scholarly journals Cross-Comparison of Albedo Products for Glacier Surfaces Derived from Airborne and Satellite (Sentinel-2 and Landsat 8) Optical Data

2017 ◽  
Vol 9 (2) ◽  
pp. 110 ◽  
Author(s):  
Kathrin Naegeli ◽  
Alexander Damm ◽  
Matthias Huss ◽  
Hendrik Wulf ◽  
Michael Schaepman ◽  
...  
Keyword(s):  
Optical Data ◽  
Landsat 8 ◽  
Sentinel 2

scholarly journals CIRRUS REMOVAL IN MULTISPECTRAL DATAWITHOUT 1.38μM SPECTRAL DATA

2016 ◽  
Vol III-7 ◽  
pp. 41-44
Author(s):  
Aliaksei Makarau ◽  
Rudolf Richter ◽  
Viktoria Zekoll ◽  
Peter Reinartz
Keyword(s):  
Additive Model ◽  
Optical Data ◽  
Landsat 8 ◽  
Cirrus Cloud ◽  
Landsat 8 Oli ◽  
Spectral Channel ◽  
Wide Range ◽  
Low Altitude ◽  
Spectral Channels ◽  
Sentinel 2

Cirrus is one of the most common artifacts in the remotely sensed optical data. Contrary to the low altitude (1-3 km) cloud the cirrus cloud (8-20 km) is semitransparent and the extinction (cirrus influence) of the upward reflected solar radiance can be compensated. The widely employed and almost ’de-facto’ method for cirrus compensation is based on the 1.38μm spectral channel measuring the upwelling radiance reflected by the cirrus cloud. The knowledge on the cirrus spatial distribution allows to estimate the per spectral channel cirrus attenuation and to compensate the spectral channels. A wide range of existing and expected sensors have no 1.38μm spectral channel. These sensors data can be corrected by the recently developed haze/cirrus removal method. The additive model of the estimated cirrus thickness map (CTM) is applicable for cirrus-conditioned extinction compensation. Numeric and statistic evaluation of the CTM-based cirrus removal on more than 80 Landsat-8 OLI and 30 Sentinel-2 scenes demonstrates a close agreement with the 1.38μm channel based cirrus removal.

scholarly journals Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands

2020 ◽  
Vol 12 (17) ◽  
pp. 2708 ◽  
Author(s):  
Qi Wang ◽  
Jiancheng Li ◽  
Taoyong Jin ◽  
Xin Chang ◽  
Yongchao Zhu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Optical Sensors ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Model ◽  
Vegetation Indices ◽  
Optical Data ◽  
Landsat 8 ◽  
Different Sources ◽  
Sentinel 2

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R2 = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.

scholarly journals Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review

2020 ◽  
Vol 12 (18) ◽  
pp. 3062 ◽  
Author(s):  
Michel E. D. Chaves ◽  
Michelle C. A. Picoli ◽  
Ieda D. Sanches
Keyword(s):  
Systematic Review ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Near Infrared ◽  
Data Cube ◽  
Optical Data ◽  
Landsat 8 ◽  
Detailed Knowledge ◽  
Sentinel 2

Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land cover (LULC) provide a new perspective in remote sensing data analysis. Jointly, these sources permit researchers to improve operational classification and change detection, guiding better reasoning about landscape and intrinsic processes, as deforestation and agricultural expansion. However, the results of their applications have not yet been synthesized in order to provide coherent guidance on the effect of their applications in different classification processes, as well as to identify promising approaches and issues which affect classification performance. In this systematic review, we present trends, potentialities, challenges, actual gaps, and future possibilities for the use of L8/OLI and S2/MSI for LULC mapping and change detection. In particular, we highlight the possibility of using medium-resolution (Landsat-like, 10–30 m) time series and multispectral optical data provided by the harmonization between these sensors and data cube architectures for analysis-ready data that are permeated by publicizations, open data policies, and open science principles. We also reinforce the potential for exploring more spectral bands combinations, especially by using the three Red-edge and the two Near Infrared and Shortwave Infrared bands of S2/MSI, to calculate vegetation indices more sensitive to phenological variations that were less frequently applied for a long time, but have turned on since the S2/MSI mission. Summarizing peer-reviewed papers can guide the scientific community to the use of L8/OLI and S2/MSI data, which enable detailed knowledge on LULC mapping and change detection in different landscapes, especially in agricultural and natural vegetation scenarios.

