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  • PANGAEA  (5)
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  • 1
    facet.materialart.
    Unknown
    Remote-sensing reflectance determined from spectral data set within intercalibration campaign from Lake Süßer See (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-07-03
    Keywords: Date/time end; Date/time start; Flag; Germany; Hyperspectral radiometer, TriOS Mess- und Datentechnik GmbH, RAMSES; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Principal investigator; Remote sensing reflectance at 320 nm; Remote sensing reflectance at 320 nm, standard deviation; Remote sensing reflectance at 321 nm; Remote sensing reflectance at 321 nm, standard deviation; Remote sensing reflectance at 322 nm; Remote sensing reflectance at 322 nm, standard deviation; Remote sensing reflectance at 323 nm; Remote sensing reflectance at 323 nm, standard deviation; Remote sensing reflectance at 324 nm; Remote sensing reflectance at 324 nm, standard deviation; Remote sensing reflectance at 325 nm; Remote sensing reflectance at 325 nm, standard deviation; Remote sensing reflectance at 326 nm; Remote sensing reflectance at 326 nm, standard deviation; Remote sensing reflectance at 327 nm; Remote sensing reflectance at 327 nm, standard deviation; Remote sensing reflectance at 328 nm; Remote sensing reflectance at 328 nm, standard deviation; Remote sensing reflectance at 329 nm; Remote sensing reflectance at 329 nm, standard deviation; Remote sensing reflectance at 330 nm; Remote sensing reflectance at 330 nm, standard deviation; Remote sensing reflectance at 331 nm; Remote sensing reflectance at 331 nm, standard deviation; Remote sensing reflectance at 332 nm; Remote sensing reflectance at 332 nm, standard deviation; Remote sensing reflectance at 333 nm; Remote sensing reflectance at 333 nm, standard deviation; Remote sensing reflectance at 334 nm; Remote sensing reflectance at 334 nm, standard deviation; Remote sensing reflectance at 335 nm; Remote sensing reflectance at 335 nm, standard deviation; Remote sensing reflectance at 336 nm; Remote sensing reflectance at 336 nm, standard deviation; Remote sensing reflectance at 337 nm; Remote sensing reflectance at 337 nm, standard deviation; Remote sensing reflectance at 338 nm; Remote sensing reflectance at 338 nm, standard deviation; Remote sensing reflectance at 339 nm; Remote sensing reflectance at 339 nm, standard deviation; Remote sensing reflectance at 340 nm; Remote sensing reflectance at 340 nm, standard deviation; Remote sensing reflectance at 341 nm; Remote sensing reflectance at 341 nm, standard deviation; Remote sensing reflectance at 342 nm; Remote sensing reflectance at 342 nm, standard deviation; Remote sensing reflectance at 343 nm; Remote sensing reflectance at 343 nm, standard deviation; Remote sensing reflectance at 344 nm; Remote sensing reflectance at 344 nm, standard deviation; Remote sensing reflectance at 345 nm; Remote sensing reflectance at 345 nm, standard deviation; Remote sensing reflectance at 346 nm; Remote sensing reflectance at 346 nm, standard deviation; Remote sensing reflectance at 347 nm; Remote sensing reflectance at 347 nm, standard deviation; Remote sensing reflectance at 348 nm; Remote sensing reflectance at 348 nm, standard deviation; Remote sensing reflectance at 349 nm; Remote sensing reflectance at 349 nm, standard deviation; Remote sensing reflectance at 350 nm; Remote sensing reflectance at 350 nm, standard deviation; Remote sensing reflectance at 351 nm; Remote sensing reflectance at 351 nm, standard deviation; Remote sensing reflectance at 352 nm; Remote sensing reflectance at 352 nm, standard deviation; Remote sensing reflectance at 353 nm; Remote sensing reflectance at 353 nm, standard deviation; Remote sensing reflectance at 354 nm; Remote sensing reflectance at 354 nm, standard deviation; Remote sensing reflectance at 355 nm; Remote sensing reflectance at 355 nm, standard deviation; Remote sensing reflectance at 356 nm; Remote sensing reflectance at 356 nm, standard deviation; Remote sensing reflectance at 357 nm; Remote sensing reflectance at 357 nm, standard deviation; Remote sensing reflectance at 358 nm; Remote