Geography Research Works

Permanent URI for this collection

http://hdl.handle.net/1903/1641

Browse

Now showing 1 - 20 of 134

A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation
(MDPI, 2016-03-22) Claverie, Martin; Matthews, Jessica L.; Vermote, Eric F.; Justice, Christopher O.
In- land surface models, which are used to evaluate the role of vegetation in the context of global climate change and variability, LAI and FAPAR play a key role, specifically with respect to the carbon and water cycles. The AVHRR-based LAI/FAPAR dataset offers daily temporal resolution, an improvement over previous products. This climate data record is based on a carefully calibrated and corrected land surface reflectance dataset to provide a high-quality, consistent time-series suitable for climate studies. It spans from mid-1981 to the present. Further, this operational dataset is available in near real-time allowing use for monitoring purposes. The algorithm relies on artificial neural networks calibrated using the MODIS LAI/FAPAR dataset. Evaluation based on cross-comparison with MODIS products and in situ data show the dataset is consistent and reliable with overall uncertainties of 1.03 and 0.15 for LAI and FAPAR, respectively. However, a clear saturation effect is observed in the broadleaf forest biomes with high LAI (>4.5) and FAPAR (>0.8) values.
A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index
(MDPI, 2021-04-01) Hosseini, Mehdi; McNairn, Heather; Mitchell, Scott; Robertson, Lauren Dingle; Davidson, Andrew; Ahmadian, Nima; Bhattacharya, Avik; Borg, Erik; Conrad, Christopher; Dabrowska-Zielinska, Katarzyna; de Abelleyra, Diego; Gurdak, Radoslaw; Kumar, Vineet; Kussul, Nataliia; Mandal, Dipankar; Rao, Y. S.; Saliendra, Nicanor; Shelestov, Andrii; Spengler, Daniel; Verón, Santiago R.; Homayouni, Saeid; Becker-Reshef, Inbal
The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m−2 and mean absolute error (MAE) of 0.51 m2m−2 . The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m−2 and MAE of 0.61 m2m−2 ) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m−2 and MAE of 0.30 m2m−2 ). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance.
A Disease Control-Oriented Land Cover Land Use Map for Myanmar
(MDPI, 2021-06-13) Chen, Dong; Shevade, Varada; Baer, Allison; He, Jiaying; Hoffman-Hall, Amanda; Ying, Qing; Li, Yao; Loboda, Tatiana V.
Malaria is a serious infectious disease that leads to massive casualties globally. Myanmar is a key battleground for the global fight against malaria because it is where the emergence of drug-resistant malaria parasites has been documented. Controlling the spread of malaria in Myanmar thus carries global significance, because the failure to do so would lead to devastating consequences in vast areas where malaria is prevalent in tropical/subtropical regions around the world. Thanks to its wide and consistent spatial coverage, remote sensing has become increasingly used in the public health domain. Specifically, remote sensing-based land cover/land use (LCLU) maps present a powerful tool that provides critical information on population distribution and on the potential human-vector interactions interfaces on a large spatial scale. Here, we present a 30-meter LCLU map that was created specifically for the malaria control and eradication efforts in Myanmar. This bottom-up approach can be modified and customized to other vector-borne infectious diseases in Myanmar or other Southeastern Asian countries.
A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)
(MDPI, 2015-01-29) Whitcraft, Alyssa K.; Becker-Reshef, Inbal; Justice, Christopher O.
