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Evaluating the accuracy of satellite-based methods to estimate residential proximity to agricultural crops

Journal of Exposure Science & Environmental Epidemiology (2022)Cite this article

Abstract

Background

Epidemiologic investigations increasingly employ remote sensing data to estimate residential proximity to agriculture as a means of approximating individual-level pesticide exposure. Few studies have examined the accuracy of these methods and the implications for exposure misclassification.

Objectives

Compare metrics of residential proximity to agricultural land between a groundtruth approach and commonly-used satellite-based estimates.

Methods

We inspected 349 fields and identified crops in current production within a 0.5 km radius of 40 residences in Idaho. We calculated the distance from each home to the nearest agricultural field and the total acreage of agricultural fields within a 0.5 km buffer. We compared these groundtruth estimates to satellite-derived estimates from three widely used datasets: CropScape, the National Land Cover Database (NLCD), and Landsat imagery (using Normalized Difference Vegetation Index thresholds).

Results

We found poor to moderate agreement between the classification of individuals living within 0.5 km of an agricultural field between the groundtruth method and the comparison datasets (53.1–77.6%). All satellite-derived estimates overestimated the acreage of agricultural land within 0.5 km of each home (average = 82.8–148.9%). Using two satellite-derived datasets in conjunction resulted in substantial improvements; specifically, combining CropScape or NLCD with Landsat imagery had the highest percent agreement with the groundtruth data (92.8–93.8% agreement).

Significance

Residential proximity to agriculture is frequently used as a proxy for pesticide exposure in epidemiologic investigations, and remote sensing-derived datasets are often the only practical means of identifying cultivated land. We found that estimates of agricultural proximity obtained from commonly-used satellite-based datasets are likely to result in exposure misclassification. We propose a novel approach that capitalizes on the complementary strengths of different sources of satellite imagery, and suggest the combined use of one dataset with high temporal resolution (e.g., Landsat imagery) in conjunction with a second dataset that delineates agricultural land use (e.g., CropScape or NLCD).

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Fig. 1: Distance to the nearest cultivated agricultural field and total acreage of cultivated agricultural fields estimated within 0.5 km of each participant's home from the ground-truth and each of the satellite-based methods.
Fig. 2: Identification of cultivated fields and total acreage of cultivated fields within a 0.5 km radius of participant’s home from each method (participant living in an area of high-density development).
Fig. 3: Identification of cultivated fields and total acreage of cultivated fields within a 0.5 km radius of participant’s home from each method (participant living in an area of high-density development).
Fig. 4: Identification of cultivated fields and total acreage of cultivated fields within a 0.5 km radius of participant’s home from each method (participant living in an area of medium-density development).
Fig. 5: Identification of cultivated fields and total acreage of cultivated fields within a 0.5 km radius of participant’s home from each method (participant living in an area of high-density development).
Fig. 6: Identification of cultivated fields and total acreage of cultivated fields within a 0.5 km radius of participant’s home from each method (participant living in an area of low-density development).
Fig. 7: Identification of cultivated fields and total acreage of cultivated fields within a 0.5 km radius of participant’s home from each method (participant living in an area of low-density development).

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Data availability

To protect the privacy of our participants, data from this analysis are not available.

References

  1. Hyland C, Laribi O. Review of take-home pesticide exposure pathway in children living in agricultural areas. Environ Res. 2017;156:559–70.

    Article  CAS  PubMed  Google Scholar 

  2. Lopez-Galvez N, Wagoner R, Quiros-Alcala L, Ornelas Van Horne Y, Furlong M, Avila E, et al. Systematic Literature Review of the Take-Home Route of Pesticide Exposure via Biomonitoring and Environmental Monitoring. Int J Environ Res Public Health. 2019;16:2177.

    Article  CAS  PubMed Central  Google Scholar 

  3. Deziel NC, Freeman LEB, Graubard BI, Jones RR, Hoppin JA, Thomas K, et al. Relative Contributions of Agricultural Drift, Para-Occupational, and Residential Use Exposure Pathways to House Dust Pesticide Concentrations: Meta-Regression of Published Data. Environ Health Perspect. 2017;125:296–305.

