Yunjun Yao

Land cover classification using Landsat 8 Operational Land Imager data in Beijing, China

The successful launch of Landsat 8 provides a new data source for monitoring land cover, which has the potential to significantly improve the characterization of the earth’s surface. To assess data performance, Landsat 8 Operational Land Imager (OLI) data were first compared with Landsat 7 ETM + data using texture features as the indicators. Furthermore, the OLI data were investigated for land cover classification using the maximum likelihood and support vector machine classifiers in Beijing. The results indicated that (1) the OLI data quality was slightly better than the ETM + data quality in the visible bands, especially the near-infrared band of OLI the data, which had a clear improvement; clear improvement was not founded in the shortwave-infrared bands. Moreover, (2) OLI data had a satisfactory performance in terms of land cover classification. In summary, OLI data were a reliable data source for monitoring land cover and provided the continuity in the Landsat earth observation.

Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation

Surface broadband emissivity (BBE) in the thermal infrared spectrum is essential for calculating the surface total longwave net radiation in land surface models. However, almost all narrowband emissivities estimated from satellite observations are in the 3-14-μm spectral region. Previous studies converted these narrowband emissivities to BBE over different spectral ranges, such as 3-14, 8-12, 8-13.5, and 8-14 μm . Errors in the calculated total longwave net radiation must be quantified systematically using these BBEs. Moreover, the best spectral range for longwave net radiation must be determined. The key to addressing these issues is the use of the realistic emissivity spectra. By applying modern radiative transfer tools, we derived the emissivity spectra of water, snow, and minerals at 1-200 μm . Using these emissivity spectra, we first investigated the accuracy of replacing all-wavelength surface longwave net radiation with the surface longwave net radiation in the 3-100-, 4-100-, 2.5-100-, 2.5-200-, and 1-200-μm spectral domains. Surface longwave net radiation at 2.5-200 μm was found to be optimal, with a bias and root mean square (rms) of less than 0.928 and 0.993 W/m2, respectively. We calculated the errors when estimating surface longwave net radiation at 2.5-200 μm with BBE in different spectral ranges. The results show that BBE at 8-13.5 μm had the lowest error and the corresponding bias and rms were less than 0.002 and 1.453 W/m2, respectively. When the 2.5-200-μm surface longwave net radiation calculated by the 8-13.5-μm BBE was used to replace the all-wavelength surface longwave net radiation, the average bias and rms were 1.473 and 2.746 W/m2, respectively. Using the most representative emissivity spectra, we derived the conversion formulas for calculating BBE at 8-13.5 μm from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Moderate Resolution Imaging Spectrometer (MODIS) narrowband emissivity products.

Land Cover Classification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data

Temporal-related features are important for improving land cover classification accuracy using remote sensing data. This study investigated the efficacy of phenological features extracted from time series MODIS Normalized Difference Vegetation Index (NDVI) data in improving the land cover classification accuracy of Landsat data. The MODIS NDVI data were first fused with Landsat data via the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to obtain NDVI data at the Landsat spatial resolution. Next, phenological features, including the beginning and ending dates of the growing season, the length of the growing season, seasonal amplitude, and the maximum fitted NDVI value, were extracted from the fused time series NDVI data using the TIMESAT tool. The extracted data were integrated with the spectral data of the Landsat data to improve classification accuracy using a maximum likelihood classifier (MLC) and support vector machine (SVM) classifier. The results indicated that phenological features had a statistically significant effect on improving the land cover classification accuracy of single Landsat data (an approximately 3% increase in overall classification accuracy), especially for vegetation type discrimination. However, the phenological features did not improve on statistical measures including the maximum, the minimum, the mean, and the standard deviation values of the time series NDVI dataset, especially for human-managed vegetation types. Regarding different classifiers, SVM could achieve better classification accuracy than the traditional MLC classifier, but the improvement in accuracy obtained using advanced classifiers was inferior to that achieved by involving the temporally derived features for land cover classification.

Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations

Accurate estimation of the satellite-based global terrestrial latent heat flux (LE) at high spatial and temporal scales remains a major challenge. In this study, we introduce a Bayesian model averaging (BMA) method to improve satellite-based global terrestrial LE estimation by merging five process-based algorithms. These are the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product algorithm, the revised remote-sensing-based Penman-Monteith LE algorithm, the Priestley-Taylor-based LE algorithm, the modified satellite-based Priestley-Taylor LE algorithm, and the semi-empirical Penman LE algorithm. We validated the BMA method using data for 2000–2009 and by comparison with a simple model averaging (SA) method and five process-based algorithms. Validation data were collected for 240 globally distributed eddy covariance tower sites provided by FLUXNET projects. The validation results demonstrate that the five process-based algorithms used have variable uncertainty and the BMA method enhances the daily LE estimates, with smaller root mean square errors (RMSEs) than the SA method and the individual algorithms driven by tower-specific meteorology and Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data provided by the NASA Global Modeling and Assimilation Office (GMAO), respectively. The average RMSE for the BMA method driven by daily tower-specific meteorology decreased by more than 5 W/m2 for crop and grass sites, and by more than 6 W/m2 for forest, shrub, and savanna sites. The average coefficients of determination (R2) increased by approximately 0.05 for most sites. To test the BMA method for regional mapping, we applied it for MODIS data and GMAO-MERRA meteorology to map annual global terrestrial LE averaged over 2001–2004 for spatial resolution of 0.05°. The BMA method provides a basis for generating a long-term global terrestrial LE product for characterizing global energy, hydrological, and carbon cycles.

Monitoring Drought over the Conterminous United States Using MODIS and NCEP Reanalysis-2 Data

Abstract Monitoring land surface drought using remote sensing data is a challenge, although a few methods are available. Evapotranspiration (ET) is a valuable indicator linked to land drought status and plays an important role in surface drought detection at continental and global scales. In this study, the evaporative drought index (EDI), based on the estimated actual ET and potential ET (PET), is described to characterize the surface drought conditions. Daily actual ET at 4-km resolution for April–September 2003–05 across the continental United States is estimated using a simple improved ET model with input solar radiation acquired by Moderate-Resolution Imaging Spectroradiometer (MODIS) at a spatial resolution of 4 km and input meteorological parameters from NCEP Reanalysis-2 data at a spatial resolution of 32 km. The PET is also calculated using some of these data. The estimated actual ET has been rigorously validated with ground-measured ET at six Enhanced Facility sites in the Southern Great Plains ...

Global Land Surface Fractional Vegetation Cover Estimation Using General Regression Neural Networks From MODIS Surface Reflectance

Fractional vegetation cover (FVC) plays an important role in earth surface process simulations, climate modeling, and global change studies. Several global FVC products have been generated using medium spatial resolution satellite data. However, the validation results indicate inconsistencies, as well as spatial and temporal discontinuities of the current FVC products. The objective of this paper is to develop a reliable estimation algorithm to operationally produce a high-quality global FVC product from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance. The high-spatial-resolution FVC data were first generated using Landsat TM/ETM+ data at the global sampling locations, and then, the general regression neural networks (GRNNs) were trained using the high-spatial-resolution FVC data and the reprocessed MODIS surface reflectance data. The direct validation using ground reference data from validation of land European Remote Sensing instruments sites indicated that the performance of the proposed method (R2=0.809, RMSE =0.157) was comparable with that of the GEOV1 FVC product (R2=0.775, RMSE =0.166), which is currently considered to be the best global FVC product from SPOT VEGETATION data. Further comparison indicated that the spatial and temporal continuity of the estimates from the proposed method was superior to that of the GEOV1 FVC product.