Global agricultural monitoring utilizes a variety of Earth observations (EO) data spanning different spectral, spatial, and temporal resolutions in order to gather information on crop area, type, condition, calendar, and yield, among other applications. Categorical requirements for space-based monitoring of major agricultural production areas have been articulated based on best practices established by the Group on Earth Observation’s (GEO) Global Agricultural Monitoring Community (GEOGLAM) of Practice, in collaboration with the Committee on Earth Observation Satellites (CEOS). We present a method to transform generalized requirements for agricultural monitoring in the context of GEOGLAM into spatially explicit (0.05°) Earth observation (EO) requirements for multiple resolutions of data. This is accomplished through the synthesis of the necessary remote sensing-based datasets concerning where (crop mask, when (growing calendar, and how frequently imagery is required (considering cloud cover impact throughout the agricultural growing season. Beyond this provision of the framework and tools necessary to articulate these requirements, investigated in depth is the requirement for reasonably clear moderate spatial resolution (10–100 m) optical data within 8 days over global within-season croplands of all sizes, a data type prioritized by GEOGLAM and CEOS. Four definitions of “reasonably clear” are investigated: 70%, 80%, 90%, or 95% clear. The revisit frequency required (RFR) for a reasonably clear view varies greatly both geographically and throughout the growing season, as well as with the threshold of acceptable clarity. The global average RFR for a 70% clear view within 8 days is 3.9–4.8 days (depending on the month), 3.0–4.1 days for 80% clear, 2.2–3.3 days for 90% clear, and 1.7–2.6 days for 95% clear. While some areas/times of year require only a single revisit (RFR = 8 days) to meet their reasonably clear requirement, generally the RFR, regardless of clarity threshold, is below to greatly below the 8 day mark, highlighting the need for moderate resolution optical satellite systems or constellations with revisit capabilities more frequent than 8 days. This analysis is providing crucial input for data acquisition planning for agricultural monitoring in the context of GEOGLAM.
A Mapping Framework to Characterize Land Use in the Sudan-Sahel Region from Dense Stacks of Landsat Data
(MDPI, 2019-03-16) Sedano, Fernando; Molini, Vasco; Azad, M. Abdul Kalam
We developed a land cover and land use mapping framework specifically designed for agricultural systems of the Sudan-Sahel region. The mapping approach extracts information from inter- and intra-annual vegetation dynamics from dense stacks of Landsat 8 images. We applied this framework to create a 30 m spatial resolution land use map with a focus on agricultural landscapes of northern Nigeria for 2015. This map provides up-to-date information with a higher level of spatial and thematic detail resulting in a more precise characterization of agriculture in the region. The map reveals that agriculture is the main land use in the region. Arable land represents on average 52.5% of the area, higher than the reported national average for Nigeria (38.4%). Irrigated agriculture covers nearly 2.2% of the total area, reaching nearly 20% of the cultivated land when traditional floodplain agriculture systems are included, above the reported national average (0.63%). There is significant variability in land use within the region. Cultivated land in the northern section can reach values higher than 75%, most land suitable for agriculture is already under cultivation and there is limited land for future agricultural expansion. Marginal lands, not suitable for permanent agriculture, can reach 30% of the land at lower altitudes in the northeast and northwest. In contrast, the southern section presents lower land use intensity that results in a complex landscape that intertwines areas farms and larger patches of natural vegetation. This map improves the spatial detail of existing sources of LCLU information for the region and provides updated information of the current status of its agricultural landscapes. This study demonstrates the feasibility of multi temporal medium resolution remote sensing data to provide detailed and up-to-date information about agricultural systems in arid and sub arid landscapes of the Sahel region.
A Method for Landsat and Sentinel 2 (HLS) BRDF Normalization
(MDPI, 2019-03-15) Franch, Belen; Vermote, Eric; Skakun, Sergii; Roger, Jean-Claude; Masek, Jeffrey; Ju, Junchang; Villaescusa-Nadal, Jose Luis; Santamaria-Artigas, Andres
The Harmonized Landsat/Sentinel-2 (HLS) project aims to generate a seamless surface reflectance product by combining observations from USGS/NASA Landsat-8 and ESA Sentinel-2 remote sensing satellites. These satellites’ sampling characteristics provide nearly constant observation geometry and low illumination variation through the scene. However, the illumination variation throughout the year impacts the surface reflectance by producing higher values for low solar zenith angles and lower reflectance for large zenith angles. In this work, we present a model to derive the bidirectional reflectance distribution function (BRDF) normalization and apply it to the HLS product at 30 m spatial resolution. It is based on the BRDF parameters estimated from the MODerate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product (M{O,Y}D09) at 1 km spatial resolution using the VJB method (Vermote et al., 2009). Unsupervised classification (segmentation) of HLS images is used to disaggregate the BRDF parameters to the HLS spatial resolution and to build a BRDF parameters database at HLS scale. We first test the proposed BRDF normalization for different solar zenith angles over two homogeneous sites, in particular one desert and one Peruvian Amazon forest. The proposed method reduces both the correlation with the solar zenith angle and the coefficient of variation (CV) of the reflectance time series in the red and near infrared bands to 4% in forest and keeps a low CV of 3% to 4% for the deserts. Additionally, we assess the impact of the view zenith angle (VZA) in an area of the Brazilian Amazon forest close to the equator, where impact of the angular variation is stronger because it occurs in the principal plane. The directional reflectance shows a strong dependency with the VZA. The current HLS BRDF correction reduces this dependency but still shows an under-correction, especially in the near infrared, while the proposed method shows no dependency with the view angles. We also evaluate the BRDF parameters using field surface albedo measurements as a reference over seven different sites of the US surface radiation budget observing network (SURFRAD) and five sites of the Australian OzFlux network.
