{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T01:48:27Z","timestamp":1778636907635,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,11]],"date-time":"2020-09-11T00:00:00Z","timestamp":1599782400000},"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>The use of effective methods for large-area drought monitoring is an important issue; hence, there have been many attempts to solve this problem. In this study, the Drought Information Satellite System (DISS) index is presented, based on the synergistic use of meteorological data and information derived from satellite images. The index allows us to monitor drought phenomena in various climatic and environmental conditions. The approach utilizes two indices for constructing a drought index: (1) the hydrothermal coefficient (HTC), which characterizes meteorological conditions across the study area over a long-term period; and (2) the temperature condition index (TCI) derived from Moderate-resolution Imaging Spectroradiometer (MODIS) data, which refers instantaneous land surface temperature (LST) to long-term extreme values. The model for drought assessment based on the DISS index was applied for generating drought index maps for Poland for the 2001\u20132019 vegetation seasons. The performance of the index was verified through comparison of the extent of agricultural drought to the reduction in cereal and maize yield. Analysis of variance revealed a significant relationship between the area of drought determined by the drought index and the decrease in cereal yield due to unfavorable growth conditions. The presented study proves that the proposed drought index can be an effective tool for large-area drought monitoring under variable environmental conditions.<\/jats:p>","DOI":"10.3390\/rs12182944","type":"journal-article","created":{"date-parts":[[2020,9,11]],"date-time":"2020-09-11T09:05:16Z","timestamp":1599815116000},"page":"2944","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Drought Model DISS Based on the Fusion of Satellite and Meteorological Data under Variable Climatic Conditions"],"prefix":"10.3390","volume":"12","author":[{"given":"Katarzyna","family":"Dabrowska-Zielinska","sequence":"first","affiliation":[{"name":"Remote Sensing Centre, Institute of Geodesy and Cartography, Modzelewskiego 27, 02-679 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3245-8561","authenticated-orcid":false,"given":"Alicja","family":"Malinska","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, Institute of Geodesy and Cartography, Modzelewskiego 27, 02-679 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zbigniew","family":"Bochenek","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, Institute of Geodesy and Cartography, Modzelewskiego 27, 02-679 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1058-0304","authenticated-orcid":false,"given":"Maciej","family":"Bartold","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, Institute of Geodesy and Cartography, Modzelewskiego 27, 02-679 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Radoslaw","family":"Gurdak","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, Institute of Geodesy and Cartography, Modzelewskiego 27, 02-679 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karol","family":"Paradowski","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, Institute of Geodesy and Cartography, Modzelewskiego 27, 02-679 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Magdalena","family":"Lagiewska","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, Institute of Geodesy and Cartography, Modzelewskiego 27, 02-679 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,11]]},"reference":[{"key":"ref_1","unstructured":"Palmer, W.C. (1965). Meteorological Drought Research Paper 45, US Weather Bureau."},{"key":"ref_2","first-page":"174","article-title":"The relationship of drought frequency and duration to time scales","volume":"58","author":"Mckee","year":"1993","journal-title":"Am. Meteorol. Soc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.jhydrol.2012.11.028","article-title":"Hydrological response to climate variability at different time scales: A study in the Ebro basin","volume":"477","author":"Zabalza","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Diakowska, E., Stanek, P., Iwa\u0144ski, S., and G\u0105siorek, E. (2018, January 7). Estimation of the occurrence of drought in Poland by 2060 based on the HTC index and probability distributions. Proceedings of the ITM Web of Conferences, XLVIII Seminar of Applied Mathematics, Wroc\u0142aw, Poland.","DOI":"10.1051\/itmconf\/20182300006"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Winkler, K., Gessner, U., and Hochschild, V. (2017). Identifying droughts affecting agriculture in Africa based