A cloud detection algorithm-generating method for remote sensing data at visible to short-wave infrared wavelengths

Sun, Lin; Mi, Xueting; Wei, Jing; Wang, Jian; Tian, Xinpeng; Yu, Hailing; Gan, Ping

doi:10.1016/j.isprsjprs.2016.12.005

Cited by 88 publications

(40 citation statements)

References 51 publications

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“…Ocean around Antarctica, and they are also affected by other atmospheric components, such as water vapor, aerosols, carbon dioxide, and ozone, which are not the focus of this paper (Jing & Lin, 2017;Lin et al, 2016;Mei et al, 2012;Sun et al, 2015;Wang et al, 2017;Wu et al, 2017). The cloud-cover anomalies resulted in strong negative cloud forcing in winter 2011, which interrupted the surface energy balance and had an important influence on the sea-ice growth.…”

Section: Journal Of Geophysical Research: Atmospheresmentioning

confidence: 99%

The Contributions of Winter Cloud Anomalies in 2011 to the Summer Sea‐Ice Rebound in 2012 in the Antarctic

Wang

Yuan

et al. 2019

JGR Atmospheres

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Unlike the rapid decline of Arctic sea ice in the warming climate, Antarctic sea‐ice extent exhibits a modest positive trend in the period of near four decades. In recent years, the fluctuation in Antarctic sea ice has been strengthened, including a decrease toward the lowest sea‐ice extent in February 2011 for the period of 1978–2016 and a strong rebound in the summer of 2012. The sea‐ice recovery mainly occurs in the Weddell Sea, Bellingshausen Sea, Amundsen Sea, southern Ross Sea, and the eastern Somov Sea. This study offers a new mechanism for this summertime sea‐ice rebound. We demonstrate that cloud‐fraction anomalies in winter 2011 contributed to the positive Antarctic sea‐ice anomaly in summer 2012. The results show that the negative cloud‐fraction anomalies in winter 2011 related to the large‐scale atmospheric circulation resulted in a substantial negative surface‐radiation budget, which cooled the surface and promoted more sea‐ice growth. The sea‐ice growth anomalies due to the negative cloud forcing propagated by sea‐ice motion vectors from September 2011 to January 2012. The distribution of the sea‐ice anomalies corresponded well with the sea‐ice concentration anomalies in February 2012 in the Weddell Sea and eastern Somov Sea. Thus, negative cloud‐fraction anomalies in winter can play a vital role in the following summer sea‐ice distribution.

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Section: Journal Of Geophysical Research: Atmospheresmentioning

confidence: 99%

The Contributions of Winter Cloud Anomalies in 2011 to the Summer Sea‐Ice Rebound in 2012 in the Antarctic

Wang

Yuan

et al. 2019

JGR Atmospheres

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“…Landsat 8 images have a temporal resolution of 16 days. However, cloud cover rendered most of them useless [47,48] for our study. Selected images included only available images with no or insignificant cloud cover in the footprint of the tower.…”

Section: Spectral Vegetation Indicesmentioning

confidence: 99%

Estimation of Carbon Fluxes from Eddy Covariance Data and Satellite-Derived Vegetation Indices in a Karst Grassland (Podgorski Kras, Slovenia)