A New Set of MODIS Land Products (MCD18): Downward Shortwave Radiation and Photosynthetically Active Radiation
(MDPI, 2020-01-03) Wang, Dongdong; Liang, Shunlin; Zhang, Yi; Gao, Xueyuan; Brown, Meredith G. L.; Jia, Aolin
Surface downward shortwave radiation (DSR) and photosynthetically active radiation (PAR), its visible component, are key parameters needed for many land process models and terrestrial applications. Most existing DSR and PAR products were developed for climate studies and therefore have coarse spatial resolutions, which cannot satisfy the requirements of many applications. This paper introduces a new global high-resolution product of DSR (MCD18A1) and PAR (MCD18A2) over land surfaces using the MODIS data. The current version is Collection 6.0 at the spatial resolution of 5 km and two temporal resolutions (instantaneous and three-hour). A look-up table (LUT) based retrieval approach was chosen as the main operational algorithm so as to generate the products from the MODIS top-of-atmosphere (TOA) reflectance and other ancillary data sets. The new MCD18 products are archived and distributed via NASA’s Land Processes Distributed Active Archive Center (LP DAAC). The products have been validated based on one year of ground radiation measurements at 33 Baseline Surface Radiation Network (BSRN) and 25 AmeriFlux stations. The instantaneous DSR has a bias of −15.4 W/m2 and root mean square error (RMSE) of 101.0 W/m2, while the instantaneous PAR has a bias of −0.6 W/m2 and RMSE of 45.7 W/m2. RMSE of daily DSR is 32.3 W/m2, and that of the daily PAR is 13.1 W/m2. The accuracy of the new MODIS daily DSR data is higher than the GLASS product and lower than the CERES product, while the latter incorporates additional geostationary data with better capturing DSR diurnal variability. MCD18 products are currently under reprocessing and the new version (Collection 6.1) will provide improved spatial resolution (1 km) and accuracy.
A Sample-Based Forest Monitoring Strategy Using Landsat, AVHRR and MODIS Data to Estimate Gross Forest Cover Loss in Malaysia between 1990 and 2005
(MDPI, 2013-04-15) Giree, Namita; Stehman, Stephen V.; Potapov, Peter; Hansen, Matthew C.
Insular Southeast Asia is a hotspot of humid tropical forest cover loss. A sample-based monitoring approach quantifying forest cover loss from Landsat imagery was implemented to estimate gross forest cover loss for two eras, 1990–2000 and 2000–2005. For each time interval, a probability sample of 18.5 km × 18.5 km blocks was selected, and pairs of Landsat images acquired per sample block were interpreted to quantify forest cover area and gross forest cover loss. Stratified random sampling was implemented for 2000–2005 with MODIS-derived forest cover loss used to define the strata. A probability proportional to x (πpx) design was implemented for 1990–2000 with AVHRR-derived forest cover loss used as the x variable to increase the likelihood of including forest loss area in the sample. The estimated annual gross forest cover loss for Malaysia was 0.43 Mha/yr (SE = 0.04) during 1990–2000 and 0.64 Mha/yr (SE = 0.055) during 2000–2005. Our use of the πpx sampling design represents a first practical trial of this design for sampling satellite imagery. Although the design performed adequately in this study, a thorough comparative investigation of the πpx design relative to other sampling strategies is needed before general design recommendations can be put forth.
