scholarly journals Using Window Regression to Gap-Fill Landsat ETM+ Post SLC-Off Data

2018 ◽  
Vol 10 (10) ◽  
pp. 1502 ◽  
Author(s):  
Evan Brooks ◽  
Randolph Wynne ◽  
Valerie Thomas
Keyword(s):  
Computation Time ◽  
Window Size ◽  
Landsat 8 ◽  
Ancillary Data ◽  
Data Intensive ◽  
Landsat 7 ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Temporal Dimensions ◽  
Data Layers

The continued development of algorithms using multitemporal Landsat data creates opportunities to develop and adapt imputation algorithms to improve the quality of that data as part of preprocessing. One example is de-striping Enhanced Thematic Mapper Plus (ETM+, Landsat 7) images acquired after the Scan Line Corrector failure in 2003. In this study, we apply window regression, an algorithm that was originally designed to impute low-quality Moderate Resolution Imaging Spectroradiometer (MODIS) data, to Landsat Analysis Ready Data from 2014–2016. We mask Operational Land Imager (OLI; Landsat 8) image stacks from five study areas with corresponding ETM+ missing data layers, using these modified OLI stacks as inputs. We explored the algorithm’s parameter space, particularly window size in the spatial and temporal dimensions. Window regression yielded the best accuracy (and moderately long computation time) with a large spatial radius (a 7 × 7 pixel window) and a moderate temporal radius (here, five layers). In this case, root mean square error for deviations from the observed reflectance ranged from 3.7–7.6% over all study areas, depending on the band. Second-order response surface analysis suggested that a 15 × 15 pixel window, in conjunction with a 9-layer temporal window, may produce the best accuracy. Compared to the neighborhood similar pixel interpolator gap-filling algorithm, window regression yielded slightly better accuracy on average. Because it relies on no ancillary data, window regression may be used to conveniently preprocess stacks for other data-intensive algorithms.

scholarly journals Consequences of the 2019 Greenland Ice Sheet Melt Episode on Albedo

2021 ◽  
Vol 13 (2) ◽  
pp. 227
Author(s):  
Arthur Elmes ◽  
Charlotte Levy ◽  
Angela Erb ◽  
Dorothy K. Hall ◽  
Ted A. Scambos ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Surface Albedo ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Surface Energy Budget ◽  
Landsat 8 ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Seasonal Snow Cover

In mid-June 2019, the Greenland ice sheet (GrIS) experienced an extreme early-season melt event. This, coupled with an earlier-than-average melt onset and low prior winter snowfall over western Greenland, led to a rapid decrease in surface albedo and greater solar energy absorption over the melt season. The 2019 melt season resulted in significantly more melt than other recent years, even compared to exceptional melt years previously identified in the moderate-resolution imaging spectroradiometer (MODIS) record. The increased solar radiation absorbance in 2019 warmed the surface and increased the rate of meltwater production. We use two decades of satellite-derived albedo from the MODIS MCD43 record to show a significant and extended decrease in albedo in Greenland during 2019. This decrease, early in the melt season and continuing during peak summer insolation, caused increased radiative forcing of the ice sheet of 2.33 Wm−2 for 2019. Radiative forcing is strongly influenced by the dramatic seasonal differences in surface albedo experienced by any location experiencing persistent and seasonal snow-cover. We also illustrate the utility of the newly developed Landsat-8 albedo product for better capturing the detailed spatial heterogeneity of the landscape, leading to a more refined representation of the surface energy budget. While the MCD43 data accurately capture the albedo for a given 500 m pixel, the higher spatial resolution 30 m Landsat-8 albedos more fully represent the detailed landscape variations.

