REMOTE SENSING PRODUCTS IN AGRICULTURE AND DROUGHT MONITORING IN ROMANIA

Transcription

1 Ministry of Environment, Water and Forests NATIONAL METEOROLOGICAL ADMINISTRATION Şos. Bucureşti - Ploieşti nr. 97, sector 1, , Bucharest, ROMANIA Phone: / Fax: , relatii@meteoromania.ro / REMOTE SENSING PRODUCTS IN AGRICULTURE AND DROUGHT MONITORING IN ROMANIA Oana-Alexandra OPREA ROMANIA May 2016, NMS, Tbilisi, Georgia Training course on the use of satellite products for drought monitoring and agro-meteorological applications

2 NATIONAL METEOROLOGICAL ADMINISTRATION, ROMANIA LABORATORY OF AGROMETEOROLOGY DROUGHT MONITORING IN ROMANIA REMOTE SENSING PRODUCTS IN AGRICULTURE May 2016, NMS, Tbilisi, Georgia 2

3 NATIONAL METEOROLOGICAL ADMINISTRATION, ROMANIA May 2016, NMS, Tbilisi, Georgia 3

4 NATIONAL METEOROLOGICAL ADMINISTRATION, ROMANIA METEOROLOGICAL PROFILE National Meteorological Administration is the national authority in the meteorological field in Romania, with a continous service since NMA is subordinated to the Ministry of Environment and Forests (MEF), functioning on the basis of Law 216/

5 NATIONAL METEOROLOGICAL ADMINISTRATION, ROMANIA The National Meteorological Observation Network within the NMA is made up of 7 Regional Meteorological Centres / RMC. Romania is a founding member of the International Meteorological Organization (IMO), and beginning with 1948 it has become a full member of the World Meteorological Organization (WMO). 5

6 National Meteorological Observation Network of Romania 7 Regional Meteorological Centres; 159 weather meteorological stations, 126 being automatic (MAWS); 66 weather stations integrating a special program of agrometeorological measurements soil moisture and phenological data (winter wheat, maize, sunflower, rape, fruit trees and vineyards. METEOROLOGICAL NETWORK AGROMETEOROLOGICAL NETWORK 6

7 LABORATORY OF AGROMETEOROLOGY May 2016, NMS, Tbilisi, Georgia 7

8 Laboratory of Agrometeorology of NMA develops specialized products such as: 1. Basic products: -weekly, monthly and seasonal agrometeorological diagnoses/forecasts -agrometeorological dedicated reports 2. Specialized products (i.e. maps): parameters and maps of thermal vulnerability and risks at sub-regional level (temperature, sunstroke, tropical nights, hot days, etc); parameters of water stress at regional and sub-regional level (rainfall, ETP, atmospheric relative humidity, soil water shortage, precipitation deficit, etc); aridity indices (standardized at full network level). The weekly Agrometeorological Bulletin includes the specific information (air temperature, rainfall, ETP, soil moisture, crop water requirement) needed for assessment of drought occurrence. This data collected from the National Observation Network is analyzed and compared with the critical thresholds in order to evaluate the threat and make recommendations to decision-makers and farmers. Also, the soil moisture maps, weekly agrometeorological informations and seasonal forecasts which are updated daily according with the flow operational activity are free on the NMA web-page ( for informational and decisional purpose in terms of technological measures that can be applied in drought 8 conditions.

9 Laboratory of Agrometeorology of NMA The meteorological data (from synoptic meteorological database/oracle) processing and interpretation are made using specific applications, such as AGRO-SYNOP, AGROSERV and AGRO-TEMPSOL. The agrometeorological data represent specialized information coming from the network s weather stations with agrometeorological programme, representative for areas of agricultural interest in Romania. This information is corroborated with in-situ measurements of soil moisture and field observations of crop development stage and apparition of water stress to plants. After the information is collected and transmitted to NMA Centre in Bucharest, soil water balance is computed the crops water requirements and water stress are analyzed in order to assess the available water resources for crops. During a crop year are developed an average of 166 specialized maps that show zoning agrometeorological parameters (air and soil temperature, precipitation, soil moisture reserve, vegetation indices, etc.) for the entire agricultural area of the country May 2016, NMS, Tbilisi, Georgia 9

