IPC. Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 1. The Integrated Food Security Phase Classification

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1 IPC The Integrated Food Security Phase Classification Guidelines for using Remote Sensing Derived Information in support of the IPC Analysis Rembold F.*, Korpi K.*, Rojas O.* 1 *Joint Research Centre of the European Commission, Via Fermi 2749, Ispra, Italy 1 Valuable feedback on the guidelines was also received from René Gommes, FOODSEC JRC Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 1

2 GUIDELINES FOR USING REMOTE SENSING DERIVED INFORMATION IN SUPPORT OF THE IPC ANALYSIS Rembold F.*, Korpi K.*, Rojas O.* 2 *Joint Research Centre of the European Commission, Via Fermi 2749, Ispra, Italy 1. Context and objective This document aims at illustrating the usefulness of remotely sensed vegetation indexes and rainfall estimates in food security analysis based on the IPC (Integrated Food Security Phase Classification) approach. Its general purpose is to increase the common understanding and improve the best use of remote sensing derived indicators by the different experts (socio-economists, agronomists, nutritionists etc.) involved in the IPC processes. As such it is primarily addressed to IPC country practitioners, although food security analysts in general could also find it useful. One of the main constraints faced by food security analysts is the scarcity of good quality data on different dimensions of food security. This general problem for food security analysis is also affecting the IPC process where for many of the key reference outcomes input data availability and quality limitations present a major problem. Remote sensing data are in a way an exception to this situation. Near real time data on vegetation activity and rainfall are available for practically all regions of the world at different scales and time frequency. Moreover, a number of institutions and programs are engaged in their analysis and distribution to countries that are prone to food insecurity. On the other hand it is clear that remote sensing data should be used mainly as indirect evidence where sufficient direct evidence on key reference outcomes 3 (mainly food availability) are not available, or where remote sensing information can be used to support other key reference outcomes such as livelihood assets. Also, remote sensing data should never be used without detailed accompanying metadata and ground data, clearly 2 Valuable feedback on the guidelines was also received from René Gommes, FOODSEC JRC 3 For definitions of key reference outcomes and indirect evidence please refer to the IPC technical guidance, i.e. the manual Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 2

3 described legends and information on the reliability of the derived products and maps. The basic requirement for the use of this guide is to be familiar with the IPC and to understand the basic concepts on remote sensing that are provided in section 2 hereafter. The guide is mainly inspired by the MARS (Monitoring Agricultural ResourceS) unit experience with the use of remote sensing derived indicators for crop monitoring in Eastern African and should enable its users to easily interpret rainfall information and vegetation indexes in the framework of an IPC analysis. However, a link is made with numerous other institutions and teams both for data retrieval and methodological aspects which go clearly beyond the crop monitoring experience of the MARS project. This document is a technical contribution of the JRC in the framework of the global IPC partnership with involves 8 implementations agencies: FAO, WFP, SC UK and US, CARE, Oxfam, FewsNet and JRC). Any comments and suggestions for improving this guide are welcome and should be sent to marsfood@jrc.ec.europa.eu 2. Basic concepts about vegetation indexes and rainfall estimates 2.1. Use of satellite images for vegetation monitoring Agricultural vegetation develops from sowing to harvest as a function of meteorological driving variables (e.g. temperature, sunlight, and precipitation). The growth is further modified by soil characteristics, plant material (genetics), crop management etc. As changes in crop vigor, density, health and productivity affects canopy optical properties, crop characteristics have been monitored by the use of satellite images since the early days of remote sensing. Satellite images can play a role in providing information about crop stage, crop conditions and crop yield from the field level to extended geographic areas. The large spatial coverage and high temporal revisit frequency 4 of low resolution satellite images 5 makes them particularly useful for near-real time information collection at regional scale. Such information is required in many domains. For example, national and international agricultural agencies, insurance companies and international agricultural boards require maps of crop type to prepare inventories about what was grown in certain areas and when. Commodity brokers and governmental agencies are interested in crop yields and cropped surfaces since 4 Revisit frequency is defined as the interval of time needed by a satellite to complete its orbit cycle, i.e. to pass over the same point on the ground 5 Low resolution is normally used for pixel size of 1 km or more Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 3

