Remote Sensing Applications for Agriculture Monitoring in Northern

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					              Remote Sensing Applications for Agriculture Monitoring in Northern Eurasia

                                      S. Bartalev1, E. Loupian1, I. Neyshtadt1 and I. Savin2
  1
      Space Research Institute, Russian Academy of Sciences, Moscow, Profsoyuznaya str. 84/32, Russia – beml@smis.iki.rssi.ru
  2
       Institute for the Protection and Security of the Citizen, EC JRC, Ispra, TP 441, 21020 Ispra (VA), Italy – igor.savin@jrc.it



Abstract – Northern Eurasian ecosystems present                         monitoring. Blue (459-479 nm) and shortwave infrared
challenges to social and ecosystem scientists assessing                 (1628-1652 nm) spectral channels with 500 m resolution
the human dimensions of land cover and land use                         have been used for cloud and snow cover detection at the
change. One of the main factors affecting the state of                  pre-processing stage.
the environment in Northern Eurasia is the change of
area and structure of agricultural lands. Reliable and
up-to-date information on agricultural lands is critical                                     3.   TEST REGIONS
issue of the day and is needed for sustainable socio-
economic development of the region and global climate                   Rostov oblast and Krasnodarsky kray were selected as test
change modeling. The paper gives an overview of first                   regions. Total area of both selected test regions is about
results of the monitoring of agricultural lands in Russia               200 th. sq. km. These regions situated in the south of
based on remote sensing data, including the                             Russia produce about 45% of sunflower croppage and 20%
methodology and set of derived thematic products on                     of wheat. An extension of developed methods over the all
various aspects of agricultural lands status and                        main agriculture regions of Russia is planned for the near
dynamic.                                                                future.

Keywords: Agriculture, MODIS, Northern Eurasia, Land-                                       4.    METHODOLOGY
Use.
                                                                        The developed methodology is based on analysis of the
                      1.   INTRODUCTION                                 remote sensing data time-series. Using the time series of
                                                                        vegetation index pixels can be classified into different
Over the past 25 years the Northern Eurasian region has                 classes. Every class corresponds to one particular crop or
experienced a dramatic changes in arable lands usage.                   group of crops.
Since the dissolution of the Soviet Union individual farmers            Data processing is divided into two main parts: pre-
got the independence in their agriculture activities.                   processing chain and thematic processing. At the first step
Nowadays all information concerning arable lands usage in               the mask of pixels which are not suitable for thematic
Russia is provided by farmers. Lack of reliable information             processing is produced. This mask includes cloud cover,
points at the necessity of development of agriculture                   snow cover, cloud shadows and pixels with resolution
monitoring system based on remote sensing data, which                   worse than stated. Algorithms for pre-processing use data
will be independent from the information provided by                    about reflectance in red and near infrared bands for
farmers. Since 2003 under the treaty with Russian Ministry              cloud/snow detection, data about solar illumination and
of Agriculture, Space Research Institute has been                       instrument viewing geometry for detecting of cloud
developing agriculture monitoring system for main Russian               shadows and for removing pixels with insufficient
agriculture regions (Bartalev et al., 2004). This paper                 resolution. The composite image of the 8 daily products is
discuses methods and first results of arable lands mapping,             produced using mentioned above mask. These composite
and classification of crops received with use of the                    images are suitable for thematic analysis.
Terra/Aqua-MODIS time-series data.                                      Second step is the thematic processing. The Perpendicular
                                                                        Vegetation Index (PVI) is used for crop growth monitoring.
                     2.    SATTELITE DATA                               It is calculated in the following way:

Terra/Aqua-MODIS instrument data have been used as the                              PVI = −0.83* R1 + 0.56 * R 2 − 0.005
primary data source for agriculture monitoring. Spectral                Where R1 and R2 are correspondingly the reflectance in red and
bands and spatial resolution fit the needs of agriculture               near-infrared channels.
vegetation monitoring. As input data standard daily
MODIS products were used MOD09GHK, MOD09GQK,                            PVI time series for each pixel, where one point in time
MODMGGAD,                     and           MOD09GST                    series represents 8 days, were calculated with the aim to
(http://lpdaac.usgs.gov/main.asp).   Mentioned products                 classify annual PVI time series into several representative
provide information about surface reflectance, solar                    land use classes. Figure 1 shows virtual PVI time series for
illumination and instrument viewing geometry. Red (620-                 several crops. These time series were obtained combining
670 nm) and near infrared (841-875 nm) spectral channels                virtual land use data and MODIS composite images.
with 250 m resolution have been used for vegetation
                                                                  It is significant to mention that the usage of fixed thresholds
                                                                  provides much more reliable results since we don’t adjust
                                                                  classification parameters to various years and regions.

                                                                                      RESULTS and VALIDATION

                                                                  The following maps were produced using MODIS data
                                                                  from 2001 to 2004: clean fallow, winter crops, and
                                                                  sunflower. An arable lands map was compiled based on
                                                                  crop masks for the years 2001-2004. This map was
                                                                  obtained by overlapping individual crop maps. Fig. 2 shows
                                                                  the spatial distribution of arable lands in Rostov oblast.

 Figure 1. Different crops have different vegetation index
           behavior during the growing season.