10 Soil Moisture in-situ measurements and GIS techniques During 2004 till present, the agrometeorological network was modernized, being endowed with specialized equipment such as 66 portable soil moisture measuring systems, in order to perform a current monitoring of the soil moisture reserves throughout the crops active vegetation period (March-November). The quantity of supplied water in soil is directly determined using the sensors in different observation points (agrometeorological platforms) representative for agriculture. The data collection is made every 10 days at the level of the Meteorological Services, by the agrometeorological specialists in the network, then transmitted via computer using the new SYSTEM SOFTWARE AGROMETEO to the Laboratory of Agrometeorology in order to carry out maps regarding the reserve (mc/ha) accessible to winter wheat and maize plants, at calendar dates of agricultural interest and at different soil depths (0-20 cm, 0-50 cm and cm). The Application for spatial representation (GIS) of agrometeorological parameters included the air and soil temperature, 10 precipitation and soil moisture modules.

11 MODULE Soil moisture May 2016, NMS, Tbilisi, Georgia 11

12 In agrometeorological operational activity using a number of parameters agrometeorological / agro-climatic risk / heat stress, atmospheric and hydrological that define, characterize and identify producing unique and / or complex agricultural drought. An Agrometeorological indicator of water stress very important is the supply of the soil moisture available to the crops. Soil water supply express the degree of soil per plant about the water requirement of the crop in specific characteristic data and on different soil depths (0-20 cm, 0-50 cm and cm) using a model of soil water balance. Classes of the soil moisture / %AWC % (Avaible Water Capacity) Extreme pedological drought / 0-20%AWC; Severe pedological drought / 20-35%AWC; Moderate pedological drought / 35-50%AWC; Satisfactory supply / 50-70%AWC; Almost optimum supply / 70-85%AWC; Optimal Supply / %AWC; Excess supply / >100%AWC. 12

13 STEPS and futher steps in agrometeorological operational activity: - EU Funding Period for and periods / Operational Sectoral Programme for Environment (POS-MEDIU) - NMA project: The development of the national system of monitoring and warning of extreme weather phenomena for the protection of life and property materials. - In the next period will be implemented the activities related of modernization of meteo and agrometerological networks: 1. Meteorological network 31 weather meteo stations (MWAS) in order to complete the automatic meteo network and dedicated software for processing data in automatic flow. 2. Agrometeorlogical network: - Modernization of agromet network / 25 soil moisture portable systems / new systems implemented within 5 November Windows Server /CISC x86 6-core - National data base platform / type SQL Server Modernization of applications in operational activity dedicated software for agrometeorological data and indicators (national level) 13

14 The conceptual scheme of SYSTEM SOFTWARE AGROMETEO has next components: Local level / agrometeorological station metadata National level web application Validation of data at regional level by 7 responsible with agrometeorological activity using a friendly web interface May 2016, NMS, Tbilisi, Georgia 14

15 SYSTEM ARCHITECTURE THE LOCAL APPLICATION NATIONAL APPLICATION May 2016, NMS, Tbilisi, Georgia 15

16 Manage and configuration platform Phenological data management Moisture management data View data Manage user accoun THE LOCAL APPLICATION May 2016, NMS, Tbilisi, Georgia 16

17 National AGROMETEO Application is a web-application based on a module dedicated to agro-meteorological responsables from each Regional Meteorological Centre (are users of regional type) Consolidate phenological reports Data correction Data validation Save data NATIONAL APPLICATION May 2016, NMS, Tbilisi, Georgia 17

18 Type of messages: - Phenology - Metadata - Soil moisture Soil moisture data May 2016, NMS, Tbilisi, Georgia 18

19 Agrometeorological web-software application / 80 INDICES AGROMETEOROLOGICAL INDICES May 2016, NMS, Tbilisi, Georgia 19

