Satellite Observations and Malaria: New Opportunities for Research and Applications
2021; Elsevier BV; Volume: 37; Issue: 6 Linguagem: Inglês
10.1016/j.pt.2021.03.003
ISSN1471-5007
AutoresMichael C. Wimberly, Kirsten M. de Beurs, Tatiana Loboda, William Pan,
Tópico(s)Viral Infections and Vectors
ResumoLong-term satellite records supply data on environmental variables that influence malaria transmission cycles.High-resolution land use and land cover maps from satellite observations provide information about human activities that affect mosquito habitats and exposure to mosquito bites.New sources of very-high-resolution satellite data create opportunities for precise, localized mapping of mosquito habitat and human settlements.Global availability of free synthetic aperture radar data facilitates mapping of buildings, water, and land use in cloudy conditions that are characteristic of many tropical regions.New cloud-based technologies for remote sensing data access, processing, and analysis are lowering the barriers to data use for malaria applications. Satellite remote sensing provides a wealth of information about environmental factors that influence malaria transmission cycles and human populations at risk. Long-term observations facilitate analysis of climate–malaria relationships, and high-resolution data can be used to assess the effects of agriculture, urbanization, deforestation, and water management on malaria. New sources of very-high-resolution satellite imagery and synthetic aperture radar data will increase the precision and frequency of observations. Cloud computing platforms for remote sensing data combined with analysis-ready datasets and high-level data products have made satellite remote sensing more accessible to nonspecialists. Further collaboration between the malaria and remote sensing communities is needed to develop and implement useful geospatial data products that will support global efforts toward malaria control, elimination, and eradication. Satellite remote sensing provides a wealth of information about environmental factors that influence malaria transmission cycles and human populations at risk. Long-term observations facilitate analysis of climate–malaria relationships, and high-resolution data can be used to assess the effects of agriculture, urbanization, deforestation, and water management on malaria. New sources of very-high-resolution satellite imagery and synthetic aperture radar data will increase the precision and frequency of observations. Cloud computing platforms for remote sensing data combined with analysis-ready datasets and high-level data products have made satellite remote sensing more accessible to nonspecialists. Further collaboration between the malaria and remote sensing communities is needed to develop and implement useful geospatial data products that will support global efforts toward malaria control, elimination, and eradication. Since 2000, considerable progress has been made in reducing the global burden of malaria, shrinking the malaria map, and moving toward the goal of malaria eradication [1.O'Meara W.P. et al.Changes in the burden of malaria in sub-Saharan Africa.Lancet Infect. Dis. 2010; 10: 545-555Abstract Full Text Full Text PDF PubMed Scopus (354) Google Scholar, 2.Bhatt S. et al.The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015.Nature. 2015; 526: 207-211Crossref PubMed Scopus (1243) Google Scholar, 3.Feachem R.G.A. et al.Shrinking the malaria map: progress and prospects.Lancet. 2010; 376: 1566-1578Abstract Full Text Full Text PDF PubMed Scopus (294) Google Scholar]. However, there is concern that declines in malaria cases and deaths have slowed [4.World Health Organization World Malaria Report 2019. WHO, 2019Crossref Google Scholar]. Although the reasons for this slowdown are multifaceted, an important factor is the limited set of tools and approaches that are currently available for combating malaria. The recent Lancet Commission report on malaria eradication emphasized that new technologies, including innovations in the field of malaria informatics, are needed to facilitate more effective data-driven management of malaria interventions [5.Feachem R.G. et al.Malaria eradication within a generation: ambitious, achievable, and necessary.Lancet. 2019; 394: 1056-1112Abstract Full Text Full Text PDF PubMed Scopus (102) Google Scholar]. Geospatial data, including satellite observations, were highlighted as a key data source for monitoring human populations and their environments in support of malaria eradication. This recommendation is concordant with other assessments that have emphasized the importance of spatial decision support systems to enable national and subnational program management as well as regional and global strategic planning [6.Hemingway J. et al.Tools and strategies for malaria control and elimination: what do we need to achieve a grand convergence in malaria?.PLoS Biol. 2016; 14e1002380Crossref PubMed Scopus (104) Google Scholar]. The value of satellite observations for malaria research has long been recognized, with the earliest reviews appearing more than two decades ago [7.Hay S. et al.From predicting mosquito habitat to malaria seasons using remotely sensed data: practice, problems and perspectives.Parasitol. Today. 