{"id":5103,"date":"2016-11-02T03:58:13","date_gmt":"2016-11-02T08:58:13","guid":{"rendered":"http:\/\/gisgeography.com\/?p=5103"},"modified":"2025-04-03T19:16:25","modified_gmt":"2025-04-04T00:16:25","slug":"remote-sensing-earth-observation-guide","status":"publish","type":"post","link":"https:\/\/gisgeography.com\/remote-sensing-earth-observation-guide\/","title":{"rendered":"What is Remote Sensing? The Definitive Guide"},"content":{"rendered":"<style>.kb-image5103_9720ec-56 .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<figure class=\"wp-block-kadence-image kb-image5103_9720ec-56 size-medium_large\"><img loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"479\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/What-Is-Remote-Sensing-1-768x479.jpg\" alt=\"What Is Remote Sensing\" class=\"kb-img wp-image-20717\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/What-Is-Remote-Sensing-1-768x479.jpg 768w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/What-Is-Remote-Sensing-1-300x187.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/What-Is-Remote-Sensing-1-678x423.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/What-Is-Remote-Sensing-1-50x31.jpg 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/What-Is-Remote-Sensing-1-200x125.jpg 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/What-Is-Remote-Sensing-1-425x265.jpg 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/What-Is-Remote-Sensing-1-550x343.jpg 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/What-Is-Remote-Sensing-1-115x72.jpg 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/What-Is-Remote-Sensing-1-1265x788.jpg 1265w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/What-Is-Remote-Sensing-1-850x530.jpg 850w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/What-Is-Remote-Sensing-1.jpg 1364w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \/><\/figure>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">What is Remote Sensing?<\/h3>\n\n\n\n<p><strong>Remote sensing<\/strong> is the science of obtaining the physical properties of an area without being there. It allows users to capture, visualize, and analyze objects and features on the Earth\u2019s surface. By collecting imagery, we can classify it into land cover and other types of analyses.<\/p>\n\n\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h5 class=\"wp-block-heading has-text-align-center\">Table of Contents<\/h5>\n\n\n<style>.kb-row-layout-id5103_d4bea5-52 > .kt-row-column-wrap{align-content:start;}:where(.kb-row-layout-id5103_d4bea5-52 > .kt-row-column-wrap) > .wp-block-kadence-column{justify-content:start;}.kb-row-layout-id5103_d4bea5-52 > .kt-row-column-wrap{column-gap:var(--global-kb-gap-md, 2rem);row-gap:var(--global-kb-gap-md, 2rem);padding-top:var(--global-kb-spacing-sm, 1.5rem);padding-bottom:var(--global-kb-spacing-sm, 1.5rem);}.kb-row-layout-id5103_d4bea5-52 > .kt-row-column-wrap > div:not(.added-for-specificity){grid-column:initial;}.kb-row-layout-id5103_d4bea5-52 > .kt-row-column-wrap{grid-template-columns:repeat(3, minmax(0, 1fr));}.kb-row-layout-id5103_d4bea5-52 > .kt-row-layout-overlay{opacity:0.30;}@media all and (max-width: 1024px){.kb-row-layout-id5103_d4bea5-52 > .kt-row-column-wrap > div:not(.added-for-specificity){grid-column:initial;}}@media all and (max-width: 1024px){.kb-row-layout-id5103_d4bea5-52 > .kt-row-column-wrap{grid-template-columns:repeat(3, minmax(0, 1fr));}}@media all and (max-width: 767px){.kb-row-layout-id5103_d4bea5-52 > .kt-row-column-wrap > div:not(.added-for-specificity){grid-column:initial;}.kb-row-layout-id5103_d4bea5-52 > .kt-row-column-wrap{grid-template-columns:minmax(0, 1fr);}}<\/style><div class=\"kb-row-layout-wrap kb-row-layout-id5103_d4bea5-52 alignnone wp-block-kadence-rowlayout\"><div class=\"kt-row-column-wrap kt-has-3-columns kt-row-layout-equal kt-tab-layout-inherit kt-mobile-layout-row kt-row-valign-top\">\n<style>.kadence-column5103_b42763-04 > .kt-inside-inner-col,.kadence-column5103_b42763-04 > .kt-inside-inner-col:before{border-top-left-radius:0px;border-top-right-radius:0px;border-bottom-right-radius:0px;border-bottom-left-radius:0px;}.kadence-column5103_b42763-04 > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column5103_b42763-04 > .kt-inside-inner-col{flex-direction:column;}.kadence-column5103_b42763-04 > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column5103_b42763-04 > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column5103_b42763-04{position:relative;}@media all and (max-width: 1024px){.kadence-column5103_b42763-04 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column5103_b42763-04 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column5103_b42763-04 inner-column-1\"><div class=\"kt-inside-inner-col\"><style>.kb-image5103_6ba2cd-51 .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<figure class=\"wp-block-kadence-image kb-image5103_6ba2cd-51\"><a href=\"#SensorTypes\" class=\"kb-advanced-image-link\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"281\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Sensor-Types-300x281.png\" alt=\"Sensor Types\" class=\"kb-img wp-image-40761\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Sensor-Types-300x281.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Sensor-Types-50x47.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Sensor-Types-200x188.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Sensor-Types-115x108.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Sensor-Types-360x338.png 360w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Sensor-Types.png 401w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><\/figure>\n<\/div><\/div>\n\n\n<style>.kadence-column5103_1bbb67-6b > .kt-inside-inner-col,.kadence-column5103_1bbb67-6b > .kt-inside-inner-col:before{border-top-left-radius:0px;border-top-right-radius:0px;border-bottom-right-radius:0px;border-bottom-left-radius:0px;}.kadence-column5103_1bbb67-6b > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column5103_1bbb67-6b > .kt-inside-inner-col{flex-direction:column;}.kadence-column5103_1bbb67-6b > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column5103_1bbb67-6b > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column5103_1bbb67-6b{position:relative;}@media all and (max-width: 1024px){.kadence-column5103_1bbb67-6b > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column5103_1bbb67-6b > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column5103_1bbb67-6b inner-column-2\"><div class=\"kt-inside-inner-col\"><style>.kb-image5103_a64784-e0 .