20 Laboratory of Agrometeorology of NMA NATIONAL PROJECTS Sectoral plan for research and development the Ministry of Agriculture and Rural Development / period ADER 2020 Geo-referenced system of indicators at different spatial and temporal scales for vulnerability assessment and agro-ecosystem adaptation measures to face the global changes. System of inventorying, monitoring and assessment of indicators on the agreement with the European directives of agri-environmental specific farms of semi- resistance. Geo-referenced database creation on regional climate risks for the main agricultural and horticultural crops for species of domestic animals. Risk assessment concerning the mycotoxin contamination of grain annual productions in Romania. The zoning varieties of species, rootstocks and fruit trees varieties basins, depending of climatic conditions and socio-economic. The portal for soil information `in mirror` to that achieved by Joint Research Centre in Europe (JRC). Informational system for agriculture and its compatibility with general cadastre S.I.A. 20

21 Laboratory of Agrometeorology of NMA INTERNATIONAL PROJECTS INTERREG IVC/WATERCoRe Project: Water Scarcity and drought Coordinated activities in European Regions, Interregional Cooperation Programme INTERREG IVC: Priority 2: Environment and risk prevention Water management Participant in the WMO Comission for Agricultural Meteorology (CAgM). Technical Meetings and Reports. SEE Project ORIENTGATE A structured network for integration of climate knowledge into policy and territorial planning ( ) Pilot Study 2: Climate change adaptation measures in Romanian agriculture. Green path to Sustainable Development Project ( ), Program RO07 Adaptation to climate change through grants SEE ; Project financed by funds provided of Iceland, Liechtenstein and Norway through European Economic Area Financial Mechanism EEA GRANTS IRIDA: Innovative remote and ground sensors, data and tools into a decision support system for agriculture water management Program ERA- NET Cofund Water Works 2014, Research and Innovation for Developing Technological Solutions and Services for Water Systems. 21 ERA4CS: ERA for Climate Services, Program EU H2020, ERANET.

22 Drought is a complex phenomenon, characterized by insufficient moisture in the atmosphere and soil in the root system and growth potential evapotranspiration. It can be studied from several points of view, namely meteorological, hydrological, agrometeorological, economic, environmental, etc. Drought affects primarily vegetal cover natural and anthropogenic, as some of the most aggressive risk phenomena impact on living conditions and the environment. Causes the complex, some pertaining to the climate change, especially as regards southern Europe, where the trend has already been noticed for diminished precipitation, which leads to diminished accumulated water resources. Experiments carried-out with climatic models have shown that this situation will worsen in future, especially in the southern and southeastern Europe, where the precipitation deficit will keep enhancing, in step with the global warming. Climate change predictions point to a warmer world within the next 50 years, yet the impact of rising temperatures on rainfall distribution patterns in much of the world remains far less certain 22

23 To have complex agro meteorological information it is necessary to improve the operational capabilities of monitoring using advanced remote sensing techniques and Geographic Information Systems (GIS). The use of remote sensing data has important advantages useful to monitor the effects of drought on vegetation: the information covers the entire territory, the repetition of images provides multitemporal measurements and vegetation indices derived from satellite data allow to identify areas affected by drought taking into account different types of vegetation and environmental conditions. Data sets provided by satellite systems can be used in global, regional or local studies, to obtain input data used to produce various models of energy balance, water balance, etc. Vegetation indices can be calculated from low spatial resolution data from different sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS), but because of low spatial resolution it is difficult to study a particular area of interest at a vegetation type level. High resolution data, like Landsat can be useful vegetation monitoring at local level. 23

24 INTERNET free access of meteorological forecasts and agrometeorological information Agrometeorological forecasts Warnings at national level and now-casting forecasts at local level Soil moisture maps - Seasonal forecasts (1-3 months) - Regional forecasts (2 weeks) - Notes on the drought evolution May 2016, NMS, Tbilisi, Georgia 24