    Article  CAS  PubMed  Google Scholar 

  4. Deziel NC, Friesen MC, Hoppin JA, Hines CJ, Thomas K, Freeman LE. A review of nonoccupational pathways for pesticide exposure in women living in agricultural areas. Environ Health Perspect. 2015;123:515–24.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Larsen AE, Gaines SD, Deschênes O. Agricultural pesticide use and adverse birth outcomes in the San Joaquin Valley of California. Nat Commun. 2017;8:302.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Gemmill A, Gunier Robert B, Bradman A, Eskenazi B, Harley Kim G. Residential Proximity to Methyl Bromide Use and Birth Outcomes in an Agricultural Population in California. Environ Health Perspect. 2013;121:737–43.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Rappazzo KM, Warren JL, Meyer RE, Herring AH, Sanders AP, Brownstein NC, et al. Maternal residential exposure to agricultural pesticides and birth defects in a 2003 to 2005 North Carolina birth cohort. Birth Defects Res Part A: Clin Mol Teratol. 2016;106:240–9.

    Article  CAS  Google Scholar 

  8. Rull RP, Ritz B, Shaw GM. Neural Tube Defects and Maternal Residential Proximity to Agricultural Pesticide Applications. Am J Epidemiol. 2006;163:743–53.

    Article  PubMed  Google Scholar 

  9. Carmichael SL, Yang W, Ma C, Roberts E, Kegley S, English P, et al. Joint effects of genetic variants and residential proximity to pesticide applications on hypospadias risk. Birth Defects Res Part A: Clin Mol Teratol. 2016;106:653–8.

    Article  CAS  Google Scholar 

  10. Meyer Kristy J, Reif John S, Veeramachaneni DNR, Luben Thomas J, Mosley Bridget S, Nuckols John R. Agricultural Pesticide Use and Hypospadias in Eastern Arkansas. Environ Health Perspect. 2006;114:1589–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Carozza SE, Li B, Wang Q, Horel S, Cooper S. Agricultural pesticides and risk of childhood cancers. Int J Hyg Environ Health. 2009;212:186–95.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Gómez-Barroso D, García-Pérez J, López-Abente G, Tamayo-Uria I, Morales-Piga A, Pardo Romaguera E, et al. Agricultural crop exposure and risk of childhood cancer: new findings from a case–control study in Spain. Int J Health Geogr. 2016;15:18.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Rull RP, Gunier R, Von Behren J, Hertz A, Crouse V, Buffler PA, et al. Residential proximity to agricultural pesticide applications and childhood acute lymphoblastic leukemia. Environ Res. 2009;109:891–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hyland C, Gunier RB, Metayer C, Bates MN, Wesseling C, Mora AM. Maternal residential pesticide use and risk of childhood leukemia in Costa Rica. Int J Cancer. 2018;143:1295–304.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lombardi C, Thompson S, Ritz B, Cockburn M, Heck JE. Residential proximity to pesticide application as a risk factor for childhood central nervous system tumors. Environ Res. 2021;197:111078.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Park AS, Ritz B, Yu F, Cockburn M, Heck JE. Prenatal pesticide exposure and childhood leukemia - A California statewide case-control study. Int J Hyg Environ Health. 2020;226:113486.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Jones RR, Yu C-L, Nuckols JR, Cerhan JR, Airola M, Ross JA, et al. Farm residence and lymphohematopoietic cancers in the Iowa Women׳s Health Study. Environ Res. 2014;133:353–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Carles C, Bouvier G, Esquirol Y, Piel C, Migault L, Pouchieu C, et al. Residential proximity to agricultural land and risk of brain tumor in the general population. Environ Res. 2017;159:321–30.

    Article  PubMed  CAS  Google Scholar 

  19. El-Zaemey S, Heyworth J, Fritschi L. Noticing pesticide spray drift from agricultural pesticide application areas and breast cancer: a case-control study. Aust NZ J Public Health. 2013;37:547–55.

    Article  Google Scholar 

  20. Raanan R, Gunier Robert B, Balmes John R, Beltran Alyssa J, Harley Kim G, Bradman A, et al. Elemental Sulfur Use and Associations with Pediatric Lung Function and Respiratory Symptoms in an Agricultural Community (California, USA). Environ Health Perspect. 2017;125:087007.

  21. Coker E, Gunier R, Bradman A, Harley K, Kogut K, Molitor J, et al. Association between Pesticide Profiles Used on Agricultural Fields near Maternal Residences during Pregnancy and IQ at Age 7 Years. Int J Environ Res Public Health. 2017;14:506.

