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JECAM | Joint Experiment for Crop Assessment and Monitoring

China - Shandong

Project Overview

Crop identification and Crop Area Estimation

Crop Condition/Stress

Estimation of Biophysical Variables

Yield Prediction and Forecasting

Crop Residue

Phonological Events

The mapping resolution is 30 metres.

Timeliness (with regards to growing season): in 1 – 2 months.

The current project phase is research and operational pilot.


Project Reports

2016 Site Progress Report

2015 Site Progress Report

2014 Site Progress Report

Publications:

Dong T, Meng J, Shang J, et al. Evaluation of Chlorophyll-Related Vegetation Indices Using Simulated Sentinel-2 Data for Estimation of Crop Fraction of Absorbed Photosynthetically Active Radiation[J]. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 2015, 8(8): 4049-4059.

Dong T, Meng J, Shang J, et al. Modified vegetation indices for estimating crop fraction of absorbed photosynthetically active radiation[J]. International Journal of Remote Sensing, 2015, 36(12): 3097-3113.

Meng J, Xu J, You X. Optimizing soybean harvest date using HJ-1 satellite imagery[J]. Precision Agriculture, 2015, 16(2): 164-179.

Zhang M, Wu BF, China Shandong JECAM site Progress Updates in 2015, Oral presentation at JECAM/GEOGLAM Science Meeting, Brussels, Belgium, 16-17 November, 2015.

Francois Waldner, Santiago Veron, Deigo de Abeyllera, Miao Zhang, Bingfang Wu, etc, Cropland mapping in five contrasted agrosystems dominated by large sized fields, submitted to International Journal of Remote Sensing.

Bingfang Wu, Jihua Meng, Qiangzi Li, Nana Yan, Xin Du, Miao Zhang, 2014. Remote sensing-based global crop monitoring: experiences with China’s CropWatch system. International Journal of Digital Earth, 7(2): 113-137. DOI: 10.1080/17538947.2013.821185.

Miao Zhang, Bingfang Wu, Jihua Meng. Quantifying winter wheat residue biomass with a spectral angle index derived from China Environmental Satellite data. International Journal of Applied Earth Observation and Geoinformation, 2014, 32: 105-113.

Miao Zhang, Bingfang Wu, Jihua Meng, et al. Fallow land mapping for better crop monitoring in Huang-Huai-Hai Plain using HJ-1 CCD data[C]//IOP Conference Series: Earth and Environmental Science. IOP Publishing,2014,17(1): 012048.

Miao Zhang, Bingfang Wu, Mingzhao Yu, et al. Monthly monitoring of uncropped arable land: concepts and implementation – a case study in Argentina. Journal of Remote Sensing, accepted, 2014 (ahead of print).

Taifeng Dong, Jihua Meng, Jiali Shang, Jiangui Liu, Bingfang Wu. An evaluation of chlorophyll-related vegetation indices using simulated Sentinel-2 data for estimation of crop fraction of absorbed photosynthetically active radiation (FPAR),IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. DOI 10.1109/JSTARS.2015.2400134.

Taifeng Dong, Jihua Meng, Jiali Shang, Jiangui Liu, Ted Huffman, Bingfang Wu. Modified vegetation index for estimating crop fraction of absorbed photosynthetically active radiation, Submitted to International Journal of Remote Sensing, 2015.

Bingfang Wu, Miao Zhang and Yang Zheng. Crop area estimation based on high spatial and temporal Rapideye images, Presented in Sentinel-2 agriculture workshop & Sentinel-2 for Science workshop, May 2014.

Wu Bingfang, Zhang Miao, Hongwei Zeng, New indicators for global crop monitoring in CropWatch. Oral presentation in the 35th International Symposium on Remote Sensing of Environment (ISRSE35), April 2013, China, Beijing.

Meng Jihua, Wu Bingfang, Du Xin, Zhang Miao. Estimating regional winter wheat leaf N concentration with MERIS by integrating a field observation based model and histogram matching. Transactions of the ASABE, 2013, 56(4): 1589-1598.

Wu B, Meng J, Li Q, et al. Remote sensing-based global crop monitoring: experiences with China's CropWatch system[J]. International Journal of Digital Earth, 2013 (ahead-of-print), DOI:10.1080/17538947.2013.821185.

Miao Zhang, Bingfang Wu, Mingzhao Yu, et al. Crop condition assessment using an uncropped arable land ratio to adjust NDVI, submitted to Remote Sensing, under review.

