{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T15:11:33Z","timestamp":1774365093715,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2016,3,24]],"date-time":"2016-03-24T00:00:00Z","timestamp":1458777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Reliable drought information is of utmost importance for efficient drought management. This paper presents a fully operational processing chain for mapping drought occurrence, extent and strength based on Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data at 250 m resolution. Illustrations are provided for the territory of Kenya. The processing chain was developed at BOKU (University of Natural Resources and Life Sciences, Vienna, Austria) and employs a modified Whittaker smoother providing consistent (de-noised) NDVI \u201cMonday-images\u201d in near real-time (NRT), with time lags between zero and thirteen weeks. At a regular seven-day updating interval, the algorithm constrains modeled NDVI values based on reasonable temporal NDVI paths derived from corresponding (multi-year) NDVI \u201cclimatologies\u201d. Contrary to other competing approaches, an uncertainty range is produced for each pixel, time step and time lag. To quantify drought strength, the vegetation condition index (VCI) is calculated at pixel level from the de-noised NDVI data and is spatially aggregated to administrative units. Besides the original weekly temporal resolution, the indicator is also aggregated to one- and three-monthly intervals. During spatial and temporal aggregations, uncertainty information is taken into account to down-weight less reliable observations. Based on the provided VCI, Kenya\u2019s National Drought Management Authority (NDMA) has been releasing disaster contingency funds (DCF) to sustain counties in drought conditions since 2014. The paper illustrates the successful application of the drought products within NDMA by providing a retrospective analysis applied to droughts reported by regular food security assessments. We also present comparisons with alternative products of the US Agency for International Development (USAID)\u2019s Famine Early Warning Systems Network (FEWS NET). We found an overall good agreement (R2 = 0.89) between the two datasets, but observed some persistent (seasonal and spatial) differences that should be assessed against external reference information.<\/jats:p>","DOI":"10.3390\/rs8040267","type":"journal-article","created":{"date-parts":[[2016,3,24]],"date-time":"2016-03-24T11:55:16Z","timestamp":1458820516000},"page":"267","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":143,"title":["Operational Drought Monitoring in Kenya Using MODIS NDVI Time Series"],"prefix":"10.3390","volume":"8","author":[{"given":"Anja","family":"Klisch","sequence":"first","affiliation":[{"name":"Institute for Surveying, Remote Sensing and Land Information, University of Natural Resources and Life Sciences (BOKU), Peter Jordan Strasse 82, 1190 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2169-8009","authenticated-orcid":false,"given":"Clement","family":"Atzberger","sequence":"additional","affiliation":[{"name":"Institute for Surveying, Remote Sensing and Land Information, University of Natural Resources and Life Sciences (BOKU), Peter Jordan Strasse 82, 1190 Vienna, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2016,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1175\/1520-0477-83.8.1149","article-title":"A review of twentieth-century drought indices used in the United States","volume":"83","author":"Heim","year":"2002","journal-title":"B Am. Meteorol. Soc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2893","DOI":"10.5194\/hess-16-2893-2012","article-title":"Towards an integrated soil moisture drought monitor for East Africa","volume":"16","author":"Anderson","year":"2012","journal-title":"Hydrol. Earth. Syst. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wilhite, D. (2005). Drought and Water Crises: Science, Technology, and Management Issues, CRC Press.","DOI":"10.1201\/9781420028386"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1177\/1070496507306222","article-title":"Documenting drought-related disasters: A global reassessment","volume":"16","author":"Below","year":"2007","journal-title":"J. Environ. Dev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.apgeog.2012.02.001","article-title":"Challenges for drought mitigation in Africa: The potential use of geospatial data and drought information systems","volume":"34","author":"Gimeno","year":"2012","journal-title":"Appl. Geogr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.jhydrol.2010.07.012","article-title":"A review of drought concepts","volume":"391","author":"Mishra","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1029\/2007EO390001","article-title":"Earlier famine warning possible using remote sensing and models","volume":"88","author":"Brown","year":"2007","journal-title":"EOS"},{"key":"ref_8","unstructured":"Brown, M. (2008). Famine Early Warning Systems and Remote Sensing Data, Springer."},{"key":"ref_9","first-page":"238","article-title":"Historical extension of operational NDVI products for livestock insurance in Kenya","volume":"28","author":"Vrieling","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.3390\/rs5041704","article-title":"Using low resolution satellite imagery for yield prediction and yield anomaly detection","volume":"5","author":"Rembold","year":"2013","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S. (2015). Remote Sensing of Water Resources, Disasters, and Urban Studies, CRC Press.","DOI":"10.1201\/b19321"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S. (2015). Remote Sensing of Water Resources, Disasters, and Urban Studies, CRC Press.","DOI":"10.1201\/b19321"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S. (2015). Remote Sensing of Water Resources, Disasters, and Urban Studies, CRC Press.","DOI":"10.1201\/b19321"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S. (2015). Remote Sensing of Water Resources, Disasters, and Urban Studies, CRC Press.","DOI":"10.1201\/b19321"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S. (2015). Remote Sensing of Water Resources, Disasters, and Urban Studies, CRC Press.","DOI":"10.1201\/b19321"},{"key":"ref_16","unstructured":"Paron, P., Di Baldassarre, G., and Shroder, J.F. (2015). Hydro-Meteorological Hazards, Risks and Disasters, Elsevier."