scholarly journals Effect of Textural Features in Remote Sensed Data on Rubber Plantation Extraction at Different Levels of Spatial Resolution

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 399
Author(s):  
Chenchen Zhang ◽  
Chong Huang ◽  
He Li ◽  
Qingsheng Liu ◽  
Jing Li ◽  
...  
Keyword(s):  
Forest Growth ◽  
Natural Forest ◽  
Optical Data ◽  
Spectral Information ◽  
Rubber Plantation ◽  
Landsat 8 ◽  
Textural Features ◽  
Natural Forests ◽  
Rubber Plantations ◽  
Sentinel 2

The expansion of rubber (Hevea brasiliensis) plantations has been a critical driver for the rapid transformation of tropical forests, especially in Thailand. Rubber plantation mapping provides basic information for surveying resources, updating forest subplot information, logging, and managing the forest. However, due to the diversity of stand structure, complexity of the forest growth environment, and the similarity of spectral characteristics between rubber trees and natural forests, it is difficult to discriminate rubber plantation from natural forest using only spectral information. This study evaluated the validity of textural features for rubber plantation recognition at different spatial resolutions using GaoFen-1 (GF-1), Sentinel-2, and Landsat 8 optical data. C-band Sentinel-1 10 m imagery was first used to map forests (including both rubber plantations and natural forests) and non-forests, then the pixels identified as forests in the Sentinel-1 imagery were compared with GF-1, Sentinel-2, and Landsat 8 images to separate rubber plantations and natural forest using two different approaches: a method based on spectral information characteristics only and a method combining spectral and textural features. In addition, we extracted textural features of different window sizes (3 × 3 to 31 × 31) and analyzed the influence of window size on the separability of rubber plantations and natural forests. Our major findings include: (1) the suitable texture extraction window sizes of GF-1, Sentinel-2, and Landsat 8 are 31 × 31, 11 × 11 to 15 × 15, and 3 × 3 to 7 × 7, respectively; (2) correlation (COR) is a robust textural feature in remote sensing images with different resolutions; and (3) compared with classification by spectral information only, the producer’s accuracy of rubber plantations based on GF-1, Sentinel-2, and Landsat 8 was improved by 8.04%, 9.44%, and 8.74%, respectively, and the user’s accuracy was increased by 4.63%, 4.54%, and 6.75%, respectively, when the textural features were introduced. These results demonstrate that the method combining textural features has great potential in delineating rubber plantations.

scholarly journals CIRRUS REMOVAL IN MULTISPECTRAL DATAWITHOUT 1.38μM SPECTRAL DATA

2016 ◽  
Vol III-7 ◽  
pp. 41-44
Author(s):  
Aliaksei Makarau ◽  
Rudolf Richter ◽  
Viktoria Zekoll ◽  
Peter Reinartz
Keyword(s):  
Additive Model ◽  
Optical Data ◽  
Landsat 8 ◽  
Cirrus Cloud ◽  
Landsat 8 Oli ◽  
Spectral Channel ◽  
Wide Range ◽  
Low Altitude ◽  
Spectral Channels ◽  
Sentinel 2

Cirrus is one of the most common artifacts in the remotely sensed optical data. Contrary to the low altitude (1-3 km) cloud the cirrus cloud (8-20 km) is semitransparent and the extinction (cirrus influence) of the upward reflected solar radiance can be compensated. The widely employed and almost ’de-facto’ method for cirrus compensation is based on the 1.38μm spectral channel measuring the upwelling radiance reflected by the cirrus cloud. The knowledge on the cirrus spatial distribution allows to estimate the per spectral channel cirrus attenuation and to compensate the spectral channels. A wide range of existing and expected sensors have no 1.38μm spectral channel. These sensors data can be corrected by the recently developed haze/cirrus removal method. The additive model of the estimated cirrus thickness map (CTM) is applicable for cirrus-conditioned extinction compensation. Numeric and statistic evaluation of the CTM-based cirrus removal on more than 80 Landsat-8 OLI and 30 Sentinel-2 scenes demonstrates a close agreement with the 1.38μm channel based cirrus removal.

scholarly journals Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping

2020 ◽  
Vol 12 (4) ◽  
pp. 727 ◽  
Author(s):  
Manuela Hirschmugl ◽  
Janik Deutscher ◽  
Carina Sobe ◽  
Alexandre Bouvet ◽  
Stéphane Mermoz ◽  
...  
Keyword(s):  
Time Series ◽  
Patch Size ◽  
Forest Disturbance ◽  
Optical Data ◽  
Landsat 8 ◽  
Data Sets ◽  
Disturbed Forest ◽  
Optical Time ◽  
The Individual ◽  
Sentinel 2

