Print This Page

JECAM | Joint Experiment for Crop Assessment and Monitoring

Taiwan (TARI) Site

Project Overview

This project is conducted by the Taiwan Agricultural Research Institute (TARI) and Center for Space and Remote Sensing Research (CSRSR), National Central University, Taiwan. The studies will be based on the following statement:

Crop identification and acreage estimation: Estimates are required end-of-season. An early inseason estimate is desirable.
Residue and Tillage mapping/ Soil moisture/ Crop biophysical variables (LAI): So far, these factors have not yet been observed. In the future, these factors will be observed.


The mapping resolution is 20m. The timeliness (with regards to growing season) varies depending on the mapping activity


Project Reports

2017 Site Progress Report

2016 Site Progress Report

2014 Site Progress Report

Publications:

  1. N.T. Son, C.F. Chen, C.R. Chen, L.Y. Chang, S.H. Chiang. 2016. Integrating MODIS LAI with CERES-Rice model for rice yield estimation in Taiwan. International Meeting on Land Use and Emissions in South/Southeast Asia. Monday, October 17, 2016 to Wednesday, October 19, 2016. Ho Chi Minh City, Vietnam.
     
  2. C.F. Chen, N.T. Son, C.R. Chen, L.Y. Chang, and S.H. Chiang. 2016. Rice crop mapping using sentinel-1A phenological metrics. EGU General Assembly, 17–22 April 2016, Vienna, Austria.
     
  3. C.F. Chen, J.B. Chen, N.T.Son, C.R. Chen, S.H. Chiang. 2016. A phenology-based approach for rice crop mapping from multi-temporal Sentinel-1A data in Taiwan. Fall AGU Meeting from 12–16 December 2016 in San Francisco, USA.
  4. C.R. Chen, C.F. Chen, N. T. Son, L.Y. Chang. (2013). Blending multi-temporal SPOT and MODIS imageries for rice crop phenology detection. ACRS 2013 Conference, Bali, Indonesia.
     
  5. C.F. Chen, C.R. Chen, N. T. Son, N.B. Chang, L.Y. Chang. (2014). Mapping rice crops in Taiwan with multitemporal MODIS-SPOT data fusion. In prep. for Remote Sensing journal (SCI).
     
  6. C.R. Chen, C.F. Chen, N.T. Son and K. V. Lau (2015) Statistical rice yield modelling using blended MODIS-Landsat based crop phenology metrics in Taiwan, Presented at 2015 Fall Meeting, AGU, San Francisco, Calif., 14-18 Dec.

Presentation:

C.F. Chen. (2013). Rice crop monitoring using time-series satellite data. International Corporation on Using Remote Sensing for Crop Area Monitoring and Yield Estimation Workshop, TARI, Taiwan.


Implementation Plans

Plans for Next Growing Season:


Site Description

Locations

Taiwan TARI
Site Extent   Centroid: 24.041, 120.661
Top left: 24.1, 120.608 Bottom Right: 23.983, 120.713

Location:

The study site (Changhua and Yulin counties) covering approximately 3,170 km2 was selected for rice crop mapping and yield estimation (Figure 1).

Topography:

The elevation ranges from 0 –1,777 m above the mean sea level.

Soils:

Silty loan

Drainage class/irrigation:

moderate to imperfect

Crop calendar:

There are two rice-cropping seasons per year. The first crop is from February–March to June–July, and the second is from August–September to November–December.


Field size:

Ranging from 0.5 – 1.1 ha.

Climate and weather:

Subtropical monsoon with the occurrence of typhoons and drought events from time to time during the year.

Agricultural methods used:

Transplanting

Photograph(s): 


Figure 1. Location of the study site with reference to the geography of Taiwan.


Specific Project Objectives & Deliverables

Results:

1.Rice crop mapping


Figure 2. Results of rice crop mapping using Sentinel-1A VH data: (a) classification map, and (b) ground reference data.

 


Figure 3. Results of rice crop mapping using Sentinel-1A VV data: (a) classification map, and (b) ground reference data.

 

2. Rice yield estimation


Figure 4. Spatial distribution of simulated rice yields for the first crop in 2014.
 


In Situ Observations

  1. Parameter: rice growth, rice parameters
    Data Collection Protocol:
    Frequency: monthly

EO Data Requirements

Approximate Start Date of Acquisition: October 1 - This period has the low cloud coverage rate in Taiwan; It has the advantage of crop identification for optical image.
Approximate End Date of Acquisition: March 1 - This period has the low cloud coverage rate in Taiwan; It has the advantage of crop identification for optical image.
Spatial Resolution: 20m minimum, 5m preferred
Temporal Frequency: Weekly
Latency of Data Delivery: Weekly
Wavelengths Required: RGB, NIR, SWIR
Across Swath: 50km
Along Track: 50km

SAR Data Requirements

Approximate Start Date of Acquisition: May 1 - to cooperate with optical image
Approximate End Date of Acquisition: October 1 - to cooperate with optical image
Spatial Resolution: 10m minimum, 3m preferred
Temporal Frequency: Weekly
Latency of Data Delivery: Weekly
Wavelengths Required: X, C and L
Polarization Dual (VV,VH)
Incidence Angle Restrictions: Various
Across Track: 50km
Along Track: 50km

Locations

Taiwan TARI

Centroid
Latitude: 24.041
Longitude: 120.661

Site Extent
Top left
Latitude: 24.1
Longitude: 120.661
Bottom Right
Latitude: 23.983
Longitude: 120.713


Optical Sensors

FORMOSAT-2
Imaging Mode:
Spatial Resolution: 8m
Acquisition Frequency: 1-25 days
Pre-Processing Level:
Application:

MODIS
Imaging Mode:
Spatial Resolution: 500m
Acquisition Frequency: 8 days/daily
Pre-Processing Level:
Application:

SPOT-5
Imaging Mode: multii-spectral
Spatial Resolution: 10m
Acquisition Frequency: 15 days
Pre-Processing Level: Radiometrically Corrected and Geo-coded
Application:

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)