},{"key":"ref_17","first-page":"80","article-title":"An introduction to the drought monitor","volume":"12","author":"Svoboda","year":"2000","journal-title":"Drought Network News"},{"key":"ref_18","unstructured":"Sivakumar, M., Motha, R., Wilhite, D., and Qu, J. (2011, January 14\u201315). Towards a compendium on national drought policy. Proceedings of the Expert Meeting on the Preparation of a Compendium on National Drought Policy, Washington, DC, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.rse.2015.12.003","article-title":"Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists","volume":"174","author":"Vrieling","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1111\/j.1477-8947.2008.00211.x","article-title":"The need for integration of drought monitoring tools for proactive food security management in sub-Saharan Africa","volume":"32","author":"Tadesse","year":"2008","journal-title":"Nat. Resour. Forum"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"25","DOI":"10.2747\/1548-1603.47.1.25","article-title":"The vegetation outlook (VegOut): A new method for predicting vegetation seasonal greenness","volume":"47","author":"Tadesse","year":"2010","journal-title":"GISci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1175\/BAMS-D-12-00124.1","article-title":"A drought monitoring and forecasting system for sub-Sahara African water resources and food security","volume":"95","author":"Sheffield","year":"2014","journal-title":"Bull. Am. Meteor. Soc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.rse.2010.09.006","article-title":"Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery","volume":"115","author":"Rojas","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.rse.2008.09.003","article-title":"Noise reduction of NDVI time series: An empirical comparison of selected techniques","volume":"113","author":"Hird","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3689","DOI":"10.1080\/01431161003762405","article-title":"Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements","volume":"32","author":"Atzberger","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.rse.2012.04.001","article-title":"Intercomparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology","volume":"123","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2024","DOI":"10.3390\/rs6032024","article-title":"Comparison of eight techniques for reconstructing multi-satellite sensor time-series NDVI data sets in the Heihe River basin, China","volume":"6","author":"Geng","year":"2014","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.rse.2015.04.014","article-title":"An approach for evaluating the impact of gaps and measurement errors on satellite land surface phenology algorithms: Application to 20 year NOAA AVHRR data over Canada","volume":"164","author":"Kandasamy","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2015.12.023","article-title":"An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data","volume":"174","author":"Shao","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3631","DOI":"10.1021\/ac034173t","article-title":"A perfect smoother","volume":"75","author":"Eilers","year":"2003","journal-title":"Anal. Chem."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1002\/wics.125","article-title":"Splines, knots, and penalties","volume":"2","author":"Eilers","year":"2010","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1080\/17538947.2010.505664","article-title":"A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America","volume":"4","author":"Atzberger","year":"2011","journal-title":"Int. J. Digit. Earth"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"257","DOI":"10.3390\/rs6010257","article-title":"Phenological metrics derived over the European continent from NDVI3g data and MODIS time series","volume":"6","author":"Atzberger","year":"2014","journal-title":"Remote Sens."},{"key":"ref_34","first-page":"10619","article-title":"The 30 years TAMSAT African rainfall climatology and time series (TARCAT) data set","volume":"119","author":"Maidment","year":"2014","journal-title":"J. Geophys. Res. A"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2805","DOI":"10.1175\/JAMC-D-14-0016.1","article-title":"Extension of the TAMSAT satellite-based rainfall monitoring over Africa and from 1983 to present","volume":"53","author":"Tarnavsky","year":"2014","journal-title":"J. Appl. Meteorol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1111\/j.1749-8198.2009.00244.x","article-title":"Biophysical remote sensing and climate data in famine early warning systems","volume":"3","author":"Brown","year":"2009","journal-title":"Geogr. Compass"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2472","DOI":"10.1080\/01431161.2014.883090","article-title":"A phenology-based method to derive biomass production anomalies for food security monitoring in the Horn of Africa","volume":"35","author":"Meroni","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","unstructured":"Kenya GIS Data World Resources Institute (WRI). Available online: http:\/\/www.wri.org\/resources\/data-sets\/kenya-gis-data."