Anderson de Jesus Pereira , Larissa Godarelli Farinassi , Bruno Ricardo Silva Costa , Israel de Oliveira Junior , Robson Argolo dos Santos , Lucio André de Castro Jorge , Luís Henrique Bassoi
{"title":"利用无人机和近端传感器的植被指数评估葡萄藤水分状况和砧木效应","authors":"Anderson de Jesus Pereira , Larissa Godarelli Farinassi , Bruno Ricardo Silva Costa , Israel de Oliveira Junior , Robson Argolo dos Santos , Lucio André de Castro Jorge , Luís Henrique Bassoi","doi":"10.1016/j.rsase.2025.101708","DOIUrl":null,"url":null,"abstract":"<div><div>Unmanned aerial vehicle (UAV)-based sensing and proximal sensing are effective techniques for monitoring grapevine canopies through vegetation indices (VI), enabling the assessment of plant growth-related factors. This study aimed to evaluate whether VIs derived from both sensing platforms correlate with leaf grapevine and soil water status. The similarity between the two platforms in estimating VI and their effectiveness in detecting variations in plant growth associated with different rootstocks were examined. Grapevines cv. Syrah grafted onto IAC 572 and Paulsen 1103 rootstocks were monitored across a 1.1 ha vineyard in Southeastern Brazil. UAV-based sensing involved capturing images with a multispectral sensor mounted on an UAV, while proximal sensing was conducted by measuring canopy reflectance with an active sensor. The Normalized Difference Red Edge (NDRE) derived from UAV-based sensing showed the strongest correlation with stomatal conductance <em>g</em><sub><em>s</em></sub> (r = 0.84, p < 0.001, pseudo-R<sup>2</sup> = 0.73) and relative leaf water content RLWC (r = 0.73, p < 0.001, R<sup>2</sup> = 0.60). No significant correlation was observed between the soil water parameter θ at 0–0.2 m and VI estimates from either platform (p > 0.05). Significant correlations (p < 0.001) were found between NDRE (r = 0.70, pseudo-R<sup>2</sup> = 0,58) and Normalized Difference Vegetation Index (NDVI) (r = 0.72, pseudo-R<sup>2</sup> = 0,54), derived from both sensing methods. Both indices also detected differences in grapevine vigor related to rootstock influence. UAV-based measures provided stronger correlations with these traits than proximal measurements.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101708"},"PeriodicalIF":4.5000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing grapevine water status and rootstock effects using vegetation indices from UAV and proximal sensors\",\"authors\":\"Anderson de Jesus Pereira , Larissa Godarelli Farinassi , Bruno Ricardo Silva Costa , Israel de Oliveira Junior , Robson Argolo dos Santos , Lucio André de Castro Jorge , Luís Henrique Bassoi\",\"doi\":\"10.1016/j.rsase.2025.101708\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Unmanned aerial vehicle (UAV)-based sensing and proximal sensing are effective techniques for monitoring grapevine canopies through vegetation indices (VI), enabling the assessment of plant growth-related factors. This study aimed to evaluate whether VIs derived from both sensing platforms correlate with leaf grapevine and soil water status. The similarity between the two platforms in estimating VI and their effectiveness in detecting variations in plant growth associated with different rootstocks were examined. Grapevines cv. Syrah grafted onto IAC 572 and Paulsen 1103 rootstocks were monitored across a 1.1 ha vineyard in Southeastern Brazil. UAV-based sensing involved capturing images with a multispectral sensor mounted on an UAV, while proximal sensing was conducted by measuring canopy reflectance with an active sensor. The Normalized Difference Red Edge (NDRE) derived from UAV-based sensing showed the strongest correlation with stomatal conductance <em>g</em><sub><em>s</em></sub> (r = 0.84, p < 0.001, pseudo-R<sup>2</sup> = 0.73) and relative leaf water content RLWC (r = 0.73, p < 0.001, R<sup>2</sup> = 0.60). No significant correlation was observed between the soil water parameter θ at 0–0.2 m and VI estimates from either platform (p > 0.05). Significant correlations (p < 0.001) were found between NDRE (r = 0.70, pseudo-R<sup>2</sup> = 0,58) and Normalized Difference Vegetation Index (NDVI) (r = 0.72, pseudo-R<sup>2</sup> = 0,54), derived from both sensing methods. Both indices also detected differences in grapevine vigor related to rootstock influence. UAV-based measures provided stronger correlations with these traits than proximal measurements.</div></div>\",\"PeriodicalId\":53227,\"journal\":{\"name\":\"Remote Sensing Applications-Society and Environment\",\"volume\":\"39 \",\"pages\":\"Article 101708\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing Applications-Society and Environment\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352938525002617\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing Applications-Society and Environment","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352938525002617","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Assessing grapevine water status and rootstock effects using vegetation indices from UAV and proximal sensors
Unmanned aerial vehicle (UAV)-based sensing and proximal sensing are effective techniques for monitoring grapevine canopies through vegetation indices (VI), enabling the assessment of plant growth-related factors. This study aimed to evaluate whether VIs derived from both sensing platforms correlate with leaf grapevine and soil water status. The similarity between the two platforms in estimating VI and their effectiveness in detecting variations in plant growth associated with different rootstocks were examined. Grapevines cv. Syrah grafted onto IAC 572 and Paulsen 1103 rootstocks were monitored across a 1.1 ha vineyard in Southeastern Brazil. UAV-based sensing involved capturing images with a multispectral sensor mounted on an UAV, while proximal sensing was conducted by measuring canopy reflectance with an active sensor. The Normalized Difference Red Edge (NDRE) derived from UAV-based sensing showed the strongest correlation with stomatal conductance gs (r = 0.84, p < 0.001, pseudo-R2 = 0.73) and relative leaf water content RLWC (r = 0.73, p < 0.001, R2 = 0.60). No significant correlation was observed between the soil water parameter θ at 0–0.2 m and VI estimates from either platform (p > 0.05). Significant correlations (p < 0.001) were found between NDRE (r = 0.70, pseudo-R2 = 0,58) and Normalized Difference Vegetation Index (NDVI) (r = 0.72, pseudo-R2 = 0,54), derived from both sensing methods. Both indices also detected differences in grapevine vigor related to rootstock influence. UAV-based measures provided stronger correlations with these traits than proximal measurements.
期刊介绍:
The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems