Farmland Drought Assessment Based on the Assimilation of Multitemporal SAR into AquaCrop Model(ID: 10448)

Hao Yang1, Guijun Yang1, Xiuliang Jin1, Stefano Pignatti2, Raffaele Casa3, Simone Pascucci2 , Paolo C. Silvestro3

1NERCITA , Beijing Academy of Agriculture and Forestry Sciences (CHINA) 2CNR-IMAA (ITALY) 3DAFNE, University of Tuscia (ITALY)

Precipitation Anomaly

Soil Anomaly

Crop Anomaly

Driving Factor System Response

Farmland Drought Development

Drought Loss Evaluation

Drought Risk Assessment

Farmland Drought Assessment

Farmland Drought Monitoring

Key Variables Monitoring

Shaanxi (Northwest of China)

Beijing (North China)

Inner Mongolia (Northeast of China)

Experiment Campaign

2 3

1

Project Overview

Biomass retrieval by SAR data

Assimilation of AquaCrop model

Our work

Content

Study Area： Shangkuli Farmland, 3000 hectares typical agricultural land use of Northeast China > 18 hectares, each field

1. Biomass estimation by multi-temporal compact SAR data

SAR data: 5 Radarsat-2, C-band, 8m

5

Sowing Elongation Flowering Filling Ripening

Ground truth data: Crop/Soil/Environment

𝑺𝑺 Sinclair matrix

𝒌𝒌𝒍𝒍, 𝒌𝒌𝒑𝒑Scatteringvectors 𝑪𝑪 Covariance matrix 𝑻𝑻 Coherency matrix

𝐶𝐶 = 𝑘𝑘𝑙𝑙𝑘𝑘𝑙𝑙∗𝑇𝑇

𝑇𝑇 = 𝑘𝑘𝑝𝑝𝑘𝑘𝑝𝑝∗𝑇𝑇

𝑘𝑘𝑙𝑙 = 𝑆𝑆ℎℎ 2𝑆𝑆ℎ𝑣𝑣 𝑆𝑆𝑣𝑣𝑣𝑣𝑇𝑇

𝑘𝑘𝑝𝑝 =12𝑆𝑆ℎℎ + 𝑆𝑆ℎℎ 𝑆𝑆ℎℎ − 𝑆𝑆ℎℎ 2𝑆𝑆ℎ𝑣𝑣 𝑇𝑇

Fully polarization SAR (FP)

H V H V

H V H V

H V H V 𝑺𝑺 =

𝒔𝒔𝒉𝒉𝒉𝒉 𝒔𝒔𝒉𝒉𝒗𝒗𝒔𝒔𝒉𝒉𝒗𝒗 𝒔𝒔𝒗𝒗𝒗𝒗

Scattering matrix Trans

Rec

2 PRF

CP CP

X X

Y Y 𝒌𝒌𝑪𝑪𝑪𝑪 = 𝑪𝑪𝑪𝑪𝑪𝑪

𝑪𝑪𝑪𝑪𝑪𝑪

Scattering vector

rece

ptio

n

reception

V

H

RC transmit reception

reception

V

H

transmit

RC

receive

RC transmit

LC receive π/4 𝐷𝐷𝐶𝐶𝐷𝐷 𝐶𝐶𝑇𝑇𝐶𝐶𝐶𝐶

Mode Transmit Receive 1 Receive 2 𝑘𝑘𝐶𝐶𝐶𝐶

π/4 45° H V 𝑆𝑆ℎℎ + 𝑆𝑆ℎ𝑣𝑣 𝑆𝑆𝑣𝑣𝑣𝑣 + 𝑆𝑆ℎ𝑣𝑣 𝑇𝑇/ 2

DCP RC RC LC 𝑆𝑆𝑅𝑅𝑅𝑅 𝑆𝑆𝑅𝑅𝑅𝑅 𝑇𝑇

CTLR (Hybrid or π/2) RC H V 𝑆𝑆ℎℎ − 𝑖𝑖𝑆𝑆ℎ𝑣𝑣 −𝑖𝑖𝑆𝑆𝑣𝑣𝑣𝑣 + 𝑆𝑆ℎ𝑣𝑣 𝑇𝑇/ 2

Trans

Rec

X

Y

1 PRF

Compact polarization SAR (CP)