Additionally a simple Land Cover map, which includes
several types of land cover: water bodies, built-up areas,
deciduous forests, coniferous forests, grasslands, bare
sands, salt-marshes, and swamps was produced.
The special algorithms to classify three types of agriculture
land use: clean fallow, winter crops and sunflower were
developed.
A unique physical feature of clean fallow fields is that fields
are not covered by vegetation during all vegetation season.
This feature underlies the classification method. The
                   1                                                       Figure 2. White – arable lands in Rostov oblast.
characteristic f = ∑ pvii , where I – is the set of
                              2

                   N i∈ I
composite images from the 1 of March to the 1 of                  A comparison of these maps with official statistical data
September for the current year was used for the pixels            and field observation data was done for validation of the
classification. Further all pixels were classified using one      quality of the results. The statistical information about
threshold value for all territory and for all years.              sown areas for each crop for every sub-region was used as
Winter crops detection algorithm is based on prior                official data. Additionally, field observation records for
knowledge about winter crops development. Temporal                about 200 fields with information about cultivated crop
dynamics of winter crops can be characterized by two main         were available for validation of the results. An example of
periods: the sowing period, the growing period. For each          comparison of the classification results with field data is
pixel we calculate PVI for two principal time frames: 1st of      shown at fig.3.
September – 5th of October, and 1st of November – 31st of
December. Then this two-dimensional attribute space was
classified into “winter crops” and “other” classes using
predefined threshold values. Used threshold values are the
same for all years, but different for Rostov and Krasnodar
regions.     Using two different threshold values was
necessary due to geographical differences between the
regions.
Sunflower is one of the main crops in the concerned                                          Winter crops, 2003 year    Sunflower, 2003 year
                                                                   Clean fallow, 2003 year
regions. Air temperature data was used to define two                                                              – Ground truth data
principal time frames responsible for sowing and flowering                                                        – Remote sensing classification
of the crop.       The relation between cumulative air             Figure 3. An example of the results validation based on
temperature and dates of sowing and flowering was                 ground truth data for one collective farm in Rostov region.
established based on expert analysis of local agronomical
practice. For these dates we derived The PVI values from
composite images were derived for these dates, and two-           Table A presents a relative error between official data and
dimensional attribute space was constructed. Threshold            remote sensing classification at the level of administrative
classification was performed with fixed thresholds to             region. A considerable discrepancy can be caused by a
distinguish “sunflower” pixels. Threshold values were             number of factors. First of all there is a doubt about
fixed for all years and regions. In more detail algorithms        accuracy of official data. Additionally, discrepancy in
are presented in Bartalev et al., 2004.                           winter crop acreage estimation can be caused by improper
                                                                  agriculture practices.
 Table A. Relative error between official data and remote
              sensing classification results.
                                                                                         5.   CONCLUSION
                                 Rostov oblast
 Type/Year           2001      2002      2003          2004         With the advent of new generation of remote sensing
 Winter Crops          –        0,38      0,04          –           systems like MODIS, new challenges arise to develop
 Sunflower           0,24       0,17     -0,23           –          methods of compiling of land use maps for various
                              Krasnodarskij kray                    applications. The approach to obtain annual land use maps
 Type/Year           2001      2002      2003          2004         for agriculture applications is under consideration in given
 Winter Crops          –        0,28      0,19         0,28         paper. In summary, this study has demonstrated the
 Sunflower           0,10     -0,14      -0,07           –          potential of MODIS data and reliability of the crop
                                                                    identification algorithms for the operative mapping of
                                                                    different crops within large territories. In the near future
Table B represents comparison between the results of
                                                                    the investigations shall be concentrated on producing of
classification and official statistical data at sub-region level.
                                                                    arable lands map for all main Russian agriculture regions,
In this case a correlation coefficient was selected as a
                                                                    and on extension of crop masks to neighboring regions.
divergence characterizing value.

Table B. Correlation between remote sensing classification                              6. REFERENCES
results and official statistical data on crop acreage for sub-
                                                                    Agriculture, hunting and forestry in Russia, Rosstat, 478p.,
                            regions.
                                                                    Moscow, 2004. (In Russian)
                                                                    Bartalev S.A., Loupian E.A., Neyshtadt I.A. et al., Remote
                                Rostov oblast
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 Type/Year           2001      2002      2003          2004
                                                                    Proceedings “Current aspects of remote sensing of earth
 Fallow              0,83      0,87      0,84           –           from space” conference, Moscow, 2004a. (In Russian, In
 Winter Crops          –       0,85      0,81           –           Press)
 Sunflower           0,76      0,80      0,84            –          Bartalev S.A., Burcev M.A., Ershov D.V. et al., Automated
                              Krasnodarskij kray                    receiving, processing and distribution system for agriculture
 Type/Year           2001      2002      2003          2004         monitoring, Proceedings “Current aspects of remote sensing
 Winter Crops          –       0,90      0,86          0.90         of earth from space” conference, Moscow, 2004b. (In
 Sunflower           0,83      0,88      0,74            –          Russian, In Press)
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(Fig. 4).                                                           Sowing area, croppage and yield of crops and perennial
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                                                                    crops, Hydrometeoizdat, 74 p., Saint-Petersburg, 2000. (In
                                                                    Russian)




Figure 4. Official statistical data about arable lands acreage
for sub-regional level versus results of the satellite
monitoring data.