    Article  PubMed Central  CAS  Google Scholar 

  22. Gunier RB, Bradman A, Harley KG, Kogut K, Eskenazi B. Prenatal Residential Proximity to Agricultural Pesticide Use and IQ in 7-Year-Old Children. Environ Health Perspect. 2017;125:057002.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Hyland C, Bradshaw PT, Gunier RB, Mora AM, Kogut K, Deardorff J, et al. Associations between pesticide mixtures applied near home during pregnancy and early childhood with adolescent behavioral and emotional problems in the CHAMACOS study. Environ Epidemiol. 2021;5:e150.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Sagiv SK, Harris MH, Gunier RB, Kogut KR, Harley KG, Deardorff J, et al. Prenatal Organophosphate Pesticide Exposure and Traits Related to Autism Spectrum Disorders in a Population Living in Proximity to Agriculture. Environ Health Perspect. 2018;126:047012.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Shelton JF, Geraghty EM, Tancredi DJ, Delwiche LD, Schmidt RJ, Ritz B, et al. Neurodevelopmental Disorders and Prenatal Residential Proximity to Agricultural Pesticides: The CHARGE Study. Environ Health Perspect. 2014;122:1103–9.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Brouwer M, Huss A, van der Mark M, Nijssen PCG, Mulleners WM, Sas AMG, et al. Environmental exposure to pesticides and the risk of Parkinson’s disease in the Netherlands. Environ Int. 2017;107:100–10.

    Article  CAS  PubMed  Google Scholar 

  27. Costello S, Cockburn M, Bronstein J, Zhang X, Ritz B. Parkinson’s Disease and Residential Exposure to Maneb and Paraquat From Agricultural Applications in the Central Valley of California. Am J Epidemiol. 2009;169:919–26.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Wang A, Costello S, Cockburn M, Zhang X, Bronstein J, Ritz B. Parkinson’s disease risk from ambient exposure to pesticides. Eur J Epidemiol. 2011;26:547–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wang A, Cockburn M, Ly TT, Bronstein JM, Ritz B. The association between ambient exposure to organophosphates and Parkinson's disease risk. Occup Environ Med. 2014;71:275.

    Article  PubMed  CAS  Google Scholar 

  30. Manthripragada AD, Costello S, Cockburn MG, Bronstein JM, Ritz B. Paraoxonase 1, agricultural organophosphate exposure, and Parkinson disease. Epidemiol (Camb, Mass). 2010;21:87–94.

    Article  Google Scholar 

  31. Dereumeaux C, Fillol C, Quenel P, Denys S. Pesticide exposures for residents living close to agricultural lands: a review. Environ Int. 2020;134:105210.

    Article  CAS  PubMed  Google Scholar 

  32. Teysseire R, Manangama G, Baldi I, Carles C, Brochard P, Bedos C, et al. Assessment of residential exposures to agricultural pesticides: a scoping review. PloS ONE. 2020;15:e0232258–e.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Barr DB. Biomonitoring of exposure to pesticides. J Chem Health Saf. 2008;15:20–9.

    Article  CAS  Google Scholar 

  34. O’Leary ES, Vena JE, Freudenheim JL, Brasure J. Pesticide exposure and risk of breast cancer: a nested case-control study of residentially stable women living on Long Island. Environ Res. 2004;94:134–44.

    Article  PubMed  CAS  Google Scholar 

  35. Brody JG, Vorhees DJ, Melly SJ, Swedis SR, Drivas PJ, Rudel RA. Using GIS and historical records to reconstruct residential exposure to large-scale pesticide application. J Expo Anal Environ Epidemiol. 2002;12:64–80.

    Article  PubMed  Google Scholar 

  36. Rull RP, Ritz B, Shaw GM. Validation of self-reported proximity to agricultural crops in a case–control study of neural tube defects. J Expo Sci Environ Epidemiol. 2006;16:147–55.

    Article  PubMed  Google Scholar 

  37. Plascak JJ, Griffith WC, Workman T, Smith MN, Vigoren E, Faustman EM, et al. Evaluation of the relationship between residential orchard density and dimethyl organophosphate pesticide residues in house dust. J Expo Sci Environ Epidemiol. 2019;29:379–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Warren JL, Luben TJ, Sanders AP, Brownstein NC, Herring AH, Meyer RE. An evaluation of metrics for assessing maternal exposure to agricultural pesticides. J Expo Sci Environ Epidemiol. 2014;24:497–503.