4 global trading prices of agricultural commodities depend largely on their seasonal production levels. Finally, international humanitarian agencies rely on early and reliable information on crop production to organize emergency response and food assistance interventions. The relationship between the spectral properties of crops and their biomass/yield was recognized since the very first spectrometric field experiments. The use of spectral data was studied extensively by using satellite imagery after the launch of the first civil Earth observation satellite (Landsat1) in But it is only with the growing availability of low resolution satellite images from the meteorological satellite series NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) in the early 80 s, that similar analyses are extended to large areas, including many countries in arid and semiarid climates. Thanks to their large swath width 6, low resolution systems have a much better synoptic view and temporal revisit frequency compared to high resolution sensors. The individual scenes span a width of up to 3000 km, such that the entire Earth surface is scanned every day and the specific costs per ground area unit are very low. The intrinsic drawback of these sensors is of course related to their low spatial resolution, with pixel sizes of about 1 km², i.e. far above typical field sizes. As a consequence, recorded spectral radiances are mostly mixed information from several surface types. This seriously complicates the interpretation (and validation) of the signal as well as the reliability of the derived information products. However, for crop monitoring, early warning and yield forecasting at the national scale a 1 km² resolution is quite suitable. Table 1 resumes the properties of the most common optical low and medium resolution sensors used for vegetation monitoring. Table 1: Properties of the most common optical low and medium resolution sensors used for vegetation monitoring Sensor Platform Spectral range AVHHR SEAWIFS NOAA POES 9-14 Orbview- 2 VIS, NIR, MWIR VIS, NIR Number of bands Resolution Swath width Repeat coverage Launch m 2400km 12 hours m 4500m 1500km 2800km 1day A satellite swath is the area on the earth surface observed by a sensor while moving along its orbit. Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 4

5 VEGETATION SPOT 4, 5 MODIS EOS AM1/PM1 VIS, NIR, SWIR VIS, NIR, SWIR, TIR MERIS ENVISAT VIS, NIR m 2200km 1day m m (1200m) 2330km <2days km <3days Vegetation indexes Vegetation indexes are mathematical transformations of the original multispectral data which are aimed at enhancing the information about vegetation properties while reducing the effect of external factors (mainly atmospheric and soil effects). The basic principle of the vegetation indexes is the strong absorption of light by leaf pigments in the visible (mainly red) range of the spectrum and on the high reflectance of leaf mesophyll in the near infrared range. This strong reflectance difference between the red and the near infrared channels is typical for vegetation and allows discrimination from other targets such as soil or water. Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 5

6 Figure 1. Variation of reflectance for different ground targets (called also spectral signature). NB: vegetation indexes are based on the large difference of green vegetation and soil reflectance between the red and near infrared wavelengths of electromagnetic radiation. Among the different VIs based on these two spectral channels, it is the NDVI (Normalized Difference Vegetation Index), which is the most popular indicator for studying vegetation health and crop production. Research in vegetation monitoring has shown that NDVI is closely related to the LAI (leaf area index) and to the photosynthetic activity of green vegetation. NDVI is an indirect measure of primary productivity through its quasi-linear relation with the fapar (Fraction of Absorbed Photosynthetically Active Radiation). Although NDVI has proven to be an extremely useful indicator for vegetation monitoring, it is affected by some well known limitations as for example effects of soil humidity and surface anisotropy 7. As a consequence NDVI values may vary due to soil humidity and depending on the particular anisotropy of the target as well as on the angular geometry of illumination and observation at the time of the measurements. Composite products used in most applications tend to limit these effects but they cannot be corrected for completely. For most applications 10-daily images are used, where the daily images are combined in so called MVC (Maximum Value Composit) products to eliminate at least partially the effects of cloud cover and perturbing atmospheric artefacts. The assumption here is that both clouds and atmospheric effects such as vapour and haze are generally lowering NDVI, so by taking the maximum NDVI values during a time step of some days these effects are automatically reduced. A ten-days time step (or dekad) is also a reasonable period for monitoring changes in crop phenology and is widely used in agrometeorology and crop monitoring Rainfall estimates Africa has a limited network of rain gauge stations and for diverse reasons, such as economic and civil insecurity coupled with a low perceived relevance of weather services a large number of existing rainfall records is incomplete. On the other hand rainfall is crucial for crop growth and is therefore an important factor for the monitoring of agricultural and pastoral production and consequently for food security. In addition to the poor availability of rainfall records and as 7 Anisotropy is the property of a surface not to reflect incoming radiation with the same angle and does generally increase with roughness. Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 6