1998; 14: 306-313Abstract Full Text Full Text PDF PubMed Scopus (127) Google Scholar, 8.Thomson M. et al.Mapping malaria risk in Africa: What can satellite data contribute?.Parasitol. Today. 1997; 13: 313-318Abstract Full Text PDF PubMed Scopus (76) Google Scholar, 9.Rogers D.J. et al.Satellite imagery in the study and forecast of malaria.Nature. 2002; 415: 710-715Crossref PubMed Scopus (323) Google Scholar]. Since then, considerable changes in global antimalaria efforts and advances in the field of remote sensing have taken place. Myriad connections between the environmental phenomena observed by satellite-borne sensors and different aspects of the malaria transmission cycle have been identified (Figure 1). However, the challenges of discovering, accessing, and processing relevant satellite data (Figure 2) still limit their use for malaria projects. The purpose of this review is to present an up-to-date assessment of satellite missions relevant to malaria and identify opportunities where new sources of remote sensing data can be leveraged to support novel applications. Major themes include long-term satellite records of environmental changes that affect malaria risk, new sources of satellite data with higher spatial resolution, measurement frequency, and global coverage, and emerging technologies that can increase the accessibility and usability of remote sensing data in the malaria sector.Figure 2Spatial and Temporal Resolutions of Satellite Missions and Data Products with Applications to Malaria.Show full captionColors represent the main applications of satellite data at different resolutions. The lighter green color of the Commercial VHR box indicates the variable frequency of image acquisition. In principle, most VHR commercial satellites can collect data on a daily or near-daily repeat cycle. In practice, these satellites image only a fraction of the Earth's surface each day, acquisition strategies are based on demand from customers, and remeasurement frequency can range from days to years. Abbreviations: AVHRR, advanced very-high-resolution radiometer; CHIRPS/CHIRTS, climate hazards group infrared precipitation/temperature with stations; GLPDR, global land parameter data record; IMERG, integrated multisatellite retrievals for global precipitation measurement; LULC, land use and land cover; MODIS, moderate resolution imaging spectroradiometer; NISAR, NASA-ISRO synthetic aperture radar; SMAP, soil moisture active-passive; SRTM, shuttle radar topography mission; VHR, very high resolution; VIIRS, visible infrared imaging radiometer suite.View Large Image Figure ViewerDownload Hi-res image Download (PPT) Colors represent the main applications of satellite data at different resolutions. The lighter green color of the Commercial VHR box indicates the variable frequency of image acquisition. In principle, most VHR commercial satellites can collect data on a daily or near-daily repeat cycle. In practice, these satellites image only a fraction of the Earth's surface each day, acquisition strategies are based on demand from customers, and remeasurement frequency can range from days to years. Abbreviations: AVHRR, advanced very-high-resolution radiometer; CHIRPS/CHIRTS, climate hazards group infrared precipitation/temperature with stations; GLPDR, global land parameter data record; IMERG, integrated multisatellite retrievals for global precipitation measurement; LULC, land use and land cover; MODIS, moderate resolution imaging spectroradiometer; NISAR, NASA-ISRO synthetic aperture radar; SMAP, soil moisture active-passive; SRTM, shuttle radar topography mission; VHR, very high resolution; VIIRS, visible infrared imaging radiometer suite. Malaria transmission cycles are sensitive to climate variability, and satellite observations provide accurate, reliable, and timely information about these variations. Precipitation influences the hydrological cycles of aquatic habitats for anopheline larvae [10.Smith M. et al.Incorporating hydrology into climate suitability models changes projections of malaria transmission in Africa.Nat. Commun. 