Improved drought detection to support crop insurance models: powerpoint

Anomaly assessment for drought monitoring, as required for index insurance applications, is commonly done by comparing actual NDVI measurements against their historical records on a pixel-by-pixel basis. Limited years of satellite records with operational real-time availability result in time-series with a relative low count in annual repeats, e.g., the VEGETATION sensor onboard SPOT and Proba-V has completed at present only 19 full annual repeats. This number is too low for agricultural index insurance models that require accurate assessments of impacts of perils (e.g. of drought) on crop performance during a specific growing season. Generally, they want to see at least 30 annual repeats. Then, considering that the index models typically focus on the left-tail part of the NDVI frequency-distribution, to extract NDVI-thresholds that correspond to drought incidence statistics, the obtainable model accuracy further drops. Typical trigger points used by index insurance models concern the 15th and 5th percentile statistics, which implies that even with thirty annual repeats, these percentiles cannot be robustly estimated. Derived results are thus hardly acceptable to actuaries of insurance companies and financial institutes alike. In this paper, we propose an innovative way to assess NDVI anomalies that significantly increases the statistical basis for their calculation. Rather than focusing only on a single pixel, we make use of the fact that large clusters of pixels respond relatively homogeneously to anomalies in weather patterns. These clusters have a similar land cover and land use, which are determined by climatic conditions, including its variability, most notably drought frequency, duration and severity. The clusters can be derived with unsupervised classification by analyzing the similarity in their long-term NDVI-profiles. Using NDVI-measurements of all pixels in a cluster and all their annual repeats, allows accurate extraction of needed left-tail percentile statistics. Subsequently, seasonal anomalies for individual pixels are then compared against these percentiles. Based on this logic, we have designed an index insurance model that is currently implemented in Ethiopia, and that utilizes real-time Proba-V data as broadcasted through EUMETCast. The model showed an excellent correspondence with surveyed data on farmers experiences. An added value is that the presented approach can easily be scaled to other regions, and that it can also be utilized to benefit real-time drought early warning schemes

The ITC GEONETCast toolbox:a geo capacity building component for education and training in global earth observation and geo information provision to society

In many countries throughout the world, the use of earth observation data for environmental or societal purposes still remains underexplored, in spite increasing earth observation (EO) data provision. The root cause is mainly a still inadequate generic knowledge to use remote sensing data and derive information products. The GEONETCast data dissemination system of GEOSS, the Global earth observation system of systems, is steadily working towards removing barriers for EO data access and use. Efficient processing and analysis tools, accessible by end-users, need to be urgently developed in order to exploit the full potential of this global data dissemination and information system. The ITC GEONETCast Toolbox, an open access earth observation data retrieval and application development environment is presented here. It can act as gap filler in the knowledge transfer chain from EO data providers to the local end-users in the different societal benefit areas of GEOSS

Mapping Small-Scale Irrigation Areas Using Expert Decision Rules and the Random Forest Classifier in Northern Ethiopia

The mapping of small-scale irrigation areas is essential for food security and water resource management studies. The identification of small-scale irrigation areas is a challenge, but it can be overcome using expert knowledge and satellite-derived high-spatial-resolution multispectral information in conjunction with monthly normalized difference vegetation index (NDVI) time series, and additional terrain information. This paper presents a novel approach to characterize small-scale irrigation schemes that combine expert knowledge, multi-temporal NDVI time series, multispectral high-resolution satellite images, and the random forest classifier in the Zamra catchment, North Ethiopia. A fundamental element of the approach is mapping small-scale irrigation areas using expert decision rules to incorporate the available water resources. We apply expert decision rules to monthly NDVI composites from September 2020 to August 2021 along with the digital elevation model (DEM) data on the slope, drainage order, and distance maps to derive the sample set. The samples werebased on the thresholds obtained by expert knowledge from field surveys. These data, along with the four spectral bands of a cloud-free Planet satellite image composite, 12 NDVI monthly composites, slope, drainage order, and distance map were used as input into a random forest classifier which was trained to classify pixels as either irrigated or non-irrigated. The results show that the analysis allows the mapping of small-scale irrigation areas with high accuracy. The classification accuracy for identifying irrigated areas showed a user accuracy ranging from 81% to 87%, along with a producer accuracy ranging from 64% to 79%. Furthermore, the classification accuracy and the kappa coefficient for the classified irrigation schemes were 80% and 0.70, respectively. As a result, these findings highlight a substantial level of agreement between the classification results and the reference data. The use of different expert knowledge-based decision rules, as a method, can be applied to extract small-scale and larger irrigation areas with similar agro-ecological characteristics.<br/

