Evaluation of Landsat-8 and Sentinel-2A Aerosol Optical Depth Retrievals Across Chinese Cities and Implications for Medium Spatial Resolution Urban Aerosol Monitoring

In urban environments, aerosol distributions may change rapidly due to building and transport infrastructure and human population density variations. The recent availability of medium resolution Landsat-8 and Sentinel-2 satellite data provide the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. AOD retrieved from 30 m Landsat-8 and 10 m Sentinel-2A data using the Land Surface Reflectance Code (LaSRC) were compared with coincident ground-based Aerosol Robotic Network (AERONET) Version 3 AOD data for 20 Chinese cities in 2016. Stringent selection criteria were used to select contemporaneous data; only satellite and AERONET data acquired within 10 min were considered. The average satellite retrieved AOD over a 1470 m1470 m window centered on each AERONET site was derived to capture fine scale urban AOD variations. AERONET Level 1.5 (cloud-screened) and Level 2.0 (cloud-screened and also quality assured) data were considered. For the 20 urban AERONET sites in 2016 there were 106 (Level 1.5) and 67 (Level 2.0) Landsat-8 AERONET AOD contemporaneous data pairs, and 118 (Level 1.5) and 89 (Level 2.0) Sentinel-2A AOD data pairs. The greatest AOD values (>1.5) occurred in Beijing, suggesting that the Chinese capital was one of the most polluted cities in China in 2016. The LaSRC Landsat-8 and Sentinel-2A AOD retrievals agreed well with the AERONET AOD data (linear regression slopes > 0.96; coefficient of determination r(exp 2) > 0.90; root mean square deviation < 0.175) and demonstrate that the LaSRC is an effective and applicable medium resolution AOD retrieval algorithm over urban environments. The Sentinel-2A AOD retrievals had better accuracy than the Landsat-8 AOD retrievals, which is consistent with previously published research.The implications of the research and the potential for urban aerosol monitoring by combining the freely available Landsat-8 and Sentinel-2 satellite data are discussed

Computationally Inexpensive Landsat 8 Operational Land Imager (OLI) Pansharpening

Pansharpening algorithms fuse higher spatial resolution panchromatic with lower spatial resolution multispectral imagery to create higher spatial resolution multispectral images. The free-availability and systematic global acquisition of Landsat 8 data indicate an expected need for global coverage and so computationally efficient Landsat 8 pansharpening. This study adapts and evaluates the established, and relatively computationally inexpensive, Brovey and context adaptive Gram Schmidt component substitution (CS) pansharpening methods for application to the Landsat 8 15 m panchromatic and 30 m red, green, blue, and near-infrared bands. The intensity images used by these CS pansharpening methods are derived as a weighted linear combination of the multispectral bands in three different ways using band spectral weights set (i) equally as the reciprocal of the number of bands; (ii) using fixed Landsat 8 spectral response function based (SRFB) weights derived considering laboratory spectra; and (iii) using image specific spectral weights derived by regression between the multispectral and the degraded panchromatic bands. The spatial and spectral distortion and computational cost of the different methods are assessed using Landsat 8 test images acquired over agricultural scenes in South Dakota, China, and India. The results of this study indicate that, for global Landsat 8 application, the context adaptive Gram Schmidt pansharpening with an intensity image defined using the SRFB spectral weights is appropriate. The context adaptive Gram Schmidt pansharpened results had lower distortion than the Brovey results and the least distortion was found using intensity images derived using the SRFB and image specific spectral weights but the computational cost using the image specific weights was greater than the using the SRFB weights. Recommendations for large area Landsat 8 pansharpening application are described briefly and the SRFB spectral weights are provided so users may implement computationally inexpensive Landsat 8 pansharpening themselves

Demonstration of Large Area Land Cover Classification with a One Dimensional Convolutional Neural Network Applied to Single Pixel Temporal Metric Percentiles