25 DROUGHT MONITORING IN ROMANIA May 2016, NMS, Tbilisi, Georgia 25

26 Drought is a complex phenomenon, characterized by insufficient moisture in the atmosphere and soil in the root system and growth potential evapotranspiration. It can be studied from several points of view, namely meteorological, hydrological, agrometeorological, economic, environmental, etc. Drought affects primarily vegetal cover natural and anthropogenic, as some of the most aggressive risk phenomena impact on living conditions and the environment. Causes the complex, some pertaining to the climate change, especially as regards southern Europe, where the trend has already been noticed for diminished precipitation, which leads to diminished accumulated water resources. Experiments carried-out with climatic models have shown that this situation will worsen in future, especially in the southern and southeastern Europe, where the precipitation deficit will keep enhancing, in step with the global warming. Climate change predictions point to a warmer world within the next 50 years, yet the impact of rising temperatures on rainfall distribution patterns in much of the world remains far less certain 26

27 Drought monitoring system in Romania Agrometeorological and climatic drought indices : heat stress, soil moisture, standardized precipitation evapotranspiration index, etc / operationally activity Drought related-indices derived from remote sensing data / operationally and research activity - LAI / Leaf Area Index - NDVI / Normalized Differences Vegetation Index - NDWI / Normalized Difference Water Index - NDDI / Normalized Difference Drought Index - fapar / Fraction of Absorbed Photosynthetically Active Radiation Index Drought indices / research activity - DVI / Drought Vulnerability Index - DROGHT-ADAPT web platform May 2016, NMS, Tbilisi, Georgia 27

28 AGROMETEOROLOGICAL DROUGHT INDICATORS SCORCHING HEAT INTENSITY May 2016, NMS, Tbilisi, Georgia 28

29 AGROMETEOROLOGICAL DROUGHT INDICATORS Strong pedological drought Moderate pedological drought Satisfactory supply Almost supply Soil moisture in winter weat crop / 17 April May 2016, NMS, Tbilisi, Georgia 29

30 NDVI vegetation index image obtained by processing PROBA-V Extreme pedological drought Strong pedological drought Moderate pedological drought Satisfactory supply Soil moisture in winter weat crop / Maintaining heat stress and hydric from the air and soil Less dense vegetation (NDVI ) Rare vegetation (NDVI ) Rich and dense vegetation (NDVI ) May 2016, NMS, Tbilisi, Georgia 30

31 Drought Vulnerability Index for maize crop during the critical period for water plant needs (August) The most critical areas is recorded in the south, south-east and west regions 31

32 Drought vulnerability index (DVI) based on climatic variables DVI = W i KN, where: DVI = Drought Vulnerability Index N = Number of indicators under consideration W I = Weights of drought vulnerability indicators, where I = 1, 2.N k = Upper limit of vulnerability weights (e.g. scale = 0-k, where k is highest value of W I Drought vulnerability scales DVI Vulnerability Scales Color scale No or less vulnerability Low vulnerability Medium vulnerability High vulnerability Very high vulnerability Extreme vulnerability 32

33 Drought vulnerability component scale Vulnerability level No vulnerability Low Vulnerability High vulnerability Extreme vulnerability Scales Heat stress SPEI Soil Moisture 0 No <10 0 No deficit < No deficit 100%AWC stress 1 Low Low to -1 1 Low %AWC stress deficit deficit 2 Moderat e stress 3 Strong stress Moderate dry to -2 2 Moderate deficit >51 3 Very Dry <-.3 3 Strong deficit 35-65%AWC 0-35%AWC Heat stress SPEI Soil Moisture 33

34 CONSIDERATIONS on Drought Vulnerability Index (DVI) This approach is based on the combination of several climatic indicators over long periods of time (>30 years ). Also, these indicators based on climatic variables have major influences on plant vegetative processes. The climate variables such as air temperature, precipitation and evapotranspiration associated with soil data have a great influence on the aridization processes. The soil type and crop data are also important. In term of meteorological definition, a drought period is defined by a significant deficit in the rainfall regime. The heat waves produce thermal stress to plants even if water is not limited especially during the summer period. Pedological drought refers to a significant deficit in the soil moisture. For agriculture, drought is defined by parameters affecting crops growth and yield. All these type of drought affect agricultural production loss varying function of their intensity and duration. Vulnerability has been expressed as a function of exposure and intensity at different level in time and space. The approach is useful in evaluating the vulnerability of crop systems to drought and may help the decision makers to formulate more specific and targeted climate adaptation policies to reduce production losses in 34 agriculture.