Miao Zhang, Piccard Isabelle, Bydekerke Lieven, Bingfang Wu. Comparison of field campaigns for crop monitoring in China (Yucheng) and Europe (Belgium). Oral presentation in Global Vegetation Monitoring and Modeling, February 3rd to 7th, Avignon, France.


Implementation Plans

In Situ Data

The main variables measured and instruments we are using are shown in Table 1. All the variables were measured once a month from April to September except for the following. Yield, harvest index and crop type mapping were measured once per growing season.

 

Main variables

Instruments or processing method

Above ground dry biomass

Oven dried and weight

Yield

Oven dried and weight

Harvest index

Calculated by yield and AGB

Density/canopy height

Tape measured

Crop type field boundary

GPS record using GIS system

Table 1   In situ Variables and Instruments - Shandong

 

The biggest challenge is weather condition during the field observations. It is sunny in the morning while cloudy at noon; this may influence measurements of field spectral and FAPAR.

The shift from growing cereals to cash crops makes the field observation at all the same fields as the previous year impossible. We had to slightly change the selected fields during the field campaign.

 

Plans for the Next Growing Season:

In 2016 and the future, we will measure the same variables as in 2015.

We already submitted the acquisition plan for newly launched GF-5 geostationary satellite images with 50m resolution at daily temporal resolution.


Site Description

Locations

Shandong
Site Extent   Centroid: 36.831, 116.569
Top left: 37.331, 116.319 Bottom Right: 36.331, 116.819

Location

Topography

Soils

Drainage class/irrigation

Crop calendar

Field size

Climate and weather

 

Figure 1: Crop condition map of North China Plain uncropped arable land ratio adjusted NDVI for early May, 2011. The maps show the condition of the crop compared to the previous year.


Specific Project Objectives & Deliverables

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Retrieval by Chlorophyll-related Vegetation Indices

Over the China Shandong JECAM site, chlorophyll-related vegetation indices (VIs) were selected and tested for their capability in crop FAPAR estimation using simulated Sentinel-2 data. These indices can be categorized into four classes:

Two crops with distinctive canopy and leaf structure, wheat and corn, were studied. Regression analysis was conducted between measured FAPAR and different vegetation indices derived from Sentinel-2 reflectance simulated from field spectral measurements. At the same time, the effects of the red-edge reflectance on crop FAPAR estimation and the impact of different crop types on FAPAR estimation were explored. It is found that VIs using the near-infrared and red-edge reflectance, including the modified Simple Ratio2 (mSR2), the red-edge Simple Ratio (SR705), the Red-edge Normalized Difference Vegetation Index (ND705), MERIS terrestrial chlorophyll index (MTCI), and the Revised Optimized Soil-Adjusted Vegetation Index (OSAVI[705, 750]), were strongly correlated with FAPAR, especially in the high biomass range. Among all the indices, RDVI705 and mSR2 were more linearly correlated with FAPAR, whereas the other indices deviated slightly from a linear correlation.

When the red-edge reflectance was used, the ratio indices (e.g., mSR2 and SR705) had a stronger correlation with crop FAPAR than the normalized difference indices (e.g., ND705). Sensitivity analysis showed that mSR2 had the strongest linear correlation with FAPAR for the two crops across the growing season. Further analysis indicated that indices using the red-edge reflectance might be useful for FAPAR retrieval. Indices using the red-edge reflectance are independent of crop types. This suggests the potential for high resolution and high quality mapping of FPAR for precision farming using Sentinel-2 data.

The same approach was also applied to investigate the relationship between above ground biomass and various vegetation indices.

Figure 1   Relationships between FAPAR and Vegetation Indices with a Linear Coefficient of Determination greater than 0.83

Annual Cropland Mapping

Together with other four JECAM sites, we applied five different existing methodologies over five JECAM sites using same dataset (7-day 250m resolution MODIS NDVI time series). Confusion matrices and derived accuracy indicators were produced with and without equalizing class proportions of validation samples and correcting for the spatial resolution bias. A decision tree was used as the general method from China JECAM site.

Figure 2   Features Extracted from Smoothed Time Series NDVI Data

The MODIS time-series were not directly inputs for the classification as it was reported that when a high number of input data are used for classification, some make negligible or even negative contributions in terms of classification accuracy (Zhao et al., 2008; Zhang et al., 2012). In order to avoid such issues and to reduce the classification computing time, four temporal features were extracted from smoothed Normalized Difference Vegetation Index (NDVI) temporal profiles: the maximum vegetation index values observed at the date of the peak, the average vegetation index during the growing season as well as the green-up ratio and withering ratio.  Cropping intensity derived from time-series NDVI data is also considered to identify cropland and non-cropland. For pixels with two growing seasons, four temporal features were only extracted from the first growing season. The smoothing was achieved by applying a Savitzky-Golay filter (Savitzky and Golay,1964;Tsai and Philpot,1998). Based on the extracted parameters and the training samples, a decision tree was generated using the Classification and Regression Tree (CART) algorithm and applied to the whole study area to produce a land cover map.

