Post on 27-Mar-2018
transcript
8-4-2014
Challenge the future
Delft University of Technology
Remote Sensing of water requirements and water use in agriculture
Massimo Menenti
2 Water Energy Food Nexus, Rome, Italy 25 – 27 March 2014
Overview
Crop water use: water requirements, water productivity and
water shortage
1. Monitoring crop water requirements using multi-spectral
image data
2. Assessing water productivity using multi-spectral and
thermal infrared image data
3. Actual ET land surface energy balance
4. Monitoring water resources: EO + modeling
5. Drought detection and early warning using remote
sensing indicators of crop condition
3 Water Energy Food Nexus, Rome, Italy 25 – 27 March 2014
Irrigation water management (Large Irrigation schemes)
Crop water Requirement (CWR) Net Irrigation water Requirement
(NIWR) Assessment of Irrigation performance
Remote Sensing: Time Series
Remote Sensing: Biophysical Variables
Soil water balance
Irrigation Adequacy and Effectiveness
Crop Water Requirements vs. Water Use
4 Water Energy Food Nexus, Rome, Italy 25 – 27 March 2014
Multispectral satellite data
Time Series
MODELING “SWAP” Analytical Approach Kc-NDVI Method
IP3 IP2
Preprocessing: Geometric, Radiometric, Atmospheric Corrections
Work Flow
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Allocation per unit area IP1
Allocation per water
requirements
IP2
Dealing with shortages IP3
Irrigation Performance Indicators
IP1ij
ij ij
i i
V A
V A
/
/
ij
nk
k
ijkk
ijV
AEp
1
10**
2IP
ij
nk
k
ijkkwk
jiV
AEaEa
IP
1
, 10**
3
In which:
Vi = Volume supplied to unit i (m3);
Vij = Volume received at reference unit j,
within higher order unit i (m3);
Ai = Irrigated area in unit i (ha);
Aij = Irrigated area in reference unit j,
within higher order unit i (ha);
ijkA = Area of crop k in unit j (ha);
n = Total number of crops k.
E pk = Potential evapotranspiration of crop k
(mm);
k,wEa = Actual evapotranspiration of crop k
following the application of an irrigation
volume Vij (mm);
kEa = Actual evapotranspiration that would
occur without any irrigation (mm);
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Hydraulic Basin of
Oum Er-rabia
High service of Irrigated perimeter Ce Low service of Irrigated perimeter
Al Massira Dam
Oum Er-rabia River
Sprinkler Irrigation
Localized Irrigation
Surface Irrigation Corn
Wheat
Sugar Beet Fourrages
Study Area & Data collection
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RapidEye (REIS)
Landsat 8 (OLI)
SPOT4 (HRVIR1)
GEOMETRIC CORRECTION: - Projection System: UTM, WGS-84, zone 29. - Topographic map: (1/50 000).
RADIOMETRIC AND ATMOSPHERIC CORRECTION: -Radiometric calibration by conversion of DN values into TOA (Top of Atmosphere) radiance, -The TOC (Top of Canopy) reflectance was obtained using ENVI FLAASH model (Incorporate MODTRAN Model) -The MODTRAN Atmospheric Model based on Latitudinal/Seasonal Dependence of surface Temperature : Mid Latitude Summer/ Tropical. -The Visibility for Clear weather Condition: 40 to 100 Km
SENSOR DATE Area SPECTRAL RESOLUTION (um)
SR ORBIT
SPOT4-HRVIR1 FromJanuary to June2013
FAREGH XS1: 0.500 - 0.590 20m Altitude:832 km revisit: 5 days
XS2: 0.610 - 0.680
XS3: 0.790 - 0.890
