(c) ann
mean bias of the MOD16 vs. LSA-SAF MSG ETa

40. Discussion
 LSA-SAF MSG ETa is closer to EC measurements than
MOD16.
 MOD16 ETa is overestimated for forested areas due to
not taking account of the leaf shadowing effect.
 MOD16 and LSA-SAF MSG ETa products perform best
for the sites located in a temperate and fully humid
climate
 In the semi-arid water-limited regions, significant
differences between MOD16 and LSA-SAF MSG ETa
products were observed.
 MOD16 performs best for the CRO (cereal) and GRA
(meadow) sites located in a temperate and fully humid
climate, whereas underestimates the ETa for the OSH

41. Contd...
 The stomatal response to radiation is not taken into
account in the formulation of stomatal resistance in
the MOD16 algorithm. (Stewart, 1988)
 The parameterization of aerodynamic resistance in
the MOD16 algorithm does not include the influence
of wind speed. (Mc Vicar et al., 2012)

42. Limitations of Remote Sensing
 Expensive method
 Requires a special kind of training to analyze the
images.
 Information provided by remote sensing data
may not be complete and may be temporary.
 Does not give better results in cloudy days.

43. Conclusion
 Long turn-around time of image acquisition and
the cost for the high resolution satellites include
challenges in ET estimation using RS images.
 The selection of the most appropriate approach is
varied based on:
 Accuracy
 Budget
 Time limitations
 Desired spatial and temporal resolutions,
 Availability of ground data and meteorological
data.

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49. THANK YOU