CO2 Flux over the Contiguous United States in 2016 Inverted by WRF-Chem/DART from OCO-2 XCO2 Retrievals

High spatial resolution carbon dioxide (CO2) flux inversion systems are needed to support the global stocktake required by the Paris Agreement and to complement the bottom-up emission inventories. Based on the work of Zhang, a regional CO2 flux inversion system capable of assimilating the column-averaged dry air mole fractions of CO2 (XCO2) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations had been developed. To evaluate the system, under the constraints of the initial state and boundary conditions extracted from the CarbonTracker 2017 product (CT2017), the annual CO2 flux over the contiguous United States in 2016 was inverted (1.08 Pg C yr−1) and compared with the corresponding posterior CO2 fluxes extracted from OCO-2 model intercomparison project (OCO-2 MIP) (mean: 0.76 Pg C yr−1, standard deviation: 0.29 Pg C yr−1, 9 models in total) and CT2017 (1.19 Pg C yr−1). The uncertainty of the inverted CO2 flux was reduced by 14.71% compared to the prior flux. The annual mean XCO2 estimated by the inversion system was 403.67 ppm, which was 0.11 ppm smaller than the result (403.78 ppm) simulated by a parallel experiment without assimilating the OCO-2 retrievals and closer to the result of CT2017 (403.29 ppm). Independent CO2 flux and concentration measurements from towers, aircraft, and Total Carbon Column Observing Network (TCCON) were used to evaluate the results. Mean bias error (MBE) between the inverted CO2 flux and flux measurements was 0.73 g C m−2 d−1, was reduced by 22.34% and 28.43% compared to those of the prior flux and CT2017, respectively. MBEs between the CO2 concentrations estimated by the inversion system and concentration measurements from TCCON, towers, and aircraft were reduced by 52.78%, 96.45%, and 75%, respectively, compared to those of the parallel experiment. The experiment proved that CO2 emission hotspots indicated by the inverted annual CO2 flux with a relatively high spatial resolution of 50 km consisted well with the locations of most major metropolitan/urban areas in the contiguous United States, which demonstrated the potential of combing satellite observations with high spatial resolution CO2 flux inversion system in supporting the global stocktake.

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Fusion of MODIS and Landsat-Like Images for Daily High Spatial Resolution NDVI

Remote Sensing ◽

10.3390/rs12081297 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1297

Author(s):

Roberto Filgueiras ◽

Everardo Chartuni Mantovani ◽

Elpídio Inácio Fernandes-Filho ◽

Fernando França da Cunha ◽

Daniel Althoff ◽

...

Keyword(s):

Spatial Resolution ◽

Regression Models ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

High Spatial Resolution ◽

Coefficient Of Determination ◽

Mean Bias Error ◽

Bias Error ◽

Landsat 8 ◽

Modis Ndvi

One of the obstacles in monitoring agricultural crops is the difficulty in understanding and mapping rapid changes of these crops. With the purpose of addressing this issue, this study aimed to model and fuse the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) using Landsat-like images to achieve daily high spatial resolution NDVI. The study was performed for the period of 2017 on a commercial farm of irrigated maize-soybean rotation in the western region of the state of Bahia, Brazil. To achieve the objective, the following procedures were performed: (i) Landsat-like images were upscaled to match the Landsat-8 spatial resolution (30 m); (ii) the reflectance of Landsat-like images was intercalibrated using the Landsat-8 as a reference; (iii) Landsat-like reflectance images were upscaled to match the MODIS sensor spatial resolution (250 m); (iv) regression models were trained daily to model MODIS NDVI using the upscaled Landsat-like reflectance images (250 m) of the closest day as the input; and (v) the intercalibrated version of the Landsat-like images (30 m) used in the previous step was used as the input for the trained model, resulting in a downscaled MODIS NDVI (30 m). To determine the best fitting model, we used the following statistical metrics: coefficient of determination (r2), root mean square error (RMSE), Nash–Sutcliffe efficiency index (NSE), mean bias error (MBE), and mean absolute error (MAE). Among the assessed regression models, the Cubist algorithm was sensitive to changes in agriculture and performed best in modeling of the Landsat-like MODIS NDVI. The results obtained in the present research are promising and can enable the monitoring of dynamic phenomena with images available free of charge, changing the way in which decisions are made using satellite images.

