GEO Joint Experiment for Crop Assessment and Monitoring (JECAM):
Template for Research Progress Report
Date:
12.02.2017
JECAM Test Site Name:
RUJECAM
Team Leader:
Igor Savin (V.V. Dokuchaev Soil Science Institute)
Team Members:
Yuri Verniuk (ATI PFUR)
David Sharychev (ATI PFUR)
Irina Veretelnikova (V.V. Dokuchaev Soil Science Institute)
Kristina Li (ATI PFUR)
Ekaterina Shishkonakova (V.V. Dokuchaev Soil Science
Institute)
Use of Information
In addition to the report we would also like to use the information and images you provide to
update the jecam.org website. Do you agree to this use of your information? Y/N
Project Objectives
Have the original objectives for your site changed? Y/N
Please briefly describe the areas that you are working on from the list below (i.e. topic, general
methods, intended operational outcome, if any):
We are working on:
1. Winter crop identification early in a season based on MODIS data
2. Monitoring of soil moisture in rooting layer and in ploughed horizon based on MODIS and
Hyperion data
3. Winter crop phenological development based on MODIS and Landsat data.
4. Monitoring of soil erosion based on Landsat and Hyperion data.
Site Description

Location
The site is located in the south of Tula region of Russia (Plavsk district).

Topography
The territory is characterized by slightly undulated plane, dissected by small rivers' valleys.

Soils
The dominant soil is chernozem with silty-clay texture and high humus content. The soil is eroded
on slopes.

Drainage class/irrigation
The soil is moderately drained. Irrigation is absent.

Crop calendar
Winter crops are sowing in September. The flowering is at the end of May, harvesting – in July.

Field size
Typical field size is near 100 hectares.

Climate and weather
The climate is temperate with moderately cold winter (air temperature is near -100C) and warm
summer (air temperature is near +250C). Amount of precipitation is near 450 mm per year.

Agricultural methods used
Field visits. Soil and crop samples collection and analysis in the lab.
In Situ Observations:
Crop type. Discrimination among crop types in georeferenced plots. Frequency: once per crop
season.
Soil moisture content. Measurements in selected georeferenced representative points.
Frequency: before crop sowing, in the middle of the season, after crop harvesting.
Crop phenology. Visual determination of phenological states. Frequency: each month during the
growing season.
Crop status. Using LAI and CF modelled based on analysis of nadir photos of crop canopy.
Soil erosion status (soil humus content). Samples collection in selected georeferenced
representative points and analysis of humus content in laboratory. Frequency: once in the year,
after the harvest.

Photograph(s)
Fig.1. “Fish-eye” photo over the winter wheat (germination phase)
Fig.2. Soil sampling and start of acquisition of UAV image
Fig.3. UAV image of test site in autumn
Earth Observation (EO) Data Received/Used
We used mainly MODIS and Landsat data, which were downloaded from USGS Global
Visualization web site (http://glovis.usgs.gov/). We use daily MODIS data for the year, and all
available Landsat scenes.
In situ Data
Describe the in situ data collected, with methods and challenges, if any.
For communication purposes, please provide photographs of site work if available.
Crop type. Discrimination among crop types in georeferenced plots (27 plots). Frequency: once
per crop season.
Crop status was defined one time per month using hemispherical photo analysis by CanEye
software.
Soil moisture content. Measurements in selected georeferenced representative points.
Frequency: before crop sowing, in the middle of the season, after crop harvesting (near 30
points).
Crop phenology. Visual determination of phenological states. Frequency: each month during the
growing season (near 30 plots).
Soil erosion status (soil humus content). Samples collection in selected georeferenced
representative points and analysis of humus content in laboratory. Frequency: once in the year,
after the harvest (near 30 samples).
Fig.5. Field work at the test point
Collaboration
Have you been approached to participate in a collaborative project with other sites? Y/N
If yes, please describe the nature of the collaboration ( i.e. Who, objective, brief status).
Results
Describe your key results, positive and negative.
For communication purposes, please provide some graphic representation(s) of the results.
To what extent have the project objectives been met?
Can this approach be called ‘best practice’?
Have you modified the project objectives? If so, in what way?
We have conducted field visits one time per month from April 2016 up to August 2016.
It has been found that weeds on many fields affect the NDVI and LAI, calculated based on Landsat
and MODIS data (Fig.6). The effect of weeds differs from field to field due to crop type and crop
development status.
Fig.6. Example of NDVI time profile (MODIS) for test plot with spring barley (1 – zone where NDVI
is predefined by barley, 2 – zone of increasing influence of weeds, 3 – zone where NDVI is
predefined primarily by weeds)
Plans for Next Growing Season
Next growing season, will you maintain your current approach, or modify the approach? If you
plan to modify, please describe your new approach.
In the season 2017 we plan to continue usage of the same approaches, and we plan to test usage
of UAV for field crop data collection.
Do you anticipate ordering the same type/quantity of EO data next year? Y/N
If not, what type and quantity of EO data do you plan to acquire?
Additionally, to MODIS and Landsat, we plan to test usage of RESURS-P multi- and hyperspectral
data.
Publications
Please list any publications from your JECAM related research since last year’s report
(presentations, peer reviewed papers, technical reports, etc).
Savin I., Prudnikova E., Vasilyeva N., Bairamov A. SEASONAL CHANGES OF TILLED SOIL SURFACE
AS INFORMATION FACTOR FOR EFFICIENT SOIL MAPPING USING REMOTE SENSING DATA - in:
Digital Soil Maps for Everyone 2016. С. 75.
Savin I.Yu., Prudnikova E.Yu., Vasilyeva N.A., Veretelnikova I.V., Bairamov A.N. THE COLOR OF
SOILS AS A BASIS FOR PROXIMAL SENSING OF THEIR COMPOSITION // Бюллетень Почвенного
института им. В.В. Докучаева (Bulletin of V.V. Dokuchaev Soil Science Institute). 2016. Т. 86. С.
46-52. (In Russian)