    Article  PubMed  Google Scholar 

  39. Avruskin GA, Meliker JR, Jacquez GM. Using satellite derived land cover information for a multi-temporal study of self-reported recall of proximity to farmland. J Expo Sci Environ Epidemiol. 2008;18:381–91.

    Article  PubMed  Google Scholar 

  40. Ward MH, Nuckols JR, Weigel SJ, Maxwell SK, Cantor KP, Miller RS. Identifying populations potentially exposed to agricultural pesticides using remote sensing and a Geographic Information System. Environ Health Perspect. 2000;108:5–12.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Ward MH, Lubin J, Giglierano J, Colt JS, Wolter C, Bekiroglu N, et al. Proximity to crops and residential exposure to agricultural herbicides in iowa. Environ Health Perspect. 2006;114:893–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Maxwell SK, Airola M, Nuckols JR. Using Landsat satellite data to support pesticide exposure assessment in California. Int J Health Geogr. 2010;9:46.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Maxwell SK. Downscaling Pesticide Use Data to the Crop Field Level in California Using Landsat Satellite Imagery: Paraquat Case Study. Remote Sensing. 2011;3:1805–16.

    Article  Google Scholar 

  44. VoPham T, Wilson JP, Ruddell D, Rashed T, Brooks MM, Yuan JM, et al. Linking pesticides and human health: a geographic information system (GIS) and Landsat remote sensing method to estimate agricultural pesticide exposure. Appl Geogr. 2015;62:171–81.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Gibson EA, Goldsmith J, Kioumourtzoglou M-A. Complex Mixtures, Complex Analyses: an Emphasis on Interpretable Results. Curr Environ health Rep. 2019;6:53–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Hamra GB, Buckley JP. Environmental exposure mixtures: questions and methods to address them. Curr. 2018;5:160–5.

    Google Scholar 

  47. Brody JG, Aschengrau A, McKelvey W, Rudel RA, Swartz CH, Kennedy T. Breast cancer risk and historical exposure to pesticides from wide-area applications assessed with GIS. Environ Health Perspect. 2004;112:889–97.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. VoPham T, Brooks MM, Yuan JM, Talbott EO, Ruddell D, Hart JE, et al. Pesticide exposure and hepatocellular carcinoma risk: A case-control study using a geographic information system (GIS) to link SEER-Medicare and California pesticide data. Environ Res. 2015;143:68–82.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Marusek JC, Cockburn MG, Mills PK, Ritz BR. Control selection and pesticide exposure assessment via GIS in prostate cancer studies. Am J Prev Med. 2006;30:S109–16.

    Article  PubMed  Google Scholar 

  50. Bukalasa JS, Brunekreef B, Brouwer M, Koppelman GH, Wijga AH, Huss A, et al. Associations of residential exposure to agricultural pesticides with asthma prevalence in adolescence: the PIAMA birth cohort. Environ Int. 2018;121:435–42.

    Article  CAS  PubMed  Google Scholar 

  51. Meyer KJ, Reif JS, Veeramachaneni DNR, Luben TJ, Mosley BS, Nuckols JR. Agricultural pesticide use and hypospadias in eastern Arkansas. Environ Health Perspect. 2006;114:1589–95.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Ochoa-Acuña H, Carbajo C. Risk of limb birth defects and mother’s home proximity to cornfields. Sci Total Environ. 2009;407:4447–51.

    Article  PubMed  CAS  Google Scholar 

  53. Xiang H, Nuckols JR, Stallones L. A geographic information assessment of birth weight and crop production patterns around mother’s residence. Environ Res. 2000;82:160–7.

