DataSheet5_WATERHYPERNET: a prototype network of automated in situ measurements of hyperspectral water reflectance for satellite validation and water quality monitoring.ZIP by Kevin G. Ruddick (18197815)

“…Analysis of the differences between satellite and in situ water reflectance measurements, particularly unmasked outliers, can provide recommendations on where satellite data processing algorithms need to be improved. In a massively multi-mission context, including Newspace constellations, hyperspectral missions and missions with broad spectral bands not designed for “water colour”, the advantage of hyperspectral over multispectral in situ measurements is clear. …”

DataSheet3_WATERHYPERNET: a prototype network of automated in situ measurements of hyperspectral water reflectance for satellite validation and water quality monitoring.ZIP by Kevin G. Ruddick (18197815)

“…Analysis of the differences between satellite and in situ water reflectance measurements, particularly unmasked outliers, can provide recommendations on where satellite data processing algorithms need to be improved. In a massively multi-mission context, including Newspace constellations, hyperspectral missions and missions with broad spectral bands not designed for “water colour”, the advantage of hyperspectral over multispectral in situ measurements is clear. …”

DataSheet10_WATERHYPERNET: a prototype network of automated in situ measurements of hyperspectral water reflectance for satellite validation and water quality monitoring.CSV by Kevin G. Ruddick (18197815)

“…Analysis of the differences between satellite and in situ water reflectance measurements, particularly unmasked outliers, can provide recommendations on where satellite data processing algorithms need to be improved. In a massively multi-mission context, including Newspace constellations, hyperspectral missions and missions with broad spectral bands not designed for “water colour”, the advantage of hyperspectral over multispectral in situ measurements is clear. …”

DataSheet6_WATERHYPERNET: a prototype network of automated in situ measurements of hyperspectral water reflectance for satellite validation and water quality monitoring.ZIP by Kevin G. Ruddick (18197815)

“…Analysis of the differences between satellite and in situ water reflectance measurements, particularly unmasked outliers, can provide recommendations on where satellite data processing algorithms need to be improved. In a massively multi-mission context, including Newspace constellations, hyperspectral missions and missions with broad spectral bands not designed for “water colour”, the advantage of hyperspectral over multispectral in situ measurements is clear. …”

DataSheet1_WATERHYPERNET: a prototype network of automated in situ measurements of hyperspectral water reflectance for satellite validation and water quality monitoring.CSV by Kevin G. Ruddick (18197815)

“…Analysis of the differences between satellite and in situ water reflectance measurements, particularly unmasked outliers, can provide recommendations on where satellite data processing algorithms need to be improved. In a massively multi-mission context, including Newspace constellations, hyperspectral missions and missions with broad spectral bands not designed for “water colour”, the advantage of hyperspectral over multispectral in situ measurements is clear. …”

DataSheet8_WATERHYPERNET: a prototype network of automated in situ measurements of hyperspectral water reflectance for satellite validation and water quality monitoring.CSV by Kevin G. Ruddick (18197815)

“…Analysis of the differences between satellite and in situ water reflectance measurements, particularly unmasked outliers, can provide recommendations on where satellite data processing algorithms need to be improved. In a massively multi-mission context, including Newspace constellations, hyperspectral missions and missions with broad spectral bands not designed for “water colour”, the advantage of hyperspectral over multispectral in situ measurements is clear. …”

DataSheet7_WATERHYPERNET: a prototype network of automated in situ measurements of hyperspectral water reflectance for satellite validation and water quality monitoring.ZIP by Kevin G. Ruddick (18197815)

“…Analysis of the differences between satellite and in situ water reflectance measurements, particularly unmasked outliers, can provide recommendations on where satellite data processing algorithms need to be improved. In a massively multi-mission context, including Newspace constellations, hyperspectral missions and missions with broad spectral bands not designed for “water colour”, the advantage of hyperspectral over multispectral in situ measurements is clear. …”

DataSheet4_WATERHYPERNET: a prototype network of automated in situ measurements of hyperspectral water reflectance for satellite validation and water quality monitoring.ZIP by Kevin G. Ruddick (18197815)

“…Analysis of the differences between satellite and in situ water reflectance measurements, particularly unmasked outliers, can provide recommendations on where satellite data processing algorithms need to be improved. In a massively multi-mission context, including Newspace constellations, hyperspectral missions and missions with broad spectral bands not designed for “water colour”, the advantage of hyperspectral over multispectral in situ measurements is clear. …”

DataSheet9_WATERHYPERNET: a prototype network of automated in situ measurements of hyperspectral water reflectance for satellite validation and water quality monitoring.CSV by Kevin G. Ruddick (18197815)

“…Analysis of the differences between satellite and in situ water reflectance measurements, particularly unmasked outliers, can provide recommendations on where satellite data processing algorithms need to be improved. In a massively multi-mission context, including Newspace constellations, hyperspectral missions and missions with broad spectral bands not designed for “water colour”, the advantage of hyperspectral over multispectral in situ measurements is clear. …”

Table1_WATERHYPERNET: a prototype network of automated in situ measurements of hyperspectral water reflectance for satellite validation and water quality monitoring.docx by Kevin G. Ruddick (18197815)

“…Analysis of the differences between satellite and in situ water reflectance measurements, particularly unmasked outliers, can provide recommendations on where satellite data processing algorithms need to be improved. In a massively multi-mission context, including Newspace constellations, hyperspectral missions and missions with broad spectral bands not designed for “water colour”, the advantage of hyperspectral over multispectral in situ measurements is clear. …”

DataSheet2_WATERHYPERNET: a prototype network of automated in situ measurements of hyperspectral water reflectance for satellite validation and water quality monitoring.ZIP by Kevin G. Ruddick (18197815)

“…Analysis of the differences between satellite and in situ water reflectance measurements, particularly unmasked outliers, can provide recommendations on where satellite data processing algorithms need to be improved. In a massively multi-mission context, including Newspace constellations, hyperspectral missions and missions with broad spectral bands not designed for “water colour”, the advantage of hyperspectral over multispectral in situ measurements is clear. …”

Video_2_Development and Clinical Application of a Novel Non-contact Early Airflow Limitation Screening System Using an Infrared Time-of-Flight Depth Image Sensor.MP4 by Hiroki Takamoto (9364688)

“…The EAFL-SS comprised an 850 nm infrared ToF depth image sensor (224 × 171 pixels) and custom-built data processing algorithms to visualize anterior-thorax three-dimensional motions in real-time. …”

Video_1_Development and Clinical Application of a Novel Non-contact Early Airflow Limitation Screening System Using an Infrared Time-of-Flight Depth Image Sensor.MP4 by Hiroki Takamoto (9364688)

“…The EAFL-SS comprised an 850 nm infrared ToF depth image sensor (224 × 171 pixels) and custom-built data processing algorithms to visualize anterior-thorax three-dimensional motions in real-time. …”

<b>Intelligent Compaction Measurement Value</b><b> Dataset by </b><b>SPARC Intelligent Compaction Analyzer (ICA)</b> by Rajitha Ranasinghe (13147044)

“…</li><li>Refine and validate data processing algorithms.</li><li>Research and develop experimental and established Intelligent Compaction Measurement Values (ICMVs).…”