Aerosol Retrieval over Land from the Directional Polarimetric Camera Aboard on GF-5
(MDPI, 2022-11-11) Wang, Shupeng; Gong, Weishu; Fang, Li; Wang, Weihe; Zhang, Peng; Lu, Naimeng; Tang, Shihao; Zhang, Xingying; Hu, Xiuqing; Sun, Xiaobing
The DPC (Directional Polarization Camera) onboard the Chinese GaoFen-5 (GF-5) satellite is the first operational aerosol monitoring instrument capable of performing multi-angle polarized measurements in China. Compared with POLDER (Polarization and Directionality of Earth’s Reflectance) which ended its mission in December 2013, DPC has similar band design, with a maximum of 12 imaging angles and a relatively higher spatial resolution of 3.3 km. The global aerosol optical depth (AOD) over land from October to December in 2018 was retrieved with multi-angle polarization measurements of DPC. Comparisons with MODIS (Moderate Resolution Imaging Spectroradiometer) AOD products show relatively good agreement over fine-aerosol-particle-dominated areas such as northern China and Huanghuai areas in eastern China, the southern foothills of the Himalayas and India. AERONET (Aerosol Robotic Network) measurements over Beijing, Xianghe and Kanpur were used to evaluate the accuracy of DPC AOD retrievals. The correlation coefficients are greater than 0.9 and the RMSE are lower than 0.08 for Beijing and Xianghe stations. For Kanpur, a relatively lower correlation of 0.772 and larger RMSE of 0.082 are found.
An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products
(MDPI, 2021-02-16) Li, Xiuxia; Liang, Shunlin; Jin, Huaan
Leaf area index (LAI) and normalized difference vegetation index (NDVI) are key parameters for various applications. However, due to sensor tradeoff and cloud contaminations, these data are often temporally intermittent and spatially discontinuous. To address the discontinuities, this study proposed a method based on spectral matching of 30 m discontinuous values from Landsat data and 500 m temporally continuous values from Moderate-resolution Imaging Spectroradiometer (MODIS) data. Experiments have proven that the proposed method can effectively yield spatiotemporally continuous vegetation products at 30 m spatial resolution. The results for three different study areas with NDVI and LAI showed that the method performs well in restoring the time series, fills in the missing data, and reasonably predicts the images. Remarkably, the proposed method could address the issue when no cloud-free data pairs are available close to the prediction date, because of the temporal information “borrowed” from coarser resolution data. Hence, the proposed method can make better use of partially obscured images. The reconstructed spatiotemporally continuous data have great potential for monitoring vegetation, agriculture, and environmental dynamics.
An Analytic BRDF Model of Canopy Radiative Transfer and Its Inversion
(Institute of Electrical and Electronics Engineers, 1993-09) Liang, Shunlin; Strahler, Alan H.
Radiative transfer modeling of the bidirectional reflectance distribution function (BRDF) of leaf canopies is a powerful tool to relate multiangle remotely sensed data to biophysical parameters of the leaf canopy and to retrieve such parameters from multiangle imagery. However, the approximate approaches for multiple scattering that are used in the inversion of existing models are quite limited, and the sky radiance frequently is simply treated as isotropic. This paper presents an analytical model based on a rigorous canopy radiative transfer equation in which the multiple-scattering component is approximated by asymptotic theory and the single-scattering calculation, which requires numerical integration to properly accommodate the hotspot effect, is also simplified. Because the model is sensitive to angular variation in sky radiance, we further provide an accompanying new formulation for directional radiance in which the unscattered solar radiance and single-scattering radiance are calculated exactly, and multiple-scattering is approximated by the well-known two-stream Dirac delta function approach. A series of validations against exact calculations indicates that both models are quite accurate, especially when the viewing angle is smaller than 55 degrees. The Powell algorithm is then used to retrieve biophysical parameters from multiangle observations based on both the canopy and the sky radiance distribution models. The results using the soybean data of Ranson et al. to recover four of nine soybean biophysical parameters indicate that inversion of the present canopy model retrieves leaf area index well. Leaf angle distribution was not retrieved as accurately for the same dataset, perhaps because these measurements do not describe the hotspot well. Further experiments are required to explore the applicability of this canopy model.