Figure 3   Cropland Agreement Map using 5 Different Methods

 

Results using five different methods were overlapped to evaluate the agreement of the five cropland maps. Overall accuracy (OA) using five different methods over China JECAM site all exceeded 0.9. OA using the decision tree method over five different JECAM sites also presented high accuracy (higher than 0.9) except for the Sao Paulo, Brazil site.


In Situ Observations

  1. Parameter: Density/canopy height
    Data Collection Protocol:

    Tape measured

    Frequency:
  2. Parameter: Crop Biophysical Parameter
    Data Collection Protocol:

    none

    Frequency: Monthly
  3. Parameter: Fractional vegetation cover
    Data Collection Protocol:

    Fish-eye camera

    Frequency:
  4. Parameter: Crop Type
    Data Collection Protocol:

    none

    Frequency: Once every crop season
  5. Parameter: Crop calendar
    Data Collection Protocol:

    Visual interpretation

    Frequency:
  6. Parameter: Crop type mapping
    Data Collection Protocol:

    Field interpretation

    Frequency:
  7. Parameter: Crop type proportion
    Data Collection Protocol:

    GVG instrument

    Frequency:
  8. Parameter: Irrigation
    Data Collection Protocol:

    Interview and record

    Frequency:
  9. Parameter: Dry amount of above ground biomass, yield
    Data Collection Protocol:

    Oven dried and weight

    Frequency:
  10. Parameter: Crop Biochemical Parameter
    Data Collection Protocol:

    none

    Frequency: Once every crop season
  11. Parameter: shandong-harvest
    Data Collection Protocol:

    Calculated by yield and AGB

    Frequency:

EO Data Requirements

Approximate Start Date of Acquisition: April 1 (min), March 1 (preferred)
Approximate End Date of Acquisition: October 10 (min), October 15 (preferred)
Spatial Resolution: 30 (min), 10 (preferred)
Temporal Frequency: 30d (min), 15d (preferred)
Latency of Data Delivery: weekly
Wavelengths Required: B,G,R,NIR (min), B,G,R,NIR, Red Edge band (preferred)
Across Swath: 70km (min), 100km (preferred)
Along Track: 70km (min), 100km (preferred)

SAR Data Requirements

Approximate Start Date of Acquisition: April 1
Approximate End Date of Acquisition: October 15
Spatial Resolution: 30 m (min), 10 m (preferred)
Temporal Frequency: 5 times over season
Latency of Data Delivery: weekly
Wavelengths Required: X, C and L
Polarization Dual (VV,VH); polarimetric
Incidence Angle Restrictions: No restriction
Across Track: 70km (min), 100km (preferred)
Along Track: 70km (min), 100km (preferred)

Locations

Shandong

Centroid
Latitude: 36.831
Longitude: 116.569

Site Extent
Top left
Latitude: 37.331
Longitude: 116.569
Bottom Right
Latitude: 36.331
Longitude: 116.819


Optical Sensors

GF-1&ZY03
Imaging Mode:
Spatial Resolution:
Acquisition Frequency: 8 times, Mar 20-Sep 30, 2014
Pre-Processing Level: 1A/2A
Application:

MODIS
Imaging Mode:
Spatial Resolution: H27V05, 250m/500m/1km
Acquisition Frequency: 46 scenes, Jan 1 to Dec 31, 2014
Pre-Processing Level: Level 2
Application:

HJ-1 CCD
Imaging Mode: Pushbroom
Spatial Resolution: 30 m
Acquisition Frequency: 12 times from Mar-Dec 2014
Pre-Processing Level: 2
Application:

Proba-V
Imaging Mode:
Spatial Resolution: X29Y03, 100m resolution
Acquisition Frequency: 57 images
Pre-Processing Level: every five days from Mar 20 to Dec 31, 2014
Application:

CBERS 01/02-CCD
Imaging Mode: Pushbroom
Spatial Resolution: 19.5 m
Acquisition Frequency: Monthly
Pre-Processing Level: 0
Application:

SPOT-4
Imaging Mode:
Spatial Resolution:
Acquisition Frequency:
Pre-Processing Level: 2A
Application:

RapidEye
Imaging Mode: Multi-spectral
Spatial Resolution: 6.5 m
Acquisition Frequency:
Pre-Processing Level:
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)