Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture

Abstract

© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. Desert locusts have attacked crops since antiquity. To prevent or mitigate its effects on local communities, it is necessary to precisely locate its breeding areas. Previous works have relied on precipitation and vegetation index datasets obtained by satellite remote sensing. However, these products present some limitations in arid or semiarid environments. We have explored a parameter: soil moisture (SM); and examined its influence on the desert locust wingless juveniles. We have used two machine learning algorithms (generalized linear model and random forest) to evaluate the link between hopper presences and SM conditions under different time scenarios. RF obtained the best model performance with very good validation results according to the true skill statistic and receiver operating characteristic curve statistics. It was found that an area becomes suitable for breeding when the minimum SM values are over 0.07m 3 /m 3 during 6 days or more. These results demonstrate the possibility to identify breeding areas in Mauritania by means of SM, and the suitability of ESA CCI SM product to complement or substitute current monitoring techniques based on precipitation datasets.