Annual Carbon Emissions from Deforestation in the Amazon Basin between 2000 and 2010
(PLOS (Public Library of Science), 2015-05-07) Song, Xiao-Peng; Huang, Chengquan; Saatchi, Sassan S.; Hansen, Matthew C.; Townshend, John R.
Reducing emissions from deforestation and forest degradation (REDD+) is considered one of the most cost-effective strategies for mitigating climate change. However, historical deforestation and emission rates―critical inputs for setting reference emission levels for REDD+―are poorly understood. Here we use multi-source, time-series satellite data to quantify carbon emissions from deforestation in the Amazon basin on a year-to-year basis between 2000 and 2010.We first derive annual deforestation indicators by using the Moderate Resolution Imaging Spectroradiometer Vegetation Continuous Fields (MODIS VCF) product. MODIS indicators are calibrated by using a large sample of Landsat data to generate accurate deforestation rates, which are subsequently combined with a spatially explicit biomass dataset to calculate committed annual carbon emissions. Across the study area, the average deforestation and associated carbon emissions were estimated to be 1.59 ± 0.25M ha•yr−1 and 0.18 ± 0.07 Pg C•yr−1 respectively, with substantially different trends and inter-annual variability in different regions. Deforestation in the Brazilian Amazon increased between 2001 and 2004 and declined substantially afterwards, whereas deforestation in the Bolivian Amazon, the Colombian Amazon, and the Peruvian Amazon increased over the study period. The average carbon density of lost forests after 2005 was 130 Mg C•ha−1, ~11%lower than the average carbon density of remaining forests in year 2010 (144 Mg C•ha−1). Moreover, the average carbon density of cleared forests increased at a rate of 7 Mg C•ha−1•yr−1 from 2005 to 2010, suggesting that deforestation has been progressively encroaching into high-biomass lands in the Amazon basin. Spatially explicit, annual deforestation and emission estimates like the ones derived in this study are useful for setting baselines for REDD+ and other emission mitigation programs, and for evaluating the performance of such efforts.
Assessing Terrestrial Ecosystem Resilience using Satellite Leaf Area Index
(MDPI, 2020-02-11) Wu, Jinhui; Liang, Shunlin
Quantitative approaches to measuring and assessing terrestrial ecosystem resilience, which expresses the ability of an ecosystem to recover from disturbances without shifting to an alternative state or losing function and services, is critical and essential to forecasting how terrestrial ecosystems will respond to global change. However, global and continuous terrestrial resilience measurement is fraught with difficulty, and the corresponding attribution of resilience dynamics is lacking in the literature. In this study, we assessed global terrestrial ecosystem resilience based on the long time-series GLASS LAI product and GIMMS AVHRR LAI 3g product, and validated the results using drought and fire events as the main disturbance indicators. We also analyzed the spatial and temporal variations of global terrestrial ecosystem resilience and attributed their dynamics to climate change and environmental factors. The results showed that arid and semiarid areas exhibited low resilience. We found that evergreen broadleaf forest exhibited the highest resilience (mean resilience value (from GLASS LAI): 0.6). On a global scale, the increase of mean annual precipitation had a positive impact on terrestrial resilience enhancement, while we found no consistent relationships between mean annual temperature and terrestrial resilience. For terrestrial resilience dynamics, we observed three dramatic raises of disturbance frequency in 1989, 1995, and 2001, respectively, along with three significant drops in resilience correspondingly. Our study mapped continuous spatiotemporal variation and captured interannual variations in terrestrial ecosystem resilience. This study demonstrates that remote sensing data are effective for monitoring terrestrial resilience for global ecosystem assessment.
Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery
(MDPI, 2021-02-26) Skakun, Sergii; Kalecinski, Natacha I.; Brown, Meredith G. L.; Johnson, David M.; Vermote, Eric F.; Roger, Jean-Claude; Franch, Belen
Crop yield monitoring is an important component in agricultural assessment. Multi-spectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 μm), red-edge (0.726 μm), and near-infrared (NIR − 0.865 μm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R2) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields.
