An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data

We propose a simple, spatially invariant and probabilistic year-round Empirical Standardized Soil Mois-ture Index (ESSMI) that is designed to classify soil moisture anomalies from harmonized multi-satellitesurface data into categories of agricultural drought intensity. The ESSMI is computed by fitting a nonpara-metric empirical probability density function (ePDF) to historical time-series of soil moisture observationsand 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 soilconditions. Drought intensity is defined as the number of negative standard deviations between theobserved soil moisture value and the respective normal climatological conditions. To evaluate the per-formance of the ESSMI, we fitted the ePDF to the Essential Climate Variable Soil Moisture (ECV SM) v02.0data 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 proba-bility density functions (bPDF and nPDF, respectively). Goodness-of-fit results attained with time-seriesof ECV SM values averaged at monthly, seasonal, half-yearly and yearly timescales suggest that the ePDFprovides triggers of agricultural drought onset and intensity that are more accurate and precise than thebPDF and nPDF. Furthermore, by accurately mapping the occurrence of major drought events over thelast three decades, the ESSMI proved to be spatio-temporal consistent and the ECV SM data to providea well calibrated and homogenized soil moisture climatology for the region. Maize, soybean and wheatcrop yields in the region are highly correlated (r > 0.82) with cumulative ESSMI values computed duringthe months of critical crop growing, indicating that the nonparametric index of soil moisture anomaliescan be used for agricultural drought assessment.

SAIOTE CARRAO Hugo Miguel;  RUSSO Simone;  SEPULCRE G;  BARBOSA Paulo; 

2016-01-12

ELSEVIER SCIENCE BV

JRC95298

0303-2434,   

http://www.sciencedirect.com/science/article/pii/S0303243415300015,    https://publications.jrc.ec.europa.eu/repository/handle/JRC95298,   

doi:10.1016/j.jag.2015.06.011,