ASSIMILATION OF REMOTELY SENSED DATA INTO CROP MODELS FOR FARMLAND DROUGHT ASSESSMENT: A
COMPARISON OF MODELS OF DIFFERING COMPLEXITY
Silvestro P.C. 1, Casa R.1, Yang H.2,4, Pignatti S.3,Pascucci S.3, Yang G.2,4
1DAFNE, University of Tuscia (ITALY) 2CNR-IMAA (ITALY) 3NERCITA , Beijing (CHINA) 4Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, CHINA
OBJECTIVE
Assimilation of biophysical variables retrieved from remote sensing into dynamic process-based crop water response models
Development of alternative techniques for the assessment of drought impact on crops, suitable for high spatial and temporal resolution applications
Assimilation and Optimization
-LAI - CC
crop models, taking into
account water stress
Yield Estimation Crop Growth
Simulation
Remote Sensing Data
BV-NET
Study Site and Material
Xiaotanshang, Beijing (China)
Yangling, Shaanxi, (China) Type Date Measurements
Field Data 30/3/2013 High, density, SPAD, TDR, LAI ,
Fresh and dry Biomass
Field Data 27/4 2013 High, density, SPAD, TDR, LAI ,
Fresh and dry Biomass Field Data 1/6/2013 TDR, Yield
Climate 2013
Precipitacion, Average wind speed, Temperature (min, MAX,
average), sunshine duration HJ1B 05/03/2013 multispectral HJ1B 20/03/2013 multispectral HJ1B 28/03/2013 multispectral HJ1A 30/03/2013 multispectral
Landsat 8 03/04/2013 multispectral HJ1A 07/04/2013 multispecral HJ1A 26/04/2013 multipectral HJ1A 11/05/2013 multispectral
Type Notes Climate Precipitation, Average wind speed, Temperature (min, max,
average), Sunshine duration
Sowing Date
27 Sep, 7Oct, 20 Oct 2008 25 Sep, 5 Oct, 15 Oct 2009 25 Sep, 5 Oct, 15 Oct 2010
Yield measurament
Grain yield was measured following maturation from samples obtained from a 1.5 m 2 area in each plot, with three
replications for each treatment. Field
Management List of different management, required only by Aquacrop
model
Biomass Biomass was determined from a 0.25 m2 area by randomly cutting four representative plants from each plot . All plant samples were
oven dried at 70°C to a constant weight, and final dry weight recorded.
Canpy Cover Canopy Cover was eximated as function of LAI (Hsiao et al. ) . The LAI-2000 Plant Canopy Analyzer (LI-COR Inc.,Lincoln, NE, USA) was
used in measuring for determination of LAI. Irrigation Day of irrigation, amount of irrigated water (in mm)
Soil Characteristics
Information s about: Field Capacity, Wilting Point and Saturation
We thank NERCITA for the granting of the experimental data
Retrieval of LAI and Canopy Cover (CC) from data acquired by HJ satellites images
PRO-SAIL Spectra
Simulations
Artificial Neural
Network Training
Weights and Bias for ANN
Application
Satellite Data ( HJ1A ,
HJ1B, Landsat)
ANN Application
Map of LAI and CC
Field measurements of LAI (validation)
HJ1A image, Yanglin 2013
CC (above) and LAI (Below) Temporal evolution
Average values of LAI retrieved by ANN Application Average estimated LAI values (green triangles) and their standard deviation (green shaded area) retrieved by HJ1A and HJ1B for field measurement sites. Average LAI value (red circle) and standard deviation (red bar) retrieved from Landsat 8 data. Average (black circles) and standard deviation of ground measured LAI.
28 March 30 March
27 April 11 May
28 March 30 March
11 May
28 March 30 March
27 April 11 May
28 March 30 March
27 April 11 May
28 March 30 March
Assimiliation Methods: two different ways for two different models
A simple model for yield estimation as a function of Photosynthetically Active Radiation (Duchemin&al 2008). Modified version introduces the water balance according to the equations described in the FAO 56 document . Model with a reduced number of parameters, easy to use, fast to compute Source code available
Modified SAFY
Ensamble Kalman Filter Assimilation
Aquacrop Water productivity model, which simulates canopy biomass and yield in response to water transpired by the crop. Main state variable: Canopy Cover Calibration is not easy because of 100 and more parameters Slow to optimize and source code not accessible
Simplex based parameters optimization
Sensitivity Analysis
Simplify the calibration of models Reduce the number of parameters used
in assimilation methods
Individuation of influential parameters in order to:
Methods
EFAST The Extended Fourier Amplitude Sensitivity Test is a variance-based method, which decomposes the total output variance V(Y) to estimate the influence of individual parameters. EFAST is an accuracy Global Sensitivity Analysis, but its computational cost is very high. For this reason if a model is described by many parameters it is necessary to apply previously a screening method (such as Morris) in order to reduce the number of parameter to analyze.
