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JECAM | Joint Experiment for Crop Assessment and Monitoring

China - Jiangsu

Project Overview

Crop identification and Crop Area Estimation: Identify rice fields with its polarimetric responses and scattering mechanisms and estimate the rice acreage accurately.

Crop Condition/Stress: Rice phenological stage retrieval, providing timely and accurate information about rice growth condition, in order to plan cultivation practices (irrigation, fertilization, etc.).

Yield Prediction and Forecasting: A quantitative relationship between polarization variables and rice key parameters (biomass, LAI) will be established. Then, a crop model, taking into account the variation of the time - domain and environmental stress, will be employed for rice yield prediction.


Project Reports

2017 Site Progress Report

2016 Site Progress Report

2015 Site Progress Report

2014 Site Progress Report

Presentations:

  1. Kun Li, Zhi Yang, Yun Shao, Long Liu and Fengli Zhang. Rice Phenology Retrieval Automatically Using Polarimetric SAR. In proceedings of the 2016 International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2016, July 10-15.
  2. Long Liu, Yun Shao, Kun Li, and Zhi Yang. Modeling Microwave Backscattering from Parabolic Rice Leaf. In proceedings of the 2016 International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2016, July 10-15.
  3. Zhi Yang, Kun Li, Yun Shao, Brian Brisco and Long Liu, Retrieval of paddy rice variables during the growth season with a modified water cloud model on polarimetric radar images. IEEE Geoscience and Remote Sensing Symposium (IGARSS). Beijing, China. 2016, July 10-15.

 

Peer reviewed papers:

  1. Zhi Yang, Kun Li, Yun Shao, Brian Brisco and Long Liu. Estimation of Paddy Rice Variables with a Modified Water Cloud Model and Improved Polarimetric Decomposition Using Multi-Temporal RADARSAT-2 Images. Remote Sensing 2016, 8(10), 878.
     
  2. Long Liu, Yun Shao, Kun Li, and Zhi Yang. Modeling the scattering behavior of rice ears. IEEE Geoscience and Remote Sensing Letters. (Accepted)
     
  3. Long Liu*, Kun Li, Shao Yun, Pinel Nicolas, Zhi Yang, Gong Huaze, and Wang Longfei. 2015. Extension of the Monte Carlo Coherent Microwave Scattering Model to Full Stage of Rice. IEEE Geoscience and Remote Sensing Letters, Vol.12, No.5, pp. 988-992. 
     
  4. Y Shao, K Li, B Brisco, L Liu and Z Yang. 2013. The Potential of Polarimetric and Compact SAR Data in Rice Identification. Proceedings of 35th International Symposium on Remote Sensing of Environment(ISRSE35).
     
  5. Y Shao, K Li, L Liu and Z Yang. 2013. Rice Monitoring with Polarimetric RADARSAT-2 data. Proceedings of the 33rd Asian Conference On Remote Sensing.
     
  6. Zhi Yang, Kun Li*, Long Liu, Yun Shao, Brian Brisco, and Weiguo Li. 2014. Rice Growth Monitoring using Simulated Compact Polarimetric C-band SAR. Radio Science, Vol.49, No.12, pp.1300-1315.
     
  7. Long Liu*, Kun Li, Shao Yun, Pinel Nicolas, Zhi Yang, Gong Huaze, and Wang Longfei. 2015. Extension of the Monte Carlo Coherent Microwave Scattering Model to Full Stage of Rice. IEEE Geoscience and Remote Sensing Letters, Vol.12, No.5, pp.988-992.

Implementation Plans

Plans for Next Growing Season:

First, we will validate our methods with the dataset acquired in 2016. Second, we will further validate our methods at another place, Zhejiang, China. Third, we will improve the multi-sphere ear scattering model and retrieve ear biomass using the model.


Site Description

Locations

Jiangsu
Site Extent   Centroid: 33.116, 118.971
Top left: 33.256, 118.828 Bottom Right: 32.976, 119.114

The test site is located in Jinhu (33°15'22.33"N~32°58'35.00"N,118°49'39.97"~119° 6'51.67"), Jiangsu Province, east of China, with the area of 600km2 (Fig 1). The terrain is flat, with the average altitude mostly less than 10m. The climate belongs to the transition region between the subtropical and the temperate zone, with four distinct seasons. The annual average temperature of the test site is about 13 to 16℃. The average precipitation is about 800 to 1200 mm every year, and more than half of the precipitation occurs from June to September. The sunshine hours can be up to 2400 every year. The soil type of this region is mostly yellow brown clay, which is more favorable for rice plant development. The main paddy varieties in this area are hybrid and japonica rice. There is one rice crop a year, with the growth cycle about 150 days, from early June to late October or early November.