Frequent cloud cover and fast regrowth often hamper topical forest disturbance monitoring with optical data. This study aims at overcoming these limitations by combining dense time series of optical (Sentinel-2 and Landsat 8) and SAR data (Sentinel-1) for forest disturbance mapping at test sites in Peru and Gabon. We compare the accuracies of the individual disturbance maps from optical and SAR time series with the accuracies of the combined map. We further evaluate the detection accuracies by disturbance patch size and by an area-based sampling approach. The results show that the individual optical and SAR based forest disturbance detections are highly complementary, and their combination improves all accuracy measures. The overall accuracies increase by about 3% in both areas, producer accuracies of the disturbed forest class increase by up to 25% in Peru when compared to only using one sensor type. The assessment by disturbance patch size shows that the amount of detections of very small disturbances (< 0.2 ha) can almost be doubled by using both data sets: for Gabon 30% as compared to 15.7–17.5%, for Peru 80% as compared to 48.6–65.7%.

Mapping and monitoring of landslide-dammed lakes using Sentinel-2 time series - a case study after the 2016 Kaikōura Earthquake in New Zealand

2020 ◽  
Author(s):  
Lorena Abad ◽  
Daniel Hölbling ◽  
Raphael Spiekermann ◽  
Zahra Dabiri ◽  
Günther Prasicek ◽  
...  
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Google Earth ◽  
Aerial Photographs ◽  
Optical Data ◽  
Landsat 8 ◽  
Multiple Time ◽  
Potential Hazard ◽  
Sentinel 2

&lt;p&gt;On November 14, 2016, a 7.8 magnitude earthquake struck the Kaik&amp;#333;ura region on the South Island of New Zealand. The event triggered numerous landslides, which dammed rivers in the area and led to the formation of hundreds of dammed lakes. Landslide-dammed lakes constitute a natural risk, given their propensity to breach, which can lead to flooding of downstream settlements and infrastructure. Hence, detecting and monitoring dammed lakes is a key step for risk management strategies. Aerial photographs and helicopter reconnaissance are frequently used for damage assessments following natural hazard events. However, repeated acquisitions of aerial photographs and on-site examinations are time-consuming and expensive. Moreover, such assessments commonly only take place immediately after an event, and long-term monitoring is rarely performed at larger scales.&lt;/p&gt;&lt;p&gt;Satellite imagery can support mapping and monitoring tasks by providing an overview of the affected area in multiple time steps following the main triggering event without deploying major resources. In this study, we present an automated approach to detect landslide-dammed lakes using Sentinel-2 optical data through the Google Earth Engine (GEE). Our approach consists of a water detection algorithm adapted from Donchyts et al., 2016 [1], where a dynamic threshold is applied to the Normalized Difference Water Index (NDWI). The water bodies are detected on pre- and post-event monthly mosaics, where the cloud coverage of the composed images is below 30 %, resulting in one pre-event (December 2015) and 14 post-event monthly mosaics. Subsequently, a differencing change detection method is performed between pre- and post-event mosaics. This allows for continuous monitoring of the lake status, and for the detection of new lakes forming in the area at different points in time.&lt;/p&gt;&lt;p&gt;A random sample of lakes delineated from Google Earth high-resolution imagery, acquired right after the Kaik&amp;#333;ura earthquake, was used for validation. The pixels categorized as &amp;#8216;dammed lakes&amp;#8217; were intersected with the validation data set, resulting in a detection rate of 70 % of the delineated lakes. Ten key dams, identified by local authorities as a potential hazard, were further examined and monitored to identify lake area changes in multiple time steps, from December 2016 to March 2019. Taking advantage of the GEE cloud computing capabilities, the proposed automated approach allows fast time series analysis of large areas. It can be applied to other regions where landslide-dammed lakes need to be monitored over long time scales (months &amp;#8211; years). Furthermore, the approach could be combined with outburst flood modeling and simulation to support initial rapid risk assessment.&lt;/p&gt;&lt;p&gt;&amp;#160;[1]&amp;#160;&amp;#160; Donchyts, G., Schellekens, J., Winsemius, H., Eisemann, E., &amp; van de Giesen, N. (2016). A 30 m resolution surface water mask including estimation of positional and thematic differences using Landsat 8, SRTM and OpenStreetMap: A case study in the Murray-Darling basin, Australia. Remote Sensing, 8(5).&lt;/p&gt;&lt;div&gt; &lt;div&gt;&amp;#160;&lt;/div&gt; &lt;/div&gt;

scholarly journals Coseismic displacements of the 14 November 2016 <i>M</i><sub>w</sub> 7.8 Kaikoura, New Zealand, earthquake using the Planet optical cubesat constellation

