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Monitoring of crop growth and development conditions in Romania using integrated

weather and satellite-derived data  

Dr. G. STANCALIE, Dr. A. MARICA, Dr. E. SAVIN,

A. NERTAN, C. FLUERARU National Meteorological Administration

Bucharest, RomaniaCAgM Expert Team on Weather, Climate and Farmer, Geneva, 15 - 18

November 2004

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IntroductionIntroduction

The agrometeorological activity undergoing within National Meteorological Administration (NMA), integrates complex issues concerning the current and future evolution of the vegetation state of the crops and water supply of soils with respect to the meteorological parameters evolution, being a particularly important activity whose final objective is to elaborate/edit the agrometeorological bulletins and disseminate information at the level of the decision making factors in agriculture and private farmers.

Since orbital sensing technologies have undergone unprecedented development, the use of multispectral satellite data in conjunction with traditional means is able to ensure the improvement of the classical determination methods of the agrometeorological parameters, greatly contributing to improve management of agrometeorological phenomena, like the drought. The paper presents the important modernization achievements of the Romanian meteorological infrastructure, obtained by the implementation of the National Integrated Meteorological System (SIMIN project) and the development trend of the Romanian agrometeorological monitoring system. The new possibilities for the monitoring of crop growth and development conditions based on integrated weather and satellite-derived data are also presented.

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The Actual Romanian The Actual Romanian Agrometeorological Monitoring SystemAgrometeorological Monitoring System

AGROMONITORING- Module of reception, selection and extraction of meteorological data for interested stations and parameters (Synop Programme)- Module of high processing of data on information structures (air temperatures, soil temperatures, precipitations, evapotranspiration, soil humidity);- Module of data storing and manipulation used as input for agrometeorological- models

Processed data (outputs):- Agrometeorological indices (thermal and hydric)- Monotoring of the vegetation state of the crops - Dynamic of the soil humidity (deficits/excess)

- Potential and real evapotranspiration - Elaboration/editing agrometeorological bulletins

Agrometeorological stations

- Primary data:- Agro-meteo observations and measurements. - Automatic data pick-up - Data primary processing

Meteorological stations Sinoptic/Automatic

- Air temperatures min./max. - Precipitations, - Relative humidity- Sunshine duration- Wind speed

Real-time monitoring of meteorological and agrometeorological

parameters

ServerMeteorological database

(Oracle)

ServerAgrometeorological

database

DISEMINATION

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The improved integrated support system for the agrometeorological warning and the identification

of the areas with agricultural risk in Romania

In the last period an important modernization of the Romanian meteorological infrastructure, based on high technology, has been achieved by the implementation of the National Integrated Meteorological System (SIMIN project), conceived as an integrated system, receiving and processing the data coming from multiple observation sub-systems (surface, radar, satellite sub-systems, etc.) and a lot of types of equipment. Within this project several systems with major significance have been provided: meteorological DOPPLER Radar Network, automatic Weather Network, lightning detection network, satellite reception stations for Meteosat Second Generation, telecommunication networks, visualization system (Nex-REAP) integrating all available information, etc.

The main benefit of this investment is relevant for weather forecasting, allowing the detection, surveillance and anticipation of severe meteorological phenomena, which have also an important impact on the agriculture. SIMIN includes a quasi-real time dissemination components of the meteorological information, so that the local authorities and the other users could be promptly informed about the occurrence of severe meteorological events, being able to take, in time, the necessary steps to avoid disasters and damages.

5Highly motivated decisionsHighly motivated decisions

Avoiding parallel expensesAvoiding parallel expensesA useful systemA useful system

Infrastructure improvementInfrastructure improvementA major contributionA major contribution

High precisionHigh precision

Prompt detectionPrompt detection

Efficient anticipationEfficient anticipation

Permanent surveillancePermanent surveillance

Extended parametricsExtended parametrics

Hazard reducingHazard reducingA vital serviceA vital service

Performance and reliability growthPerformance and reliability growthA national demandA national demand

Life and property protectionLife and property protectionA public interestA public interest

GENERAL GENERAL OBJECTIVESOBJECTIVES

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Module 1: Module 1: Surface measurement networkSurface measurement networkModule 1: Module 1: Surface measurement networkSurface measurement network

LEGEND

Weather station

Automatic weather stationSIMIN automatic weather station

60 new automatic weather stations, out of which 46 with soil

temperature sensors

COMPONENTSCOMPONENTS

53 weather stations with agrometeorological

program

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Module 3: Module 3: Satellite numerical dataSatellite numerical dataModule 3: Module 3: Satellite numerical dataSatellite numerical data