scholarly journals TVDI Obtido de Imagens OLI/TIRS e MODIS

2019 ◽  
Vol 34 (4) ◽  
pp. 573-583
Author(s):  
Lucimara Wolfarth Schirmbeck ◽  
Denise Cybis Fontana ◽  
Juliano Schirmbeck ◽  
Carolina Bremm
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Infrared Sensor ◽  
Moderate Resolution ◽  
Dryness Index ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Resumo O objetivo do estudo foi analisar a variabilidade no TVDI (Temperature-Vegetation Dryness Index) obtido de sensores orbitais com resoluções distintas, em região agrícola no sul do Brasil. Utilizou-se três imagens OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) do satélite Landsat 8, e 12 imagens MODIS (Moderate Resolution Imaging Spectroradiometer) do satélite Terra. Dados coletados em campo serviram como base para classificação de imagem OLI/TIRS e mapeamento de áreas de arroz, soja, campos naturais, mata ciliar e solo exposto. O TVDI foi obtido por duas parametrizações em períodos distintos, utilizando as dispersões entre Temperatura de Superfície (TS) e NDVI (Normalized Difference Vegetation Index). O TVDI obtido para ambos sensores apresentou padrão similar possibilitando diferenciar os alvos. Na média de todas as datas e classes, o TVDI obtido das imagens MODIS foi superior em 0,128 unidades ao TVDI obtido com o OLI/TIRS. Quando utilizado OLI/TIRS há um melhor detalhamento espacial das condições hídricas, mas com menor repetição ao longo da safra; já utilizando o TVDI-MODIS é possível monitorar as condições hídricas em escala regional, com menor detalhamento espacial, mas com maior repetitividade no tempo. O TVDI estimado pelos sensores OLI/TIRS e MODIS, pode ser utilizado de forma conjunta, trazendo informações complementares.

scholarly journals The Spatial–Spectral–Environmental Extraction Endmember Algorithm and Application in the MODIS Fractional Snow Cover Retrieval

2020 ◽  
Vol 12 (22) ◽  
pp. 3693
Author(s):  
Hongyu Zhao ◽  
Xiaohua Hao ◽  
Jian Wang ◽  
Hongyi Li ◽  
Guanghui Huang ◽  
...  
Keyword(s):  
Snow Cover ◽  
Absolute Error ◽  
Satellite Observation ◽  
Landsat 8 ◽  
Endmember Extraction ◽  
Analysis Model ◽  
Fractional Snow Cover ◽  
Extraction Algorithm ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Endmember extraction is a primary and indispensable component of the spectral mixing analysis model applicated to quantitatively retrieve fractional snow cover (FSC) from satellite observation. In this study, a new endmember extraction algorithm, the spatial–spectral–environmental (SSE) endmember extraction algorithm, is developed, in which spatial, spectral and environmental information are integrated together to automatically extract different types of endmembers from moderate resolution imaging spectroradiometer (MODIS) images. Then, combining the linear spectral mixture analysis model (LSMA), the SSE endmember extraction algorithm is practically applied to retrieve FSC from standard MODIS surface reflectance products in China. The new algorithm of MODIS FSC retrieval is named as SSEmod. The accuracy of SSEmod is quantitatively validated with 16 higher spatial-resolution FSC maps derived from Landsat 8 binary snow cover maps. Averaged over all regions, the average root-mean-square-error (RMSE) and mean absolute error (MAE) are 0.136 and 0.092, respectively. Simultaneously, we also compared the SSEmod with MODImLAB, MODSCAG and MOD10A1. In all regions, the average RMSE of SSEmod is improved by 2.3%, 2.6% and 5.3% compared to MODImLAB for 0.157, MODSCAG for 0.157 and MOD10A1 for 0.189. Therefore, our SSE endmember extraction algorithm is reliable for the MODIS FSC retrieval and may be also promising to apply other similar satellites in view of its accuracy and efficiency.

