Remote Sens.2015, 7(8), 9844-9864; doi:10.3390/rs70809844 (registering DOI) - published 31 July 2015 Show/Hide Abstract
Abstract: Land-surface reflectance, estimated from satellite observations through atmospheric corrections, is an essential parameter for further retrieval of various high level land-surface parameters, such as leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and surface albedo. Although great efforts have been made, land-surface reflectance products still contain considerable noise caused by, e.g., cloud or mixed-cloud pixels, which results in temporal and spatial inconsistencies in subsequent downstream products. In this study, a new method is developed to remove the residual clouds in the Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface reflectance product and reconstruct time series of surface reflectance for the red, near infrared (NIR), and shortwave infrared (SWIR) bands. A smoothing method is introduced to calculate upper envelopes of vegetation indices (VIs) from the surface reflectance data and the cloud contaminated reflectance data are identified using the time series VIs and the upper envelopes of the time series VIs. Surface reflectance was then reconstructed according to cloud-free surface reflectance by incorporating the upper envelopes of the time series VIs as constraint conditions. The method was applied to reconstruct time series of surface reflectance from MODIS/TERRA surface reflectance product (MOD09A1). Temporal consistency analysis indicates that the new method can reconstruct temporally-continuous time series of land-surface reflectance. Comparisons with cloud-free MODIS/AQUA surface reflectance product (MYD09A1) over the BELMANIP (Benchmark Land Multisite Analysis and Intercomparison of Products) sites in 2003 demonstrate that the new method provides better performance for the red band (R2 = 0.8606 and RMSE = 0.0366) and NIR band (R2 = 0.6934 and RMSE = 0.0519), than the time series cloud detection (TSCD) algorithm (R2 = 0.5811 and RMSE = 0.0649; and R2 = 0.5005 and RMSE = 0.0675, respectively).
Remote Sens.2015, 7(8), 9822-9843; doi:10.3390/rs70809822 (registering DOI) - published 31 July 2015 Show/Hide Abstract
Abstract: Maximum flood extent—a key data need for disaster response and mitigation—is rarely quantified due to storm-related cloud cover and the low temporal resolution of optical sensors. While change detection approaches can circumvent these issues through the identification of inundated land and soil from post-flood imagery, their accuracy can suffer in the narrow and complex channels of increasingly developed and heterogeneous floodplains. This study explored the utility of the Operational Land Imager (OLI) and Independent Component Analysis (ICA) for addressing these challenges in the unprecedented 2013 Flood along the Colorado Front Range, USA. Pre- and post-flood images were composited and transformed with an ICA to identify change classes. Flooded pixels were extracted using image segmentation, and the resulting flood layer was refined with cloud and irrigated agricultural masks derived from the ICA. Visual assessment against aerial orthophotography showed close agreement with high water marks and scoured riverbanks, and a pixel-to-pixel validation with WorldView-2 imagery captured near peak flow yielded an overall accuracy of 87% and Kappa of 0.73. Additional tests showed a twofold increase in flood class accuracy over the commonly used modified normalized water index. The approach was able to simultaneously distinguish flood-related water and soil moisture from pre-existing water bodies and other spectrally similar classes within the narrow and braided channels of the study site. This was accomplished without the use of post-processing smoothing operations, enabling the important preservation of nuanced inundation patterns. Although flooding beneath moderate and sparse riparian vegetation canopy was captured, dense vegetation cover and paved regions of the floodplain were main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the flood edge. Nevertheless, the unsupervised nature of ICA, in conjunction with the global availability of Landsat imagery, offers a straightforward, robust, and flexible approach to flood mapping that requires no ancillary data for rapid implementation. Finally, the spatial layer of flood extent and a summary of impacts were provided for use in the region’s ongoing hydrologic research and mitigation planning.
Remote Sens.2015, 7(8), 9796-9821; doi:10.3390/rs70809796 (registering DOI) - published 31 July 2015 Show/Hide Abstract
Abstract: We developed a polarimetric coherent electromagnetic scattering model for Poyang Lake wetland vegetation. Realistic canopy structures including curved leaves and the lodging situation of the vegetation were taken into account, and the situation at the ground surface was established using an Advanced Integral Equation Model combined with Oh’s 2002 model. This new model can reasonably describe the coherence effect caused by the phase differences of the electromagnetic fields scattered from different particles by different scattering mechanisms. We obtained good agreement between the modeling results and C-band data from the Radarsat-2 satellite. A simulation of scattering from the vegetation in Poyang Lake showed that direct vegetation scattering and the single-ground-bounce mechanism are the dominant scattering mechanisms in the C-band and L-band, while the effects of the double-ground-bounce mechanism are very small. We note that the curvature of the leaves and the lodging characteristics of the vegetation cannot be ignored in the modeling process. Monitoring soil moisture in the Poyang Lake wetland with the C-band data was not feasible because of the density and depth of Poyang Lake vegetation. When the density of Poyang Lake Carex increases, the backscattering coefficient either decreases or remains stable.