COMPONENTSCOMPONENTS

METEOSAT/NOAA data METEOSAT/NOAA data reception and processing stationreception and processing station

• MET 7 (operational program )MET 7 (operational program )

• MSG (next generation)MSG (next generation)

METEOSAT/NOAA data METEOSAT/NOAA data reception and processing stationreception and processing station

• MET 7 (operational program )MET 7 (operational program )

• MSG (next generation)MSG (next generation)

BucharestBucharest

• Continue data (Continue data (5 Km resolution5 Km resolution))• Continue data (Continue data (5 Km resolution5 Km resolution))

• Permanent data (every 30 min)Permanent data (every 30 min)• Permanent data (every 30 min)Permanent data (every 30 min)

• Panoramic (hemispheric) dataPanoramic (hemispheric) data• Panoramic (hemispheric) dataPanoramic (hemispheric) data

• Multispectral (3 channel) dataMultispectral (3 channel) data• Multispectral (3 channel) dataMultispectral (3 channel) data

8SIMIN COMMUNICATION INFRASTRUCTURE DESIGNSIMIN COMMUNICATION INFRASTRUCTURE DESIGN

NMA

SATELLITE COMMUNICATIONS

DATA, VOICE, FAX,

E-MAIL, VIDEO

Module 7: Module 7: Specialized communication networkSpecialized communication networkModule 7: Module 7: Specialized communication networkSpecialized communication network

LOCAL NETWORK

SATELLITE COMMUNICATION

NETWORK

RADAR S R P

C O F

COMPONENTSCOMPONENTS

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National Forecasting Centre

- Bucharest - ROU

Regional Forecasting Centres

- Bucharest - MUN - Constanta - DOB - Bacau - MOL - Cluj - TRN - Sibiu - TRS - Arad - BAC - Craiova - OLT

MUN

TRN

DOBOLT

TRS

BACMOL

Bucharest

Module 6.1: Module 6.1: Forecasting networkForecasting networkModule 6.1: Module 6.1: Forecasting networkForecasting network

11 T

MS an

d 41 C

MS

11 T

MS an

d 41 C

MS

connec

tions

connec

tions

Territorial Meteorological Station (TMS) • County Meteorological Station (CMS)

COMPONENTSCOMPONENTS

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Interoperable System for Meteorology

DATA PROCESSING

DECISION PRODUCTS ELABORATION

IP TELECONF

COF

DATA PROCESSING DECISION PRODUCTS

ELABORATIONTC

TC

DATA BASE and CLIMATOLOGY

RFC

-METCOMPUTEData processing and

data storage

USERS:Strategicprivate

Automated weather stationsand actual weather reports

Automated weather stations

Dedicated automaticagro-meteostations

Automated air qualitystations (fixed and mobile)

TC

TC

MODELING

TC

TC

SIMIN

INTEROPERATE

ISS CLIMA

ISS AGRO

END PRODUCTSWarnings & ForecastsStudiesRisk mapping

MET TRAINExcellence training Center Workshop, seminars, conferences at national and international level

LEGENDRFC – Reg.forecast centerCOF – Center Operating FacilitiesTC – Communication terminalISS – Integrated Support System

Satellite multi-mission receiving stationNOAA-AVHRR

SPOT-VEGETATIONTERRA-MODIS

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The information flowchart of the integrated support The information flowchart of the integrated support system for agrometeorological warning and system for agrometeorological warning and identification of agricultural hazard areasidentification of agricultural hazard areas

Satellite multi-mission receiving stationNOAA-AVHRRSPOT-VEGETATIONTERRA-MODIS

Agrometeorological Stations (fixed and mobile)

Automatic meteorological

stations

Regional Meteorological Centers

Subsystem for the management of the agrometeorological database

- Main function: data storage, analyzing and data updating. - The input data for the analysis procedures will allow to determine a certain number of hazard indicators of the territory in various agro-climatic conditions.

ServerUsers- strategic - private

Subsystem for the management of the thematic, cartographic database

-The GIS info-plans and the required information to a structural analysis of the territory.

-The GIS database can be personalized and completed through introducing new layers in view to update meta-data associated to each thematic info-plan.

Subsystem for displaying the structural vulnerability- Better knowledge of the agroclimatic vulnerability structure;- Crisis management and long term planning having in view the definition of the vulnerability context and its dynamics at various spatial scales (regional, national);- Elaborated results in map-drawing format.

