Transcript Document

Application of GI-based
Procedures for Soil Moisture
Mapping
and Crop Vegetation Status
Monitoring in Romania
Dr. Adriana MARICA, Dr. Gheorghe STANCALIE, Daniel
ALEXANDRU, Argentina NERTAN, Cristian FLUERARU
National Meteorological Administration
Bucharest, Romania
WMO, FAO & COST 718 WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR
AGRICULTURE
Bologna, 14-17 June 2005
Presentation outline:
1. Introduction: Recent developments in the use of GI-based
procedures in Romania;
2. Application of GIS for mapping historical soil moisture data
(methodology & results);
3. Examples of derived products obtained by using Arc View
applications for improving operational agrometeorological
activity;
4. Application of remote sensing data to monitor crop
vegetation status;
5. Conclusions.
1. Introduction:
 The
agrometeorological
activity
undergoing
within
National
Meteorological Administration (NMA), integrates complex issues
concerning the evolution of the water supply of soils and vegetation state
of the crops with respect to the meteorological parameters evolution,
being a particularly important activity whose final objective is to
elaborate/edit the agrometeorological bulletins;
 In
our institut there is an intense activity in the developing
applications of new methods and technologies to provide additional
improved agrometeorological information for decision-makers and other
users;
 Our
recent developments in the
summarized in this presentation include:
use
of
GI-based
procedures
 statistical analysis procedure on historical series of soil moisture
data to produce agroclimatic classification in order to promote
sustainable land and water management;
 NDVI image analysis procedure to monitor current crop
vegetation conditions and its variation with time.
2. Application of GIS for mapping historical soil
moisture data
Objectives:
 to analyze the spatial and temporal evolution of soil moisture over a
30-year period in order to identify the intervals and zones with high
risk at the occurrence of extreme climatic events;
 to provide information on the long term average of soil moisture,
that are very useful for placing current or expected conditions in a
historical context.
The application for spatial representation of soil moisture is
performed in VBA (Visual Basic for Applications) for Arc View 8.3
and is structured on two components:
a) the data collected and the different information layers that
are organized in a database and all the information is
integrated in a GIS;
b)
spatial representation of soil moisture by using “Kriging”
interpolation method.
Input data used:
• Monthly data of measured soil moisture (m3/ha) from
50 agrometeorological stations over the 1970-2000
interval, for:
 two soil depths (0-20 cm and 0-100 cm);
 three calendar dates specific to the phenological
phases of maize crop (April 30, July 31 and August 31);
• Available water capacity -AWC (m3/ha) for different
soil types;
• Calculated % of AWC.
Classification of the soil moisture levels:
% of AWC
0
20
35
50
70
- 20%
- 35%
- 50%
- 70%
- 100%
>100%
HUMIDITY CLASSES