7 highlighted by the World Meteorological Organization (WMO) during the World Climate Research Program Conference in 1998, there is a steady decline of the standard observation network, which is a strong limitation for climate related research as well as for operational agricultural monitoring. Because of these limitations in the availability and quality of measured data, rainfall estimates, which generally depend on climate circulation models or/and satellite observations, are used. Among the most common estimated rainfall data currently available for Africa are the rainfall forecasts of the European Centre for Medium-Range Weather Forecast (ECMWF), and the rainfall estimates (RFE) produced by the Climate Prediction Centre (CPC) of the National Oceanic and Atmospheric Administration (NOAA). The latter are used operationally by Famine Early Warning System - Network (FEWS-NET), an activity funded by the United States Agency for International Development (USAID). The European Centre of Medium-Range Weather Forecast (ECMWF) at Reading in the UK runs a general circulation model (GCM) 8 ( at a resolution of 0,18 degrees which simulates a series of meteorological variables at 6 hours time steps including rainfall forecasts. Since analyzed fields do not exist for rainfall, daily precipitation values can be computed as the differences between 36 hours and 12 hours forecasts, and 10-daily data as the sum of those daily rainfall forecasts. These data are used for example by the FOOD-SEC action of the Joint Research Centre (JRC) in an operational way for monitoring agricultural and pastoral vegetation on a real-time basis for the Horn of Africa. Recent versions of the ECMWF model have been used to reprocess historical data to obtain a climatological time series. In particular ERA-40 is an advanced back-processing analysis of the period from 1957 to 2001 using modern data assimilation technologies and parameterizations and all data available from each day in that period (Uppala et al. 2004) running at a 2,5 degrees resolution. ERA- Interim is a similar project reprocessing data from 1989 to 2007 at a more detailed spatial resolution (0,7 degrees). The RFE is a rainfall estimate of NOAA's Climate Prediction Centre currently used by FEWS-NET and several United Nations agencies such as the Food and Agriculture Organization (FAO) and World Food Programme (WFP) for agricultural monitoring in a large number of African countries. It basically uses satellite imagery from the geostationary 9 Meteosat Second Generation (MSG) 8 GCM s are mathematical models which simulate continuously the conditions of the atmosphere by dividing it in vertical cells corresponding to a horizontal grid on the surface. 9 Geostationary satellites rotate with the same speed as the earth and are therefore observing the same area of the surface as opposed to orbiting satellites which rotate around the earth. Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 7

8 and estimates convective rainfall 10 as a function of top of cloud temperatures (the so called cold cloud duration model or CCD) and using GTS stations for calibration. There exist two RFE versions (RFE 1.0 and RFE 2.0) produced with slightly different methodologies and different input data at a resolution: RFE 1.0 uses an interpolation method to combine Meteosat derived rainfall and Global Telecommunication System (GTS) data, and includes warm cloud information for the dekadal estimates; the data is available for the period RFE 2.0 uses additional techniques to better estimate rainfall while continuing the use of CCD and GTS. Two new satellite rainfall estimation instruments are incorporated into RFE 2.0, namely, the Special Sensor Microwave/Imager (SSM/I) on board Defence Meteorological Satellite Program satellites, and the Advanced Microwave Sounding Unit (AMSU) on board NOAA satellites. RFE 2.0 rainfall estimates are available only from Several other rainfall estimates exist, like for example the Tropical Rainfall Measuring Mission (TRMM) and the Tropical Applications of Meteorology using SATellite (TAMSAT) at Reading University, while other methods combine global circulation model outputs with rainfall estimates (FAO RFE). However, all the data described here are estimates and do therefore contain errors and show deviations in different regions of Africa. At the same time, validation or better calibration of these estimates is again hindered by the scarce availability of ground measurements. This makes it extremely difficult to assess the quality or reliability of each dataset and can have important implications for food security applications. 3. Specific use of vegetation indexes and rainfall estimates in IPC analysis 3.1. Climate data analysis and detection of anomalies Climatic conditions analysis for an agricultural season is based directly on the interpretation of meteorological data and remote sensing derived indicators at station level, pixel level or for administrative units. Rainfall is normally the main limiting factor for crop development in arid and semi arid regions and is the first indicator to look at, by following the dekadal rainfall and cumulated rainfall. Min. 10 Linked to convection of air masses (movement of liquids or gases due to temperature differences) Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 8