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<div class=\"wp-block-kadence-image kb-image5103_a64784-e0\"><figure class=\"aligncenter\"><a href=\"#Types\" class=\"kb-advanced-image-link\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"281\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Remote-Sensing-Types-300x281.png\" alt=\"Remote Sensing Types\" class=\"kb-img wp-image-40762\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Remote-Sensing-Types-300x281.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Remote-Sensing-Types-50x47.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Remote-Sensing-Types-200x188.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Remote-Sensing-Types-115x108.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Remote-Sensing-Types-360x338.png 360w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Remote-Sensing-Types.png 401w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><\/figure><\/div>\n<\/div><\/div>\n\n\n<style>.kadence-column5103_c65f15-01 > .kt-inside-inner-col,.kadence-column5103_c65f15-01 > .kt-inside-inner-col:before{border-top-left-radius:0px;border-top-right-radius:0px;border-bottom-right-radius:0px;border-bottom-left-radius:0px;}.kadence-column5103_c65f15-01 > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column5103_c65f15-01 > .kt-inside-inner-col{flex-direction:column;}.kadence-column5103_c65f15-01 > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column5103_c65f15-01 > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column5103_c65f15-01{position:relative;}@media all and (max-width: 1024px){.kadence-column5103_c65f15-01 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column5103_c65f15-01 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column5103_c65f15-01 inner-column-3\"><div class=\"kt-inside-inner-col\"><style>.kb-image5103_2e62c4-56 .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<div class=\"wp-block-kadence-image kb-image5103_2e62c4-56\"><figure class=\"aligncenter\"><a href=\"#ElectromagneticSpectrum\" class=\"kb-advanced-image-link\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"281\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Electromagnetic-Spectrum-300x281.png\" alt=\"Electromagnetic Spectrum\" class=\"kb-img wp-image-40763\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Electromagnetic-Spectrum-300x281.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Electromagnetic-Spectrum-50x47.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Electromagnetic-Spectrum-200x188.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Electromagnetic-Spectrum-115x108.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Electromagnetic-Spectrum-360x338.png 360w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Electromagnetic-Spectrum.png 401w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><\/figure><\/div>\n<\/div><\/div>\n\n<\/div><\/div>\n\n<style>.kb-row-layout-id5103_91c4ac-1f > .kt-row-column-wrap{align-content:start;}:where(.kb-row-layout-id5103_91c4ac-1f > .kt-row-column-wrap) > .wp-block-kadence-column{justify-content:start;}.kb-row-layout-id5103_91c4ac-1f > .kt-row-column-wrap{column-gap:var(--global-kb-gap-md, 2rem);row-gap:var(--global-kb-gap-md, 2rem);padding-top:var(--global-kb-spacing-sm, 1.5rem);padding-bottom:var(--global-kb-spacing-sm, 1.5rem);}.kb-row-layout-id5103_91c4ac-1f > .kt-row-column-wrap > div:not(.added-for-specificity){grid-column:initial;}.kb-row-layout-id5103_91c4ac-1f > .kt-row-column-wrap{grid-template-columns:repeat(3, minmax(0, 1fr));}.kb-row-layout-id5103_91c4ac-1f > .kt-row-layout-overlay{opacity:0.30;}@media all and (max-width: 1024px){.kb-row-layout-id5103_91c4ac-1f > .kt-row-column-wrap > div:not(.added-for-specificity){grid-column:initial;}}@media all and (max-width: 1024px){.kb-row-layout-id5103_91c4ac-1f > .kt-row-column-wrap{grid-template-columns:repeat(3, minmax(0, 1fr));}}@media all and (max-width: 767px){.kb-row-layout-id5103_91c4ac-1f > .kt-row-column-wrap > div:not(.added-for-specificity){grid-column:initial;}.kb-row-layout-id5103_91c4ac-1f > .kt-row-column-wrap{grid-template-columns:minmax(0, 1fr);}}<\/style><div class=\"kb-row-layout-wrap kb-row-layout-id5103_91c4ac-1f alignnone wp-block-kadence-rowlayout\"><div class=\"kt-row-column-wrap kt-has-3-columns kt-row-layout-equal kt-tab-layout-inherit kt-mobile-layout-row kt-row-valign-top\">\n<style>.kadence-column5103_4504f1-00 > .kt-inside-inner-col,.kadence-column5103_4504f1-00 > .kt-inside-inner-col:before{border-top-left-radius:0px;border-top-right-radius:0px;border-bottom-right-radius:0px;border-bottom-left-radius:0px;}.kadence-column5103_4504f1-00 > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column5103_4504f1-00 > .kt-inside-inner-col{flex-direction:column;}.kadence-column5103_4504f1-00 > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column5103_4504f1-00 > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column5103_4504f1-00{position:relative;}@media all and (max-width: 1024px){.kadence-column5103_4504f1-00 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column5103_4504f1-00 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column5103_4504f1-00 inner-column-1\"><div class=\"kt-inside-inner-col\"><style>.kb-image5103_ffcdd2-23 .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<div class=\"wp-block-kadence-image kb-image5103_ffcdd2-23\"><figure class=\"aligncenter\"><a href=\"#ImageClassification\" class=\"kb-advanced-image-link\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"281\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Image-Classification-300x281.png\" alt=\"Image Classification\" class=\"kb-img wp-image-40764\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Image-Classification-300x281.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Image-Classification-50x47.