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Evaluation of Six High-Spatial Resolution Clear-Sky Surface Upward Longwave Radiation Estimation Methods with MODIS

Remote Sensing ◽

10.3390/rs12111834 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1834

Author(s):

Boxiong Qin ◽

Biao Cao ◽

Hua Li ◽

Zunjian Bian ◽

Tian Hu ◽

...

Keyword(s):

Spatial Resolution ◽

High Spatial Resolution ◽

Hybrid Methods ◽

Longwave Radiation ◽

Surface Radiation ◽

Radiation Budget ◽

Estimation Methods ◽

Mean Bias Error ◽

Clear Sky ◽

Surface Radiation Budget

Surface upward longwave radiation (SULR) is a critical component in the calculation of the Earth’s surface radiation budget. Multiple clear-sky SULR estimation methods have been developed for high-spatial resolution satellite observations. Here, we comprehensively evaluated six SULR estimation methods, including the temperature-emissivity physical methods with the input of the MYD11/MYD21 (TE-MYD11/TE-MYD21), the hybrid methods with top-of-atmosphere (TOA) linear/nonlinear/artificial neural network regressions (TOA-LIN/TOA-NLIN/TOA-ANN), and the hybrid method with bottom-of-atmosphere (BOA) linear regression (BOA-LIN). The recently released MYD21 product and the BOA-LIN—a newly developed method that considers the spatial heterogeneity of the atmosphere—is used initially to estimate SULR. In addition, the four hybrid methods were compared with simulated datasets. All the six methods were evaluated using the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Surface Radiation Budget Network (SURFRAD) in situ measurements. Simulation analysis shows that the BOA-LIN is the best one among four hybrid methods with accurate atmospheric profiles as input. Comparison of all the six methods shows that the TE-MYD21 performed the best, with a root mean square error (RMSE) and mean bias error (MBE) of 14.0 and −0.2 W/m2, respectively. The RMSE of BOA-LIN, TOA-NLIN, TOA-LIN, TOA-ANN, and TE-MYD11 are equal to 15.2, 16.1, 17.2, 21.2, and 18.5 W/m2, respectively. TE-MYD21 has a much better accuracy than the TE-MYD11 over barren surfaces (NDVI < 0.3) and a similar accuracy over non-barren surfaces (NDVI ≥ 0.3). BOA-LIN is more stable over varying water vapor conditions, compared to other hybrid methods. We conclude that this study provides a valuable reference for choosing the suitable estimation method in the SULR product generation.

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Evaluating the Impacts of Continuous and Rotational Grazing on Tallgrass Prairie Landscape Using High-Spatial-Resolution Imagery

Agronomy ◽

10.3390/agronomy9050238 ◽

2019 ◽

Vol 9 (5) ◽

pp. 238 ◽

Cited By ~ 4

Author(s):

Shengfang Ma ◽

Yuting Zhou ◽

Prasanna H. Gowda ◽

Liangfu Chen ◽

Patrick J. Starks ◽

...

Keyword(s):

United States ◽

Land Cover ◽

Spatial Resolution ◽

Temporal Resolution ◽

Tallgrass Prairie ◽

Landscape Metrics ◽

High Spatial Resolution ◽

Shrub Encroachment ◽

Rotational Grazing ◽

Grass Growth

This study evaluated the impacts of different grazing treatments (continuous (C) and rotational (R) grazing) on tallgrass prairie landscape, using high-spatial-resolution aerial imagery (1-m at RGB and near-infrared bands) of experimental C and R pastures within two replicates (Rep A and Rep B) in the southern Great Plains (SGP) of the United States. The imagery was acquired by the National Agriculture Imagery Program (NAIP) during the agricultural growing season of selected years (2010, 2013, 2015, and 2017) in the continental United States. Land cover maps were generated by combining visual interpolation, a support vector machine, and a decision tree classifier. Landscape metrics (class area, patch number, percentage of landscape, and fragmentation indices) were calculated from the FRAGSTATS (a computer software program designed to compute a wide variety of landscape metrics for categorical map patterns) based on land cover results. Both the metrics and land cover results were used to analyze landscape dynamics in the experiment pastures. Results showed that both grass and shrubs of different pastures differed largely in the same year and had significant annual dynamics controlled by climate. High stocking intensity delayed grass growth. A large proportion of bare soil occurred in sub-paddocks of rotational grazing that were just grazed or under grazing. Rep A experienced rapid shrub encroachment, with a large proportion of shrub at the beginning of the experiment. Shrub may occupy 41% of C and 15% of R in Rep A by 2030, as revealed by the linear regression analysis of shrub encroachment. In contrast, shrub encroachment was not significant in Rep B, which only had a small number of shrub patches at the beginning of the experiment. This result indicates that the shrub encroachment is mainly controlled by the initial status of the pastures instead of grazing management. However, the low temporal resolution of the NAIP imagery (one snapshot in two or three years) limits our comparison of the continuous and rotational grazing at the annual scale. Future studies need to combine NAIP imagery with other higher temporal resolution imagery (e.g., WorldView), in order to better evaluate the interannual variabilities of grass productivity and shrub encroachment.