9 temperatures can be important in mountainous areas or max. temperatures in areas where there is the possibility of heat stress. Rainfall and temperatures can be used to derive ETP (potential evapotranspiration). NDVI again is a direct observation of vegetation performance and is therefore a good indicator of climatic conditions preceding the date of observation. In order to assess the performance of the current season the most common analysis is the comparison of the current situation to previous seasons or to what can be assumed to be the average or normal condition. In the same way comparison with reference years, for example years known to be very dry or very wet, can be done. This provides a qualitative indication of how good or bad the current season is when compared with other seasons or with the average situation. These areas showing significant deviations are mapped as anomalous and should be analyzed in more detail. Difference algorithms go from the simple absolute difference to sophisticated expressions that take into account the range of historical variations from min to max (eg. VCI). In general for arid and semiarid regions the simple absolute difference is preferable to relative indicators because the latter tend to overemphasize variations in areas with small values, resulting in large relative variations in very dry areas. An alternative to overcome this problem could be the masking of desert areas. Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 9

10 Figure 2. Example of absolute difference for NDVI and rainfall in June Note the dry areas in Sudan, Kenya and Northern Ethiopia. Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 10

11 3.2. Time series processing and profile analysis Repetitive satellite observations can provide updated information on the growth and phenological 11 development of natural vegetation and crops during a seasonal cycle. To facilitate the interpretation of many consecutive images, the indicators can be spatially aggregated and analysed as so called temporal (or seasonal) profiles. These temporal profiles are extracted for representative pixels where crops are dominant (i) by averaging pixel values inside an administrative area, or (ii) by averaging values only for cropped pixels within an administrative area. Regional mean NDVI products may be computed for different level administrative regions. If a land cover map (e.g. AFRICOVER) is available, mean (or weighted) NDVI products can be calculated for different land cover/land use categories. Several approaches have been elaborated for extracting crop specific signatures from the mixed low resolution pixel. A simple and common one is the Crop Specific NDVI (CNDVI) method which adds proportional weights to the NDVI values based on the fraction of crop area within each low resolution pixel for an administrative area. In general, the smaller the administrative unit, the more detailed/reliable the profile is, especially in case where latitude or altitude gradients characterize the administrative units. The profiles give a complete picture of the vegetation development during the seasonal cycle, and can be compared with other (for example, previous) crop seasons and the long term average vegetation profile. For early warning purposes graphs showing at the same time the annual development of vegetation index and rainfall (on 2 axes) for a land use class in a particular administrative area have proved particularly useful over the last years, since in addition to showing possible stress situations as compared to normal they also evidence the relationship between rainfall and vegetation indexes. Since NDVI and rainfall come from different sources they can be considered as independent and a convergence of the two variables is a strong indicator in case of positive or negative anomalies. If only one of the 2 shows an anomaly and the other is close to normal, more attention is needed for interpretation and the accuracy of the data has to be checked carefully (eg. clouds, problems of RFE such as non convective rainfall in coastal areas, anomalies in vegetation greenness not linked to rainfall etc ). 11 The study of vegetation dynamics in terms of climatically-driven changes that take place over a growing season is called phenology. Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 11

12 CNDVI Bay - High potential sorghum M ar Apr M ay Jun Jul Aug Sep Oct Nov Dec Jan Feb rainfall rainfall CNDVI CNDVI CNDVI mm CNDVI Galgaduud - Pastoral vegetation M ar Apr M ay Jun Jul Aug Sep Oct Nov Dec Jan Feb rainfall rainfall CNDVI CNDVI CNDVI mm Figure 3. Example of seasonal graphs for CNDVI and rainfall (ECWMF). A shows a clear positive anomaly for the main sorghum producing area in Southern Somalia while B is a case of very dry season for pastoral areas. The seasonal vegetation and rainfall graphs can be completed by additional graphs which show the difference of the current season with all the previous ones. In this case the use of Z-scores 12 is recommended to standardize the vegetation and rainfall values. The 0 line represents the average situation, while positive or negative deviations within the +-1 Z scores deviation range mean that the current season differs from 70% of the previous seasons assuming a normal distribution. In this case the vegetation index and rainfall values are cumulated over the crop season. The deviation takes into account only the amplitude of vegetation index and rainfall variation, but not the distribution. This means that it is possible to observe a below average vegetation index with high rainfall, due to 12 In statistics, a standard score or z-score indicates how many standard deviations an observation or datum is above or below the mean. Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 12

