DOI: 10.1016/j.jag.2015.03.008
Scopus记录号: 2-s2.0-84943516048
论文题名: Detection of anomalous crop condition and soil variability mapping using a 26 year Landsat record and the Palmer crop moisture index
作者: Venteris E ; R ; , Tagestad J ; D ; , Downs J ; L ; , Murray C ; J
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2015
卷: 39 起始页码: 160
结束页码: 170
语种: 英语
英文关键词: Corn
; Geologic injection
; Monitoring
; NDVI
; Soil mapping
; Soybean
Scopus关键词: developing world
; index method
; Landsat
; maize
; mapping
; NDVI
; soil moisture
; soybean
; spatial variation
; temporal variation
; vegetation mapping
; Illinois
; United States
; Glycine max
; Zea mays
英文摘要: Cost-effective and reliable vegetation monitoring methods are needed for applications ranging from traditional agronomic mapping, to verifying the safety of geologic injection activities. A particular challenge is defining baseline crop conditions and subsequent anomalies from long term imagery records (Landsat) in the face of large spatiotemporal variability. We develop a new method for defining baseline crop response (near peak growth) using the normalized difference vegetation index (NDVI) from 26 years (1986-2011) of Landsat data for 400km2 surrounding a planned geologic carbon sequestration site near Jacksonville, Illinois. The normal score transform (yNDVI) was applied on a field by field basis to accentuate spatial patterns and level differences due to planting times. We tested crop type and soil moisture (Palmer crop moisture index (CMI)) as predictors of expected crop condition. Spatial patterns in yNDVI were similar between corn and soybeans - the two major crops. Linear regressions between yNDVI and the cumulative CMI (CCMI) exposed complex interactions between crop condition, field location (topography and soils), and annual moisture. Wet toposequence positions (depressions) were negatively correlated to CCMI and dry positions (crests) positively correlated. However, only 21% of the landscape showed a statistically significant (p < 0.05) linear relationship. To map anomalous crop conditions, we defined a tolerance interval based on yNDVI statistics. Tested on an independent image (2013), 63 of 1483 possible fields showed unusual crop condition. While the method is not directly suitable for crop health assessment, the spatial patterns in correlation between yNDVI and CCMI have potential applications for pest damage detection and edaphological soil mapping, especially in the developing world. © 2015 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79472
Appears in Collections: 气候变化事实与影响
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作者单位: Pacific Northwest National Laboratory, P.O Box 999, MSIN K9-33, Richland, WA, United States
Recommended Citation:
Venteris E,R,, Tagestad J,et al. Detection of anomalous crop condition and soil variability mapping using a 26 year Landsat record and the Palmer crop moisture index[J]. International Journal of Applied Earth Observation and Geoinformation,2015-01-01,39