sensing reflectance at 358 nm, standard deviation; Remote sensing reflectance at 359 nm; Remote sensing reflectance at 359 nm, standard deviation; Remote sensing reflectance at 360 nm; Remote sensing reflectance at 360 nm, standard deviation; Remote sensing reflectance at 361 nm; Remote sensing reflectance at 361 nm, standard deviation; Remote sensing reflectance at 362 nm; Remote sensing reflectance at 362 nm, standard deviation; Remote sensing reflectance at 363 nm; Remote sensing reflectance at 363 nm, standard deviation; Remote sensing reflectance at 364 nm; Remote sensing reflectance at 364 nm, standard deviation; Remote sensing reflectance at 365 nm; Remote sensing reflectance at 365 nm, standard deviation; Remote sensing reflectance at 366 nm; Remote sensing reflectance at 366 nm, standard deviation; Remote sensing reflectance at 367 nm; Remote sensing reflectance at 367 nm, standard deviation; Remote sensing reflectance at 368 nm; Remote sensing reflectance at 368 nm, standard deviation; Remote sensing reflectance at 369 nm; Remote sensing reflectance at 369 nm, standard deviation; Remote sensing reflectance at 370 nm; Remote sensing reflectance at 370 nm, standard deviation; Remote sensing reflectance at 371 nm; Remote sensing reflectance at 371 nm, standard deviation; Remote sensing reflectance at 372 nm; Remote sensing reflectance at 372 nm, standard deviation; Remote sensing reflectance at 373 nm; Remote sensing reflectance at 373 nm, standard deviation; Remote sensing reflectance at 374 nm; Remote sensing reflectance at 374 nm, standard deviation; Remote sensing reflectance at 375 nm; Remote sensing reflectance at 375 nm, standard deviation; Remote sensing reflectance at 376 nm; Remote sensing reflectance at 376 nm, standard deviation; Remote sensing reflectance at 377 nm; Remote sensing reflectance at 377 nm, standard deviation; Remote sensing reflectance at 378 nm; Remote sensing reflectance at 378 nm, standard deviation; Remote sensing reflectance at 379 nm; Remote sensing reflectance at 379 nm, standard deviation; Remote sensing reflectance at 380 nm; Remote sensing reflectance at 380 nm, standard deviation; Remote sensing reflectance at 381 nm; Remote sensing reflectance at 381 nm, standard deviation; Remote sensing reflectance at 382 nm; Remote sensing reflectance at 382 nm, standard deviation; Remote sensing reflectance at 383 nm; Remote sensing reflectance at 383 nm, standard deviation; Remote sensing reflectance at 384 nm; Remote sensing reflectance at 384 nm, standard deviation; Remote sensing reflectance at 385 nm; Remote sensing reflectance at 385 nm, standard deviation; Remote sensing reflectance at 386 nm; Remote sensing reflectance at 386 nm, standard deviation; Remote sensing reflectance at 387 nm; Remote sensing reflectance at 387 nm, standard deviation; Remote sensing reflectance at 388 nm; Remote sensing reflectance at 388 nm, standard deviation; Remote sensing reflectance at 389 nm; Remote sensing reflectance at 389 nm, standard deviation; Remote sensing reflectance at 390 nm; Remote sensing reflectance at 390 nm, standard deviation; Remote sensing reflectance at 391 nm; Remote sensing reflectance at 391 nm, standard deviation; Remote sensing reflectance at 392 nm; Remote sensing reflectance at 392 nm, standard deviation; Remote sensing reflectance at 393 nm; Remote sensing reflectance at 393 nm, standard deviation; Remote sensing reflectance at 394 nm; Remote sensing reflectance at 394 nm, standard deviation; Remote sensing reflectance at 395 nm; Remote sensing reflectance at 395 nm, standard deviation; Remote sensing reflectance at 396 nm; Remote sensing reflectance at 396 nm, standard deviation; Remote sensing reflectance at 397 nm; Remote sensing reflectance at 397 nm, standard deviation; Remote sensing reflectance at 398 nm; Remote sensing reflectance at 398 nm, standard deviation; Remote sensing reflectance at 399 nm; Remote sensing reflectance at 399 nm, standard deviation; Remote sensing reflectance at 400 nm; Remote sensing reflectance at 400 nm, standard deviation; Remote sensing
    Type: Dataset
    Format: text/tab-separated-values, 2325 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 2
    facet.materialart.