2020; 11: 1-9Crossref PubMed Scopus (2) Google Scholar], while temperature and humidity affect the vital rates that drive mosquito population dynamics, parasite development in the mosquito, and parasite transmission [11.Mordecai E.A. et al.Thermal biology of mosquito-borne disease.Ecol. Lett. 2019; 22: 1690-1709Crossref PubMed Scopus (91) Google Scholar]. Climate also affects malaria indirectly through influences on land use, settlement patterns, and human population movements [12.Stresman G.H. Beyond temperature and precipitation. Ecological risk factors that modify malaria transmission.Acta Trop. 2010; 116: 167-172Crossref PubMed Scopus (52) Google Scholar]. The densities of in situ weather stations, as well as the quality and completeness of the data collected, are limited in many of the low- and middle-income countries where malaria is a public health concern. Therefore, satellite data are an important information source for characterizing multidecadal trends and monitoring ongoing changes in these areas. Relevant satellite measurements include precipitation estimates, land surface temperature (see Glossary), and spectral indices like the normalized difference vegetation index (NDVI) that are sensitive to vegetation and moisture (Box 1).Box 1Optical and Thermal Remote SensingSensors on Earth-observing satellites detect energy within one of more spectral bands. Each band captures radiation that is reflected or emitted by different objects on the Earth's surface within a rather narrow range of electromagnetic radiation. Bands in the visible portion of the spectrum detect blue (~400 nm), green (~500 nm), and red (~700 nm) light, while bands in the near-infrared (750–1000 nm) and shortwave-infrared (1200–2500 nm) portions of the spectrum have varying sensitivities to vegetation, water, and soils. Measurements of radiance taken by the sensors are transformed into reflectance, which measures the fraction of incoming solar radiation reflected from the Earth's surface back to the sensor.Reflectance data are commonly used to compute spectral indices. The NDVI is calculated from near-infrared and red bands and is sensitive to healthy green vegetation. The normalized difference moisture index (NDMI) is calculated from near-infrared and shortwave-infrared bands and is sensitive to vegetation moisture stress. Because spatial and temporal variation in vegetation tracks fluctuations in temperature and precipitation, these indices provide an indirect measurement of underlying environmental patterns that influence mosquitoes and malaria. Other spectral indices like the normalized difference water index (NDWI), calculated from green and near-infrared bands, can detect open water bodies that provide larval habitats. Depending on the sensor, these indices can be mapped at spatial resolutions ranging from less than a meter to thousands of meters.Thermal sensors detect emitted radiation from the thermal infrared portion of the spectrum (3–14 μm). Measurements of radiance in the thermal wavelengths can be used to estimate land surface temperature, which measures how much heat radiates from the uppermost part of the Earth's surface. This surface may be a grass lawn, bare soil, the roof of a building, or the leaves at the top of a forest. Mosquitoes are directly influenced by air temperature, which is generally correlated with land surface temperature. During the day, near-surface air temperature often deviates from land surface temperature because of the effects of solar radiation, wind, and soil moisture. During the night, solar radiation does not influence land surface temperature and the correlation with air temperature is usually stronger than during the day. Because of these differences, land surface temperature is most useful as a relative indicator of temperature variation in space and time when used as an environmental indicator of malaria risk. Sensors on Earth-observing satellites detect energy within one of more spectral bands. Each band captures radiation that is reflected or emitted by different objects on the Earth's surface within a rather narrow range of electromagnetic radiation. Bands in the visible portion of the spectrum detect blue (~400 nm), green (~500 nm), and red (~700 nm) light, while bands in the near-infrared (750–1000 nm) and shortwave-infrared (1200–2500 nm) portions of the spectrum have varying sensitivities to vegetation, water, and soils. Measurements of radiance taken by the sensors are transformed into reflectance, which measures the fraction of incoming solar radiation reflected from the Earth's surface back to the sensor. Reflectance data are commonly used to compute spectral indices. The NDVI is calculated from near-infrared and red bands and is sensitive to healthy green vegetation. The normalized difference moisture index (NDMI) is calculated from near-infrared and shortwave-infrared bands and is sensitive to vegetation moisture stress. Because spatial and temporal variation in vegetation tracks fluctuations in temperature and precipitation, these indices provide an indirect measurement of underlying environmental patterns that influence mosquitoes and malaria. Other spectral indices like the normalized difference water index (NDWI), calculated from green and near-infrared bands, can detect open water bodies that provide larval habitats. Depending on the sensor, these indices can be mapped at spatial resolutions ranging from less than a meter to thousands of meters. Thermal sensors detect emitted radiation from the thermal infrared portion of the spectrum (3–14 