Evaluating the MSG satellite Multi-Sensor Precipitation Estimate for extreme rainfall monitoring over northern Tunisia

Knowledge and evaluation of extreme precipitation is important for water resources and flood risk management, soil and land degradation, and other environmental issues. Due to the high potential threat to local infrastructure, such as buildings, roads and power supplies, heavy precipitation can have an important social and economic impact on society. At present, satellite derived precipitation estimates are becoming more readily available. This paper aims to investigate the potential use of the Meteosat Second Generation (MSG) Multi-Sensor Precipitation Estimate (MPE) for extreme rainfall assessment in Tunisia. The MSGMPE data combine microwave rain rate estimations with SEVIRI thermal infrared channel data, using an EUMETSAT production chain in near real time mode. The MPE data can therefore be used in a now-casting mode, and are potentially useful for extreme weather early warning and monitoring. Daily precipitation observed across an in situ gauge network in the north of Tunisia were used during the period 2007–2009 for validation of the MPE extreme event data. As a first test of the MSGMPE product's performance, very light to moderate rainfall classes, occurring between January and October 2007, were evaluated. Extreme rainfall events were then selected, using a threshold criterion for large rainfall depth (>50 mm/day) occurring at least at one ground station. Spatial interpolation methods were applied to generate rainfall maps for the drier summer season (from May to October) and the wet winter season (from November to April). Interpolated gauge rainfall maps were then compared to MSGMPE data available from the EUMETSAT UMARF archive or from the GEONETCast direct dissemination system. The summation of the MPE data at 5 and/or 15 min time intervals over a 24 h period, provided a basis for comparison. The MSGMPE product was not very effective in the detection of very light and light rain events. Better results were obtained for the slightly more moderate and moderate rain event classes in terms of percentage of detected events, correlation coefficient, and ratio bias. The results for extreme events were mixed, with high pixel correlations of R=0.75 achieved for some events, while for other events the correlation between satellite and ground observation was rather weak. MPE data for northern Tunisia seem more reliable during the summer season and for larger event scales. The MSGMPE data have demonstrated to be very informative for early warning purposes, but need to be combined with other near real time data or information to give reliable and quantitative estimates of extreme rainfall

Sustained data access and tools as key ingredients to strengthening EO capacities : examples from land application perspective + powerpoint

Sustainably managing agriculture and forests is key for development, in particular in Africa, and for facing global challenges such as climate change or food security, but requires reliable information. As Earth Observation (EO) satellite data can contribute to these information needs, more and more institutes integrate this technology into their daily work. Facing ever-growing and evolving EO data sources (e.g. new satellites and sensors) and access technology (both online and via EUMETCast satellite broadcast), their applications require software tools to particularly facilitate (i) the exchange of data between the analysis tools, so users can take advantage of each tool’s strengths, and (ii) the processing and analysis of time series. A first example is the Land Surface Analysis Satellite Application Facility (LSA-SAF), that entered the second part of the Continuous Development and Operations Phase (CDOP-2), under the lead of the Portuguese Institute for Sea and Atmosphere (IPMA), in 2011. VITO, joining the LSA-SAF network for the first time and building on previous experiences (e.g. http://www.metops10.vito.be), aims to contribute by producing and delivering operational, 10-daily vegetation indicators based on MetOp-AVHRR. Furthermore, a software tool is developed to aid exploitation of LSA-SAF products, provisionally called “MSG Toolbox”. A second example is the AGRICAB project, that receives funding from the European Union’s 7th Framework Programme for Research (FP7) and aims to build a comprehensive framework for strengthening capacities in the use of EO for agriculture and forestry management in Africa. This framework starts from sustained access to relevant satellite data (e.g. CBERS-3, DEIMOS) and derived products, such as those from the European Copernicus Global Land service, the 15 year time series of SPOT-VEGETATION (and its transition to PROBA-V) and Meteosat Second Generation (e.g. rainfall estimates). It combines local and EO data with tools and training into applications on crop monitoring, area statistics and yield forecasting, livestock insurance and modelling, forest and fire management, all fitted to the needs of stakeholders in the African focus countries