et al. 2019

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The Eddy Covariance method (EC) is widely used for measuring carbon (C) and energy fluxes at high frequency between the atmosphere and the ecosystem, but has some methodological limitations and a spatial restriction to an area, called a footprint. Remotely sensed information is usually used in combination with eddy covariance data in order to estimate C fluxes over larger areas. In fact, spectral vegetation indices derived from available satellite data can be combined with EC measurements to estimate C fluxes outside of the tower footprint. Following this approach, the present study aimed to model C fluxes for a karst grassland in Slovenia. Three types of model were considered: (1) a linear relationship between Net Ecosystem Exchange (NEE) or Gross Primary Production (GPP) and each vegetation index; (2) a linear relationship between GPP and the product of a vegetation index with PAR (Photosynthetically Active Radiation); and (3) a simplified LUE (Light Use-Efficiency) model assuming a constant LUE. We compared the performance of several vegetation indices derived from two remote platforms (Landsat and Proba-V) as predictors of NEE and GPP, based on three accuracy metrics, the coefficient of determination (R2), the Root Mean Square Error (RMSE) and the Akaike Information Criterion (AIC). Two types of aggregation of flux data were explored: midday average and daily average fluxes. The vapor pressure deficit (VPD) was used to separate the growing season into two phases, a wet and a dry phase, which were considered separately in the modelling process, in addition to the growing season as a whole. The results showed that NDVI is the best predictor of GPP and NEE during the wet phase, whereas water-related vegetation indices, namely LSWI and MNDWI, were the best predictors during the dry phase, both for midday and daily aggregates. Model 1 (linear relationship) was found to be the best in many cases. The best regression equations obtained were used to map GPP and NEE for the whole study area. Digital maps obtained can practically contribute, in a cost-effective way to the management of karst grasslands.

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“…For this reason, datasets that show clouds, cloud shadows, suspended dust, and other obvious features that may negatively impact the analysis were filtered out by means of a quicklook photointerpretation, requiring the AOI to be covered by L8 in order to be visually clear. The use of a visual assessment to determine clouds, shadows, suspended dust, or other problematic elements was made due to the false positives that cloud detection algorithms present, especially for high-reflectance surfaces like deserts [39], along with the absence of thermal bands in S2 that made it difficult to generate a reliable cloud mask [40]. L8 quicklooks were downloaded from the Earth Resources Science and Observation Data Center (EROS) of the United States Geological Survey (USGS) and S2 quicklooks were from the Copernicus Hub.…”

Section: Spatial and Temporal Uniformitymentioning

confidence: 99%

Analysis of Spatial and Temporal Variability in Libya-4 with Landsat 8 and Sentinel-2 Data for Optimized Ground Target Location

et al. 2019

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Pseudo-Invariant Calibration Sites (PICS) have been widely used by the remote sensing community in recent decades for post-launch absolute calibration, cross-calibration, and the monitoring of radiometric stability. The Committee on Earth Observation Satellites (CEOS) has established several official PICS for these purposes. Of these, Libya-4 is the most commonly used, due to its high uniformity and stability. The site was chosen as a large-area site for medium resolution sensors, and with high-resolution sensors now common, smaller sites are being identified. This work has identified an improved area of interest (AOI) within Libya-4 by using combined Landsat 8 and Sentinel 2 data. The Optimized Ground Target (OGT) was determined by calculating the coefficient of variation along with the use of a quasi-Newton optimization algorithm combined with the Basin–Hopping global optimization technique to constrain a search area small enough to perform a final brute-force refinement. The Coefficient of Variation CV of the proposed OGT is significantly lower than that in the original CEOS area, with differences between the CV of both zones in the order of 1% in the visible near-infrared (VNIR) bands. This new AOI has the potential to improve the cross-calibration between high-resolution sensors using the PICS methodology through an OGT with more homogeneous and stable characteristics.

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A cloud detection algorithm-generating method for remote sensing data at visible to short-wave infrared wavelengths

Cited by 88 publications

References 51 publications

The Contributions of Winter Cloud Anomalies in 2011 to the Summer Sea‐Ice Rebound in 2012 in the Antarctic

The Contributions of Winter Cloud Anomalies in 2011 to the Summer Sea‐Ice Rebound in 2012 in the Antarctic

Estimation of Carbon Fluxes from Eddy Covariance Data and Satellite-Derived Vegetation Indices in a Karst Grassland (Podgorski Kras, Slovenia)

Analysis of Spatial and Temporal Variability in Libya-4 with Landsat 8 and Sentinel-2 Data for Optimized Ground Target Location

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