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Detecting Desert Locust Breeding Grounds: A Satellite-Assisted Modeling Approach

1
Earth System Science Center, The University of Alabama in Huntsville, 320 Sparkman Dr., Huntsville, AL 35805, USA
2
NASA SERVIR Science Coordination Office, Marshall Space Flight Center, 320 Sparkman Dr., Huntsville, AL 35805, USA
3
ENSCO, Inc., Huntsville, AL 35805, USA
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NASA Short-Term Prediction Research and Transition (SPoRT) Center, Marshall Space Flight Center, Huntsville, AL 35805, USA
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Universities Space Research Association, Huntsville, AL 35805, USA
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United Nations Food and Agriculture Organization, 00153 Rome, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Deepak R. Mishra
Remote Sens. 2021, 13(7), 1276; https://doi.org/10.3390/rs13071276
Received: 24 February 2021 / Revised: 20 March 2021 / Accepted: 25 March 2021 / Published: 27 March 2021
(This article belongs to the Section Environmental Remote Sensing)
The objective of this study is to evaluate the ability of soil physical characteristics (i.e., texture and moisture conditions) to better understand the breeding conditions of desert locust (DL). Though soil moisture and texture are well-known and necessary environmental conditions for DL breeding, in this study, we highlight the ability of model-derived soil moisture estimates to contribute towards broader desert locust monitoring activities. We focus on the recent DL upsurge in East Africa from October 2019 though June 2020, utilizing known locust observations from the United Nations Food and Agriculture Organization (FAO). We compare this information to results from the current literature and combine the two datasets to create “optimal thresholds” of breeding conditions. When considering the most optimal conditions (all thresholds met), the soil texture combined with modeled soil moisture content predicted the estimated DL egg-laying period 62.5% of the time. Accounting for the data errors and uncertainties, a 3 × 3 pixel buffer increased this to 85.2%. By including soil moisture, the areas of optimal egg laying conditions decreased from 33% to less than 20% on average. View Full-Text
Keywords: desert locust; soil moisture; modeling desert locust; soil moisture; modeling
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MDPI and ACS Style

Ellenburg, W.L.; Mishra, V.; Roberts, J.B.; Limaye, A.S.; Case, J.L.; Blankenship, C.B.; Cressman, K. Detecting Desert Locust Breeding Grounds: A Satellite-Assisted Modeling Approach. Remote Sens. 2021, 13, 1276. https://doi.org/10.3390/rs13071276

AMA Style

Ellenburg WL, Mishra V, Roberts JB, Limaye AS, Case JL, Blankenship CB, Cressman K. Detecting Desert Locust Breeding Grounds: A Satellite-Assisted Modeling Approach. Remote Sensing. 2021; 13(7):1276. https://doi.org/10.3390/rs13071276

Chicago/Turabian Style

Ellenburg, W. L., Vikalp Mishra, Jason B. Roberts, Ashutosh S. Limaye, Jonathan L. Case, Clay B. Blankenship, and Keith Cressman. 2021. "Detecting Desert Locust Breeding Grounds: A Satellite-Assisted Modeling Approach" Remote Sensing 13, no. 7: 1276. https://doi.org/10.3390/rs13071276

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