35 REMOTE SENSING PRODUCTS IN AGRICULTURE May 2016, NMS, Tbilisi, Georgia 35

36 To have complex agro meteorological information it is necessary to improve the operational capabilities of monitoring using advanced remote sensing techniques and Geographic Information Systems (GIS). The use of remote sensing data has important advantages useful to monitor the effects of drought on vegetation: the information covers the entire territory, the repetition of images provides multitemporal measurements and vegetation indices derived from satellite data allow to identify areas affected by drought taking into account different types of vegetation and environmental conditions. Data sets provided by satellite systems can be used in global, regional or local studies, to obtain input data used to produce various models of energy balance, water balance, etc. Vegetation indices can be calculated from low spatial resolution data from different sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS), but because of low spatial resolution it is difficult to study a particular area of interest at a vegetation type level. High resolution data, like Landsat can be useful vegetation monitoring at local level. 36

37 In order to monitor the vegetation statement, high resolution satellite images have been used to obtain the dedicated vegetation indexes. These indexes are good indicators of drought and they are used also by the scientific community (European Drought Observatory). Landsat data for the period : Landsat 5 ( ); Landsat 8 ( ) The MODIS Surface Reflectance 8-Day L3 Global 500 m products (MOD09A1). Provides bands 1 7 at 500 m resolution in an 8-day gridded level-3 product in the sinusoidal projection. Science Data Sets provided for this product include reflectance values for Bands 1 7, quality assessment, and the day of the year for the pixel along with solar, view, and zenith angles. Pleiades data: 37

38 USING REMOTE SENSING DATA FOR DROUGHT MONITORING GIS database The GIS database contains info-layers in a relational structure, that are: sub-basins and basin limits; land topography (15m cell size DEM); hydrographic and canal networks; transport network (roads, railways); localities; administrative boundaries; agro meteorological stations; land cover/land use, updated from satellite images 38

39 The land cover / use categories of the test area based on the CLC database: cities/villages, arable land, pastures, vineyards, forests and semi-natural areas, wetlands, water). Land cover/use categories over the Caracal study area Detailed land cover/use of the test area based on Pleiadés image of 10 May The unsupervised image classification, followed by classes regrouping finally led to 6 main land cover / use classes: winter crops (wheat), summer crops (corn, sunflower), pastures, barren soil, urban and water. 39

40 The Normalized Difference Vegetation Index (NDVI) is a non-linear transformation of visible bands (Red) and near infrared (NIR), being defined as the difference between these two bands divided by their sum:ndvi= NIR-VISNIR+VIS NDVI is a "measure" of development and vegetation density and is associated with biophysical parameters as: biomass, leaf area index (LAI), used widely in crop growth models, the percentage of vegetation cover of the land, photosynthetic activity of vegetation. NDVI values range from -1.0 to 1.0, with negative values indicating clouds and water, positive values near zero indicating bare soil, and higher positive values of NDVI ranging from sparse vegetation ( ) to dense green vegetation (0.6 and above). 40

41 USING REMOTE SENSING DATA FOR DROUGHT MONITORING Vegetation indices NDVI is an indicator of presence, density and health of vegetation compared to a pixel (1km 2 ); the positive values are colored in shades of green to dark green and negative values are colored in shades from yellow to brown, indicating a lack of vegetation or bad health The NDVI spatial distribution obtained from MODIS data (MOD09A1) (droughty year) 41

42 USING REMOTE SENSING DATA FOR DROUGHT MONITORING Vegetation indices NDVI is an indicator of presence, density and health of vegetation compared to a pixel (1km 2 ); the positive values are colored in shades of green to dark green and negative values are colored in shades from yellow to brown, indicating a lack of vegetation or bad health The NDVI spatial distribution obtained from MODIS data (MOD09A1):

