干旱区生态环境知识资源中心(Arid): Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data

Arid

DOI	10.3390/agronomy14061238
	Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data
	Kerry, Ruth; Ingram, Ben; Sanders, Kirsten; Henrie, Abigail; Hammond, Keegan; Hawks, Dave; Hansen, Neil; Jensen, Ryan; Hopkins, Bryan
通讯作者	Kerry, R
来源期刊	AGRONOMY-BASEL
EISSN	2073-4395
出版年	2024
卷号	14 期号:6
英文摘要	Turfgrass irrigation consumes a large amount of the scarce freshwater in arid/semi-arid regions. Approximately 50% of this irrigation water is wasted. It has been suggested that determining patterns of spatial variability in soil moisture to modify applications with valve-in-head sprinkler technology can greatly reduce waste. Variable rate irrigation (VRI) studies in traditional agricultural settings have shown that VRI zones do not stay static temporally and need to be frequently redetermined. Electrical conductivity (ECa) data from Geonics EM38 surveys and data from Red, Green, Blue (RGB) and Thermal Infra-Red (Th.IR) drone surveys are less time-consuming and therefore expensive to collect than a dense field survey of soil moisture and grass health to produce accurate geostatistical maps. Drone flights and ECa surveys are compared here for their ability to accurately estimate spatial patterns of soil volumetric water content (VWC) using simple linear regression and z-score transformations for prediction-non-geostatistical approaches that require less data. Overall, ECa readings collected in the horizontal mode were the most consistent at capturing spatial patterns in soil moisture. Predictions from regression produced lower root mean squared errors (RMSEs) for the larger datasets. However, z-score transformation produced lower RMSEs when the sample number was very small and preserved the scale of values better than the regression approach. The results suggested that predictions from ECa and drone data were useful for capturing key features in soil moisture patterns for 2-3 weeks, suggesting that a periodic reassessment of zones is needed. Using ECa and drone data in an urban environment is more labor-intensive than in an agricultural field, so it is likely that automating periodic re-surveying of ECa data for zone definition would only be cost-effective for golf courses or high-income sports fields. Elsewhere, using static zones with variable rates applied to each zone may be more efficient.
英文关键词	soil mapping management zones variable rate irrigation (VRI) sensed data
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001254507900001
WOS关键词	ELECTRICAL-CONDUCTIVITY ; GRASSES ; MAPS
WOS类目	Agronomy ; Plant Sciences
WOS研究方向	Agriculture ; Plant Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/402787
推荐引用方式 GB/T 7714	Kerry, Ruth,Ingram, Ben,Sanders, Kirsten,et al. Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data[J],2024,14(6).
APA	Kerry, Ruth.,Ingram, Ben.,Sanders, Kirsten.,Henrie, Abigail.,Hammond, Keegan.,...&Hopkins, Bryan.(2024).Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data.AGRONOMY-BASEL,14(6).
MLA	Kerry, Ruth,et al."Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data".AGRONOMY-BASEL 14.6(2024).