Extremly drought
Strongly drought
Moderate drought
Satisfactory supply
Optimal supply
Above normal
moisture values
To assess long term average of soil moisture:
-the frequency (%) of humidity class was computed
-the prevalent class of soil moisture in different climatic
and soil type zones was established
Soil moisture database integration in a GIS
The map with the agrometeorological stations
The final map with the spatial representation of
soil moisture
Information concerning the values of soil misture content, %
of AWC, soil moisture deficit and humidity class
Average Soil Moisture, 1970-2000, at 30 April
Maize available
water in 0-20 cm
depth of soil
Moisture data
CURTEA DE ARGES
Moisture reserve
value (m3/ha):
420
% AWC:
86
Water deficit:
70
Significance:
OS
Legend
230 - 300 mc/ha
Satisfactory supply (50-70% of AWC)
300 - 400 mc/ha
Near optimal supply (70-85% of AWC)
Optimal supply (85-1oo% of AWC)
400 - 471 mc/ha
zona montana
OK
CANCEL
Average Soil Moisture, 1970-2000, at 31 July
Maize available
water in 0-100 cm
depth of soil
Moisture data
FUNDULEA
Moisture reserve
value (m3/ha):
825
% AWC:
48
Water deficit:
885
Significance:
MD
OK
Legend
445 - 600 mc/ha
600 - 900 mc/ha
900 - 1200 mc/ha
1200 - 1385 mc/ha
zona montana
Stronghly drought (20-35% of AWC)
Moderate drought (35-50% ofAWC)
Satisfactory supply (50-70% of AWC)
Near optimal supply (70-85% of AWC)
CANCEL
Average Soil Moisture, 1970-2000, at 31 August
Maize available
water in 0-100 cm
depth of soil
Moisture data
TECUCI
Legend
318 - 400 mc/ha
400 - 600 mc/ha
600 - 900 mc/ha
900 - 1200 mc/ha
1200 - 1410 mc/ha
zona montana
Extremely drought (0-20% of AWC)
Stronghly drought (20-35% of AWC)
Moderate drought (35-50% of AWC)
Satisfactory supply (50-70% of AWC)
Near optimal supply (70-85% of AWC)
Moisture reserve
value (m3/ha):
465
% AWC:
32
Water deficit:
985
Significance:
SD
OK
CANCEL
Example of yearly soil moisture variabiity, in
the period 1970-2000, at CALARASI station
Soil Moisture, 1970-2000, 30 April
600
500
mc/ha
400
Soil Moisture, 1970-2000, 31 July
300
2500
200
2000
1997
1994
1991
1988
1985
1982
1979
1976
1973
1970
0
mc/ha
2000
Soil m oist.
Avg. 1970-2000
AWC
100
Soil m oist.
Avg. 1970-2000
AWC
1500
1000
500
2000
1997
1994
1991
1988
1985
1982
1979
1976
1970
2500
1973
0
Soil Moisture, 1970-2000, 31 August
Soil Moist.
Avg. 1970-2000
AWC
1500
1000
Drought years occured in
50-60% of cases (15-18 years)
500
2000
1997
1994
1991
1988
1985
1982
1979
1976
1973
0
1970
mc/ha
2000
Monthly precipitation (JPG file)
Weekly Soil Moisture (JPG file)
Weekly Soil Temperature (JPG file)
Weekly precipitation (JPG file)
 Tmed>0°C, 1Feb-10April
4. Application of remote sensing data to
monitor crop vegetation conditions
Applications of NDVI values derived from SPOT-VEGETATION
imagery are used to:
 follow dynamic of crop vegetation status;
• infer crop progress (crop growth and development) and
conditions;
• analyze the temporal shape of the NDVI;
• estimate beginning of growing season, leaf growth, etc. based
on temporal profile analysis.
Key issues for NDVI series data analysis:
• overlay of a mask in order to isolate agricultural lands;
• overlay of reference points selection of representative cropcultivated areas with at least 4 sq.km surfaces (equiv.of 4
pixels) using terrain information or high resolution satellitederived landcover maps;
• accurate localization of the reference points using GPS;
• extraction of NDVI values;
• analysis & interpretation.
 The monitoring of crop vegetation conditions and its
variation with time is based on the analysis of the dekadal
maps of NDVI;
 Begining from the last dekade of March 2005 these
maps have been included and analysed in the
Agrometeorological Bulletin;
 Generally, the winter crops status was better as
compared with previous year, especially for the crops sown
in optimal time, but a delay in crop development with about
1-2 weeks was observed.
Exemples of application remote sensing
technology for monitoring crop vegetation status
NDVI / Spot-Vegetation / 11-20 March 2005
NDVI / Spot-Vegetation / 1-10 April 2005
NDVI / Spot-Vegetation / 11-20 April 2005
NDVI / Spot-Vegetation / 21-30 April 2005
NDVI / Spot-Vegetation / 1-10 May 2005
Conclusions:

The examples given in this presentation shown that the
combination of ground-measurements, GIS and satellite
data gives promising results for the mapping soil moisture
to define agroclimatic potentialities and restrictions, and for
monitoring crop vegetation status;

The capability of GIS technology is significant, enabling
users to develop accurate and precise maps based on
quantitative data to be analysed in combination with other
sources of data;

The continues application of GIS and remote sensing
technologies to evaluate soil moisture and vegetation state
of
the
crops
will
contribute
to
improving
the
agrometeorological information in order to provide better
decision support in agricultural management;
Conclusions
 The assessment and near-real time monitoring of the risk
agrometeorological factors and their zoning over the
country’s agricultural territory, during vegetation season of
crops will allow timely identification of the agricultural
areas the most vulnerable and the dissemination of the
information to the users for taking adequate measures
(irrigation, fertilizing, agrotechnics to preserve the water in
the soil, etc.);
Thank you