on remote sensing time series between 2000 and 2016: Rainfall anomalies and vegetation condition in the context of ENSO. Remote Sens., 9.","DOI":"10.3390\/rs9080831"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1080\/01431161.2015.1093190","article-title":"Agricultural drought monitoring using MODIS-based drought indices over the USA Corn Belt","volume":"36","author":"Wu","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","first-page":"245","article-title":"A comprehensive drought monitoring method integrating MODIS and TRMM data","volume":"23","author":"Du","year":"2013","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ahmadi, B., Ahmadalipour, A., Tootle, G., and Moradkhani, H. (2019). Remote Sensing of Water Use Efficiency and Terrestrial Drought Recovery across the Contiguous United States. Remote Sens., 11.","DOI":"10.3390\/rs11060731"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"014016","DOI":"10.1088\/1748-9326\/aa5258","article-title":"Global gross primary productivity and water use efficiency changes under drought stress","volume":"12","author":"Yu","year":"2017","journal-title":"Environ. Res. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.ecoinf.2017.03.005","article-title":"Characterization of droughts during 2001\u20132014 based on remote sensing: A case study of Northeast China","volume":"39","author":"Cong","year":"2017","journal-title":"Ecol. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1175\/1520-0477(1995)076<0655:DOTLIT>2.0.CO;2","article-title":"Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data","volume":"76","author":"Kogan","year":"1995","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zambrano, F., Lillo-Saavedra, M., Verbist, K., and Lagos, O. (2016). Sixteen years of agricultural drought assessment of the BioB\u00edo region in Chile using a 250 m resolution Vegetation Condition Index (VCI). Remote Sens., 8.","DOI":"10.1117\/12.2235345"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/0273-1177(95)00079-T","article-title":"Application of vegetation index and brightness temperature for drought detection","volume":"15","author":"Kogan","year":"1995","journal-title":"Adv. Space Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1080\/01431160110070744","article-title":"Modelling of crop growth conditions and crop field In Poland using AVHRR-based indices","volume":"23","author":"Kogan","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chang, S., Wu, B.F., Yan, N., Davdai, B., and Nasanbat, E. (2017). Suitability assessment of satellite-derived drought indices for Mongolian Grassland. Remote Sens., 9.","DOI":"10.3390\/rs9070650"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0034-4257(01)00274-7","article-title":"A simple interpretation of the surface temperature\/vegetation index space for assessment of surface moisture status","volume":"79","author":"Sandholt","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4998","DOI":"10.3390\/rs6064998","article-title":"Characterization of drought development through Remote Sensing: A case study in Central Yunnan, China","volume":"6","author":"Abbas","year":"2014","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.scitotenv.2014.09.099","article-title":"Integration of remote sensing datasets for local scale assessment and prediction of drought","volume":"505","author":"Nichol","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Reinermann, S., Gessner, U., Asam, S., Kuenzer, C., and Dech, S. (2019). The Effect of Droughts on Vegetation Condition in Germany: An Analysis Based on Two Decades of Satellite Earth Observation Time Series and Crop Yield Statistics. Remote Sens., 11.","DOI":"10.3390\/rs11151783"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Peng, J., Mueller, J.-P., Blessing, S., Giering, R., Danne, O., Gobron, N., Kharbouche, S., Ludwig, R., M\u00fcller, B., and Leng, G. (2019). Can we use satellite based FAPAR to detect drought?. Sensors, 19.","DOI":"10.3390\/s19173662"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.1080\/01431161.2018.1524603","article-title":"Non-stationarity in MODIS-FAPAR time-series and its impact on operational drought detection","volume":"40","author":"Cammalleri","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2006.06.003","article-title":"A comparative study of NOAA\u2013AVHRR derived drought indices using change vector analysis","volume":"105","author":"Bayarjargal","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.rse.2016.12.010","article-title":"Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices","volume":"190","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_24","first-page":"432","article-title":"Responses of MODIS spectral indices to typical drought events from 2000 to 2012 in southwest China","volume":"18","author":"Wang","year":"2014","journal-title":"J. Remote Sens."