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Contributions of Operational Satellites in Monitoring the Catastrophic Floodwaters Due to Hurricane Harvey

Remote Sensing ◽

10.3390/rs10081256 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1256 ◽

Cited By ~ 9

Author(s):

Mitchell Goldberg ◽

Sanmei Li ◽

Steven Goodman ◽

Dan Lindsey ◽

Bill Sjoberg ◽

...

Keyword(s):

United States ◽

Emergency Management ◽

Spatial Resolution ◽

Infrared Imaging ◽

High Spatial Resolution ◽

Economic Loss ◽

The United States ◽

Flood Monitoring ◽

Flooded Areas ◽

Flood Maps

Hurricane Harvey made landfall as a Category-4 storm in the United States on 25 August 2017 in Texas, causing catastrophic flooding in the Houston metropolitan area and resulting in a total economic loss estimated to be about $125 billion. To monitor flooding in the areas affected by Harvey, we used data from sensors aboard the Suomi National Polar-Orbiting Partnership Satellite (SNPP) and the new Geostationary Operational Environmental Satellite (GOES)-16. The GOES-16 Advanced Baseline Imager (ABI) observations are available every 5 min at 1-km spatial resolution across the entire United States, allowing for the possibility of frequent cloud free views of the flooded areas; while the higher resolution 375-m imagery available twice per day from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the SNPP satellite can observe more details of the flooded regions. Combining the high spatial resolution from VIIRS with the frequent observations from ABI offers an improved capability for flood monitoring. The flood maps derived from the SNPP VIIRS and GOES-16 ABI observations were provided to the Federal Emergency Management Agency (FEMA) continuously during Hurricane Harvey. According to FEMA’s estimate on 3 September 2017, approximately 155,000 properties might have been affected by the floodwaters of Hurricane Harvey.

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An Evaluation of NDFD Weather Forecasts for Wildland Fire Behavior Prediction

Weather and Forecasting ◽

10.1175/waf-d-17-0121.1 ◽

2018 ◽

Vol 33 (1) ◽

pp. 301-315 ◽

Cited By ~ 6

Author(s):

Wesley G. Page ◽

Natalie S. Wagenbrenner ◽

Bret W. Butler ◽

Jason M. Forthofer ◽

Chris Gibson

Keyword(s):

United States ◽

Wind Speed ◽

Relative Humidity ◽

Air Temperature ◽

Wildland Fire ◽

Fire Behavior ◽

The United States ◽

Mean Bias Error ◽

Bias Error ◽

Behavior Prediction

Abstract Wildland fire managers in the United States currently utilize the gridded forecasts from the National Digital Forecast Database (NDFD) to make fire behavior predictions across complex landscapes during large wildfires. However, little is known about the NDFDs performance in remote locations with complex topography for weather variables important for fire behavior prediction, including air temperature, relative humidity, and wind speed. In this study NDFD forecasts for calendar year 2015 were evaluated in fire-prone locations across the conterminous United States during periods with the potential for active fire spread using the model performance statistics of root-mean-square error (RMSE), mean fractional bias (MFB), and mean bias error (MBE). Results indicated that NDFD forecasts of air temperature and relative humidity performed well with RMSEs of about 2°C and 10%–11%, respectively. However, wind speed was increasingly underpredicted when observed wind speeds exceeded about 4 m s−1, with MFB and MBE values of approximately −15% and −0.5 m s−1, respectively. The importance of accurate wind speed forecasts in terms of fire behavior prediction was confirmed, and the forecast accuracies needed to achieve “good” surface head fire rate-of-spread predictions were estimated as ±20%–30% of the observed wind speed. Weather station location, the specific forecast office, and terrain complexity had the largest impacts on wind speed forecast error, although the relatively low variance explained by the model (~37%) suggests that other variables are likely to be important. Based on these results it is suggested that wildland fire managers should use caution when utilizing the NDFD wind speed forecasts if high wind speed events are anticipated.