2017 ◽  
Vol 17 (5) ◽  
pp. 627-639 ◽  
Author(s):  
Andreas Kääb ◽  
Bas Altena ◽  
Joseph Mascaro
Keyword(s):  
New Zealand ◽  
Land Surface ◽  
Near Infrared ◽  
Sar Interferometry ◽  
Optical Methods ◽  
Optical Data ◽  
Time Interval ◽  
Landsat 8 ◽  
Surface Displacements ◽  
Sentinel 2

Abstract. Satellite measurements of coseismic displacements are typically based on synthetic aperture radar (SAR) interferometry or amplitude tracking, or based on optical data such as from Landsat, Sentinel-2, SPOT, ASTER, very high-resolution satellites, or air photos. Here, we evaluate a new class of optical satellite images for this purpose – data from cubesats. More specific, we investigate the PlanetScope cubesat constellation for horizontal surface displacements by the 14 November 2016 Mw 7.8 Kaikoura, New Zealand, earthquake. Single PlanetScope scenes are 2–4 m-resolution visible and near-infrared frame images of approximately 20–30 km  ×  9–15 km in size, acquired in continuous sequence along an orbit of approximately 375–475 km height. From single scenes or mosaics from before and after the earthquake, we observe surface displacements of up to almost 10 m and estimate matching accuracies from PlanetScope data between ±0.25 and ±0.7 pixels (∼ ±0.75 to ±2.0 m), depending on time interval and image product type. Thereby, the most optimistic accuracy estimate of ±0.25 pixels might actually be typical for the final, sun-synchronous, and near-polar-orbit PlanetScope constellation when unrectified data are used for matching. This accuracy, the daily revisit anticipated for the PlanetScope constellation for the entire land surface of Earth, and a number of other features, together offer new possibilities for investigating coseismic and other Earth surface displacements and managing related hazards and disasters, and complement existing SAR and optical methods. For comparison and for a better regional overview we also match the coseismic displacements by the 2016 Kaikoura earthquake using Landsat 8 and Sentinel-2 data.

scholarly journals TESTING A COMBINED MULTISPECTRAL-MULTITEMPORAL APPROACH FOR GETTING CLOUDLESS IMAGERY FOR SENTINEL-2

2020 ◽  
Vol V-3-2020 ◽  
pp. 293-300
Author(s):  
N. Colaninno ◽  
A. Marambio ◽  
J. Roca
Keyword(s):  
Seismic Event ◽  
Optical Data ◽  
Data Reconstruction ◽  
Landsat 8 ◽  
Thermal Data ◽  
Before And After ◽  
Optical Imagery ◽  
Post Disaster ◽  
Selection Of ◽  
Sentinel 2

Abstract. Earth observation and land cover monitoring are among major applications for satellite data. However, the use of primary satellite information is often limited by clouds, cloud shadows, and haze, which generally contaminate optical imagery. For purposes of hazard assessment, for instance, such as flooding, drought, or seismic events, the availability of uncontaminated optical data is required. Different approaches exist for masking and replacing cloud/haze related contamination. However, most common algorithms take advantage by employing thermal data. Hence, we tested an algorithm suitable for optical imagery only. The approach combines a multispectral-multitemporal strategy to retrieve daytime cloudless and shadow-free imagery. While the approach has been explored for Landsat information, namely Landsat 5 TM and Landsat 8 OLI, here we aim at testing the suitability of the method for Sentinel-2 Multi-Spectral Instrument. A multitemporal stack, for the same image scene, is employed to retrieve a composite uncontaminated image over a temporal period of few months. Besides, in order to emphasize the effectiveness of optical imagery for monitoring post-disaster events, two temporal stages have been processed, before and after a critical seismic event occurred in Lombok Island, Indonesia, in summer 2018. The approach relies on a clouds and cloud shadows masking algorithm, based on spectral features, and a data reconstruction phase based on automatic selection of the most suitable pixels from a multitemporal stack. Results have been tested with uncontaminated image samples for the same scene. High accuracy is achieved.

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