13 a bad rainfall distribution over time. For this reason, the Z-score graphs should always be used in combination with the seasonal graphs. Figure 4. Example of Z-score graph showing the very good crop season in terms of both cumulated RFE and CNDVI for sorghum in Hargeisa in Substantiation of IPC analysis This section is about explaining how rainfall estimates and vegetation indexes can be used as indirect evidence in the IPC analysis. In the current IPC guidance remote sensing products are included as indirect evidence for food availability in the indirect evidence (proxy indicator) table. Remote sensing products can, however, be also used to support the analysis of other Key Reference Outcomes under contributing evidence with certain assumptions. In this section the user will be guided on how to analyze available remotely sensed information (rainfall and vegetation indexes), and how to translate this analysis into an evidence statement to be recorded into the Analysis Template (please see IPC technical guidance, e.g. manual). Such statement will also include source of evidence, date, reliability score and indicative phase according to the procedure outlined in the IPC Manual. When determining reliability scores and the confidence level in the analysis it is important to bear in mind that remote sensing information is always indirect evidence. Often it is not the only evidence available for the KRO considered and all available evidence needs to be considered in order to assign a phase to that KRO. In section 6 you will find guidance on how to determine reliability scores for Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 13

14 this type of evidence. It also explains how the use of this type of evidence affects the confidence level of your analysis Water availability The direct evidence for water availability would be a measure of the number of liters per day available to each person in the unit of analysis. In general, remote sensing and meteo data do not provide direct evidence of water available for human consumption. Only in cases where absolutely no other information is available, and where it can be assumed that people depend to a high degree on availability of surface water, a close to average or above average cumulated rainfall (station data or ECMWF or RFE) for the period preceding the analysis could be used as evidence for water availability. No detailed methodologies have so far been developed for converting remote sensing data into information on surface water availability for human and animal consumption, although seasonal forecasts exist for example for generation of hydroelectric power. In the absence of a precise methodology, remote sensing data can only give indications on water availability for human or animal consumption. Any conclusions would need to be supported by ground data, available through food security assessments Health In general remote sensing and climatic data provide no direct evidence for human health status. However, certain epidemics are linked to excessive rain or follow climate related disasters such as floods and droughts. Flooding especially increases the likelihood of certain diseases, which can be divided into waterborne diseases (typhoid fever, cholera, leptospirosis and hepatitis A) and vectorborne diseases (malaria, dengue and dengue haemorrhagic fever, yellow fever, and West Nile Fever). In this sense positive rainfall anomaly in periods of the year of typical Plasmodium (malaria) and other parasites development can be used as indicators for potential epidemics. Certain agencies, for example FewsNet, WHO and IRI (International Research Institute for Climate and Society), are using remote sensing information for early warning purposes on epidemics, so far mainly on malaria and dengue fever. Examples or regional initiatives are the Malaria Early Warning System in Southern Africa, and Regional Malaria Outlook Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 14

