Object-based cloud and cloud shadow detection in Landsat imagery

https://www.tucson.ars.ag.gov/unit/Publications/PDFfiles/MSM_1948.pdf

Opportunities and limitations for image-based remote sensing in precision crop management

The National Oceanic and Atmospheric Administration (NOAA)/NASA Oceans Pathfinder sea surface temperature (SST) data are derived from measurements made by the advanced very high resolution radiometers (AVHRRs) on board the NOAA 7, 9, 11, and 14 polar orbiting satellites. All versions of the Pathfinder SST algorithm are based on the NOAA/National Environmental Satellite Data and Information Service nonlinear SST operational algorithm (NLSST). Improvements to the NLSST operational algorithm developed by the Pathfinder program include the use of monthly calibration coefficients selected on the basis of channel brightness temperature difference (T4–T5). This channel difference is used as a proxy for water vapor regime. The latest version (version 4.2) of the Pathfinder processing includes the use of decision trees to determine objectively pixel cloud contamination and quality level (0–7) of the SST retrieval. The 1985–1998 series of AVHRR global measurements has been reprocessed using the Pathfinder version 4.2 processing protocol and is available at various temporal and spatial resolutions from NASA's Jet Propulsion Laboratory Distributed Active Archive Center. One of the highlights of the Pathfinder program is that in addition to the daily global area coverage fields, a matchup database of coincident in situ buoy and satellite SST observations also is made available for independent algorithm development and validation.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/1999JC000065

Overview of the NOAA/NASA advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database

The scientific community that includes meteorologists, physical scientists, engineers, medical doctors, biologists, and environmentalists has shown interest in a better understanding of fog for years because of its effects on, directly or indirectly, the daily life of human beings. The total economic losses associated with the impact of the presence of fog on aviation, marine and land transportation can be comparable to those of tornadoes or, in some cases, winter storms and hurricanes. The number of articles including the word ``fog'' in Journals of American Meteorological Society alone was found to be about 4700, indicating that there is substantial interest in this subject. In spite of this extensive body of work, our ability to accurately forecast/nowcast fog remains limited due to our incomplete understanding of the fog processes over various time and space scales. Fog processes involve droplet microphysics, aerosol chemistry, radiation, turbulence, large/small-scale dynamics, and surface conditions (e.g., partaining to the presence of ice, snow, liquid, plants, and various types of soil). This review paper summarizes past achievements related to the understanding of fog formation, development and decay, and in this respect, the analysis of observations and the development of forecasting models and remote sensing methods are discussed in detail. Finally, future perspectives for fog-related research are highlighted.

Fog Research: A Review of Past Achievements and Future Perspectives

The free and open access to all archived Landsat images in 2008 has completely changed the way of using Landsat data. Many novel change detection algorithms based on Landsat time series have been developed We present a comprehensive review of four important aspects of change detection studies based on Landsat time series, including frequencies, preprocessing, algorithms, and applications. We observed the trend that the more recent the study, the higher the frequency of Landsat time series used. We reviewed a series of image preprocessing steps, including atmospheric correction, cloud and cloud shadow detection, and composite/fusion/metrics techniques. We divided all change detection algorithms into six categories, including thresholding, differencing, segmentation, trajectory classification, statistical boundary, and regression. Within each category, six major characteristics of different algorithms, such as frequency, change index, univariate/multivariate, online/offline, abrupt/gradual change, and sub-pixel/pixel/spatial were analyzed. Moreover, some of the widely-used change detection algorithms were also discussed. Finally, we reviewed different change detection applications by dividing these applications into two categories, change target and change agent detection.

Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications

Automatic cloud detection applied to NOAA-11 /AVHRR imagery

NOAA-11/AVHRR data from March 1990 to February 1991 have been processed to map the vegetation cover of France with a 4 km spatial resolution. After an automatic cloud detection, Normalized Difference Vegetation Index (NDVI) were computed over the whole period using visible and near-infrared reflectances corrected for atmospheric effects. Monthly NDVI values were obtained for each point of the area by keeping the maximum value. These annual profiles have been classified with a clustering algorithm and the resulting classification has been associated to vegetation cover by comparison with the 1988 vegetation survey from the French Agriculture Department.

Vegetation cover mapping over France using NOAA-11/AVHRR

RESUME ; Les donnees NOAA-11 /AVHRR de la periode mars 1990-fevrier 1991 sont traitees afin d'obtenir une cartographie de l'occupation du sol sur la France et une partie de l'Espagne a une resolution de 4 km. Apres que les nuages aient ete detectes, l'indice de vegetation (NDVI) est calcule sur toute la periode a partir des canaux visible et proche infrarouge corriges des effets atmospheriques. Des profils temporels de NDVI sont ainsi obtenus en limitant l'angle de visee et en ne gardant que la valeur maximale de chaque mois. Ces profils sont classes d'une maniere automatique par un algorithme de type " Nuees Dynamiques ". La confrontation des resultats de cette classification a des donnees de terrain provenant du Recensement General de l'Agriculture effectue en 1988 en France a l'echelle du canton permet d'evaluer la sensibilite des profils AVHRR a l'occupation du sol.

B. Farki

Papers

Automatic cloud detection applied to NOAA-11 /AVHRR imagery

Vegetation cover mapping over France using NOAA-11/AVHRR

Classification de la végétation sur la France à l'aide de l'AVHRR de NOAA-11.