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Image-Classification-200x188.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Image-Classification-115x108.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Image-Classification-360x338.png 360w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Image-Classification.png 401w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><\/figure><\/div>\n<\/div><\/div>\n\n\n<style>.kadence-column5103_94596f-ba > .kt-inside-inner-col,.kadence-column5103_94596f-ba > .kt-inside-inner-col:before{border-top-left-radius:0px;border-top-right-radius:0px;border-bottom-right-radius:0px;border-bottom-left-radius:0px;}.kadence-column5103_94596f-ba > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column5103_94596f-ba > .kt-inside-inner-col{flex-direction:column;}.kadence-column5103_94596f-ba > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column5103_94596f-ba > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column5103_94596f-ba{position:relative;}@media all and (max-width: 1024px){.kadence-column5103_94596f-ba > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column5103_94596f-ba > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column5103_94596f-ba inner-column-2\"><div class=\"kt-inside-inner-col\"><style>.kb-image5103_403151-8b .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<div class=\"wp-block-kadence-image kb-image5103_403151-8b\"><figure class=\"aligncenter\"><a href=\"#UseCases\" class=\"kb-advanced-image-link\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"281\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Uses-Applications-300x281.png\" alt=\"Uses Applications\" class=\"kb-img wp-image-40765\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Uses-Applications-300x281.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Uses-Applications-50x47.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Uses-Applications-200x188.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Uses-Applications-115x108.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Uses-Applications-360x338.png 360w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/TOC-Uses-Applications.png 401w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><\/figure><\/div>\n<\/div><\/div>\n\n\n<style>.kadence-column5103_7acb6d-9b > .kt-inside-inner-col,.kadence-column5103_7acb6d-9b > .kt-inside-inner-col:before{border-top-left-radius:0px;border-top-right-radius:0px;border-bottom-right-radius:0px;border-bottom-left-radius:0px;}.kadence-column5103_7acb6d-9b > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column5103_7acb6d-9b > .kt-inside-inner-col{flex-direction:column;}.kadence-column5103_7acb6d-9b > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column5103_7acb6d-9b > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column5103_7acb6d-9b{position:relative;}@media all and (max-width: 1024px){.kadence-column5103_7acb6d-9b > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column5103_7acb6d-9b > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column5103_7acb6d-9b inner-column-3\"><div class=\"kt-inside-inner-col\"><\/div><\/div>\n\n<\/div><\/div><\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><style>.wp-block-kadence-advancedheading.kt-adv-heading5103_d86cad-08, .wp-block-kadence-advancedheading.kt-adv-heading5103_d86cad-08[data-kb-block=\"kb-adv-heading5103_d86cad-08\"]{font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading5103_d86cad-08 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading5103_d86cad-08[data-kb-block=\"kb-adv-heading5103_d86cad-08\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading5103_d86cad-08 img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading5103_d86cad-08[data-kb-block=\"kb-adv-heading5103_d86cad-08\"] img.kb-inline-image{width:150px;vertical-align:baseline;}<\/style>\n<h3 id=\"SensorTypes\" class=\"kt-adv-heading5103_d86cad-08 wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading5103_d86cad-08\">Chapter 1. Sensor Types<\/h3>\n\n\n\n<p>Remote sensing uses a sensor to capture an image. For example, airplanes, satellites, and UAVs have specialized platforms that carry sensors.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"620\" height=\"283\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Technologies.png\" alt=\"Remote Sensing Technologies\" class=\"wp-image-19973\" title=\"Remote Sensing Technologies\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Technologies.png 620w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Technologies-300x137.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Technologies-50x23.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Technologies-200x91.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Technologies-425x194.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Technologies-550x251.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Technologies-115x52.png 115w\" sizes=\"auto, (max-width: 620px) 100vw, 620px\" \/><\/figure>\n<\/div>\n\n\n<p>The diagram below shows the major <strong>remote sensing technologies<\/strong> and their typical altitudes.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\">TYPES OF SENSORS<\/h4>\n\n\n\n<p>Each type of sensor has its own advantages and disadvantages. When you want to capture imagery, you have to consider factors like <strong>flight restrictions<\/strong>, <strong>image resolution<\/strong> and <strong>coverage<\/strong>.<\/p>\n\n\n\n<p>For example, satellites capture data on a global scale. But drones are a better fit for flying in small areas. Finally, airplanes and helicopters take the middle ground.