    Article  CAS  PubMed  Google Scholar 

  54. Friedman E, Hazlehurst M, Loftus C, Karr C, McDonald K, Suarez-Lopez J. Residential proximity to greenhouse agriculture and neurobehavioral performance in Ecuadorian children. Int J Hyg Environ Health. 2019;223:220–7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Suarez-Lopez J, Hong V, McDonald K, Suarez-Torres J, López D, Cruz F. Home proximity to flower plantations and higher systolic blood pressure among children. Int J Hyg Environ Health. 2018;221:1077–84.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Rull RP, Ritz B. Historical pesticide exposure in California using pesticide use reports and land-use surveys: an assessment of misclassification error and bias. Environ Health Perspect. 2003;111:1582–9.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Ochoa-Acuña H, Carbajo C. Risk of limb birth defects and mother’s home proximity to cornfields. Sci Total Environ. 2009;407:4447–51.

    Article  PubMed  CAS  Google Scholar 

  58. Xiang H, Nuckols JR, Stallones L. A Geographic Information Assessment of Birth Weight and Crop Production Patterns around Mother’s Residence. Environ Res. 2000;82:160–7.

    Article  CAS  PubMed  Google Scholar 

  59. Jones RR, Yu CL, Nuckols JR, Cerhan JR, Airola M, Ross JA, et al. Farm residence and lymphohematopoietic cancers in the Iowa Women’s Health Study. Environ Res. 2014;133:353–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. United States Department of Agriculture (USDA) National Agricultural Statistics Service. Reserach and Science. CropScape and Cropland Data Layers - FAQs 2021 https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php.

  61. Multi-Resolution Land Characteristics Consortium (MRLC). Land Cover 2019 https://www.mrlc.gov/data/type/land-cover#:~:text=The%20National%20Land%20Cover%20Database,land%20cover%20and%20associated%20changes.

  62. United States Geological Survey (USGS). What are the band designations for the Landsat satellites? 2021 https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites.

  63. Esri. World Imagery Basemap https://www.arcgis.com/home/item.html?id=10df2279f9684e4a9f6a7f08febac2a9.

  64. Pebesma E. Simple Features for R: Standardized Support for Spatial Vector Data. R J. 2018;10:439–46.

    Article  Google Scholar 

  65. Hijmans RJ, van Etten J raster: Geographic analysis and modeling with raster data. R package version 2.0-12. 2012 http://CRAN.R-project.org/package=raster.

  66. Dorman M, Rush J, Hough I, Russel D, Ranghetti L, Benini A, et al. nngeo: k-Nearest Neighbor Join for Spatial Data https://cran.r-project.org/web/packages/nngeo/index.html.

  67. Wickham A, Averick M, Bryan J, Chang W, McGowan L, François R, et al. Welcome to the tidyverse. J Open Source Softw. 2019;4:1686.

    Article  Google Scholar 

  68. Boryan C, Yang Z, Mueller R, Craig M. Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program. Geocarto Int. 2011;26:341–58.

    Article  Google Scholar 

  69. Chen B. Package ‘CropScapeR’ package version 1.1.1. 2021 [Available from: https://cran.r-project.org/web/packages/CropScapeR/CropScapeR.pdf].

  70. United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS). 2020 Idaho Cropland Data Lyaer | NASS/USDA 2020 https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/metadata_id20.htm.

  71. Yang L, Jin S, Danielson P, Homer C, Gass L, Bender SM, et al. A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies. ISPRS J Photogramm Remote Sens. 2018;146:108–23.

    Article  Google Scholar 

  72. Pause M, Raasch F, Marrs C, Csaplovics E. Monitoring Glyphosate-Based Herbicide Treatment Using Sentinel-2 Time Series—A Proof-of-Principle. Remote Sens. 2019;11:2541.

    Article  Google Scholar 

  73. Pettorelli N, Vik JO, Mysterud A, Gaillard JM, Tucker CJ, Stenseth NC. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol Evol. 2005;20:503–10.

    Article  PubMed  Google Scholar 

  74. United States Geological Survey (USGS). NDVI, the Foundation for Remote Sensing Phenology 2018 https://www.usgs.gov/special-topics/remote-sensing-phenology/science/ndvi-foundation-remote-sensing-phenology#:~:text=NDVI%20values%20range%20from%20%2B1.0,(approximately%200.2%20to%200.5).

  75. Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med. 2016;15:155–63.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Gibbs JL, Yost MG, Negrete M, Fenske RA. Passive Sampling for Indoor and Outdoor Exposures to Chlorpyrifos, Azinphos-Methyl, and Oxygen Analogs in a Rural Agricultural Community. Environ Health Perspect. 2017;125:333–41.