C2 matrix of CP

Stokes Matrix

Scattering vector

Compact SAR simulation by fully polarization Radarsat-2

Model-based polarization decomposition method

DBL ODD VOL Total

ODD = Odd Scattering DBL = Double Scattering VOL = Volume Scattering

Freeman decomposition for FP data

Polarization decomposition method for CP SAR

m-delta (Raney, TGRS2007) m-chi (Raney, TGRS2012)

DBL ODD VOL

Freeman

m-chi

m-delta

Single-Pol.

Dual-Pol.

Quad-Pol.

Ground photos in different Day After Sowing

The same growth pattern was found before crop ripening between m-chi-Dbl and crop biomass.

Evolution of measured dry biomass Evolution of m-chi-Dbl

The relationship between m-chi-Dbl and biomass

3 fields (red) in ripening stage

its vegetation water content become low

Conclusion

Propose a method of estimating crop biomass by CP SAR

Demonstrate the importance of polarimetry in crop monitoring by

camparing with traditional single-pol and dua-pol

CP revealed great potential in crop quantitative monitoring when

considering it can reach the level of Quad-pol and it has more imaging

width and less data volume

Motivation • Improving Water Use Efficiency (WUE)

• preventing farmland drought • saving water resource

• Yield loss assessment due to drought • boosting crop production in arid regions

Objective • Yield and WUE estimation in regional scale by combining:

• Remote sensing • AquaCrop model • Data assimilation (PSO)

𝑊𝑊𝑊𝑊𝑊𝑊 =𝑌𝑌𝑖𝑖𝑌𝑌𝑌𝑌𝑌𝑌∑𝑊𝑊𝑇𝑇

2. WUE estimation by AquaCrop and data assimilation

1. Experiment and data

2. AquaCrop Model

3. Global sensitivity

4. CC or Biomass retrieval

5. PSO assimilation

PSO Assimilation

Simulated Biomass

Input (Climate,soil …)

Output (Yield…)

AquaCrop Model

Sensitivity analysis

Retrieved Biomass

Method

Time Growth stages

05/03/2014 Regreening

29/03/2014 Jointing

22/04/2014 Heading

16/05/2014 Filling

09/06/2014 Mature

(1). Experiment Campaign

• Satellite data: – Radarsat-2 SAR – HJ-1 CCD

• Ground data: – Crop – Soil – Climate…

Agronomic management

(variety; sowing dates…)

Soil (hydraulic properties; fertility)

Weather data AQUACROP

• AquaCrop is new Crop Model to simulate yield response to water (Steduto 2009);

• Suited for predicting crop productivity, water requirement, and WUE under water-limiting conditions

(2). AquaCrop Model

Climate data

Management data Soil data

Crops data

• Data collection in Yangling site (China); • AquaCrop have been localized for wheat in China in our previous research (PLOS, 2014)

*.CLI file *.IRR file *.ETo file *.CRO file *.SOL file …

• Objective: • determine the most important

crop parameters • reduce the used parameters

number • Method:

• Extended Fourier Amplitude Sensitivity Test (EFAST)

• assess the contribution of different crop parameters to model output

(3). Global sensitivity analysis for AquaCrop

• 2 indicator: – First order sensitivity index (FOSI) – Total sensitivity index (TSI)