<\/p>\n\n\n<style>.kb-image5103_968336-ea .kb-image-has-overlay:after{opacity:0.3;}.kb-image5103_968336-ea img.kb-img, .kb-image5103_968336-ea .kb-img img{border-top:1px solid #cacaca;border-right:1px solid #cacaca;border-bottom:1px solid #cacaca;border-left:1px solid #cacaca;}@media all and (max-width: 1024px){.kb-image5103_968336-ea img.kb-img, .kb-image5103_968336-ea .kb-img img{border-top:1px solid #cacaca;border-right:1px solid #cacaca;border-bottom:1px solid #cacaca;border-left:1px solid #cacaca;}}@media all and (max-width: 767px){.kb-image5103_968336-ea img.kb-img, .kb-image5103_968336-ea .kb-img img{border-top:1px solid #cacaca;border-right:1px solid #cacaca;border-bottom:1px solid #cacaca;border-left:1px solid #cacaca;}}<\/style>\n<div class=\"wp-block-kadence-image kb-image5103_968336-ea\"><figure class=\"aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"678\" height=\"259\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Sensor-Types-Advantages-Disadvantages-678x259.png\" alt=\"Remote Sensing Sensor Types Advantages Disadvantages\" class=\"kb-img wp-image-40646\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Sensor-Types-Advantages-Disadvantages-678x259.png 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Sensor-Types-Advantages-Disadvantages-300x114.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Sensor-Types-Advantages-Disadvantages-768x293.png 768w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Sensor-Types-Advantages-Disadvantages-50x19.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Sensor-Types-Advantages-Disadvantages-200x76.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Sensor-Types-Advantages-Disadvantages-425x162.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Sensor-Types-Advantages-Disadvantages-550x210.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Sensor-Types-Advantages-Disadvantages-115x44.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Sensor-Types-Advantages-Disadvantages-360x137.png 360w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Remote-Sensing-Sensor-Types-Advantages-Disadvantages.png 925w\" sizes=\"auto, (max-width: 678px) 100vw, 678px\" \/><\/figure><\/div>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\">IMAGE RESOLUTION<\/h4>\n\n\n\n<p>For earth observation, you also have to consider <strong>image resolution<\/strong>. Remote sensing divides image resolution into three different types:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Spatial resolution<\/li>\n\n\n\n<li>Spectral resolution<\/li>\n\n\n\n<li>Temporal resolution<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\">SPATIAL RESOLUTION<\/h4>\n\n\n\n<p><strong>Spatial resolution<\/strong> is the detail in pixels of an image. High spatial resolution means more detail and smaller pixel size. Whereas, lower spatial resolution means less detail and larger pixel size.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><a href=\"http:\/\/gisgeography.com\/wp-content\/uploads\/2015\/10\/Spatial-Resolution-Comparison.png\"><img loading=\"lazy\" decoding=\"async\" width=\"425\" height=\"176\" src=\"http:\/\/gisgeography.com\/wp-content\/uploads\/2015\/10\/Spatial-Resolution-Comparison-425x176.png\" alt=\"Spatial Resolution Comparison\" class=\"wp-image-7891\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/10\/Spatial-Resolution-Comparison-425x176.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/10\/Spatial-Resolution-Comparison-300x124.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/10\/Spatial-Resolution-Comparison-50x21.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/10\/Spatial-Resolution-Comparison-200x83.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/10\/Spatial-Resolution-Comparison-115x48.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/10\/Spatial-Resolution-Comparison-375x155.png 375w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/10\/Spatial-Resolution-Comparison.png 450w\" sizes=\"auto, (max-width: 425px) 100vw, 425px\" \/><\/a><\/figure>\n<\/div>\n\n\n<p>Typically, <a href=\"https:\/\/gisgeography.com\/free-uav-drone-imagery\/\">UAV imagery<\/a> has one of the highest spatial resolution. Even though satellites are highest in the atmosphere, they are capable of 50cm pixel size or greater.<\/p>\n\n\n\n<p><strong>READ MORE:<\/strong> <a href=\"https:\/\/gisgeography.com\/maxar-satellite-imagery\/\">Maxar Satellite Imagery: Worldview, GeoEye and IKONOS<\/a><\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\">SPECTRAL RESOLUTION<\/h4>\n\n\n\n<p><strong>Spectral Resolution<\/strong> is the amount of spectral detail in a band. High spectral resolution means its bands are more narrow. Whereas low spectral resolution has broader bands covering more of the spectrum.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1000\" height=\"591\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/11\/Spectral-Resolution.jpg\" alt=\"Spectral Resolution\" class=\"wp-image-96194\" style=\"width:700px\" title=\"Spectral Resolution\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/11\/Spectral-Resolution.jpg 1000w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/11\/Spectral-Resolution-300x177.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/11\/Spectral-Resolution-678x401.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/11\/Spectral-Resolution-768x454.jpg 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n<\/div>\n\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h4 class=\"wp-block-heading\">TEMPORAL RESOLUTION<\/h4>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1275\" height=\"1238\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2025\/03\/GPS-Trilateration.jpg\" alt=\"GPS Trilateration\" class=\"wp-image-96603\" style=\"width:350px\" title=\"GPS trilateration\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2025\/03\/GPS-Trilateration.jpg 1275w, https:\/\/gisgeography.com\/wp-content\/uploads\/2025\/03\/GPS-Trilateration-300x291.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2025\/03\/GPS-Trilateration-678x658.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2025\/03\/GPS-Trilateration-768x746.jpg 768w\" sizes=\"auto, (max-width: 1275px) 100vw, 1275px\" \/><\/figure>\n<\/div>\n\n\n<p><strong>Temporal Resolution<\/strong> is the time it takes for a satellite to complete a full orbit. UAVs, airplanes, and helicopters are completely flexible. But satellites orbit the Earth in set paths.<\/p>\n\n\n\n<p>Global position system satellites are in medium Earth orbit (MEO). Because they follow a continuous orbital path, revisit times are consistent. This means our GPS receiver can <em>almost<\/em> always achieve 3 satellites or greater for high accuracy.