    Unknown
    Remote-sensing reflectance determined from spectral data set from Lake Geiseltal (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-07-03
    Keywords: Date/time end; Date/time start; Event label; Flag; Geiseltalsee-0802_RAMSES3; Geiseltalsee-0845_RAMSES3; Geiseltalsee-0950_RAMSES3; Geiseltalsee-1006_RAMSES3; Geiseltalsee-1046_RAMSES3; Germany; Hyperspectral radiometer, TriOS Mess- und Datentechnik GmbH, RAMSES; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Principal investigator; RAMSES3; RAMSES3 Hyperspectral Radiance and Irradiance Sensor; Remote sensing reflectance at 320 nm; Remote sensing reflectance at 320 nm, standard deviation; Remote sensing reflectance at 321 nm; Remote sensing reflectance at 321 nm, standard deviation; Remote sensing reflectance at 322 nm; Remote sensing reflectance at 322 nm, standard deviation; Remote sensing reflectance at 323 nm; Remote sensing reflectance at 323 nm, standard deviation; Remote sensing reflectance at 324 nm; Remote sensing reflectance at 324 nm, standard deviation; Remote sensing reflectance at 325 nm; Remote sensing reflectance at 325 nm, standard deviation; Remote sensing reflectance at 326 nm; Remote sensing reflectance at 326 nm, standard deviation; Remote sensing reflectance at 327 nm; Remote sensing reflectance at 327 nm, standard deviation; Remote sensing reflectance at 328 nm; Remote sensing reflectance at 328 nm, standard deviation; Remote sensing reflectance at 329 nm; Remote sensing reflectance at 329 nm, standard deviation; Remote sensing reflectance at 330 nm; Remote sensing reflectance at 330 nm, standard deviation; Remote sensing reflectance at 331 nm; Remote sensing reflectance at 331 nm, standard deviation; Remote sensing reflectance at 332 nm; Remote sensing reflectance at 332 nm, standard deviation; Remote sensing reflectance at 333 nm; Remote sensing reflectance at 333 nm, standard deviation; Remote sensing reflectance at 334 nm; Remote sensing reflectance at 334 nm, standard deviation; Remote sensing reflectance at 335 nm; Remote sensing reflectance at 335 nm, standard deviation; Remote sensing reflectance at 336 nm; Remote sensing reflectance at 336 nm, standard deviation; Remote sensing reflectance at 337 nm; Remote sensing reflectance at 337 nm, standard deviation; Remote sensing reflectance at 338 nm; Remote sensing reflectance at 338 nm, standard deviation; Remote sensing reflectance at 339 nm; Remote sensing reflectance at 339 nm, standard deviation; Remote sensing reflectance at 340 nm; Remote sensing reflectance at 340 nm, standard deviation; Remote sensing reflectance at 341 nm; Remote sensing reflectance at 341 nm, standard deviation; Remote sensing reflectance at 342 nm; Remote sensing reflectance at 342 nm, standard deviation; Remote sensing reflectance at 343 nm; Remote sensing reflectance at 343 nm, standard deviation; Remote sensing reflectance at 344 nm; Remote sensing reflectance at 344 nm, standard deviation; Remote sensing reflectance at 345 nm; Remote sensing reflectance at 345 nm, standard deviation; Remote sensing reflectance at 346 nm; Remote sensing reflectance at 346 nm, standard deviation; Remote sensing reflectance at 347 nm; Remote sensing reflectance at 347 nm, standard deviation; Remote sensing reflectance at 348 nm; Remote sensing reflectance at 348 nm, standard deviation; Remote sensing reflectance at 349 nm; Remote sensing reflectance at 349 nm, standard deviation; Remote sensing reflectance at 350 nm; Remote sensing reflectance at 350 nm, standard deviation; Remote sensing reflectance at 351 nm; Remote sensing reflectance at 351 nm, standard deviation; Remote sensing reflectance at 352 nm; Remote sensing reflectance at 352 nm, standard deviation; Remote sensing reflectance at 353 nm; Remote sensing reflectance at 353 nm, standard deviation; Remote sensing reflectance at 354 nm; Remote sensing reflectance at 354 nm, standard deviation; Remote sensing reflectance at 355 nm; Remote sensing reflectance at 355 nm, standard deviation; Remote sensing reflectance at 356 nm; Remote sensing reflectance at 356 nm, standard deviation; Remote sensing