{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T19:42:08Z","timestamp":1784576528530,"version":"3.55.0"},"reference-count":89,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,3,13]],"date-time":"2019-03-13T00:00:00Z","timestamp":1552435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Key R &amp; D plan from the MOST of China","award":["2017YFC0403203"],"award-info":[{"award-number":["2017YFC0403203"]}]},{"name":"The 111 Project","award":["No.B12007"],"award-info":[{"award-number":["No.B12007"]}]},{"name":"The Synergetic Innovation of Industry-University-Research Cooperation Project plan from Yangling","award":["2018CXY-23"],"award-info":[{"award-number":["2018CXY-23"]}]},{"name":"The Key Discipline Construction Project of Northwest Agriculture and Forestry University","award":["2017-C03"],"award-info":[{"award-number":["2017-C03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mapping maize water stress status and monitoring its spatial variability at a farm scale are a prerequisite for precision irrigation. High-resolution multispectral images acquired from an unmanned aerial vehicle (UAV) were used to evaluate the applicability of the data in mapping water stress status of maize under different levels of deficit irrigation at the late vegetative, reproductive and maturation growth stages. Canopy temperature, field air temperature and relative humidity obtained by a handheld infrared thermometer and a portable air temperature\/relative humidity meter were used to establish a crop water stress index (CWSI) empirical model under the weather conditions in Ordos, Inner Mongolia, China. Nine vegetation indices (VIs) related to crop water stress were derived from the UAV multispectral imagery and used to establish CWSI inversion models. The results showed that non-water-stressed baseline had significant difference in the reproductive and maturation stages with an increase of 2.1 \u00b0C, however, the non-transpiring baseline did not change significantly with an increase of 0.1 \u00b0C. The ratio of transformed chlorophyll absorption in reflectance index (TCARI) and renormalized difference vegetation index (RDVI), and the TCARI and soil-adjusted vegetation index (SAVI) had the best correlations with CWSI. R2 values were 0.47 and 0.50 for TCARI\/RDVI and TCARI\/SAVI at the reproductive and maturation stages, respectively; and 0.81 and 0.80 for TCARI\/RDVI and TCARI\/SAVI at the late reproductive and maturation stages, respectively. Compared to CWSI calculated by on-site measurements, CWSI values retrieved by VI-CWSI regression models established in this study had more abilities to assess the field variability of crop and soil. This study demonstrates the potentiality of using high-resolution UAV multispectral imagery to map maize water stress.<\/jats:p>","DOI":"10.3390\/rs11060605","type":"journal-article","created":{"date-parts":[[2019,3,14]],"date-time":"2019-03-14T04:15:29Z","timestamp":1552536929000},"page":"605","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":191,"title":["Mapping Maize Water Stress Based on UAV Multispectral Remote Sensing"],"prefix":"10.3390","volume":"11","author":[{"given":"Liyuan","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&amp;F University, Yangling 712100, Shaanxi, China"},{"name":"Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9781-6086","authenticated-orcid":false,"given":"Huihui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Water Management and Systems Research Unit, USDA-ARS, 2150 Centre Avenue, Bldg. D., Fort Collins, CO 80526, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaxiao","family":"Niu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&amp;F University, Yangling 712100, Shaanxi, China"},{"name":"Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenting","family":"Han","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&amp;F University, Yangling 712100, Shaanxi, China"},{"name":"Institute of Soil and Water Conservation, Northwest A&amp;F University, Yangling 712100, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.agrformet.2017.05.001","article-title":"Integrating growth stage deficit irrigation into a process based crop model","volume":"243","author":"Lopez","year":"2017","journal-title":"Agric. 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