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Agrometeorological database interrogation

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Useful Satellite Sensors Useful Satellite Sensors for Afor Agrometeorological

MonitoringMonitoring NOAA-AVHRR (optical, 4 or 5 channel, broad band scanner, visible to

thermal IR, 2400 km swath, 1 km at nadir); Terra/Aqua-MODIS (optical, 250 m, 1330 km swath, 36 spectral bands:

an imaging spectrometer, same orbit as Landsat 7, sees every point on Earth every 1-2 days);

SPOT/VEGETATION (Optical, 1km, broad band scanner, visible to near IR, spectral bands, synthesis 10-days products);

Landsat-ETM+ (optical, visible and near infrared (VNIR) bands - bands 1,2,3,4,and 8 (PAN) with a spectral range between 0.4 and 1.0 micrometer; 15 m (PAN only) and 30 m resolution, Swath width is 185 km);

Terra/Aqua-ASTER (optical, 10 m, 14 channels, swath of 60 km at nadir, spatial resolution: VNIR-15 m,  SWIR-30 m, TIR-90 m).

MED

IUM

& H

IGH

RES

OLU

TIO

N

AMSR-E (radar, 6 frequency channels (6.9 to 89.0 GHz), spatial resolution 5.4 to 56 Km, swath 1445 Km); SeaWinds-QuikSCAT (Ku-band active radar scatterometer, resolution 25 km x 25 km, swath of 1800 km (for a vertical polarization at an incidence angle of 54o) or 1400 km (for a horizontal polarization at incidence angle of 46o), visit period every 2.5 days regardless of cloud cover.

CO

AR

SE

RE

SO

LUTIO

N

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Assimilation of remotely sensed data into numerical prediction models (e.g. SWAT, crop models)

Prediction Monitoring and early warning Assessment of impacts for extreme meteo events

Land use type Intensity and areal extent Use of satellite data as input for crop model yield estimates.

Earth Observations from satellites are highly complementary to those collected by in-situ systems.

Satellites are often necessary for the provision of synoptic, wide-area coverage and provision of the frequent information required to put in-situ information into broader spatial monitoring of drought conditions.

CRAIOVA

00.5

11.5

22.5

33.5

44.5

55.5

ET

c (m

m/d

ay)

-50

-40

-30-20

-10

0

10

2030

40

50

Re

l. e

rro

r (%

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ETc(CROPWAT)

ETc(RS)

Rel. error (%).

Satellite – Derived Information for Satellite – Derived Information for Agricultural MonitoringAgricultural Monitoring

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Satellite Data Assimilation into Satellite Data Assimilation into Soil-Vegetation-Atmosphere Transfer Soil-Vegetation-Atmosphere Transfer

(SVAT)(SVAT) or Crop Modelsor Crop Models

Forcing technique

Estimating model state variables from remote sensing data, and directly introduce (force) them into the model at occurrences compatible with the time step of the model.

Correcting technique

Correcting the course of model dynamic variables by comparing them to remote sensing measurements.

Sequential assimilation techniques

The model is updated each time an observation is available. This observation is usually used for updating model prognostic variables.

Variational assimilation techniques

All of the available observations for a certain period of time are processed together. They are usually used for retrieving model parameters or initial values of model variables using model calibration techniques, based either on iterative algorithms or stochastic methods.

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Satellite – Derived Information for Satellite – Derived Information for AgrometeorologyAgrometeorology

The geo-referenced information, obtained from optical and radar images could be used in determination of certain parameters required for agrometeorology and for agricultural drought monitoring:

• the land surface temperature;• the actual evapotranspiration;• land (vegetation) albedo• soil moisture;• snow cover;• fraction of green vegetation, land cover/land use features; • vegetation state;• biophysical crop parameters (LAI, FPAR, etc.)

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LLand and SSurface urface TTemperatureemperature (LST) (LST)

For agrometeorology and vegetation monitoring it is important to have access to reliable estimates of surface temperature over large spatial and temporal scales.

As it is practically impossible to obtain such information from ground-based measurements, the use of satellite measurements in the thermal infrared (TIR) can give access to global and uniform estimates of surface temperature.

The TIR signature received by satellite sensors is determined by surface temperature, surface emissivity/reflectivity, atmospheric emission, absorption and scattering actions upon thermal radiation from the surface and the solar radiation in daytime.

The LST is associated with the thermal emission from the landscape “surface” including the top of the canopy for vegetated surfaces as well as other surfaces such as bare soil. Incoming solar radiation, but also variables associates with the substrate and atmospheric conditions, such as thermal inertia, soil moisture and albedo, mostly determine surface temperature response. Over vegetated surfaces, the surface canopy temperature is also indirectly controlled by available water in the root zone and more directly by evapotranspiration.

Satellite-derived LST have a lot of applications such a evapotranspiration estimation, frost detection, monitoring water-stress in crops and thermal inertia studies and may be used to improve agrometeorological models.