• 40 crop parameters in AquaCrop – ±10% – ±30% – ±50% fluctuations

• 4 variables in AquaCrop output – Static variables : Yield, Maximum

dry biomass – Dynamic variables : Canopy Cover,

Dry biomass

TSI results for yield FOSI result for yield

TSI results for time series dry biomass FOSI results for time series dry biomass

Output variable Parameter range Sensitive parameters

Yield 10% fluctuation cc, polmn,wp, stbio, hi, psto

30% fluctuation cc, wp, remd, hi, stbio, rootdep, mcc, polmn, cgc

50% fluctuation cc, polmx, wp, hi, stbio, mcc, remd, cdc, pstoshp

Maximum dry

biomass

10% fluctuation wp, cc, stbio, rootdep, polmn, mcc, psto

30% fluctuation wp, cc, stbio, mcc

50% fluctuation wp, cc, stbio, mcc

Sensitivity analysis results (TSI) under different parameter variation ranges

Results: (1) EFAST is OK (2) TSI > FOSI (3) Four variables show consistency for FOSI, but show difference for TSI;

Canopy cover and biomass estimation by

• A new combined VI was developed to estimate Canopy cover and Biomass by SAR and Optical data

Radarsat-2 HJ-1

05/03/2014 04/03/2014

29/03/2014 07/04/2014

22/04/2014 29/04/2014

16/05/2014 20/05/2014

(4). CC and Biomass retrieval from RS

Note: n=80 for modeling, n=40 for validation

(5). PSO assimilation

Method: Particle Swarm Optimization (PSO) State variables: CC or Biomass Parameters:

Result (1)

CC and Biomass estimation results by PSO method

Canopy Cover Biomass

It shows good consistency between the predicted and the measured for CC and Biomass

Result (2)

Yield WUE

Yield and WUE estimation results by PSO method (biomass as the state variable in assimilation)

Result (3) Yield WUE

Yield and WUE mapping in Yangling by PSO method (biomass as the state variable in assimilation)

Conclusion • PSO method which assimilates the Remote Sensing observation and AquaCrop

model can be used to estimate the yield and WUE in regional scale; • Biomass is more suitable than CC as a state variable in PSO assimilation;

– The estimation result of yield based on Biomass (R2=0.42,RMSE=0.81ton/ha, nRMSE=17.05%) is better than that based on CC (R2=0.31, RMSE=0.94ton/ha and nRMSE=19.79%);

– WUE result based on Biomass is also better;

• This study provide a method for monitoring the yield and WUE in regional scale by combining the AquaCrop model and RS observation;

• This study provide a guideline for improving the irrigation management of winter wheat in arid regions.

Cooperation

Cooperation

1. Hao Yang, Zengyuan Li, Erxue Chen, Chunjiang Zhao, Guijun Yang, Raffaele Casa, Stefano Pignatti, Qi Feng. Temporal Polarimetric Behavior of Oilseed Rape (Brassica napus L.) at C-Band for Early Season Sowing Date Monitoring, Remote sensing, 2014,6(11):10375-10394.

2. Hao Yang, Erxue Chen, Zengyuan Li, Chunjiang Zhao, Guijun Yang, Stefano Pignatti, Raffaele Casa, Lei Zhao. Wheat lodging monitoring using polarimetric index from RADARSAT-2 data, International Journal of Applied Earth Observation and Geoinformation,2015,34(1):157-166.

3. Hao Yang, Chunjiang Zhao, Guijun Yang,Zengyuan Li, Erxue Chen, Lin Yuan, Xiaodong Yang, Xingang Xu. Agricultural crop harvest progress monitoring by fully polarimetric synthetic aperture radar imagery. Journal of Applied Remote Sensing, 2015, 9(1), 09607, 1-11.

4. Assessing water use efficiency in winter wheat by using the AquaCrop model with remote sensing data. Agricultural Water Management. (Under Review)

5. A new optical and radar combination vegetation index for estimating LAI and biomass of winter wheat using HJ and RADARSAT-2 data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. (Under Review)

6. Estimation of water use efficiency for winter wheat based on multi-source remote sensing data and AquaCrop model using particle swarm optimization algorithm. Remote Sensing of Environment. (Under Review)

Co-Papers

Acknowledge

Our work was supported by Dragon III, also in part supported by the Chinese National Science and Technology Support Program under grants 2012BAH29B00 and by the Chinese State Key Basic Project under grants 2013CB733404.

Thanks for your attention!