Assessment of MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) for Directional Reflectance Retrieval
(MDPI, 2017-11-04) Che, Xianghong; Feng, Min; Sexton, Joseph O.; Channan, Saurabh; Yang, Yaping; Sun, Qing
Measurements of solar radiation reflected from Earth’s surface are the basis for calculating albedo, vegetation indices, and other terrestrial attributes. However, the “bi-directional” geometry of illumination and viewing (i.e., the Bi-directional Reflectance Distribution Function (BRDF)) impacts reflectance and all variables derived or estimated based on these data. The recently released MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) dataset enables retrieval of directional reflectance at arbitrary solar and viewing angles, potentially increasing precision and comparability of data collected under different illumination and observation geometries. We quantified the ability of MCD43A1 Collection 6 for retrieving directional reflectance and compared the daily Collection 6 retrievals to those of MCD43A1 Collection 5, which are retrieved on an eight-day basis. Correcting MODIS-based estimates of surface reflectance from the illumination and viewing geometry of the Terra satellite (MOD09GA) to that of the MODIS Aqua (MYD09GA) overpass, as well as MCD43A4 Collection 6 and Landsat-5 TM images show that the BRDF correction of MCD43A1 Collection 6 results in greater consistency among datasets, with higher R2 (0.63–0.955), regression slopes closer to unity (0.718–0.955), lower root mean squared difference (RMSD) (0.422–3.142), and lower mean absolute error (MAE) (0.282–1.735) compared to the Collection 5 data. Smaller levels of noise (observed as high-frequency variability within the time series) in MCD43A1 Collection 6 in comparison to Collection 5 corroborates the improvement of BRDF parameters time series. These results corroborates that the daily MCD43A1 Collection 6 product represents the anisotropy of surface features and results in more precise directional reflectance derivation at any solar and viewing geometry than did the previous Collection 5.
Assessment of the Suomi NPP VIIRS Land Surface Albedo Data Using Station Measurements and High-Resolution Albedo Maps
(MDPI, 2016-02-08) Zhou, Yuan; Wang, Dongdong; Liang, Shunlin; Yu, Yunyue; He, Tao
Land surface albedo (LSA), one of the Visible Infrared Imaging Radiometer Suite (VIIRS) environmental data records (EDRs), is a fundamental component for linking the land surface and the climate system by regulating shortwave energy exchange between the land and the atmosphere. Currently, the improved bright pixel sub-algorithm (BPSA) is a unique algorithm employed by VIIRS to routinely generate LSA EDR from VIIRS top-of-atmosphere (TOA) observations. As a product validation procedure, LSA EDR reached validated (V1 stage) maturity in December 2014. This study summarizes recent progress in algorithm refinement, and presents comprehensive validation and evaluation results of VIIRS LSA by using extensive field measurements, Moderate Resolution Imaging Spectroradiometer (MODIS) albedo product, and Landsat-retrieved albedo maps. Results indicate that: (1) by testing the updated desert-specific look-up-table (LUT) that uses a stricter standard to select the training data specific for desert aerosol type in our local environment, it is found that the VIIRS LSA retrieval accuracy is improved over a desert surface and the absolute root mean square error (RMSE) is reduced from 0.036 to 0.023, suggesting the potential of the updated desert LUT to the improve the VIIRS LSA product accuracy; (2) LSA retrieval on snow-covered surfaces is more accurate if the newly developed snow-specific LUT (RMSE = 0.082) replaces the generic LUT (RMSE = 0.093) that is employed in the current operational LSA EDR production; (3) VIIRS LSA is also comparable to high-resolution Landsat albedo retrieval (RMSE < 0.04), although Landsat albedo has a slightly higher accuracy, probably owing to higher spatial resolution with less impacts of mixed pixel; (4) VIIRS LSA retrievals agree well with the MODIS albedo product over various land surface types, with overall RMSE of lower than 0.05 and the overall bias as low as 0.025, demonstrating the comparable data quality between VIIRS and the MODIS LSA product.
Atmospheric Correction of Landsat ETM+ Land Surface Imagery: II. Validation and Applications
(Institute of Electrical and Electronics Engineers, 2002) Liang, Shunlin; Morisette, Jeffrey T.; Fang, Hongliang; Chen, Mingzhen; Shuey, Chad J.; Daughtry, Craig S. T.; Walthall, Charles L.