There are two rice planting methods in the test site, transplanting and direct-seedling, which will produce two different rice field structures (Fig 2(a) (b)) and have a certain impact on rice yields. The size of rice field parcels is 1700m2 or so. In this study, forty-two sample plots were selected in the test site, covering twenty-nine transplanting fields and thirteen direct-seedling fields. The distribution of these sample plots is also showed in Fig 1.


Figure 1. The location of test site and the distribution of the sample plots, cloud and sun mean transplant and direct-planting rice fields respectively.

 

     
(a) Transplanting                                        (b) direct-seedling

Figure 2. Rice fields in Jiangsu test site


 


Specific Project Objectives & Deliverables

Results

  1. An automated method for rice phenology monitoring
    An automated method of rice phenology retrieval was developed using a feature optimization strategy integrated support vector machine (SVM) and sequential forward selection (SFS). The flowchart of the method is displayed in Fig 3. The optimal polarimetric variables for each phenological stage were acquired, based on which eight rice phenological stages were retrieved automatically. The accuracies were higher than 90% except for the dough stage and the transition period between two stages.


    Figure 3. The flowchart of the rice phenology retrieal.
     
  2. Further validation of the method of rice parameter estimation proposed in 2015

    In 2015 a modified water cloud model (MWCM) for rice parameters estimation was proposed, in which the heterogeneity of the rice canopy were considered as well as the polarimetric information. We validate the method using R-Square and RMSE only. In 2016, we further validated the methods and compared the results with that of the tradional water cloud model (Fig 4).

    In the CASE I, the heterogeneity of rice canopy in the horizontal direction is considered without the polarimetric information. In the CASE II, the polarimetric variables from the improved polarimetric decomposition are used in the MWCM, while the heterogeneity of rice canopy in the horizontal direction is ignored. In the CASE III, both the heterogeneity of rice canopy and the polarimetric information are considered in the MWCM.

    During the whole rice growth cycle, r for the CASE III was largest, implying that the estimation accuracy is the best by considering both the heterogeneity of rice canopy and the decomposition components. From the seedling to the booting stage (from June 27 to Aug. 4), r for the CASE I (larger than 10%) is much larger than that for the CASE II (2-3%), implying that the estimation accuracy is improved much by considering the heterogeneity of rice canopy during the this period, whereas the estimation accuracy is just improved approximately 3% by applying the decomposition components. From the heading to the mature stage (from Aug. 28 to Oct. 15), r for the CASE II (approximately 5%) is larger than that for the CASE I (less than 2%), implying the application of decomposition components in the MWCM could improve the estimation accuracy more than the consideration of the rice canopy heterogeneity during the reproductive phase of rice growth cycle. In sum, the heterogeneity of rice canopy is very essential to consider in the MWCM, especially during the vegetative phase. With the rice canopy becomes dense and uniform, the heterogeneity of rice canopy decreases. The application of decomposition components in the MWCM could improve the estimation accuracy with 3-5% during the rice growth cycle.


    Figure 4. Comparison between the estimation accuracy in different cases. The estimation accuracies of (a) LAI, (b) h, and (c) m was shown as example

  3. The multi-sphere ear scattering model

    A novel rice ear scattering model, the multi-sphere ear scattering model, was developed by incorporating the micro-structure of rice ear panicles, including the ear grain parameters in particular. A virtual ear model (Fig 5) was used to simulate the ear morphology, and a multi-sphere scattering model was used to simulate the ear scattering. This novel multi-sphere ear scattering model provides a potential way of retrieving grain parameters from SAR imagery.


    Figure 5. Virtual Rice Ear with ϕ0 = 74.39◦ and ϕN = 50.70◦, ϕ0 and ϕN are the basal and distal elevation angle of the axis curve, obtained from ground measurements.



    Figure 6. The simulation results over 100 realizations.
     