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SnowClim v1.0: High-resolution snow model and data for the western United States

10.5194/gmd-2021-407 ◽

2021 ◽

Author(s):

Abby C. Lute ◽

John Abatzoglou ◽

Timothy Link

Keyword(s):

Climate Change ◽

United States ◽

Spatial Resolution ◽

Snow Water Equivalent ◽

High Spatial Resolution ◽

Western United States ◽

Climate Data ◽

Western Us ◽

Snow Water ◽

Snow Model

Abstract. Seasonal snowpack dynamics shape the biophysical and societal characteristics of many global regions. However, snowpack accumulation and duration have generally declined in recent decades largely due to anthropogenic climate change. Mechanistic understanding of snowpack spatiotemporal heterogeneity and climate change impacts will benefit from snow data products that are based on physical principles, that are simulated at high spatial resolution, and that cover large geographic domains. Existing datasets do not meet these requirements, hindering our ability to understand both contemporary and changing snow regimes and to develop adaptation strategies in regions where snowpack patterns and processes are important components of Earth systems. We developed a computationally efficient physics-based snow model, SnowClim, that can be run in the cloud. The model was evaluated and calibrated at Snowpack Telemetry sites across the western United States (US), achieving a site-median root mean square error for daily snow water equivalent of 62 mm, bias in peak snow water equivalent of −9.6 mm, and bias in snow duration of 1.2 days when run hourly. Positive biases were found at sites with mean winter temperature above freezing where the estimation of precipitation phase is prone to errors. The model was applied to the western US using newly developed forcing data created by statistically downscaling pre-industrial, historical, and pseudo-global warming climate data from the Weather Research and Forecasting (WRF) model. The resulting product is the SnowClim dataset, a suite of summary climate and snow metrics for the western US at 210 m spatial resolution (Lute et al., 2021). The physical basis, large extent, and high spatial resolution of this dataset will enable novel analyses of changing hydroclimate and its implications for natural and human systems.

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Evaluation and validation of a high spatial resolution satellite soil moisture product over the Continental United States

Journal of Hydrology ◽

10.1016/j.jhydrol.2020.125043 ◽

2020 ◽

Vol 588 ◽

pp. 125043 ◽

Cited By ~ 1

Author(s):

Bin Fang ◽

Venkataraman Lakshmi ◽

Rajat Bindlish ◽

Thomas J. Jackson ◽

Pang-Wei Liu

Keyword(s):

United States ◽

Soil Moisture ◽

Spatial Resolution ◽

High Spatial Resolution

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ACCURACY ASSESSMENT OF LANDSAT-DERIVED CONTINUOUS FIELDS OF TREE COVER PRODUCTS USING AIRBORNE LIDAR DATA IN THE EASTERN UNITED STATES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w4-241-2015 ◽

2015 ◽

Vol XL-7/W4 ◽

pp. 241-246 ◽

Cited By ~ 3

Author(s):

X. P. Song ◽

H. Tang

Keyword(s):

United States ◽

Land Cover ◽

Canopy Cover ◽

Airborne Lidar ◽

Tree Cover ◽

Mean Bias Error ◽

Bias Error ◽

Lidar Data ◽

Eastern United States ◽

Continuous Fields

Knowing the detailed error structure of a land cover map is crucial for area estimation. Facilitated by the opening of the Landsat archive, global land cover mapping at 30-m resolution has become possible in recent years. Two global Landsat-based continuous fields of tree cover maps have been generated by Sexton et al. (2013) and Hansen et al. (2013) but the accuracy of which have not been comprehensively evaluated. Here we used canopy cover derived from airborne small-footprint Lidar data as a reference to evaluate the accuracy of these two datasets as well as the National Land Cover Database 2001 canopy cover layer (Homer et al. 2004) in two entire counties in Maryland, United States. Our results showed that all three Landsat datasets captured well the spatial variations of tree cover in the study area with an <i>r</i><sup>2</sup> ranging between 0.54 and 0.58, a mean bias error ranging between -15% and 5% tree cover, and a root mean square error ranging between 27% and 29% tree cover. When the continuous tree cover maps were converted to binary forest/nonforest maps, all three products were proved to have an overall accuracy >= 80% but with significant differences in producer’s accuracy and user’s accuracy. Data users are thus suggested to beware of these accuracy patterns when selecting the most appropriate dataset for their specific applications.

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