},{"key":"ref_39","unstructured":"TAMSAT (Tropical Applications of Meteorology Using SATellite Data and Ground-Based Observations) University of Reading. Available online: http:\/\/www.met.reading.ac.uk\/~tamsat\/data\/."},{"key":"ref_40","unstructured":"Solano, R., Didan, K., Jacobson, A., and Huete, A. MODIS Vegetation Index User\u2019s Guide (MOD13 Series), Version 2.00, May 2010 (Collection 5). Available online: http:\/\/www.ctahr.hawaii.edu\/grem\/modis-ug.pdf."},{"key":"ref_41","unstructured":"Mattiuzzi, M., Verbesselt, J., Hengl, T., Klisch, A., Evans, B., and Lobo, A. (2012, January 23\u201325). MODIS: MODIS download and processing package. Processing Functionalities for (Multi-Temporal) MODIS Grid Data. First International Workshop on \u201cTemporal Analysis of Satellite Images\u201d, Mykonos Island, Greece."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3143","DOI":"10.3390\/rs4103143","article-title":"Exploiting the classification performance of support vector machines with multi-temporal Moderate-Resolution Imaging Spectroradiometer (MODIS) data in areas of agreement and disagreement of existing land cover products","volume":"4","author":"Vuolo","year":"2012","journal-title":"Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"12381","DOI":"10.3390\/rs61212381","article-title":"A Kalman filter-based method to generate continuous time series of medium-resolution NDVI images","volume":"6","author":"Sedano","year":"2014","journal-title":"Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.rse.2005.10.021","article-title":"Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI","volume":"100","author":"Beck","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"7141","DOI":"10.1080\/01431160802238435","article-title":"Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS","volume":"29","author":"Brown","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.rse.2006.08.002","article-title":"A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data","volume":"106","author":"Bradley","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1080\/01431169208904212","article-title":"The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series","volume":"13","author":"Viovy","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"809","DOI":"10.14358\/PERS.69.8.899","article-title":"AVHRR-based spectral vegetation index for quantitative assessment of vegetation state and productivity: Calibration and validation","volume":"69","author":"Kogan","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Rembold, F., Meroni, M., Urbano, F., Royer, A., Atzberger, C., Lemoine, G., Eerens, H., and Haesen, D. (2015). Remote sensing time series analysis for crop monitoring with the SPIRITS software: New functionalities and use examples. Front. Environ. Sci., 3.","DOI":"10.3389\/fenvs.2015.00046"},{"key":"ref_51","unstructured":"Web-tools for Vegetation Anomaly Analysis. BOKU. Available online: http:\/\/ivfl-geomap.boku.ac.at\/demo_WG\/kenya\/, http:\/\/ivfl-info.boku.ac.at\/index.php\/eo-data-processing\/data-analytics."},{"key":"ref_52","unstructured":"USGS (2011). eMODIS Africa Product Guide Version 1.0, 2011, USGS EROS Data Center."},{"key":"ref_53","unstructured":"USGS eMODIS TERRA Normalized Difference Vegetation Index (NDVI). Available online: http:\/\/earlywarning.usgs.gov\/fews\/product\/116."},{"key":"ref_54","unstructured":"Colditz, R.R., Conrad, C., Wehrmann, T., Schmidt, M., and Dech, S. (2007, January 13\u201315). Analysis of the quality of collection 4 and 5 vegetation index time series from MODIS. Proceedings of the ISPRS 5th International Symposium Spatial Data Quality, Enschede, The Netherlands."},{"key":"ref_55","unstructured":"KFSSG Kenya Long Rains Assessment Report 2009. Available online: http:\/\/documents.wfp.org\/stellent\/groups\/public\/documents\/ena\/wfp208056.pdf?iframe."},{"key":"ref_56","unstructured":"KFSSG Kenya Long Rains Assessment Report 2011. Available online: http:\/\/documents.wfp.org\/stellent\/groups\/public\/documents\/ena\/wfp240180.pdf."},{"key":"ref_57","unstructured":"KFSSG Kenya Long Rains Assessment Report 2014. Available online: http:\/\/www.ipcinfo.org\/fileadmin\/user_upload\/ipcinfo\/docs\/2014%20Kenya%20LRA%20National%20Report.pdf."},{"key":"ref_58","unstructured":"KFSSG Kenya Long Rains Assessment Report 2006. Available online: http:\/\/reliefweb.int\/sites\/reliefweb.int\/files\/resources\/F63B1A92E605B2E4C1257230004B666F-govt-ken-12sep.pdf."},{"key":"ref_59","unstructured":"KFSSG Kenya Long Rains Assessment Report 2010. Available online: http:\/\/www.fao.org\/fileadmin\/user_upload\/drought\/docs\/Kenya_2010_LRA%20Report.pdf."},{"key":"ref_60","unstructured":"KFSSG Kenya Long Rains Assessment Report 2013. Available online: https:\/\/www.humanitarianresponse.info\/system\/files\/documents\/files\/LRA%202013_National%20Report_Final.pdf."},{"key":"ref_61","unstructured":"KFSSG Kenya Short Rains Assessment Report 2005. Available online: http:\/\/documents.wfp.org\/stellent\/groups\/public\/documents\/ena\/wfp087348.pdf?iframe."},{"key":"ref_62","unstructured":"KFSSG Kenya Short Rains Assessment Report 2010. Available online: http:\/\/documents.wfp.org\/stellent\/groups\/public\/documents\/ena\/wfp241326.pdf?iframe."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/4\/267\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:21:11Z","timestamp":1760210471000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/4\/267"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,3,24]]},"references-count":62,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2016,4]]}},"alternative-id":["rs8040267"],"URL":"https:\/\/doi.org\/10.3390\/rs8040267","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,3,24]]}}}