μm). Measurements of radiance in the thermal wavelengths can be used to estimate land surface temperature, which measures how much heat radiates from the uppermost part of the Earth's surface. This surface may be a grass lawn, bare soil, the roof of a building, or the leaves at the top of a forest. Mosquitoes are directly influenced by air temperature, which is generally correlated with land surface temperature. During the day, near-surface air temperature often deviates from land surface temperature because of the effects of solar radiation, wind, and soil moisture. During the night, solar radiation does not influence land surface temperature and the correlation with air temperature is usually stronger than during the day. Because of these differences, land surface temperature is most useful as a relative indicator of temperature variation in space and time when used as an environmental indicator of malaria risk. To assess relationships between climate variations and malaria, it is essential to have long-term records combined with frequent measurements to capture short-term anomalies and seasonal cycles (Figure 2). Early applications of remote sensing for malaria research relied on NDVI and land surface temperature measured by the advanced very-high-resolution radiometer (AVHRR) instrument on United States National Oceanic and Atmospheric Administration (NOAA) weather satellites. AVHRR provides daily, global observations dating back to 1981 at a nominal resolution of 1000–4000 m [8.Thomson M. et al.Mapping malaria risk in Africa: What can satellite data contribute?.Parasitol. Today. 1997; 13: 313-318Abstract Full Text PDF PubMed Scopus (76) Google Scholar]. The moderate resolution imaging spectroradiometer (MODIS) instrument, launched aboard the United States National Aeronautics and Space Administration (NASA) Terra and Aqua satellites in 1999 and 2002, provided significant improvements in spatial resolution (250–1000 m), measurement frequency (up to four times daily in the tropics), number of spectral bands, and data quality. Spectral indices and land surface temperature from MODIS are frequently used with satellite precipitation measurements (Figure 3) as predictors in spatial models for generating malaria risk maps [13.Adigun A.B. et al.Malaria risk in Nigeria: Bayesian geostatistical modelling of 2010 malaria indicator survey data.Malar. J. 2015; 14: 156Crossref PubMed Scopus (34) Google Scholar,14.Alegana V.A. et al.Advances in mapping malaria for elimination: fine resolution modelling of Plasmodium falciparum incidence.Sci. Rep. 2016; 6: 29628Crossref PubMed Scopus (26) Google Scholar], and time series models for predicting changes in malaria risk resulting from environmental fluctuations [15.Davis J.K. et al.A genetic algorithm for identifying spatially-varying environmental drivers in a malaria time series model.Environ. Model. Softw. 2019; 119: 275-284Crossref PubMed Scopus (13) Google Scholar,16.Sewe M.O. et al.Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya.Sci. Rep. 2017; 7: 2589Crossref PubMed Scopus (20) Google Scholar]. Remotely sensed data are also used to control for environmental variation when studying the influences of other factors on malaria. A study of the effects of malaria interventions in Africa used MODIS data to control for climate variation and night-lights data from the newer visible infrared imaging radiometer suite (VIIRS) to control for urbanization [2.Bhatt S. et al.The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015.Nature. 2015; 526: 207-211Crossref PubMed Scopus (1243) Google Scholar,17.Weiss D.J. et al.Re-examining environmental correlates of Plasmodium falciparum malaria endemicity: a data-intensive variable selection approach.Malar. J. 2015; 14: 68Crossref PubMed Scopus (62) Google Scholar]. Similarly, an analysis of cross-border malaria spillover in the Amazon used meteorological and hydrological variables derived from satellite observations to control for variation in environmental risk factors [18.Gunderson A.K. et al.Malaria transmission and spillover across the Peru–Ecuador Border: A spatiotemporal analysis.Int. J. Environ. Res. Public Health. 2020; 17: 7434Crossref Scopus (2) Google Scholar]. Despite the demonstrated value of MODIS for malaria research, the spatial resolution is unsuitable for mapping finer-grained landscape details. Land use practices and the resulting land cover patterns can increase or decrease the abundance of mosquitoes and their potential to transmit malaria depending on social, ecological, and geographic contexts. Irrigated agriculture can provide larval habitats for anopheline mosquitoes and is a risk factor for malaria in many, but not all, settings [19.Ijumba J. Lindsay S. Impact of irrigation on malaria in Africa: paddies paradox.Med. Vet. Entomol. 