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

@article{Gmez2018MachineLA,
  title={Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture},
  author={Diego G{\'o}mez and Pablo Salvador and Julia Sanz and Carlos Casanova and Daniel Taratiel and Jose Luis Casanova},
  journal={Journal of Applied Remote Sensing},
  year={2018},
  volume={12}
}
Abstract. 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… Expand
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