<\/p>\n\n\n\n<p><strong>READ MORE:<\/strong> <a href=\"https:\/\/gisgeography.com\/trilateration-triangulation-gps\/\">Trilateration vs Triangulation \u2013 How GPS Receivers Work<\/a><\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\">TYPES OF ORBITS<\/h4>\n\n\n\n<p>The three types of orbits are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/gisgeography.com\/geosynchronous-geostationary-orbits\/\">Geostationary orbits<\/a> match the Earth&#8217;s rate of rotation.<\/li>\n\n\n\n<li><strong>Sun-synchronous orbits<\/strong> keep the angle of sunlight on the surface of the Earth as consistent as possible.<\/li>\n\n\n\n<li><a href=\"https:\/\/gisgeography.com\/polar-orbit-sun-synchronous-orbit\/\">Polar orbits<\/a> pass above or nearly above both poles of Earth.<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"297\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Satellite-Orbits-Altitude-300x297.png\" alt=\"Satellite Orbits Altitude\" class=\"wp-image-40738\" title=\"Satellite Orbits Altitude\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Satellite-Orbits-Altitude-300x297.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Satellite-Orbits-Altitude-150x150.png 150w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Satellite-Orbits-Altitude-50x50.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Satellite-Orbits-Altitude-200x198.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Satellite-Orbits-Altitude-425x421.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Satellite-Orbits-Altitude-550x544.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Satellite-Orbits-Altitude-115x114.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Satellite-Orbits-Altitude-360x356.png 360w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Satellite-Orbits-Altitude.png 590w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>It&#8217;s the <strong>satellite&#8217;s height<\/strong> above the Earth&#8217;s surface that determines the time it takes for a complete orbit. If a satellite has a higher altitude, the orbital period increases.<\/p>\n\n\n\n<p>We categorize orbits by their altitude:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Low Earth Orbit (LEO)<\/li>\n\n\n\n<li>Medium Earth Orbit (MEO)<\/li>\n\n\n\n<li>High Earth Orbit (HEO)<\/li>\n<\/ul>\n\n\n\n<p>We often find the weather, communications, and surveillance satellites in high Earth orbit. But CubeSats, the ISS, and other satellites are often in low Earth orbit.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\" id=\"Types\">Chapter 2. Types of Remote Sensing<\/h3>\n\n\n\n<p>The two types of remote sensing sensors are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Passive sensors<\/li>\n\n\n\n<li>Active sensors<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\">ACTIVE SENSORS<\/h4>\n\n\n\n<p>The main difference between <strong>active sensors<\/strong> is that this type of sensor illuminates its target. Then, active sensors measure the reflected light. For example, <a title=\"Canadian Space Agency Radarsat-2\" href=\"https:\/\/www.asc-csa.gc.ca\/eng\/satellites\/radarsat2\/\" target=\"_blank\" rel=\"noopener noreferrer\">Radarsat-2<\/a> is an active sensor that uses synthetic aperture radar.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"678\" height=\"297\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Active-Remote-Sensing-678x297.png\" alt=\"Active Remote Sensing\" class=\"wp-image-40739\" title=\"Active Remote Sensing\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Active-Remote-Sensing-678x297.png 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Active-Remote-Sensing-300x132.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Active-Remote-Sensing-50x22.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Active-Remote-Sensing-80x35.png 80w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Active-Remote-Sensing-200x88.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Active-Remote-Sensing-425x186.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Active-Remote-Sensing-550x241.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Active-Remote-Sensing-115x50.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Active-Remote-Sensing-360x158.png 360w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Active-Remote-Sensing.png 764w\" sizes=\"auto, (max-width: 678px) 100vw, 678px\" \/><\/figure>\n<\/div>\n\n\n<p>Imagine the flash of a camera. It brightens its target. Next, it captures the return light. This is the same principle of how active sensors work.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\">PASSIVE SENSORS<\/h4>\n\n\n\n<p><strong>Passive sensors<\/strong> measure <strong>reflected light emitted from the sun<\/strong>. When sunlight reflects off the Earth&#8217;s surface, passive sensors capture that light.<\/p>\n\n\n\n<p>For example, <a href=\"https:\/\/gisgeography.com\/landsat\/\">Landsat<\/a> and <a href=\"https:\/\/gisgeography.com\/sentinel-satellites-copernicus-programme\/\">Sentinel<\/a> are passive sensors. They capture images by sensing reflected sunlight in the electromagnetic spectrum.<\/p>\n\n\n<style>.kb-image5103_99d274-cb.kb-image-is-ratio-size, .kb-image5103_99d274-cb .kb-image-is-ratio-size{max-width:550px;width:100%;}.wp-block-kadence-column > .kt-inside-inner-col > .kb-image5103_99d274-cb.kb-image-is-ratio-size, .wp-block-kadence-column > .kt-inside-inner-col > .kb-image5103_99d274-cb .kb-image-is-ratio-size{align-self:unset;}.kb-image5103_99d274-cb figure{max-width:550px;}.kb-image5103_99d274-cb .image-is-svg, .kb-image5103_99d274-cb .image-is-svg img{width:100%;}.kb-image5103_99d274-cb .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<div class=\"wp-block-kadence-image kb-image5103_99d274-cb\"><figure class=\"aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"678\" height=\"297\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Passive-Remote-Sensing-678x297.png\" alt=\"Passive Remote Sensing\" class=\"kb-img wp-image-40740\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Passive-Remote-Sensing-678x297.png 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Passive-Remote-Sensing-300x132.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Passive-Remote-Sensing-50x22.