DOI: 10.1016/j.jag.2015.06.011
Scopus记录号: 2-s2.0-85015897968
论文题名: An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data
作者: Carrão H ; , Russo S ; , Sepulcre-Canto G ; , Barbosa P
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 48 起始页码: 74
结束页码: 84
语种: 英语
英文关键词: Agricultural drought
; Crop yield
; Remote sensing-based index
; Soil moisture
; South–Central America
Scopus关键词: crop yield
; drought
; empirical analysis
; probability density function
; remote sensing
; satellite data
; soil moisture
; Central America
; South America
; Glycine max
; Triticum aestivum
; Zea mays
英文摘要: We propose a simple, spatially invariant and probabilistic year-round Empirical Standardized Soil Moisture Index (ESSMI) that is designed to classify soil moisture anomalies from harmonized multi-satellite surface data into categories of agricultural drought intensity. The ESSMI is computed by fitting a nonparametric empirical probability density function (ePDF) to historical time-series of soil moisture observations and then transforming it into a normal distribution with a mean of zero and standard deviation of one. Negative standard normal values indicate dry soil conditions, whereas positive values indicate wet soil conditions. Drought intensity is defined as the number of negative standard deviations between the observed soil moisture value and the respective normal climatological conditions. To evaluate the performance of the ESSMI, we fitted the ePDF to the Essential Climate Variable Soil Moisture (ECV SM) v02.0 data values collected in the period between January 1981 and December 2010 at South–Central America, and compared the root-mean-square-errors (RMSE) of residuals with those of beta and normal probability density functions (bPDF and nPDF, respectively). Goodness-of-fit results attained with time-series of ECV SM values averaged at monthly, seasonal, half-yearly and yearly timescales suggest that the ePDF provides triggers of agricultural drought onset and intensity that are more accurate and precise than the bPDF and nPDF. Furthermore, by accurately mapping the occurrence of major drought events over the last three decades, the ESSMI proved to be spatio-temporal consistent and the ECV SM data to provide a well calibrated and homogenized soil moisture climatology for the region. Maize, soybean and wheat crop yields in the region are highly correlated (r > 0.82) with cumulative ESSMI values computed during the months of critical crop growing, indicating that the nonparametric index of soil moisture anomalies can be used for agricultural drought assessment. © 2015 The Authors Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80084
Appears in Collections: 气候变化事实与影响
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作者单位: European Commission (EC), Joint Research Centre (JRC), Via Enrico Fermi 2749, Ispra, VA, Italy; Earth and Life Institute (ELI), Université Catholique de Louvain, Louvain-la-Neuve, Belgium
Recommended Citation:
Carrão H,, Russo S,, Sepulcre-Canto G,et al. An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,48