{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T15:42:53Z","timestamp":1787240573828,"version":"3.56.0"},"reference-count":122,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,12]],"date-time":"2024-10-12T00:00:00Z","timestamp":1728691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004410","name":"Scientific and Technological Research Council of T\u00fcrkiye (T\u00dcB\u0130TAK), BIDEB2214-A program","doi-asserted-by":"publisher","award":["1059B142201613"],"award-info":[{"award-number":["1059B142201613"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems.<\/jats:p>","DOI":"10.3390\/rs16203799","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T07:47:05Z","timestamp":1728892025000},"page":"3799","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in T\u00fcrkiye"],"prefix":"10.3390","volume":"16","author":[{"given":"Eyyup Ensar","family":"Ba\u015fak\u0131n","sequence":"first","affiliation":[{"name":"Hydraulics Division, Civil Engineering Department, Istanbul Technical University, Istanbul 34469, T\u00fcrkiye"},{"name":"Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6053-6232","authenticated-orcid":false,"given":"Paul C.","family":"Stoy","sequence":"additional","affiliation":[{"name":"Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4402-906X","authenticated-orcid":false,"given":"Mehmet C\u00fcneyd","family":"Demirel","sequence":"additional","affiliation":[{"name":"Hydraulics Division, Civil Engineering Department, Istanbul Technical University, Istanbul 34469, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mutlu","family":"Ozdogan","sequence":"additional","affiliation":[{"name":"Department of Forest and Wildlife Ecology & Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4034-7845","authenticated-orcid":false,"given":"Jason A.","family":"Otkin","sequence":"additional","affiliation":[{"name":"Space Science and Engineering Center, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1007\/s40808-020-01010-6","article-title":"Drought Prediction Using Hybrid Soft-Computing Methods for Semi-Arid Region","volume":"7","year":"2021","journal-title":"Model. 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