2001; 15: 1-11Crossref PubMed Scopus (203) Google Scholar]. Urbanization increases malaria risk in parts of Asia where the primary vector is the urban-adapted Anopheles stephensi [20.Santos-Vega M. et al.Population density, climate variables and poverty synergistically structure spatial risk in urban malaria in India.PLoS Negl. Trop. Dis. 2016; 10e0005155Crossref PubMed Scopus (18) Google Scholar] but reduces malaria risk in Africa where vectors such as Anopheles gambiae are associated with rural habitats [21.Hay S.I. et al.Urbanization, malaria transmission and disease burden in Africa.Nat. Rev. Microbiol. 2005; 3: 81-90Crossref PubMed Scopus (361) Google Scholar]. Forest cover is a risk factor for malaria in parts of Southeast Asia where the vector Anopheles dirus is associated with closed-canopy forests [22.Kar N.P. et al.A review of malaria transmission dynamics in forest ecosystems.Parasit. Vectors. 2014; 7: 265Crossref PubMed Scopus (55) Google Scholar]. In contrast, deforestation can increase habitat suitability for vector species such as An. gambiae s.l. in Africa and Nyssorhynchus darlingi (formerly Anopheles darlingi) in South America, leading to higher malaria risk in cleared areas [23.Vittor A.Y. et al.Linking deforestation to malaria in the Amazon: characterization of the breeding habitat of the principal malaria vector, Anopheles darlingi.Am. J. Trop. Med. Hyg. 2009; 81: 5-12Crossref PubMed Google Scholar,24.Afrane Y.A. et al.Deforestation and vectorial capacity of Anopheles gambiae Giles mosquitoes in malaria transmission, Kenya.Emerg. Infect. Dis. 2008; 14: 1533Crossref PubMed Scopus (79) Google Scholar]. Human activities associated with land use practices, including agriculture and forest work, also influence exposure to bites of infected mosquitoes [25.Shah H. et al.Agricultural land use and infectious disease risks in southeast Asia: a systematic review and meta analyses.Lancet Planet. Health. 2018; 2: S20Abstract Full Text Full Text PDF Google Scholar,26.Hoffman-Hall A. et al.Mapping remote rural settlements at 30 m spatial resolution using geospatial data-fusion.Remote Sens. Environ. 2019; 233: 111386Crossref Scopus (13) Google Scholar]. To measure these landscape features with precision, higher-resolution data from satellite missions such as Landsat are needed (Figure 4A ). The Landsat program has collected optical data at 30 m resolution and thermal data at 60–120 m resolutions since the launch of Landsat 4 in 1982 (Figure 2). Although the 16-day revisit time of a Landsat satellite is considerably longer than the daily resolution of MODIS, it is suitable for measuring change in land cover at seasonal to annual time scales. Most applications of Landsat for malaria risk assessment have used one or a few images to provide a static assessment of landscape conditions at a single point in time. Examples include the influences of vegetation and water on spatial patterns of malaria cases in Ethiopia [27.Midekisa A. et al.Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia.Water Resour. Res. 2014; 50: 8791-8806Crossref PubMed Scopus (14) Google Scholar], Swaziland [28.Cohen J.M. et al.Rapid case-based mapping of seasonal malaria transmission risk for strategic elimination planning in Swaziland.Malar. J. 2013; 12: 61Crossref PubMed Scopus (49) Google Scholar], and the Brazilian Amazon [29.de Oliveira E.C. et al.Geographic information systems and logistic regression for high-resolution malaria risk mapping in a rural settlement of the southern Brazilian Amazon.Malar. J. 2013; 12: 420Crossref PubMed Scopus (28) Google Scholar], and high-resolution malaria risk mapping in Vietnam [30.Bui Q.-T. et al.Understanding spatial variations of malaria in Vietnam using remotely sensed data integrated into GIS and machine learning classifiers.Geocarto Int. 2019; 34: 1300-1314Crossref Scopus (14) Google Scholar] and Madagascar [31.Rakotoarison H.A. et al.Remote sensing and multi-criteria evaluation for malaria risk mapping to support indoor residual spraying prioritization in the Central highlands of Madagascar.Remote Sens. 2020; 12: 1585Crossref Scopus (5) Google Scholar]. Landsat is also frequently used to create classified land cover maps and derive vegetation and moisture indices for analysis and mapping of anopheline mosquito habitats [23.Vittor A.Y. et al.Linking deforestation to malaria in the Amazon: characterization of the breeding habitat of the principal malaria vector, Anopheles darlingi.Am. J. Trop. Med. Hyg. 2009; 81: 5-12Crossref PubMed Google Scholar,32.Adeola A.M. et al.Landsat satellite derived environmental metric for mapping mosquitoes breeding habitats in the Nkomazi municipality, Mpumalanga Province, South Africa.South African Geogr. J. 2017; 99: 14-28Crossref Scopus (12) Google Scholar,33.Hardy A. et al.Tropical wetland (tropwet) mapping tool: the automatic detection of open and vegetated waterbodies in Google earth engine for tropical wetlands.Remote Sens. 2020; 12: 1182Crossref Scopus (6) Google Scholar]. All new and historical Landsat data became freely available in 2010, catalyzing advances in remote-sensing science that have enabled high-resolution global land cover mapping at annual time steps [34.Wulder M.A. et al.Opening the archive: How free data has enabled the science and monitoring promise of Landsat.Remote Sens. Environ. 