Morris
The Morris method (Morris, 1991) computes for each parameter the elementary effect of individual parameter The influence of parameters is evaluated by 2 main index: the absolute value of mean (µ*; overall influence) and standard deviation (σ; higher order effects) of all elementary effects.
The main advantage of the Morris screening method is its low computational cost. It is used only for excluding the non influential parameters from EFAST analysis.
All paramters with µ* under this value are considered not influential Threshold value: µ*=0.2 Mg/he
Exclusion of the parameters below the influence
threshold value
Final assessment of the influential
parameters
Sensitivity Analysis: Results
EFAST sensitivity profiles with main (first-order; upper line) and interaction (higher-order; bottom line) effects for different years (2008-2010) of NERCITA
field experiments in Xiaotangshan
Average Morris mean effects (µ*) and spread (σ) for the 3 different years (2008-2010) of field experiments in Xiaotangshan for the SAFY model.
Results of Sensitivity Anlisys for SAFY:
EFAST method has identified the following most influence parameters for SAFY: Pgro_Kex (Extinction of Radiation in Canopy), Pgro_Ms0 (Emergence Dry Mass Value ), Pfen_MrgD (Day of Emergence ), Pfen_PrtB (Make vary the day of max LAI ), Pfen_SenA (Temp. Threshold to Start Senescence (°C))
vS 2008-2009
Results of Sensitivity Analysis for Aquacrop:
Average Morris mean effects (µ*) and spread (σ) for the 3 different years (2008-2010) of field experiments in Xiaotangshan for Aquacrop.
EFAST sensitivity profiles with main (first-order; upper line) and interaction (higher-order; bottom line) effects for different years (2008-
2010) of NERCITA field experiments in Xiaotangshan EFAST method has identified the following most influence parameters for Aquacrop: To_crop (base Temperature), mat (crop length),polmn (pollination minimum air Temp.), stbio (minimum growing degrees required for full biomass production), kc (field capacity), wp (welting point), hi (harvest index), flo (flowering beginning), cgc4ggd (increase in canopy cover)
See poster #126 by Silvestro et al.
2009-2010 2010-2011 2009-2010 2010-2011 2008-2009
vSt
vS
vSt
2008-2009 2009-2010 2010-2011 2008-2009 2009-2010 2010-2011
Simplex based Parameters Optimization applied to Aquacrop
Step 1 Individuation of main Parameters
Step 2 Model calibration for a single date (27 sep 2008 Xiaotangshan)
Step 3 Generate an ensemble of N values of parameters Pp J,k=[1,…,N] N= number of ensemble elements (50 in this case) n= number of parameters (20) δ =error drawn from N(µ,σ)
Step 4 Running Aquacrop in order to obtain N values of CC
Step 5 Addition of a random error to the measurements(CC):
jtt
jt iii
MM τ+=
Step 6 Application of the simplex algorithm. The function to minimize is: ∑
=
−=
n
i
ms
niCCiCCMSE
1
2))()((
Step 7 Retrieve simulated yield from best fitting simulation
Assimilation: Ensemble Kalman Filter applied to SAFY
Step 1 Individuation of main Parameters
Step 2 Model calibration for a single date (27 sep 2008 Xiaotangshan )
Step 4 Running SAFY in order to obtain N values of LAI for the first observation date with the addition of an error ε
Step 9 At each observation date repeat from step 5. When the last observation has been assimilated SAFY runs to the end and outputs the yield .