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Passive-Remote-Sensing-80x35.png 80w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Passive-Remote-Sensing-200x88.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Passive-Remote-Sensing-425x186.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Passive-Remote-Sensing-550x241.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Passive-Remote-Sensing-115x50.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Passive-Remote-Sensing-360x158.png 360w, https:\/\/gisgeography.com\/wp-content\/uploads\/2015\/11\/Passive-Remote-Sensing.png 764w\" sizes=\"auto, (max-width: 678px) 100vw, 678px\" \/><\/figure><\/div>\n\n\n\n<p>Passive remote sensing measures reflected energy emitted from the sun. Whereas active remote sensing illuminates its target and measures its backscatter.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\" id=\"ElectromagneticSpectrum\">Chapter 3. The Electromagnetic Spectrum<\/h3>\n\n\n\n<p>The electromagnetic spectrum ranges from short wavelengths (like X-rays) to long wavelengths (like radio waves).<\/p>\n\n\n\n<p>Our eyes only see the visible range (red, green, and blue). But other types of sensors can see beyond human vision. Ultimately, this is why remote sensing is so powerful.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"530\" height=\"50\" src=\"http:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/emspectrum.png\" alt=\"Electromagnetic Spectrum\" class=\"wp-image-1470\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/emspectrum.png 530w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/emspectrum-300x28.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/emspectrum-50x5.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/emspectrum-200x19.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/emspectrum-425x40.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/emspectrum-115x11.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/emspectrum-500x47.png 500w\" sizes=\"auto, (max-width: 530px) 100vw, 530px\" \/><\/figure>\n<\/div><\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\">ELECTROMAGNETIC SPECTRUM<\/h4>\n\n\n<style>.kb-image5103_922dce-17.kb-image-is-ratio-size, .kb-image5103_922dce-17 .kb-image-is-ratio-size{max-width:300px;width:100%;}.wp-block-kadence-column > .kt-inside-inner-col > .kb-image5103_922dce-17.kb-image-is-ratio-size, .wp-block-kadence-column > .kt-inside-inner-col > .kb-image5103_922dce-17 .kb-image-is-ratio-size{align-self:unset;}.kb-image5103_922dce-17 figure{max-width:300px;}.kb-image5103_922dce-17 .image-is-svg, .kb-image5103_922dce-17 .image-is-svg img{width:100%;}.kb-image5103_922dce-17 .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<div class=\"wp-block-kadence-image kb-image5103_922dce-17\"><figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"350\" height=\"389\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2017\/08\/ndvi-example.jpg\" alt=\"ndvi example\" class=\"kb-img wp-image-15818\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2017\/08\/ndvi-example.jpg 350w, https:\/\/gisgeography.com\/wp-content\/uploads\/2017\/08\/ndvi-example-270x300.jpg 270w, https:\/\/gisgeography.com\/wp-content\/uploads\/2017\/08\/ndvi-example-45x50.jpg 45w, https:\/\/gisgeography.com\/wp-content\/uploads\/2017\/08\/ndvi-example-180x200.jpg 180w, https:\/\/gisgeography.com\/wp-content\/uploads\/2017\/08\/ndvi-example-115x128.jpg 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2017\/08\/ndvi-example-139x155.jpg 139w\" sizes=\"auto, (max-width: 350px) 100vw, 350px\" \/><\/figure><\/div>\n\n\n\n<p>Our eyes are sensitive to the visible spectrum (390-700 nm). But engineers design sensors to capture beyond these wavelengths in the <a href=\"https:\/\/gisgeography.com\/atmospheric-window\/\">atmospheric window<\/a>.<\/p>\n\n\n\n<p>For example, near-infrared (NIR) is in the 700-1400 nm range. Vegetation reflects more green light because that&#8217;s how our eyes see it.<\/p>\n\n\n\n<p>But it&#8217;s even more sensitive to near-infrared. That&#8217;s why we use <a href=\"https:\/\/gisgeography.com\/how-to-ndvi-maps-arcgis\/\">indexes like NDVI<\/a> to classify vegetation.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\">SPECTRAL BANDS<\/h4>\n\n\n\n<p>Spectral bands are groups of wavelengths. For example, ultraviolet, visible, near-infrared, thermal infrared, and microwave are spectral bands.<\/p>\n\n\n\n<p>We categorize each spectral region based on its frequency (v) or wavelength. There are two types of imagery for passive sensors:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Multispectral imagery<\/li>\n\n\n\n<li>Hyperspectral imagery<\/li>\n<\/ul>\n\n\n\n<p>The main difference between <a href=\"https:\/\/gisgeography.com\/multispectral-vs-hyperspectral-imagery-explained\/\">multispectral and hyperspectral<\/a> is the <strong>number of bands<\/strong> and <strong>how narrow the bands are<\/strong>. <a href=\"https:\/\/gisgeography.com\/hyperspectral-imaging\/\">Hyperspectral images<\/a> have hundreds of narrow bands, multispectral images consist of 3-10 wider bands.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\">MULTISPECTRAL<\/h4>\n\n\n\n<p>Multispectral imagery generally refers to <strong>3 to 10 bands<\/strong>. For example, Landsat-8 produces 11 separate images for each scene.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"530\" height=\"100\" src=\"http:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/multi.png\" alt=\"An example of multispectral imagery\" class=\"wp-image-1307\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/multi.png 530w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/multi-300x57.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/multi-50x9.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/multi-200x38.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/multi-425x80.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/multi-115x22.