15 Forums which are organized in Eastern and Central Africa under the auspices of the ICPAC (IGAD Climate Prediction and Applications Centre) Food availability As described in paragraph 3, climate data analysis as well as NDVI and RFE seasonal profiles of agricultural vegetation can be used to draw conclusions on the food supply for the unit of analysis considered. NDVI and RFE anomaly maps can be used to focus more in detail on areas with strong negative anomalies. In particular the NDVI and RFE Z-score graphs are useful to detect situations were negative anomalies have affected previous seasons, since food availability is a function of the current season production but also stocks and seeds left from previous seasons. Normal vegetation index describes the overall vegetation situation in an area, without being able to make a difference between different types of vegetation, for example forest, fields or pasture. Therefore knowledge of the vegetation patterns, seasonal calendar, and phenological stages of cultivated crops are useful and even necessary for interpreting the remote sensing data. Crop masking is a methodology developed for separating different types of vegetation from each other, in order to provide more detailed information on conditions for specific crop types. Remote sensing products which use crop masking are so far available for certain countries in the Horn of Africa (for more information please see JRC crop monitoring bulletins website in section 7) Hazards RFE and NDVI derived indicators can be useful in the detection of climate related hazards such as droughts or floods, while more detailed remote sensing and ground information is normally needed for hazard impact assessment. In the case of droughts RFE and NDVI data can be particularly useful for early warning and early identification of risks, which can be used for the IPC projection analysis. For drought, significant negative deviations from average RFE or NDVI, especially if protracted over several dekads during a crop cycle can be used as warning signals for identifying areas with a high drought risk. Useful in this sense can be also the use of a water balance model (for example Water Satisfaction Requirement Index or WRSI), and the comparison of current NDVI values with the historical maximum and minimum (for example Vegetation Condition Index or VCI). For rainfall, the SPI (Standardized Precipitation Index) is Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 15

16 a simple and robust drought indicator. All these indicators are normally made available by Early Warning systems such as those of FEWSNET, FAO GIEWS or MARS FOODSEC Livelihoods assets For monitoring productivity and possible stress situations of pastoral vegetation, similar techniques to what has been described for agricultural monitoring can be used, like pasture specific NDVI and RFE profiles or the pastoral WRSI provided by FEWSNET. Total biomass production in pastoral areas can be assessed by using DMP (Dry Matter Product) produced by MARS following the Monteith approach (ACF Action Contre la Faim is doing this operationally in the Sahel). Water availability for livestock can be seen as function of rainfall only under specific circumstances where surface water plays a role. For surface water availability the Small Water Bodies (SWB) product developed by JRC and made available by the DEVCOCAST project can be used, but it has to be reminded that this product was validated so far only in the Sahel and has a rather coarse resolution (1 km 2 ). Any interpretation of the data requires, however, knowledge on the type of livestock bred in the area (water requirements of, for example, cattle are far higher than those of shoats) and typical water sources available for livestock. 5. Risk of worsening phase analysis For the forecast of crop and pastoral vegetation conditions over the next 1-3 months, seasonal rainfall forecasts as those regularly produced by IRI, ECMWF and ICPAC (for Eastern Africa) can be used. However, since these products are still associated with a high range of uncertainty, our recommendation is to analyse carefully all the forecasts available and look for areas of convergence. The El Niño and La Niña phenomena (sea surface temperature anomalies in the equatorial Pacific Ocean) are also known to affect the weather and rainfall in different regions around the world. El Niño and La Niña forecasts are released regularly for example by NOAA. 6. Determining reliability scores A reliability score must be assigned to each piece of evidence. This score must appear in the analysis template and is expressed by an R = x, where x can take a value of 1, 2 or 3, which respectively mean: very reliable, somewhat reliable or unconfirmed. Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 16

17 According to the IPC guidance, R values are determined by taking into consideration the quality of the evidence and its time validity. When evaluating the quality of the evidence its source and method of collection should be looked at. Vegetation indexes and rainfall estimates derived from climatic models are very timely information. When IPC analysis takes place data up to the previous dekad are normally available. Therefore under normal circumstances, with regard to time validity, remotely sensed information can be considered very reliable. On the other hand when looking at the quality of the information, the main limitations have already been mentioned in the description of the single products. Lower reliability scores should be assigned where the NDVI images are affected by clouds or where there is no clear relationship between RFE and NDVI. 7. Useful links for access to data NDVI data: RFE data: ECMWF data: Crop monitoring reports and bulletins: Flood Monitoring Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 17

18 Health, disease monitoring: For more information on these approaches see for example: _43/http;/iriportal3.ldeo.columbia.edu;7087/publishedcontent/publish/develop ment/home/new_home/homebody/2008_spotlight_features/paho_who_collaborat ing_centre.html El Niño and La Niña monitoring: Rainfall forecasts: (worldwide by regions) (Eastern and Central Africa) Guidelines for using Remote Sensing Derived Information in support of the IPC analysis 18