In Situ Observations

  1. Parameter: Dimensions of grains (length, width)
    Data Collection Protocol:

    measured by vernier caliper

    Frequency: four times during experiment cycle
  2. Parameter: Bunch diameter
    Data Collection Protocol:

    measured by vernier caliper

    Frequency: ten times during experiment cycle
  3. Parameter: Leaf insertion angle
    Data Collection Protocol:

    estimation using compass

    Frequency: ten times during experiment cycle
  4. Parameter: Weight of a panicle (moist, dry)
    Data Collection Protocol:

    field sampling, measured by balance

    Frequency: four times during experiment cycle
  5. Parameter: Plant Bunch distance
    Data Collection Protocol:

    measured by steel ruler

    Frequency: ten times during experiment cycle
  6. Parameter: Number of Leaf/stem
    Data Collection Protocol:

    select a few stems randomly, count the number, then calculate the value per stem

    Frequency: ten times during experiment cycle
  7. Parameter: Moist weight and Dry height/bunch
    Data Collection Protocol:

    field sampling, measured by drying method

    Frequency: ten times during experiment cycle
  8. Parameter: Leaf length, width and thickness
    Data Collection Protocol:

    measured by vernier caliper

    Frequency: ten times during experiment cycle
  9. Parameter: Number of stem/bunch
    Data Collection Protocol:

    select a few bunches randomly, count the number, then calculate the value per bunch

    Frequency: ten times during experiment cycle
  10. Parameter: Paddy variety
    Data Collection Protocol:

    obtain through field measurements

    Frequency: once during experiment cycle
  11. Parameter: Number of Panicle/bunch
    Data Collection Protocol:

    select a few bunches randomly, count the number, then calculate the value per bunch

    Frequency: four times during experiment cycle
  12. Parameter: Stem diameter
    Data Collection Protocol:

    measured by vernier caliper

    Frequency: ten times during experiment cycle
  13. Parameter: Method of planting
    Data Collection Protocol:

    obtain through field measurements

    Frequency: once during experiment cycle
  14. Parameter: Panicle length
    Data Collection Protocol:

    measured by steel ruler

    Frequency: four times during experiment cycle
  15. Parameter: Stem inclination
    Data Collection Protocol:

    estimation using compass

    Frequency: ten times during experiment cycle
  16. Parameter: Sowing date
    Data Collection Protocol:

    obtain through field measurements

    Frequency: once during experiment cycle
  17. Parameter: Panicle angle
    Data Collection Protocol:

    estimation using compass

    Frequency: four times during experiment cycle
  18. Parameter: Plant height
    Data Collection Protocol:

    measured by steel ruler

    Frequency: ten times during experiment cycle
  19. Parameter: Water layer height
    Data Collection Protocol:

    Measured by vernier caliper

    Frequency: Six times during experiment cycle
  20. Parameter: Number of grain/panicle
    Data Collection Protocol:

    select a few panicles randomly, count the number, then calculate the value per panicle

    Frequency: four times during experiment cycle
  21. Parameter: Number of Bunch/m2
    Data Collection Protocol:

    select a field randomly, count the number, then calculate the value per square meter

    Frequency: ten times during experiment cycle
  22. Parameter: Soil condition
    Data Collection Protocol:

    observation, field sampling, measured by drying method

    Frequency: ten times during experiment cycle

EO Data Requirements

Approximate Start Date of Acquisition: June 10, May shift slightly from one year to next due to changes in moisture/temperatures
Approximate End Date of Acquisition: October 25, May shift slightly from one year to next due to changes in moisture/temperatures
Spatial Resolution: 20m (min), 0.6m (preferred)
Temporal Frequency: 3 times over season (min), 6 times over season (preferred)
Latency of Data Delivery: weekly
Wavelengths Required: B,G,R,NIR
Across Swath: 40km (min), 60km (preferred)
Along Track: 40km (min), 60km (preferred)

SAR Data Requirements

Approximate Start Date of Acquisition: June 10, May shift slightly from one year to next due to changes in moisture/temperatures
Approximate End Date of Acquisition: October 25, May shift slightly from one year to next due to changes in moisture/temperatures
Spatial Resolution: 30m (min), 8m (preferred)
Temporal Frequency: 12 times over season
Latency of Data Delivery: weekly
Wavelengths Required: X, C and L
Polarization Full polarization (HH/HV/VH/VV)
Incidence Angle Restrictions: See the Incidence Angle in Preferred sensor
Across Track: 40km (min), 60km (preferred)
Along Track: 40km (min), 60km (preferred)

Locations

Jiangsu

Centroid
Latitude: 33.116
Longitude: 118.971

Site Extent
Top left
Latitude: 33.256
Longitude: 118.971
Bottom Right
Latitude: 32.976
Longitude: 119.114


Optical Sensors

JECAM | Joint Experiment for Crop Assessment and Monitoring | Group on Earth Observation

©2013 Joint Experiment for Crop Assessment and Monitoring © HER MAJESTY THE QUEEN IN RIGHT OF CANADA SA MAJESTE LA REINE DU CHEF DU CANADA (2012)