Step 8 Replacement of LAI simulated with the LAI calculated at step 7, and ongoing the running of model
Step 7 Application of the Kalman filter to obtain a corrected LAI and parameters values estimated
Step 3 Generate an ensemble of N values of parameters Pp J,k=[1,…,N] N= number of ensemble elemnts (50 in this case) n= number of parameters (20, reducible to 5) δ =error drawn from N(µ,σ)
jkk
jk PP δ+=
Step 5 Addition of a random error to the measurements (LAI):
jtt
jt iii
MM τ+=
Step 6 Calculating the Kalman gain (using the covariance matrix)
)var(i
i
i
it
t
Tt
T
t RRR
RK
τ+=∑
∑
Results: Ensemble Kalman Filter assimilation for SAFY
LAI
Biomass Yield
Example of LAI, Biomass and Yield simulation with
EnKF assimilation for SAFY (7 Oct 2008, Xiaotangshan field
experiments)
Xiaotangshan Results Yangling Results
RMSE = 1.21
RMSE = 0.43
RMSE = 1.47
RMSE = 0.85
RMSE = 0.61
Results: Optimization for Aquacrop
Example of LAI, Biomass and Yield simulation with Optimization for
Aquacrop (7 Oct 2008, Xiaotangshan field experiments)
Best simulation of yield within the set of simulations
(7 Oct 2008, Xiaotangshan field experiments)
Xiaotangshan Results
Results: Optimization for Aquacrop
RMSE = 0.04 RMSE = 0.22
RMSE = 0.17 RMSE = 0.29
Aquacrop Parameters Optimization
Results comparison between SAFY and Aquacrop assimilation methods
Xiaotangshan data set Yangling data set
Aquacrop
SAFY
RMSE = 0.69 RMSE = 0.04
RMSE = 0.85 RMSE = 0.43
RMSE = 0.05
RMSE = 0.61
LAI Assimilation in SAFY
RMSE = 0.69 RMSE = 0.04
RMSE = 0.85
RMSE = 0.69 RMSE = 0.04
RMSE = 0.43 RMSE = 0.85
RMSE = 0.69 RMSE = 0.04
Conclusions
Global sensitivity analysis allowed to identify the most influential parameters for Aquacrop and SAFY, to be varied during the assimilation and optimisation algorithms
Both the optimization of Aquacrop based on CC observations and LAI
assimilation into SAFY improve the yield estimation of models, allowing to use remote sensing data to identify in-season drought effects on yield.
The optimization method used for Aquacrop improves the time
performance as compared to Silvestro et al., 2014 Dragon3 Symposium, but it is not enough for a spatialized application.
The results obtained with the LAI assimilation method in SAFY are slightly
worse than for Aquacrop, but its low computational cost and low number of parameters make it a good candidate for a spatialized application at a regional scale
Activities during the period of stay in China as guest of NERCITA
Analysis about:
• LAI • Biomass • Height • Yield • Soil Moisture • Soil Roughness
• Sharing the work done in 2014 • Partecipation to the 2014 measurement campaign in Yangling •Exchange of data acquired in different measurement campaigns of several years both on Chinese and Italian territory • Planning work done this year
Recent & on-going papers Stefano Pignatti, Wenjiang Huang, Raffaele Casa, Giovanni Laneve, Pablo Marzialetti, Angelo Palombo, Simone Pascucci, Nazzareno Pierdicca, Xie Qiaoyun, Federico Santini, Paolo Cosmo Silvestro, Hao Yang, Guijun Yang, 2015. Synergistic use of radar and optical data for agricultural data products assimilation: a case study in Central Italy, Paper #8026 IGARSS 2015, July 26-31 2015, Milan (Italy) Raffaele Casa, Paolo Cosmo Silvestro, Hao Yang, Stefano Pignatti, Simone Pascucci, Guijun Yang, 2015. Development of farmland drought assessment tools based on the assimilation of remotely sensed canopy biophysical variables into crop water response models. Paper #8582 IGARSS 2015, IGARSS 2015, July 26-31 2015, Milan (Italy) Castaldi, F., Casa, R., Pelosi, F., Yang, H.,2015. Influence of acquisition time and resolution on wheat yield estimation at the field scale from canopy biophysical variables retrieved from SPOT satellite data. International Journal of Remote Sensing, 36 ,2438-2459
Future Work
Sensitivity Analysis for Aquacrop and SAFY with Yangling data set
Obtain wheat crop mask for the Yangling site
Improvement of Optimization method for Aquacrop in order
to reduce computational time
Application at regional scale in Yangling
