Satellite microwave remote sensing for environmental modeling of mosquito population dynamics

Abstract

Environmental variability has important influences on mosquito life cycles and understanding the spatial and temporal patterns of mosquito populations is critical for mosquito control and vector-borne disease prevention. Meteorological data used for model-based predictions of mosquito abundance and life cycle dynamics are typically acquired from ground-based weather stations; however, data availability and completeness are often limited by sparse networks and resource availability. In contrast, environmental measurements from satellite remote sensing are more spatially continuous and can be retrieved automatically. This study compared environmental measurements from the NASA Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and in situ weather station data to examine their ability to predict the abundance of two important mosquito species (Aedes vexans and Culex tarsalis) in Sioux Falls, South Dakota, USA from 2005 to 2010. The AMSR-E land parameters included daily surface water inundation fraction, surface air temperature, soil moisture, and microwave vegetation opacity. The AMSR-E derived models had better fits and higher forecasting accuracy than models based on weather station data despite the relatively coarse (25-km) spatial resolution of the satellite data. In the AMSR-E models, air temperature and surface water fraction were the best predictors of Aedes vexans, whereas air temperature and vegetation opacity were the best predictors of Cx. tarsalis abundance. The models were used to extrapolate spatial, seasonal, and interannual patterns of climatic suitability for mosquitoes across eastern South Dakota. Our findings demonstrate that environmental metrics derived from satellite passive microwave radiometry are suitable for predicting mosquito population dynamics and can potentially improve the effectiveness of mosquito-borne disease early warning systems.

Spatio-Temporal Epidemiology of Human West Nile Virus Disease in South Dakota

“…This multifactor index was found to be a better early warning indicator for the onset of human WNV disease than predictions based on single sources of information [35]. Previous research in South Dakota has found influences of climatic variability on the abundance of vector mosquitoes [15,17] and human disease cases [18] and we suggest that combining these types of environmental indicators with mosquito and human case surveillance data offers a strong potential for improving our ability to predict the spatial and temporal dimensions of WNV in South Dakota as well as in other regions where WNV is endemic.…”

Modeling insect population fluctuations with satellite land surface temperature

“…Accuracy in temperature measurements is important for applying integrated pest management (IPM) in both forest and agricultural settings (e.g., Fand et al, 2014;Faye et al, 2014), for exploring the possible effects of global warming on ecosystems and organisms (e.g., Logan et al, 2004;Gutierrez et al, 2009;Ponti et al, 2009) and for developing strategies for the management of vector-borne diseases of humans and animals over large geographic areas (e.g., Rogers et al, 1996;Gilioli and Mariani, 2011;Amek et al, 2012;Chen and Hsieh, 2012). Recently, land surface temperature (LST) data derived from satellites have been proposed as a possible source of temperature data to be fed into insect population models and used to explore ecological and environmental questions (Carbajal de la Fuente et al, 2009;Coops et al, 2009;Chuang et al, 2012;Lensky and Dayan, 2011;Morag et al, 2012;Steinman et al, 2012;Blum et al, 2013;Aharonson-Raz et al, 2014). Lensky and Dayan (2011) used LST data to investigate the variability in the developmental rate of an agricultural pest (Heliothis sp.)…”

Inter-Calibration of Satellite Passive Microwave Land Observations from AMSR-E and AMSR2 Using Overlapping FY3B-MWRI Sensor Measurements

“…Microwave radiometers onboard Earth observing satellites have been widely used for observing critical land surface parameters supporting environmental monitoring and global change studies [1][2][3][4]. High-quality and consistent geophysical retrievals from well-calibrated, stable radiometer observations and long-term data records are essential for accurately delineating regional patterns and distinguishing natural climate variations from more subtle environmental trends.…”