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/multi-500x94.png 500w\" sizes=\"auto, (max-width: 530px) 100vw, 530px\" \/><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Coastal aerosol (0.43-0.45 um)<\/li>\n\n\n\n<li>Blue (0.45-0.51 um)<\/li>\n\n\n\n<li>Green (0.53-0.59 um)<\/li>\n\n\n\n<li>Red (0.64-0.67 um)<\/li>\n\n\n\n<li>Near-infrared NIR (0.85-0.88 um)<\/li>\n\n\n\n<li>Short-wave infrared SWIR 1 (1.57-1.65 um)<\/li>\n\n\n\n<li>Short-wave infrared SWIR 2 (2.11-2.29 um)<\/li>\n\n\n\n<li>Panchromatic (0.50-0.68 um)<\/li>\n\n\n\n<li>Cirrus (1.36-1.38 um)<\/li>\n\n\n\n<li>Thermal infrared TIRS 1 (10.60-11.19 um)<\/li>\n\n\n\n<li>Thermal infrared TIRS 2 (11.50-12.51 um)<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\">HYPERSPECTRAL<\/h4>\n\n\n\n<p>Hyperspectral imagery has much narrower bands (10-20 nm). A hyperspectral image has <strong>hundreds of thousands of bands<\/strong>.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"530\" height=\"90\" src=\"http:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/hyper.png\" alt=\"Example of hyperspectral imagery\" class=\"wp-image-1308\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/hyper.png 530w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/hyper-300x51.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/hyper-50x8.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/hyper-200x34.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/hyper-425x72.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/hyper-115x20.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/hyper-500x85.png 500w\" sizes=\"auto, (max-width: 530px) 100vw, 530px\" \/><\/figure>\n\n\n\n<p>For example, <a title=\"Hyperion Imaging Sepctrometer\" href=\"https:\/\/earthobservatory.nasa.gov\/features\/EO1Tenth\/page3.php\" target=\"_blank\" rel=\"noopener noreferrer\">Hyperion<\/a> (part of the EO-1 satellite) produces 220 spectral bands (0.4-2.5 um).<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><style>.wp-block-kadence-advancedheading.kt-adv-heading5103_e80077-9a, .wp-block-kadence-advancedheading.kt-adv-heading5103_e80077-9a[data-kb-block=\"kb-adv-heading5103_e80077-9a\"]{font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading5103_e80077-9a mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading5103_e80077-9a[data-kb-block=\"kb-adv-heading5103_e80077-9a\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading5103_e80077-9a img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading5103_e80077-9a[data-kb-block=\"kb-adv-heading5103_e80077-9a\"] img.kb-inline-image{width:150px;vertical-align:baseline;}<\/style>\n<h3 id=\"ImageClassification\" class=\"kt-adv-heading5103_e80077-9a wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading5103_e80077-9a\">Chapter 4. Image Classification<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"242\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/obia-layers.png\" alt=\"Object Based Image Classification Layers\" class=\"wp-image-1216\" title=\"Object Based Image Classification Layers\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/obia-layers.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/obia-layers-50x40.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/obia-layers-200x161.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/obia-layers-115x93.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/07\/obia-layers-192x155.png 192w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>When you examine a photo and you try to pull out features and characteristics from it, this is the act of using <strong>image interpretation<\/strong>. We use image interpretation in forestry, military, and urban environments.<\/p>\n\n\n\n<p>We can interpret features because all objects have their own unique chemical composition. In remote sensing, we distinguish these differences by obtaining their <a href=\"https:\/\/gisgeography.com\/spectral-signature\/\">spectral signature<\/a>.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\">SPECTRAL SIGNATURES<\/h4>\n\n\n\n<p>In the mining industry, there are over <strong>4000 natural minerals<\/strong> on Earth. Each mineral has its own chemical composition that makes it different from others.<\/p>\n\n\n\n<p>It&#8217;s the object&#8217;s chemical composition that drives its spectral signature. You can classify each mineral because it has its own unique spectral signature. When you have more spectral bands, this gives greater potential in <a href=\"https:\/\/gisgeography.com\/image-classification-techniques-remote-sensing\/\">image classification<\/a>.<\/p>\n\n\n\n<p>A spectral signature is the amount of energy reflected in a particular wavelength. Differences in spectral signatures are how we tell objects apart.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h5 class=\"wp-block-heading\">IMAGE CLASSIFICATION<\/h5>\n\n\n\n<p>When you assign classes to features on the ground, this is the process of <a href=\"https:\/\/gisgeography.com\/image-classification-techniques-remote-sensing\/\">image classification<\/a>.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"678\" height=\"360\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/11\/OBIA-Cover-678x360.jpg\" alt=\"OBIA Cover\" class=\"wp-image-96116\" style=\"aspect-ratio:3\/2;object-fit:cover;width:350px\" title=\"Image Classification Techniques Object-Based\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/11\/OBIA-Cover-678x360.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/11\/OBIA-Cover-300x159.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/11\/OBIA-Cover-768x408.jpg 768w, https:\/\/gisgeography.com\/wp-content\/uploads\/2016\/11\/OBIA-Cover.jpg 1000w\" sizes=\"auto, (max-width: 678px) 100vw, 678px\" \/><\/figure>\n<\/div>\n\n\n<p>The three main methods to classify images are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/gisgeography.com\/supervised-unsupervised-classification-arcgis\/\">Supervised classification<\/a><\/li>\n\n\n\n<li>Unsupervised classification<\/li>\n\n\n\n<li><a href=\"https:\/\/gisgeography.com\/obia-object-based-image-analysis-geobia\/\">Object-based image analysis<\/a><\/li>\n<\/ul>\n\n\n\n<p>The goal of image classification is to produce land use\/land cover. By using <a href=\"https:\/\/gisgeography.com\/open-source-remote-sensing-software-packages\/\">remote sensing software<\/a>, this is how we classify water, wetlands, trees, and urban areas in land cover.