S. Migdall, Sandra Dotzler, Eva Gleisberg, F. Appel, M. Muerth, H. Bach, Giulio Weikmann, C. Paris, D. Marinelli, L. Bruzzone
{"title":"Crop Water Availability Mapping in the Danube Basin Based on Deep Learning, Hydrological and Crop Growth Modelling","authors":"S. Migdall, Sandra Dotzler, Eva Gleisberg, F. Appel, M. Muerth, H. Bach, Giulio Weikmann, C. Paris, D. Marinelli, L. Bruzzone","doi":"10.3390/engproc2021009042","DOIUrl":null,"url":null,"abstract":"The Danube Basin has been hit by several droughts in the last few years. As climate change makes weather extremes and temperature records in late winter and early spring more likely, water availability and irrigation possibilities become more important. In this paper, the crop water demand at field and national scale within the Danube Basin is presented using a dense time series of multispectral Sentinel-2 data, for crop type maps derived with deep learning techniques and physically-based models for crop parameter retrieval and crop growth modelling.","PeriodicalId":11748,"journal":{"name":"Engineering Proceedings","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/engproc2021009042","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
The Danube Basin has been hit by several droughts in the last few years. As climate change makes weather extremes and temperature records in late winter and early spring more likely, water availability and irrigation possibilities become more important. In this paper, the crop water demand at field and national scale within the Danube Basin is presented using a dense time series of multispectral Sentinel-2 data, for crop type maps derived with deep learning techniques and physically-based models for crop parameter retrieval and crop growth modelling.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