reflectance at 357 nm; Remote sensing reflectance at 357 nm, standard deviation; Remote sensing reflectance at 358 nm; Remote sensing reflectance at 358 nm, standard deviation; Remote sensing reflectance at 359 nm; Remote sensing reflectance at 359 nm, standard deviation; Remote sensing reflectance at 360 nm; Remote sensing reflectance at 360 nm, standard deviation; Remote sensing reflectance at 361 nm; Remote sensing reflectance at 361 nm, standard deviation; Remote sensing reflectance at 362 nm; Remote sensing reflectance at 362 nm, standard deviation; Remote sensing reflectance at 363 nm; Remote sensing reflectance at 363 nm, standard deviation; Remote sensing reflectance at 364 nm; Remote sensing reflectance at 364 nm, standard deviation; Remote sensing reflectance at 365 nm; Remote sensing reflectance at 365 nm, standard deviation; Remote sensing reflectance at 366 nm; Remote sensing reflectance at 366 nm, standard deviation; Remote sensing reflectance at 367 nm; Remote sensing reflectance at 367 nm, standard deviation; Remote sensing reflectance at 368 nm; Remote sensing reflectance at 368 nm, standard deviation; Remote sensing reflectance at 369 nm; Remote sensing reflectance at 369 nm, standard deviation; Remote sensing reflectance at 370 nm; Remote sensing reflectance at 370 nm, standard deviation; Remote sensing reflectance at 371 nm; Remote sensing reflectance at 371 nm, standard deviation; Remote sensing reflectance at 372 nm; Remote sensing reflectance at 372 nm, standard deviation; Remote sensing reflectance at 373 nm; Remote sensing reflectance at 373 nm, standard deviation; Remote sensing reflectance at 374 nm; Remote sensing reflectance at 374 nm, standard deviation; Remote sensing reflectance at 375 nm; Remote sensing reflectance at 375 nm, standard deviation; Remote sensing reflectance at 376 nm; Remote sensing reflectance at 376 nm, standard deviation; Remote sensing reflectance at 377 nm; Remote sensing reflectance at 377 nm, standard deviation; Remote sensing reflectance at 378 nm; Remote sensing reflectance at 378 nm, standard deviation; Remote sensing reflectance at 379 nm; Remote sensing reflectance at 379 nm, standard deviation; Remote sensing reflectance at 380 nm; Remote sensing reflectance at 380 nm, standard deviation; Remote sensing reflectance at 381 nm; Remote sensing reflectance at 381 nm, standard deviation; Remote sensing reflectance at 382 nm; Remote sensing reflectance at 382 nm, standard deviation; Remote sensing reflectance at 383 nm; Remote sensing reflectance at 383 nm, standard deviation; Remote sensing reflectance at 384 nm; Remote sensing reflectance at 384 nm, standard deviation; Remote sensing reflectance at 385 nm; Remote sensing reflectance at 385 nm, standard deviation; Remote sensing reflectance at 386 nm; Remote sensing reflectance at 386 nm, standard deviation; Remote sensing reflectance at 387 nm; Remote sensing reflectance at 387 nm, standard deviation; Remote sensing reflectance at 388 nm; Remote sensing reflectance at 388 nm, standard deviation; Remote sensing reflectance at 389 nm; Remote sensing reflectance at 389 nm, standard deviation; Remote sensing reflectance at 390 nm; Remote sensing reflectance at 390 nm, standard deviation; Remote sensing reflectance at 391 nm; Remote sensing reflectance at 391 nm, standard deviation; Remote sensing reflectance at 392 nm; Remote sensing reflectance at 392 nm, standard deviation; Remote sensing reflectance at 393 nm; Remote sensing reflectance at 393 nm, standard deviation; Remote sensing reflectance at 394 nm; Remote sensing reflectance at 394 nm, standard deviation; Remote sensing reflectance at 395 nm; Remote sensing reflectance at 395 nm, standard deviation; Remote sensing reflectance at 396 nm; Remote sensing reflectance at 396 nm, standard deviation; Remote sensing reflectance at 397 nm; Remote sensing reflectance at 397 nm, standard deviation; Remote sensing reflectance at 398 nm; Remote sensing reflectance at 398 nm, standard
    Type: Dataset
    Format: text/tab-separated-values, 73502 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 3
    facet.materialart.