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><style>.wp-block-kadence-advancedheading.kt-adv-heading5103_f70b5c-fb, .wp-block-kadence-advancedheading.kt-adv-heading5103_f70b5c-fb[data-kb-block=\"kb-adv-heading5103_f70b5c-fb\"]{font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading5103_f70b5c-fb mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading5103_f70b5c-fb[data-kb-block=\"kb-adv-heading5103_f70b5c-fb\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading5103_f70b5c-fb img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading5103_f70b5c-fb[data-kb-block=\"kb-adv-heading5103_f70b5c-fb\"] img.kb-inline-image{width:150px;vertical-align:baseline;}<\/style>\n<h3 id=\"UseCases\" class=\"kt-adv-heading5103_f70b5c-fb wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading5103_f70b5c-fb\">Chapter 5. Applications and Uses<\/h3>\n\n\n\n<p>There are <a href=\"https:\/\/gisgeography.com\/remote-sensing-applications\/\">hundreds of applications of remote sensing<\/a>. From weather forecasting to GPS, it&#8217;s satellites in space that monitor, protect, and guide us in our daily lives.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\">LOCAL ISSUES<\/h4>\n\n\n\n<p>Commonly, we use UAVs, helicopters, and airplanes for local issues. But satellites can also be useful for local study areas as well.<\/p>\n\n\n\n<p>Here are some of the common sensor technologies:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Light Detection and Ranging (LiDAR)<\/li>\n\n\n\n<li>Sound navigation ranging (Sonar)<\/li>\n\n\n\n<li>Radiometers and spectrometers<\/li>\n<\/ul>\n\n\n\n<p>We use <a href=\"https:\/\/gisgeography.com\/lidar-light-detection-and-ranging\/\">Light Detection and Ranging (LiDAR)<\/a> and Sonar. Both are ideal for building topographic models. But the main difference between the two is &#8220;where&#8221;. While LiDAR is best suited for the ground, <a href=\"https:\/\/gisgeography.com\/sonar\/\">Sonar works better underwater<\/a>.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"678\" height=\"325\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2020\/11\/esri-javascript-api-lidar-scene-678x325.jpg\" alt=\"Esri Javascript API Lidar Scene\" class=\"wp-image-97183\" style=\"width:500px\" title=\"LiDAR scene\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2020\/11\/esri-javascript-api-lidar-scene-678x325.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2020\/11\/esri-javascript-api-lidar-scene-300x144.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2020\/11\/esri-javascript-api-lidar-scene-768x369.jpg 768w, https:\/\/gisgeography.com\/wp-content\/uploads\/2020\/11\/esri-javascript-api-lidar-scene.jpg 900w\" sizes=\"auto, (max-width: 678px) 100vw, 678px\" \/><\/figure>\n<\/div>\n\n\n<p>By using these technologies, we build <a href=\"http:\/\/gisgeography.com\/dem-dsm-dtm-differences\/\">digital elevation models<\/a>. Using these topographic models, we can predict flooding risk, archaeological sites, and delineating watersheds (to name a few).<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\">GLOBAL ISSUES<\/h4>\n\n\n\n<p>As the world becomes more globalized, we are just starting to see the proliferation of remote sensing. For example, satellites tackle issues including:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Navigating with global positioning systems<\/li>\n\n\n\n<li>Climate change monitoring<\/li>\n\n\n\n<li>Arctic Surveillance<\/li>\n<\/ul>\n\n\n\n<p>Satellite information is fundamentally important if we are going to solve some of the major challenges of our time. All things considered, it&#8217;s an expanding field reaching new heights.<\/p>\n\n\n<style>.kb-image5103_f49ef4-dd .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<div class=\"wp-block-kadence-image kb-image5103_f49ef4-dd\"><figure class=\"aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"116\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/remote-sensing-trends.png\" alt=\"Remote Sensing Trends\" class=\"kb-img wp-image-855\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/remote-sensing-trends.png 500w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/remote-sensing-trends-300x70.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/remote-sensing-trends-50x12.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/remote-sensing-trends-200x46.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/remote-sensing-trends-425x99.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2014\/06\/remote-sensing-trends-115x27.png 115w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/figure><\/div>\n\n\n\n<p>For issues like climate change, natural resources, disaster management, and the environment, remote sensing provides a wealth of information on a global scale.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">References<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/earthdata.nasa.gov\/learn\/remote-sensing\" target=\"_blank\" rel=\"noopener noreferrer\">What is Remote Sensing? (NASA)<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/gisgeography.com\/earth-satellite-list\/\" target=\"_blank\" rel=\"noopener noreferrer\">50 Satellites in Space: Types and Uses of Satellites<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/gisgeography.com\/best-remote-sensing-software\/\">15 Best Remote Sensing Software<\/a><\/li>\n<\/ul>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Remote sensing is the science of obtaining information without physically being there. The 3 most common methods of capture is airplane, satellite &#038; drones.<\/p>\n","protected":false},"author":2,"featured_media":20717,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_kad_post_transparent":"default","_kad_post_title":"default","_kad_post_layout":"default","_kad_post_sidebar_id":"","_kad_post_content_style":"default","_kad_post_vertical_padding":"default","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[92],"tags":[459],"class_list":["post-5103","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-remote-sensing","tag-remote-sensing-types"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What is Remote Sensing? 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