2012; 122: 2-10Crossref Scopus (684) Google Scholar]. Landsat-derived annual measurements of forest gain and loss from a global dataset [35.Hansen M.C. et al.High-resolution global maps of 21st-century forest cover change.Science. 2013; 342: 850-853Crossref PubMed Scopus (4933) Google Scholar] were used to study the effects of forest clearing and fragmentation on the occurrence of Plasmodium knowlesi in humans in Malaysian Borneo [36.Brock P.M. et al.Predictive analysis across spatial scales links zoonotic malaria to deforestation.Proc. R. Soc. B. 2019; 28620182351Crossref PubMed Scopus (20) Google Scholar]. The Program to Calculate Deforestation in the Amazon (PRODES) dataset, which uses Landsat and other data sources to map annual deforestation in the Brazilian Legal Amazon, has been used to examine the effects of forest loss on malaria incidence in this region [37.Chaves L.S.M. et al.Abundance of impacted forest patches less than 5 km 2 is a key driver of the incidence of malaria in Amazonian Brazil.Sci. Rep. 2018; 8: 7077Crossref PubMed Scopus (42) Google Scholar, 38.Valle D. Clark J. Conservation efforts may increase malaria burden in the Brazilian Amazon.PLoS One. 2013; 8e57519Crossref PubMed Scopus (41) Google Scholar, 39.Hahn M.B. et al.Influence of deforestation, logging, and fire on malaria in the Brazilian Amazon.PLoS One. 2014; 9e85725Crossref PubMed Scopus (80) Google Scholar]. A 30-year global dataset of surface water was incorporated into a map of malaria vector suitability in Malawi [40.Frake A.N. et al.Leveraging big data for public health: Mapping malaria vector suitability in Malawi with Google Earth Engine.PLoS One. 2020; 15e0235697Crossref PubMed Scopus (2) Google Scholar], and a 14-year time series of Landsat-derived NDVI was used in a model of malaria cases at the health facility level in Zambia [41.Larsen D.A. et al.Leveraging risk maps of malaria vector abundance to guide control efforts reduces malaria incidence in Eastern Province, Zambia.Sci. Rep. 2020; 10: 1-12Crossref PubMed Scopus (2) Google Scholar]. There is potential for much broader use of Landsat time series in malaria research. One limiting factor is the availability of long-term epidemiological and entomological datasets with high enough spatial resolution to associate with land use and land cover changes. Working with Landsat time series is also technically challenging because of large data volumes and data gaps resulting from cloud cover. Data continuity is an important issue for remote-sensing applications. All satellite missions have a finite lifespan, and differences in sensor and orbital characteristics affect the measurements taken by newer missions. Harmonized products can be developed to combine satellite data from different sources into consistent, long-term datasets. An example is the suite of Integrated Multi-Satellite Retrievals for GPM (IMERG) products, which provide seamless precipitation estimates from 2000 to the present by combining data from the current Global Precipitation Measurement (GPM) mission, the older Tropical Rainfall Measurement Mission (TRMM), and other sources. The resulting data record has been used in the study of malaria and other water-related diseases [42.Kirschbaum D.B. et al.NASA's remotely sensed precipitation: A reservoir for applications users.Bull. Am. Meteorol. Soc. 2017; 98: 1169-1184Crossref Scopus (53) Google Scholar]. A significant event for the remote-sensing community will be the end of the MODIS era, with the decommissioning of Terra expected in 2026 and Aqua several years afterwards. MODIS is being replaced by the VIIRS sensor onboard NOAA polar-orbiting satellites, and there is ongoing research on harmonizing MODIS and VIIRS to generate consistent records of land surface temperature and vegetation indices [43.Guillevic P.C. et al.Validation of land surface temperature products derived from the visible infrared imaging radiometer suite (VIIRS) using ground-based and heritage satellite measurements.Remote Sens. Environ. 2014; 154: 19-37Crossref Scopus (82) Google Scholar,44.Skakun S. et al.Transitioning from MODIS to VIIRS: an analysis of inter-consistency of NDVI data sets for agricultural monitoring.Int. J. Remote Sens. 2018; 39: 971-992Crossref PubMed Scopus (34) Google Scholar]. However, it is not yet clear what types of harmonized data products will be available and how accessible and usable these data will be for work with malaria and other environmentally sensitive diseases. Household-level research and interventions require information about individual dwellings and community interventions that target high-resolution spatial features like ponds and temporary water bodies. Mapping this level of detail requires very high resolution (VHR) satellite imagery (
Referência(s)