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LST derived from NOAA/AVHRR by the

« split window” split window” methodmethod

17, July 1999

6 July 2000

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Comparison between the LST (oC) distributions on the Romanian Plaines zones, on June, 7, 1999 (normal agricultural

year) and June, 4, 2000 (very drought year)

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Particular advantage of satellite RS:• Not influenced by water re-distribution (e.g., irrigation);• Strong at phenology;• Good accuracy;• Good spatial coverage;

E.g. AQUA/MODIS: - Frequent global coverage (every day + combination with

Terra/MODIS)- Afternoon overpass … dry condition becomes clearer than

morning- Moderate spatial resolution (1km) cf. Microwave remote sensing- High-precision of temperature and vegetation indices - Albedo and emissivity are available. - Atmospheric information is available from other sensors on Aqua

EvapotranspirationEvapotranspiration ((ETET))

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Method for the Assessment of Method for the Assessment of the Crop Actual ET Using RS the Crop Actual ET Using RS

DataData

The method used for the computation of daily actual crop evapotranspiration, (ETcj), is based on the energy balance of the surface expressed in two simple versions.

The method uses the connection between evapotranspiration, net radiation and the difference between surface (Ts) and air (Ta) temperature measured around 1400 hrs. L.T. (the time of the satellite passage, e.g. NOAA-AVHRR).

The air temperature around local noon is well approximated by the daily air temperature maximum (Tamax)

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Evaporation Fraction - EFEvaporation Fraction - EF

Potential for coupling with Terra-MODIS (AM overpass)

Q

ETEF GRQ n

Net radiation (radiation absorbed on the land)

Ground heat transferAvailable Energy

- Fractional value is representative for “wetness”.

- Scalability of instantaneous observation to longer period.

- Accurate estimate of Q is difficult.

From: Crago, 1996, “Scaling up in Hydrology using Remote Sensing”

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- Vegetation Index …NDVI or EVI (S)

- Temperature on vegetation: Tveg (S)

- Incoming solar radiation: PAR (T)

-VI-Ts diagram (S)

Note: (S) …. Derived from Satellite

(T) …. Estimated theoretically

Algorithm for EF Algorithm for EF EvaluationEvaluation

Qveg Qbare

EF = fveg EFveg + (1 – fveg) EFbare

Q Q

- radiative transfer of atmosphere (T)

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LST derived from NOAA AVHRR

image of 6 July 2000

Actual evapotranspiration estimated from the NOAA AVHRR

Image of 6 July 2000

20 25 30 35 40 45 C

1 2 3 4 5 mm

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Spectral Vegetation IndicesSpectral Vegetation Indices

The Normalized Difference Vegetation Index (NDVI) is defined as:

NDVI = [(red – NIR)/(red + NIR)]

Where: red and NIR are the sensor’s channel operating in the red or NIR spectral region;

The NDVI could be used to follow seasonal dynamics of vegetation. Inferences can be made regarding phenology and crop growth development, analyzing the temporal shape of the NDVI. Parameters such as beginning of leaf growth, green peak, growing season could also be estimated based on temporal profile analysis.

Spectral Vegetation Indices (SVI) are various linear and non-linear combinations of spectral bands, maximizing sensitivity of the indices to the canopy characteristic of interest (e.g., Fraction of Photosynthetically Active Radiation Absorbed (Fapar)) while minimizing the sensitivity to the unknown and unwanted canopy characteristic (e.g., background reflectance).

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The Modified Soil Vegetation Index (MSAVI) is defined as:

MSAVI = [(red – NIR)/(red + NIR + A)](1 + A)Where: red and NIR are the sensor’s channel operating in the red or NIR sectral

region; A is a self-adjusting factor defined to adapt the soil noise correction to

the proportion of soil seen by the sensor, given by the expression:

A = 1 – [2(NIR – red)/(NIR + red)]x(red – 1.06NIR)

MSAVI is less sensitive to soil brightness variations including shadows than other spectral vegetation indices.

The soil heat flux (G), is a significant component of net radiation. For bare soil, the relationship between the net radiation Rn and G depends on the surface soil moisture, while for vegetated surface, the ratio G/Rn can be obtained from visible and near-infrared reflectance. G could be formulated in terms of the MSAVI as:

G/Rn = 0.50 exp(-2.13 MSAVI)

Observations: The expression between Rn and G is valid only for clear sky conditions.The relationship between net radiation and soil heat flux depends on the type of surface (bare versus vegetated surface) and on the distribution of the vegetation within the surface.

Spectral Vegetation Indices Spectral Vegetation Indices (cont.)(cont.)