SWIR (HRVIR):1.530 -1.750
RapidEye -REIS 12/10/2012 ZEMAMRA SIDIBENNOUR
Blue: 0.440-0.510 5m Altitude:630 km revisit:
Green: 0.520 - 0.590 Daily (off-nadir) ; 5.5 days (at nadir)
02/08/2013 Red: 0.630 - 0.685
Red-Edge: 0.690-0.730
NIR: 0.760-0.850
Landsat 8 - OLI 04/19/2013 SIDIBENNOUR ZEMAMRA
Coastal / Aerosol: 0.433 - 0.453
30 m Altitude:705 km revisit: 16 days
Blue: 0.450 - 0.515
04/26/2013 Green: 0.525 - 0.600
Red: 0.630 - 0.680
06/13/2013 NearInfrared: 0.845 - 0.885
SWIR: 1.560 - 1.660
06/29/2013 SWIR: 2.100 - 2.300
Cirrus : 1.360 - 1.390
07/15/2013 Panchromatic: 0.500 - 0.680
15m
Satellite Data (2012/2013)
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10
SPOT4 (HRVIR1) Field Work
31/01/2013 Casier Faregh 18 -19 - 20 Décembre
2012
05/02/2013 Casier Faregh 27 - 28 Fevrier 2013
10/02/2013 Casier Faregh 3 - 4 Avril 2013
25/02/2013 Casier Faregh 23-24 Mai 2013
02/03/2013 Casier Faregh 20 Juillet 2013
17/03/2013 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
SPOT4 (HRVIR1)
31/01/2013 Casier Faregh
05/02/2013 Casier Faregh
10/02/2013 Casier Faregh
25/02/2013 Casier Faregh
02/03/2013 Casier Faregh
17/03/2013 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
SPOT4 (HRVIR1)
31/01/2013 Casier Faregh
05/02/2013 Casier Faregh
10/02/2013 Casier Faregh
25/02/2013 Casier Faregh
02/03/2013 Casier Faregh
17/03/2013 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
Field Campaigns (2012/2013)
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<
2.5σ
< 2σ
RMSE=0.99 RMSE=0.86
RMSE=0.89 RMSE=0.79
Validation of ETc estimates
In-situ ETc estimated with the dual kc approach and ETc estimated with the analytical approach (a) respectively the Kc-NDVI method (b)
c cET ET
c cET ET
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Water Productivity
GLOBAL-WP model
No separate calculation, directly WP:
All key variables obtained from routine satellite measurements
Ag. Water Man. Zwart et al., 2010
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Water Productivity: definitions
4/8/2014
Variable Definition Units
HI harvest index, crop specific -
aNDVI + b
APAR/PAR fraction -
NDVI mean Normalized Difference Vegetation Index -
PAR / SEXO fraction -
SEXO extraterrestrial radiation W m-2
max maximum light use efficiency g MJ-1
T1 Dependence of light use efficiency on air temperature
-
T2 Dependence of light use efficiency on air temperature
-
grain grain water content fraction -
mean broadband surface albedo -
135 estimate of atmospheric absorption and scattering W m-2
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SEBAL for Water Productivity
7 countries, 8 projects the same methodological framework was applied
water productivity in wheat dominated areas
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Definition of WPS
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Wheat water productivity
16
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Remote Sensing of actual ET
• Soil water balance
• Surface energy balance
• Water vapour balance atmospheric column
• Vegetation control of leaf level transpiration
• Constrained by available energy: Rn, Penman – Monteith,
Priestley - Taylor
Rate – limiting process vs. state variables?