},{"key":"ref_25","first-page":"270","article-title":"Combination of multi-sensor remote sensing data for drought monitoring over Southwest China","volume":"35","author":"Hao","year":"2015","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, F., Wang, Z., Yang, H., Zhao, Y., Li, Z., and Wu, J. (2018). Capability of remotely sensed indices for representing the spatio-temporal variations of meteorological drought in the Yellow River Basin. Remote Sens., 10.","DOI":"10.20944\/preprints201811.0476.v1"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ryu, J.-H., Han, K.-S., Lee, Y.-W., Park, N.-W., Hong, S., Chung, C.-Y., and Cho, J. (2019). Different agricultural responses to extreme drought events in neighboring counties of South and North Korea. Remote Sens., 11.","DOI":"10.3390\/rs11151773"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Qu, C., Hao, X., and Qu, J.J. (2019). Monitoring Extreme agricultural drought over Horn of Africa (HOA) using remote sensing measurements. Remote Sens., 11.","DOI":"10.3390\/rs11080902"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.1080\/01431160500121727","article-title":"Comments on the use of the Vegetation Health Index over Mongolia","volume":"27","author":"Karnieli","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bento, V.A., and Trigo, I.F. (2018). Contribution of Land Surface Temperature (TCI) to Vegetation Health Index: A Comparative Study Using Clear Sky and All-Weather Climate Data Records. Remote Sens., 10.","DOI":"10.3390\/rs10091324"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1080\/19475705.2017.1337654","article-title":"SNPP\/VIIRS vegetation health to assess 500 California drought","volume":"8","author":"Kogan","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1038\/s41598-020-57910-1","article-title":"Vegetation response to precipitation anomalies under different climatic and biogeographical conditions in China","volume":"10","author":"Chen","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wei, C.H., Obringer, R., Li, D., Chen, N.C., and Niyogi, D. (2017). Gauging the severity of the 2012 Midwestern U.S. drought for agriculture. Remote Sens., 9.","DOI":"10.3390\/rs9080767"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Enenkel, M., Steiner, C., Mistelbauer, T., Dorigo, W., Wagner, W., See, L., Atzberger, D., Schneider, S., and Rogenhofer, E. (2016). A Combined Satellite-Derived Drought Indicator to Support Humanitarian Aid Organisations. Remote Sens., 8.","DOI":"10.3390\/rs8040340"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Adede, C., Oboko, R., Wagacha, P.W., and Atzberger, C. (2019). A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya\u2019s Operational Drought Monitoring. Remote Sens., 11.","DOI":"10.3390\/rs11091099"},{"key":"ref_36","first-page":"165","article-title":"About climate agricultural estimation","volume":"20","author":"Selyaninov","year":"1928","journal-title":"Proc. Agric. Meteorol."},{"key":"ref_37","first-page":"106","article-title":"Comparison of watermark soil moisture content with Selyaninov hedrothermal coefficient","volume":"2","author":"Taparauskiene","year":"2017","journal-title":"AGROFOR Int. J."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1134\/S2079096115020055","article-title":"Droughts and dynamics of synoptic processes in the south of the East European Plain at the beginning of the twenty-first century","volume":"5","author":"Cherenkova","year":"2015","journal-title":"Arid Ecosyst."},{"key":"ref_39","unstructured":"World Meteorological Organisation (2016). Handbook of Drought Indicator and Indices, WMO."},{"key":"ref_40","unstructured":"Henning, B., and Christof, W. (2014). Fixed effects panel regression. The SAGE Handbook of Regression Analysis and Causal Inference, SAGE Publications Ltd."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Hua, L., Wang, H., Sui, H., Wardlow, B., Hayes, M.J., and Wang, J. (2019). Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region. Remote Sens., 11.","DOI":"10.3390\/rs11161873"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Jiao, W., Zhang, L., Chang, Q., Cen, D.Y., and Tong, Q. (2016). Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States. Remote Sens., 8.","DOI":"10.3390\/rs8030224"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2944\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:09:00Z","timestamp":1760177340000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2944"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,11]]},"references-count":42,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12182944"],"URL":"https:\/\/doi.org\/10.3390\/rs12182944","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,11]]}}}