Remote Sens.2015, 7(8), 9769-9795; doi:10.3390/rs70809769 (registering DOI) - published 31 July 2015 Show/Hide Abstract
Abstract: Modifications of human land use and climate change are known to be a threat for the health and proper functioning of tropical wetlands. They interfere with the seasonal flood pulse, which is seen as the most important driver for biodiversity and directly controls evaporation. In order to investigate the impact of local and upstream changes on wetlands, a regional assessment of evaporation is crucial but challenging in such often remote and poorly gauged ecosystems. Evaporation is the major water balance component of these wetlands and links the flood pulse with the ecosystem. It can therefore be seen as a proxy for their functioning. In the last decades, information from space became an important data source to assess remote wetland areas. Here, we developed a new approach to quantify regional evaporation driven by inundation dynamics as its dominant control. We used three water and vegetation indices (mNDWI (modified Normalized Difference Water Index), NDVI (Normalized Difference Vegetation Index), and EVI (Enhanced Vegetation Index)) from MODIS (Moderate Resolution Imaging Spectroradiometer) surface reflectance products to assess regional inundation dynamics between the dry and wet seasons. Two years of continual in situ water level measurements at different locations in our study area, the Pantanal wetland of South America, provided the reference to evaluate our method. With process-based modeling that used the inundation dynamics to determine the water available for evaporation, we were able to estimate actual evaporation (AET) on a regional scale. Relating AET to changes in discharge due to upstream flow modifications and on local precipitation over the last 13 years, we found that the Pantanal is more vulnerable to alternated inundation dynamics than to changes in local precipitation. We concluded that coupling ground- and space-based information in this remote wetland area is a valuable first step to investigate the status of the Pantanal ecosystem.
Remote Sens.2015, 7(8), 9753-9768; doi:10.3390/rs70809753 (registering DOI) - published 31 July 2015 Show/Hide Abstract
Abstract: Biofuels are important alternatives for meeting our future energy needs. Successful bioenergy crop production requires maintaining environmental sustainability and minimum impacts on current net annual food, feed, and fiber production. The objectives of this study were to: (1) determine under-productive areas within an agricultural field in a watershed using a single date; high resolution remote sensing and (2) examine impacts of growing bioenergy crops in the under-productive areas using hydrologic modeling in order to facilitate sustainable landscape design. Normalized difference indices (NDIs) were computed based on the ratio of all possible two-band combinations using the RapidEye and the National Agricultural Imagery Program images collected in summer 2011. A multiple regression analysis was performed using 10 NDIs and five RapidEye spectral bands. The regression analysis suggested that the red and near infrared bands and NDI using red-edge and near infrared that is known as the red-edge normalized difference vegetation index (RENDVI) had the highest correlation (R2 = 0.524) with the reference yield. Although predictive yield map showed striking similarity to the reference yield map, the model had modest correlation; thus, further research is needed to improve predictive capability for absolute yields. Forecasted impact using the Soil and Water Assessment Tool model of growing switchgrass (Panicum virgatum) on under-productive areas based on corn yield thresholds of 3.1, 4.7, and 6.3 Mg·ha−1 showed reduction of tile NO3-N and sediment exports by 15.9%–25.9% and 25%–39%, respectively. Corresponding reductions in water yields ranged from 0.9% to 2.5%. While further research is warranted, the study demonstrated the integration of remote sensing and hydrologic modeling to quantify the multifunctional value of projected future landscape patterns in a context of sustainable bioenergy crop production.
Remote Sens.2015, 7(8), 9727-9752; doi:10.3390/rs70809727 (registering DOI) - published 30 July 2015 Show/Hide Abstract
Abstract: Moisture supply in the Pamir Mountains of Central Asia significantly determines the hydrological cycle and, as a result, impacts the local communities via hazards or socioeconomic aspects, such as hydropower, agriculture and infrastructure. Scarce and unreliable in situ data prevent an accurate assessment of moisture supply, as well as its temporal and spatial variability in this strongly-heterogeneous environment. On the other hand, a clear understanding of climatic and surface processes is required in order to assess water resources and natural hazards. We propose to evaluate the potential of remote sensing and regional climate model (RCM) data to overcome such issues. Difficulties arise for the direct analysis of precipitation if the events are sporadic and when the amounts are low. We hence apply a harmonic time series analysis (HANTS) algorithm to derive spatio-temporal precipitation distributions and to determine regional boundaries delimiting areas where winter or summer precipitation dominate moisture supply. We complement the study with remote sensing-based products, such as temperature, snow cover and liquid water equivalent thickness. We find a strong intra- and inter-annual variability of meteorological parameters that result in strongly variable water budget and water mobilization. Climatic variability and its effects on floods and droughts are discussed for three outstanding years. The in-house developed HANTS toolbox is a promising instrument to unravel periodic signals in remote sensing time series, even in complex areas, such as the Pamir.