Annual mapping of large forest disturbances across Canada’s forests using 250 m MODIS imagery from 2000 to 2011

2014 ◽  
Vol 44 (12) ◽  
pp. 1545-1554 ◽  
Author(s):  
L. Guindon ◽  
P.Y. Bernier ◽  
A. Beaudoin ◽  
D. Pouliot ◽  
P. Villemaire ◽  
...  
Keyword(s):  
Validation Dataset ◽  
Commission Error ◽  
Forest Disturbances ◽  
Forest Change ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Supplementary Material ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Data Layers

Disturbances such as fire and harvesting shape forest dynamics and must be accounted for when modelling forest properties. However, acquiring timely disturbance information for all of Canada’s large forest area has always been challenging. Therefore, we developed an approach to detect annual forest change resulting from fire, harvesting, or flooding using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery at 250 m spatial resolution across Canada and to estimate the within-pixel fractional change (FC). When this approach was applied to the period from 2000 to 2011, the accuracy of detection of burnt, harvested, or flooded areas against our validation dataset was 82%, 80%, and 85%, respectively. With FC, 77% of the area burnt and 82% of the area harvested within the validation dataset were correctly identified. The methodology was optimized to reduce the commission error but tended to omit smaller disturbances as a result. For example, the omitted area for harvest blocks greater than 80 ha was less than 14% but increased to between 38% and 50% for harvest blocks of 20 to 30 ha. Detection of burnt and harvested areas in some regions was hindered by persistent haze or cloud cover or by insect outbreaks. All resulting data layers are available as supplementary material.

scholarly journals The Spatial and Temporal Land Cover Patterns of the Qazaly Irrigation Zone in 2003–2018: The Case of Syrdarya River’s Lower Reaches, Kazakhstan

2019 ◽  
Vol 11 (15) ◽  
pp. 4035 ◽  
Author(s):  
Kanat Samarkhanov ◽  
Jilili Abuduwaili ◽  
Alim Samat ◽  
Gulnura Issanova
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Algorithm ◽  
Aral Sea ◽  
Landsat 8 ◽  
Spatial And Temporal Patterns ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Land Cover Maps

In this study, the spatial and temporal patterns of the land cover were monitored within the Qazaly irrigation zone located in the deltaic zone of the Syrdarya river in the surroundings of the former Aral Sea. A 16-day MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua NDVI (Normalized Difference Vegetation Index) data product with a spatial resolution of 250 meters was used for this purpose, covering the period between 2003 and 2018. Field survey results obtained in 2018 were used to build a sample dataset. The random forests supervised classification machine learning algorithm was used to map land cover, which produced good results with an overall accuracy of about 0.8. Statistics on land cover change were calculated and analyzed. The correctness of obtained classes was checked with Landsat 8 (OLI, The Operational Land Imager) images. Detailed land cover maps, including rice cropland, were derived. During the observation period, the rice croplands increased, while the generally irrigated area decreased.

scholarly journals The Impact of Seasonality and Land Cover on the Consistency of Relationship between Air Temperature and LST Derived from Landsat 7 and MODIS at a Local Scale: A Case Study in Southern Ontario

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 672
Author(s):  
Michael Burnett ◽  
Dongmei Chen
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Air Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Southern Ontario ◽  
Landsat 7 ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact

Land surface temperature (LST) and air temperature (Tair) have been commonly used to analyze urban heat island (UHI) effects throughout the world, with noted variations based on vegetation distribution. This research has compared time series LST data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) platforms, Landsat 7 Enhanced Thematic Mapper (ETM+) and Tair from weather stations in the Southern Ontario area. The influence of the spatial resolution, land cover, vegetated surfaces, and seasonality on the relationship between LST and in situ Tair were examined. The objective is to identify spatial and seasonal differences amongst these different spatial resolution LST products and Tair, along with the causes for variations at a localized scale. Results show that MODIS LST from Terra had stronger relationships with Landsat 7 LST than those from Aqua. Tair demonstrated weaker correlations with Landsat LST than with MODIS LST in sparsely vegetated and urban areas during the summer. Due to the winter’s ability to smooth heterogenous surfaces, both LST and Tair showed stronger relationships in winter than summer over every land cover, except with coarse spatial resolutions on forested surfaces.

scholarly journals Empirical Absolute Calibration Model for Multiple Pseudo-Invariant Calibration Sites