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The soil moisture index (SMI), useful to characterize the actual drought or desertification status of the ground, is defined as:

SMI = LE / LEp 1.25 LE/In

Where:LE is the actual evapotranspiration; Lep is the potential evapotranspiration; In is the net radiation.

The Stress Degree Day (SDD) is defined by:

SDD = (Ts - Ta)

Where: Ts is the soil surface temperature, Ta is the air temperature.

SDD index adds the daily hydric deficits, expresses by (Ts - Ta), offering good

information about the soil water deficit and related early crop stress, for whole

the vegetative period. ( (Ts - Ta) 0, for a surface with water deficit).

The Water Deficit Index (DFI), is based on a daily relationship between

(Ts - Ta) and the vegetation cover degree of the terrain expressed by the NDVI,

vegetation index.

Spectral Vegetation Indices Spectral Vegetation Indices (cont.)(cont.)

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Color composite image

NOAA AVHRR 6 July 2000

NDVI derived from NOAA AVHRR of 6 July 2000

clouds

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>0.2>0.1 >0.3 >0.4 >0.5cloud

NDVI 6 June 2000 NDVI 6 June 2000 –agricultural zones

Vegetation state monitoring using NDVI derived Vegetation state monitoring using NDVI derived

from NOAA AVHRR imagesfrom NOAA AVHRR images

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SPOT/VEGETATION - derived products SPOT/VEGETATION - derived products ApplicationsApplications

Applications of NDVI values derived from S10 Spot-Vegetation imagery:

analysis of multi-year NDVI values for various locations; deriving complex indices: MSAVI (Modified soil advanced vegetation index), VCI (Vegetation crop index), etc.; correlations with various agro-meteorological parameters using datasets provided by agro-meteo models and agro-meteo observation platforms.

VGT-S10 products (ten day synthesis) are compiled by merging segments acquired in a ten days. These products provide data from all spectral bands, the NDVI and auxiliary data on image acquisition parameters. A MVC synthesis can be obtained with several spatial resolution (1*1 km2 or 4*4 km2 or 8*8 km2).

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SPOT-VEGETATION - SPOT-VEGETATION - Normalized Difference Vegetation IndexNormalized Difference Vegetation Index (NDVI) (NDVI)

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SPOT-VEGETATION - SPOT-VEGETATION - Normalized Difference Vegetation IndexNormalized Difference Vegetation Index (NDVI) (NDVI)

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IIntegrated ntegrated agro-meteorologicalagro-meteorological and and satellite-derived datasatellite-derived data for crop condition for crop condition

monitoringmonitoring

CALARASI

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Ian Feb Mar Apr Mai Iun Iul Aug Sep Oct Nov Dec

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Soil moisture (% of TAM) -2003 NDVI 2003

Soil Moisture Deficit - CROPWAT(mm)

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Soil moisture measured data and SPOT-VEGETATION - NDVI values for maize condition monitoring in 2003 in 2

test-areas situated in the Romanian Plane.

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NDVI 2003 CalarasiCraiova

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IIntegrated ntegrated agro-meteorologicalagro-meteorological and and satellite-derived datasatellite-derived data for crop condition for crop condition

monitoringmonitoring

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NDVI 2003 NDVI 2004Soil Moisture 2003 Soil Moisture 2004

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MSAVI derived from SPOT-Vegetation 10 days MSAVI derived from SPOT-Vegetation 10 days composite data on Romaniacomposite data on Romania

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FFraction of raction of GGreen reen VVegetationegetation, , Land Cover/land Use Land Cover/land Use FeaturesFeatures

Landsat-TM from 12.06.2000 on the Oltenia Plaine (Caracal)

ASTER from 4.10.2000 on Baragan Plaine (Fundulea)

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Snow cover derived from AQUA/MODIS Snow cover derived from AQUA/MODIS 8 days composite data8 days composite data

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ConclusionsConclusions

The new investments in the Romanian meteorological integrated system will contribute to improving the monitoring system of the agrometeorological parameters, using highly efficient methodologies and techniques (mathematical modeling, GIS, remote sensing), in order to evaluate the vegetation state of the crops, of the moisture and soil water deficit dynamics, for the optimization of the agricultural management.

The results, such as the agrometeorological bulletins (diagnosis and forecasts) will be disseminate via Internet at different customers including the decision making factors in agriculture, extension services, insurance companies, farmers, media, etc.

The assessment and quasi-real time monitoring of the risk agrometeorological factors (drought, heat, water excess/deficit in the soil, etc.) 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 (farmers, decision makers) for taking adequate measures (irrigation, fertilizing, agrotechnics to preserve the water in the soil, etc.).