    Article  CAS  PubMed  Google Scholar 

  77. Jin S, Homer C, Yang L, Danielson P, Dewitz J, Li C, et al. Overall Methodology Design for the United States National Land Cover Database 2016 Products. Remote Sensing. 2019;11,2971.

  78. Homer C, Dewitz J, Jin S, Xian G, Costello C, Danielson P, et al. Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database. ISPRS J Photogramm Remote Sens. 2020;162:184–99.

    Article  PubMed  PubMed Central  Google Scholar 

  79. United States Geological Survey. What are the acquisition schedules for the Landsat satellites? https://www.usgs.gov/faqs/what-are-acquisition-schedules-landsat-satellites.

  80. Dolven RI. Urban Sprawl and Farmland Protection: Responding to Changes in Idaho’s Treasure Valley. Idaho L Rev. 2021;269:269–96.

  81. Maxwell A, Warner T, Vanderbilt B, Ramezan C. Land Cover Classification and Feature Extraction from National Agriculture Imagery Program (NAIP) Orthoimagery: a review. Photogrammetric Eng Remote Sens. 2017;83:737–47.

    Article  Google Scholar 

  82. Cheng Y, Vrieling A, Fava F, Meroni M, Marshall M, Gachoki S. Phenology of short vegetation cycles in a Kenyan rangeland from PlanetScope and Sentinel-2. Remote Sens Environ. 2020;248:112004.

    Article  Google Scholar 

  83. Breunig FM, Galvão LS, Dalagnol R, Dauve CE, Parraga A, Santi AL, et al. Delineation of management zones in agricultural fields using cover–crop biomass estimates from PlanetScope data. Int J Appl Earth Observation Geoinf. 2020;85:102004.

    Article  Google Scholar 

  84. Turker M, Ozdarici A. Field-based crop classification using SPOT4, SPOT5, IKONOS and QuickBird imagery for agricultural areas: a comparison study. Int J Remote Sens. 2011;32:9735–68.

    Article  Google Scholar 

  85. Kussul N, Lavreniuk M, Skakun S, Shelestov A. Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geosci Remote Sens Lett. 2017;14:778–82.

    Article  Google Scholar 

  86. Neetu RaySS. Exploring Machine Learning Classification Algorithms for Crop Classification Using Sentinel 2 Data. Int Arch Photogramm Remote Sens Spat Inf Sci. 2019;XLII-3/W6:573–8.

    Article  Google Scholar 

  87. Zurqani HA, Post CJ, Mikhailova EA, Cope MP, Allen JS, Lytle BA. Evaluating the integrity of forested riparian buffers over a large area using LiDAR data and Google Earth Engine. Sci Rep. 2020;10:14096.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Duda T, Canty M. Unsupervised classification of satellite imagery: choosing a good algorithm. Int J Remote Sens. 2002;23:2193–212.

    Article  Google Scholar 

  89. United States Department of Agriculture (USDA) Economic Research Service. Major Land Uses https://www.ers.usda.gov/topics/farm-economy/land-use-land-value-tenure/major-land-uses/.

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Acknowledgements

We gratefully acknowledge all of the study participants.

Funding

Research reported in this publication was supported by the National Institute of Environmental Health Sciences of the National Institutes of Health under Award Number K01ES028745. The content of this paper, including all findings and conclusions, is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors and Affiliations

  1. School of Public and Population Health, Boise State University, Boise, ID, USA

    Carly Hyland & Cynthia L. Curl

  2. Yale School of the Environment, New Haven, CT, USA

    Kathryn McConnell

  3. Department of Geosciences, Boise State University, Boise, ID, USA

    Edwin DeYoung

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  1. Carly Hyland
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  2. Kathryn McConnell
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  3. Edwin DeYoung
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CH: Data Collection, Formal Analysis, Writing—Original Draft; KM, Conception of Data Analysis, Formal Analysis, Writing—Reviewing and Editing; ED, Formal Analysis, Writing—Reviewing and Editing; CLC: Conceptualization, Writing—Reviewing and Editing.

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Correspondence to Carly Hyland.

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Hyland, C., McConnell, K., DeYoung, E. et al. Evaluating the accuracy of satellite-based methods to estimate residential proximity to agricultural crops. J Expo Sci Environ Epidemiol (2022). https://doi.org/10.1038/s41370-022-00467-0

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