2019 ◽  
Vol 11 (9) ◽  
pp. 1105 ◽  
Author(s):  
Bipin Raut ◽  
Morakot Kaewmanee ◽  
Amit Angal ◽  
Xiaoxiong Xiong ◽  
Dennis Helder
Keyword(s):  
Region Of Interest ◽  
Image Data ◽  
Sensor Calibration ◽  
Absolute Calibration ◽  
Landsat 8 ◽  
Calibration Model ◽  
General Applicability ◽  
Landsat 7 ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Sentinel 2A

This work extends an empirical absolute calibration model initially developed for the Libya 4 Pseudo-Invariant Calibration Site (PICS) to five additional Saharan Desert PICS (Egypt 1, Libya 1, Niger 1, Niger 2, and Sudan 1), and demonstrates the efficacy of the resulting models at predicting sensor top-of-atmosphere (TOA) reflectance. It attempts to generate absolute calibration models for these PICS that have an accuracy and precision comparable to or better than the current Libya 4 model, with the intent of providing additional opportunities for sensor calibration. In addition, this work attempts to validate the general applicability of the model to other sites. The method uses Terra Moderate Resolution Imaging Spectroradiometer (MODIS) as the reference radiometer and Earth Observing-1 (EO-1) Hyperion image data to provide a representative hyperspectral reflectance profile of the PICS. Data from a region of interest (ROI) in an “optimal region” of 3% temporal, spatial, and spectral stability within the PICS are used for developing the model. The developed models were used to simulate observations of the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+), Landsat 8 (L8) Operational Land Imager (OLI), Sentinel 2A (S2A) MultiSpectral Instrument (MSI) and Sentinel 2B (S2B) MultiSpectral Instrument (MSI) from their respective launch date through 2018. The models developed for the Egypt 1, Libya 1 and Sudan 1 PICS have an estimated accuracy of approximately 3% and precision of approximately 2% for the sensors used in the study, comparable to the current Libya 4 model. The models developed for the Niger 1 and Niger 2 sites are significantly less accurate with similar precision.

scholarly journals Bayesian Dark Target Algorithm for MODIS AOD retrieval over land

2017 ◽  
Author(s):  
Antti Lipponen ◽  
Tero Mielonen ◽  
Mikko R. A. Pitkänen ◽  
Robert C. Levy ◽  
Virginia R. Sawyer ◽  
...  
Keyword(s):  
Computation Time ◽  
Surface Reflectance ◽  
Correlation Models ◽  
Modis Aod ◽  
Uncertainty Estimates ◽  
Average Computation Time ◽  
Moderate Resolution ◽  
Fine Mode ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Aerosol Parameters

Abstract. We have developed a Bayesian Dark Target (BDT) algorithm for the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrieval over land. In the BDT algorithm, we simultaneously retrieve all pixels in a granule, utilize spatial correlation models for the unknown aerosol parameters, use a statistical prior model for the surface reflectance, and take into account the uncertainties due to fixed aerosol models. The retrieved parameters are total AOD at 550 nm, fine-mode fraction (FMF), and surface reflectances at four different wavelengths (466, 550, 644, and 2100 nm). The accuracy of the new algorithm is evaluated by comparing the AOD retrievals to Aerosol Robotic Network (AERONET) AOD. The results show that the BDT significantly improves the accuracy of AOD retrievals over the operational Dark Target (DT) algorithm. A reduction of about 29 % in the AOD root mean square error and decrease of about 80 % in the median bias of AOD were found globally when the BDT was used instead of the DT algorithm. Furthermore, the fraction of AOD retrievals inside the ±(0.05 + 15 %) expected error envelope increased from 55 % to 76 %. In addition to retrieving the values of AOD, FMF and surface reflectance, the BDT also gives pixel-level posterior uncertainty estimates for the retrieved parameters. The BDT algorithm always results in physical, non-negative AOD values, and the average computation time for a single granule was less than a minute on a modern personal computer.

Sign in / Sign up

Export Citation Format

Share Document