Evaluation of the Reanalysis Surface Incident Shortwave Radiation Products from NCEP, ECMWF, GSFC, and JMA Using Satellite and Surface Observations

Solar radiation incident at the Earth’s surface (Rs) is an essential component of the total energy exchange between the atmosphere and the surface. Reanalysis data have been widely used, but a comprehensive validation using surface measurements is still highly needed. In this study, we evaluated the Rs estimates from six current representative global reanalyses (NCEP–NCAR, NCEP-DOE; CFSR; ERA-Interim; MERRA; and JRA-55) using surface measurements from different observation networks [GEBA; BSRN; GC-NET; Buoy; and CMA] (674 sites in total) and the Earth’s Radiant Energy System (CERES) EBAF product from 2001 to 2009. The global mean biases between the reanalysis Rs and surface measurements at all sites ranged from 11.25 W/m2 to 49.80 W/m2. Comparing with the CERES-EBAF Rs product, all the reanalyses overestimate Rs, except for ERA-Interim, with the biases ranging from −2.98 W/m2 to 21.97 W/m2 over the globe. It was also found that the biases of cloud fraction (CF) in the reanalyses caused the overestimation of Rs. After removing the averaged bias of CERES-EBAF, weighted by the area of the latitudinal band, a global annual mean Rs values of 184.6 W/m2, 180.0 W/m2, and 182.9 W/m2 were obtained over land, ocean, and the globe, respectively.

Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data

Abstract Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data.

Validation and Application of the Modified Satellite-Based Priestley-Taylor Algorithm for Mapping Terrestrial Evapotranspiration

Satellite-based vegetation indices (VIs) and Apparent Thermal Inertia (ATI) derived from temperature change provide valuable information for estimating evapotranspiration (LE) and detecting the onset and severity of drought. The modified satellite-based Priestley-Taylor (MS-PT) algorithm that we developed earlier, coupling both VI and ATI, is validated based on observed data from 40 flux towers distributed across the world on all continents. The validation results illustrate that the daily LE can be estimated with the Root Mean Square Error (RMSE) varying from 10.7 W/m2 to 87.6 W/m2, and with the square of correlation coefficient (R2) from 0.41 to 0.89 (p < 0.01). Compared with the Priestley-Taylor-based LE (PT-JPL) algorithm, the MS-PT algorithm improves the LE estimates at most flux tower sites. Importantly, the MS-PT algorithm is also satisfactory in reproducing the inter-annual variability at flux tower sites with at least five years of data. The R2 between measured and predicted annual LE anomalies is 0.42 (p = 0.02). The MS-PT algorithm is then applied to detect the variations of long-term terrestrial LE over Three-North Shelter Forest Region of China and to monitor global land surface drought. The MS-PT algorithm described here demonstrates the ability to map regional terrestrial LE and identify global soil moisture stress, without requiring precipitation information.

A Comparative Study of Three Land Surface Broadband Emissivity Datasets from Satellite Data

This study compared three broadband emissivity (BBE) datasets from satellite observations. The first is a new global land surface BBE dataset known as the Global Land Surface Satellite (GLASS) BBE. The other two are the North American ASTER Land Surface Emissivity Database (NAALSED) BBE and University of Wisconsin Global Infrared Land Surface Emissivity Database (UWIREMIS) BBE, which were derived from two independent narrowband emissivity products. Firstly, NAALSED BBE was taken as the reference to evaluate the GLASS BBE and UWIREMIS BBE. The GLASS BBE was more close to NAALSED BBE with a bias and root mean square error (RMSE) of −0.001 and 0.007 for the summer season, −0.001 and 0.008 for the winter season, respectively. Then, the spatial distribution and seasonal pattern of global GLASS BBE and UWIREMIS BBE for six dominant land cover types were compared. The BBE difference between vegetated areas and non-vegetated areas can be easily seen from two BBEs. The seasonal variation of GLASS BBE was more reasonable than that of UWIREMIS BBE. Finally, the time series were calculated from GLASS BBE and UWIREMIS BBE using the data from 2003 through 2010. The periodic variations of GLASS BBE were stronger than those of UWIREMIS BBE. The long time series high quality GLASS BBE can be incorporated in land surface models for improving their simulation results.