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Parameterization of canopy conductance
(MOD – 16)
(Mu et al., 2007 and 2011)
Penman – Monteith
Transpiration limited by response of canopy conductance to vapour pressure deficit and minimum air temperature
Transpiration scaled by leaf area
Evaporation calculated independently from net radiation at the soil
Soil water availability is not included in the ET algorithm
19 Water Energy Food Nexus, Rome, Italy 25 – 27 March 2014
Parameterization with observations of
state variables: soil water content
(Miralles et al., 2011)
Soil water balance model
Priestley – Taylor: P – M for potential ET + assumptions to collapse aerodynamic into adjusted radiative term
State variable: root zone soil water content
Top soil water content adjusted by assimilating microwave observations of soil moisture
Stress factor: normalized soil water deficit
E = S x Ep (plus interception)
20 Water Energy Food Nexus, Rome, Italy 25 – 27 March 2014
Parameterization with observations of state
variables: Land Surface Temperature
(Menenti, 2001)
Residual energy balance
State variable: Land Surface Temperature (LST)
Scale ET vs LST based on context
Scale ET vs LST based on (NDVI, LST)
Scale ET vs LST based on Penman – Monteith
Separate parameterizations of transpiration (leaves) and of evaporation (soil)
Dual source models with observations of foliage and soil temperatures
21 Water Energy Food Nexus, Rome, Italy 25 – 27 March 2014
SEB: Outstanding Issues
• LE scales with LST IF radiative and convective forcing is normalized first
• Additional constraints needed to solve SEB
• Additional equations by segmenting images and assuming some parameters (e.g. ra) constant within the segment
• Add experimental constraints by using limiting cases (reference system states)
• Dry and wet reference states assumed to exist within image (SEBAL)
• Dry and wet reference states evaluated from theory (SEBI SEBS MSSEBS)
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Multi-Scale Energy Balance • SEBI based
• Physical calculation of the extreme
boundaries following the SEBS approach
(Menenti and Choudhury, 1993; Su,
2000)
• Calculation grid size depends on the
length scale of physical processes and
ABL development (MSSEBS approach,
Colin 2006)
• Expected results:
• 1km resolution Surface Energy
Balance components
• Time series on a week to 10 days
basis
• Automated processing chain,
including interface with data
providers & results repositories
Meso-scale Atmospheric Forcing grid [15-100 km]
Ta, q, u, v, p
Sw, Lw incoming radiance
ABL Calculation grid [10 x ABL height]
Full resolution calculation grid [TIR Resolution]
LST, albedo, fc, LAI, emissivity, DEM
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Effect of spatial correlation in gap filling
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Frequency and spatial distribution of gaps LST: FY-2 hourly TIR data
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Daily ET
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Daily ET 2008 – 2010
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Daily ET: how accurate?
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Gaps ground measurements
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ETest vs. ETec: Error statistics
Daily ET with Ef assumption, not hourly ET
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Model domain
• Model extent 7,221,600 km2 (Qinghai-Tibet Plateau 2,527,166 km2)
• 5 x 5 km spatial resolution
• Simulated period: 2008-2010, daily time step
0 200 400 600 800 1000km
Indus
Ganges-Brahmaputra
Salween Mekong
Yangtze
Yellow
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Integration of data
CEOP-AEGIS WP8: FutureWater, ARIESpace, IGSNRR, NIT Rourkela
WP 5: Precipitation
WP 6: Snow and ice
Model forcing
WP 4: Soil moisture
Model calibration / validation
Rescale EO data to model resolution
Prototype water balance monitoring system
Water balance and yield of the entire TP
WP 3: ET
River discharges
Monitoring the water balance
and water yield of the
Plateau
2008-2010
Air Temperature
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Water balance 2008-2010
P
1260
Major draining basins Tibetan Plateau (mm/yr)
ET
300
Base flow 360
ΔS +30
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Annual water yield 2008-2010
Indus 45 km3
Ganges-Brahmaputra
257 km3
Salween 90 km3
Mekong 97 km3
Yangtze 608 km3
Yellow 232 km3
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Drought Detection and Early Warning
Water use
Damage on ecosystem
Crop yield
Dro
ug
ht
evo
luti
on
pro
ce
ss
es
Cause
Drought impact severity
Impact evaluation
37 Water Energy Food Nexus, Rome, Italy 25 – 27 March 2014
Drought Remote Sensing Indicators
LST anomaly: Δ LST = LST – LSTmean
Normalized Temperature Anomaly Index (NTAI): N_ΔLST = Δ LST / (LSTmax - LSTmin)
(Jia et al.
2011) LST as a drought indicator
NDVI as a drought indicator
NDVI anomaly : Δ NDVI = NDVI – NDVImean
Normalized Vegetation Anomaly Index (NVAI) : N_ΔNDVI = Δ NDVI / (NDVImax - NDVImin)
44 Water Energy Food Nexus, Rome, Italy 25 – 27 March 2014
Comments
• Assessment and monitoring of crop water requirements is mature
for operational use, multiple data sources available (Landsat 8,
Sentinel 2)
• Water use, water productivity, water resources, water scarcity:
substantial contributions of satellite data
• SEB data products by combining multi-spectral observations of
the land surface and high spatial resolution CBL fields
• GEO – satellites provide redundant time series with sparse but
sufficient coverage in space and time for daily SEB data products
• GEO observations avoid assumptions on the Evaporative Fraction
• LEO – satellites should be used as a virtual SEB constellation