    Unknown
    Results of lake water analysis from Lake Geiseltal in Saxony-Anhalt during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2024-05-11
    Description: Lake Geiseltal is the largest lake of Saxony-Anhalt and the largest artificial lake of Germany (max. depth 78 m; mean depth 22.8 m; volume 423 Mio. m³; surface area 1853 ha) and can be classified as oligotrophic. It was created by the excavation of lignite in several former surface mines starting at industrial scale in 1906 (formerly only small-scale mining dated back to 1698; Knochenhauer 1996). Mining stopped 1993 after 1.4*109 tons of lignite and the same mass of overburden were excavated. To stabilize the slopes of the residual mine pits and to avoid acidification from mine drainage, a planned, large scale flooding of the residual mine pits started in 2003 by pumping water from River Saale that was cleaned up by sand filtration before (Fritz et al. 2001). In 2011, the flooding of the lake was completed (LMBV 2018). Algal productivity in the lake is low and water transparency high, the littoral compartments along the shores harbor large stocks of submerged macrophytes. This publication series includes datasets collected on Lake Geiseltal during the Inland Water Remote Sensing Validation Campaign 2017 (Bumberger et al. 2023).
    Type: Dataset
    Format: application/zip, 7 datasets
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 4
    facet.materialart.
    Unknown
    Intercalibration campaign Lake Süßer See (Germany) in Saxony-Anhalt during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2024-05-11
    Description: On Aug. 29, 2017, the central intercalibration campaign was conducted on Lake Süßer See, in which all groups were participating that had been involved in the field measurements included in the overall project campaigns. The main goal of this intercalibration campaign was to realise a direct comparison of all involved field and lab sensors/analysis in order to assess their accuracy, comparability, and reproducibility. For this purpose, all used instruments were used simultaneously at the measuring point on Lake Süßer See. This fully parallel application enabled us to directly compare the results under identical in-situ conditions and to detect and quantify instrumental deviations.
    Keywords: interdisciplinary; IWRSVC-2017
    Type: Dataset
    Format: application/zip, 22 datasets
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 5
    facet.materialart.
    Unknown
    Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2024-05-11
    Description: In this measurement campaign of five water bodies (lakes and reservoirs) several German research groups organised a joint effort to collect a data set for testing, evaluating, and potentially improving the abilities of satellite-based monitoring of water quality in standing waters. The strategy of the campaign is summarised in Figure 1 (documentation "Conceptual design of Inland Water Remote Sensing Validation Campaign 2017") and consists of three independently measured categories of data: (i) satellite-based monitoring, (ii) in situ monitoring, and (iii) bio-optical characterisation. The latter aspect, in particular, was intended in order to go beyond classical comparison of satellite-based and in-situ observations and to enable a more process-oriented and physically-based assessment of the observations made during the satellite overcasts. We concentrated our work on one week in summer 2017 and organised a synoptically measurement campaign on five lakes in Central Germany (Lake Arendsee, Lake Geiseltalsee, Kelbra Reservoir, Rappbode Reservoir, Lake Süßer See, see Tab. 1 in documentation "Main physical and limnological characteristics of the five water bodies from Inland Water Remote Sensing Validation Campaign 2017") based on various field and lab methods. The synoptically approach required the equipment of five sampling teams that are able to work independently from each other. Field- instruments used during the campaign (which required to be available in five sets) had been compared with each other in a separate intercalibration day. All lab-based measurements took place at the central lab of the Helmholtz-Centre for Environmental Research in Magdeburg using methods as outlined in Friese et al. (2014). The five water bodies were intentionally chosen because they reflect a broad range of temperate standing waters with respect to size, depth, trophic state, and the occurrence of cyanobacterial blooms. In addition, also natural and artificial water bodies are reflected by this set of lakes/reservoirs. To our knowledge, this is one of the rare multiple-teams efforts in remote sensing research on water quality making the collection of data in terms of their synoptic evaluation and broad methodological basis particularly useful and valuable.
    Keywords: IWRSVC-2017
    Type: Dataset
    Format: 9 datasets
    Location Call Number Expected Availability
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    DATA PANGAEA
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