43 Vegetation Indices for crops water stress monitoring NDWI The Normalized Difference Water Index (NDWI) is a satellite-derived index from the Near-Infrared (NIR) and Short Wave Infrared (SWIR) reflectance channels: Where: SWIR and NIR are spectral reflectance from short wave infrared band and near-infrared regions, respectively. NDWI values range from -1.0 to 1.0. The common range for green vegetation is -0.1 to 0.4. This index increases with vegetation water content or from dry soil to free water. NDWI index is a good indicator of water content of leaves and is used for detecting and monitoring the humidity of the vegetation cover. During dry periods, the vegetation is affected by water stress, which influence plant development and can cause damage to crops. Because it is influenced by plants dehydration and wilting, NDWI may be a better indicator for drought monitoring than NDVI. By providing near real-time data related to plant water stress, the water management can be improve, particularly by irrigating agricultural areas affected by drought, according to water needs. 43

44 USING REMOTE SENSING DATA FOR DROUGHT MONITORING Vegetation indices NDWI index is a good indicator of water content of leaves; the positive values (NDWI > 0.3) are colored in shades of green to dark blue and negative values (NDWI < 0.2) are colored in shades from light green to brown, indicating vegetation affected by water stress The NDWI spatial distribution obtained from MODIS data (MOD09A1) (droughty year)

45 USING REMOTE SENSING DATA FOR DROUGHT MONITORING Vegetation indices NDWI index is a good indicator of water content of leaves; the positive values (NDWI > 0.3) are colored in shades of green to dark blue and negative values (NDWI < 0.2) are colored in shades from light green to brown, indicating vegetation affected by water stress The NDWI spatial distribution obtained from MODIS data (MOD09A1)

46 Vegetation Indices for crops water stress monitoring NDDI The Normalized Difference Drought Index (NDDI) NDDI is a relatively new superior drought indicator. It is calculated as the ratio of the difference between the normalized difference vegetation index and normalized difference water index and their sum: It combines information from visible, NIR, and SWIR channel. NDDI can offer an appropriate measure of the dryness of a particular area, because it combines information on both vegetation and water. NDDI had a stronger response to summer drought conditions than a simple difference between NDVI and NDWI, and is therefore a more sensitive indicator of drought. This index can be an optimal complement to in-situ based indicators or for other indicators based on remote sensing data. 46

47 NDVI over the Caracal study area issues from the Pleiades images 10 May July August

48 NDVI, NDWI, NDDI over the Caracal study area estimated from Landsat 8 data 48

49 CONCLUSIONS The vegetation indexes extracted from satellite images, correlated with meteorological and agro-meteorological information, are good indicators of vegetation condition, and they are relevant for monitoring the beginning, duration and intensity of drought. Remote sensing techniques can enhance and improve the drought analysis, especially considering the scarce availability of measured ground truth data. The advantage of multi-annual imagery availability allows the overlay and cross-checking of doughty, normal or rainy years. Study of the potential of the new SANTINELS satellite mission for drought monitoring 49

50 CONCLUSIONS Study of the potential of the new SANTINELS satellite mission for drought monitoring Climate is the ensemble of meteorological processes and phenomena specific to a geographical region. The management and sustainable development decisions should aim to specialize the agricultural production by growing in each region the appropriate crops that have the largest benefit from the natural potential for agriculture, which is evaluated through analysis of pedoclimatic conditions. 50

51 INTERNET free access of meteorological forecasts and agrometeorological information Agrometeorological forecasts Warnings at national level and now-casting forecasts at local level Soil moisture maps - Seasonal forecasts (1-3 months) - Regional forecasts (2 weeks) - Notes on the drought evolution May 2016, NMS, Tbilisi, Georgia 51

52 Oana-Alexandra OPREA /Laboratory of Agrometeorology and Argentina Teodora NERTAN /Remote Sensing & GIS Department დიდი მადლობა ყურადღებისთვის! Thank you for your attention! ; 52