This is the second paper of the series on atmospheric correction of ETM+ land surface imagery. In the first paper, a new algorithm that corrects heterogeneous aerosol scattering and surface adjacency effects was presented. In this study, our objectives are to 1) evaluate the accuracy of this new atmospheric correction algorithm using ground radiometric measurements; 2) apply this algorithm to correct MODIS and SeaWiFS imagery; and 3) demonstrate how much atmospheric correction of ETM+ imagery can improve land cover classification, change detection, and broadband albedo calculations. Validation results indicate that this new algorithm can retrieve surface reflectance from ETM+ imagery accurately. All experimental cases demonstrate that this algorithm can be used for correcting both MODIS and SeaWiFS imagery. Although more tests and validation exercises are needed, it has been proven promising to correct different multispectral imagery operationally. We have also demonstrated that atmospheric correction does matter.
Atmospheric Correction of Landsat ETM+ Land Surface Imagery—Part I: Methods
(Institute of Electrical and Electronics Engineers, 2001-11) Liang, Shunlin; Fang, Hongliang; Chen, Mingzhen
To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, atmospheric correction is a necessary step. After reviewing historical development of atmospheric correction of Landsat thematic mapper (TM) imagery, we present a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from ETM+ imagery under general atmospheric and surface conditions. This algorithm is therefore suitable for operational applications. A new formula that accounts for adjacency effects is also presented. Several examples are given to demonstrate that this new algorithm works very well under a variety of atmospheric and surface conditions. The companion paper will validate this method using ground measurements, and illustrate the improvements of several applications due to atmospheric correction.
Automated Extraction of Surface Water Extent from Sentinel-1 Data
(MDPI, 2018-05-21) Huang, Wenli; DeVries, Ben; Huang, Chengquan; Lang, Megan W.; Jones, John W.; Creed, Irena F.; Carroll, Mark L.
Accurately quantifying surface water extent in wetlands is critical to understanding their role in ecosystem processes. However, current regional- to global-scale surface water products lack the spatial or temporal resolution necessary to characterize heterogeneous or variable wetlands. Here, we proposed a fully automatic classification tree approach to classify surface water extent using Sentinel-1 synthetic aperture radar (SAR) data and training datasets derived from prior class masks. Prior classes of water and non-water were generated from the Shuttle Radar Topography Mission (SRTM) water body dataset (SWBD) or composited dynamic surface water extent (cDSWE) class probabilities. Classification maps of water and non-water were derived over two distinct wetlandscapes: the Delmarva Peninsula and the Prairie Pothole Region. Overall classification accuracy ranged from 79% to 93% when compared to high-resolution images in the Prairie Pothole Region site. Using cDSWE class probabilities reduced omission errors among water bodies by 10% and commission errors among non-water class by 4% when compared with results generated by using the SWBD water mask. These findings indicate that including prior water masks that reflect the dynamics in surface water extent (i.e., cDSWE) is important for the accurate mapping of water bodies using SAR data.
Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery
(MDPI, 2017-08-07) DeVries, Ben; Huang, Chengquan; Lang, Megan W.; Jones, John W.; Huang, Wenli; Creed, Irena F.; Carroll, Mark L.
We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over three wetland sites across North America, including the Prairie Pothole Region, the Delmarva Peninsula and the Everglades, representing a gradient of inundation and vegetation conditions. We estimated SWF at 30-m resolution with accuracies ranging from a normalized root-mean-square-error of 0.11 to 0.19 when compared with various high-resolution ground and airborne datasets. SWF estimates were more sensitive to subtle inundated features compared to previously published surface water datasets, accurately depicting water bodies, large heterogeneously inundated surfaces, narrow water courses and canopy-covered water features. Despite this enhanced sensitivity, several sources of errors affected SWF estimates, including emergent or floating vegetation and forest canopies, shadows from topographic features, urban structures and unmasked clouds. The automated algorithm described in this article allows for the production of high temporal resolution wetland inundation data products to support a broad range of applications.

Browse

Browsing Geography Research Works by Title

Results Per Page

Sort Options