Over large areas, land cover classification has conventionally been undertaken using satellite time series. Typically temporal metric percentiles derived from single pixel location time series have been used to take advantage of spectral differences among land cover classes over time and to minimize the impact of missing observations. Deep convolutional neural networks (CNNs) have demonstrated potential for land cover classification of single date images. However, over large areas and using time series their application is complicated because they are sensitive to missing observations and they may misclassify small and spatially fragmented surface features due to their spatial patch-based implementation. This study demonstrates, for the first time, a one-dimensional (1D) CNN single pixel time series land classification approach that uses temporal percentile metrics and that does not have these issues. This is demonstrated for all the Conterminous United States (CONUS) considering two different 1D CNN structures with 5 and 8 layers, respectively. CONUS 30 m land cover classifications were derived using all the available Landsat-5 and -7 imagery over a seven-month growing season in 2011 with 3.3 million 30 m land cover class labelled samples extracted from the contemporaneous CONUS National Land Cover Database (NLCD) 16 class land cover product. The 1D CNNs and, a conventional random forest model, were trained using 10%, 50% and 90% samples, and the classification accuracies were evaluated with an independent 10% proportion. Temporal metrics were classified using 5, 7 and 9 percentiles for each of five Landsat reflective wavelength bands and their eight band ratios. The CONUS and detailed 150 × 150 km classification results demonstrate that the approach is effective at scale and locally. The 1D CNN classification land cover class boundaries were preserved for small axis dimension features, such as roads and rivers, with no stripes or anomalous spatial patterns. The 8-layer 1D CNN provided the highest overall classification accuracies and both the 5-layer and 8-layer 1D CNN architectures provided higher accuracies than the random forest by 1.9% - 2.8% which as all the accuracies were \u3e 83% is a meaningful increase. The CONUS overall classification accuracies increased marginally with the number of percentiles (86.21%, 86.40%, and 86.43% for 5, 7 and 9 percentiles, respectively) using the 8-layer 1D-CNN. Class specific producer and user accuracies were quantified, with lower accuracies for the developed land, crop and pasture/hay classes, but no systematic pattern among classes with respect to the number of temporal percentiles used. Application of the trained model to a different year of CONUS Landsat ARD showed moderately decreased accuracy (80.79% for 7 percentiles) that we illustrate is likely due to different intra-annual surface variations between years. These encouraging results are discussed with recommended research for deep learning using temporal metric percentiles

Variación espacial y estacional de Cianobacterias y sus tasas de fijación de nitrógeno en la Bahía de Sanya en el Sur del Mar de China

The nitrogen fixation rates of planktonic and intertidal benthic cyanobacteria were investigated in Sanya Bay from 2003 to 2005. Trichodesmium thiebautii was the dominant species of planktonic cyanobacteria during our study. Significant seasonal and spatial variations in Trichodesmium spp. abundance were observed (P&lt;0.01). The highest Trichodesmium concentrations occurred during intermonsoon periods and in the outer region of Sanya Bay (Outer Bay stations). At fixed station S03 the abundance of T. thiebautii ranged from 1.14&times;103 to 2060&times;103 trichomes m–2, with an annual mean of 273&times;103 trichomes m–2. The average nitrogen fixation rate per colony of T. thiebautii was 0.27 nmol N h-1 colony-1 and it did not show any obvious seasonal variations. Nitrogen fixation by planktonic cyanobacteria was highest in the Outer Bay stations, where the estimated amount of new nitrogen introduced by Trichodesmium contributed 0.03 to 1.63% of the total primary production and up to 11.64% of the new production. Statistical results showed that significant seasonal and spatial variations of nitrogen fixation rates were found among the intertidal communities. The main benthic nitrogen-fixing cyanobacteria were identified as members of the genera Anabaena, Calothrix, Lyngbya, Nostoc and Oscillatoria. The highest nitrogen fixation rate was found in microbial mats and the lowest in reefs and rocky sediments. All the benthic communities studied presented their highest nitrogen fixation activity in summer, with an average nitrogen fixation rate of 33.31 &micro;mol N h-1 m-2, whereas the lowest nitrogen activity was detected in winter, with an average nitrogen fixation rate of 5.66 &micro;mol N h-1 m-2. A Pearson correlation analysis indicated that the nitrogen fixation rate of three types of intertidal communities was significantly positively correlated to seawater temperature (P&lt;0.05), whereas only the nitrogen fixation rate of the reefs and rock communities was significantly negatively correlated to seawater salinity (P&lt;0.05).Las tasas de fijación de nitrógeno de cianobacterias intermareales y bentónicas fueron investigadas en la Bahía de Sanya, desde 2003 a 2005. Trichodesmium thiebautii era la especie dominante de las cianobacterias planctónicas durante nuestra investigación. Se observaron variaciones espaciales y estacionales significativas (P&lt;0.01) en la abundancia de Trichodesmium spp. La concentración más elevada de Trichodesmium se observó durante los períodos de intermonzón y en la región exterior de la Bahía de Sanya (estaciones fuera de la Bahía). En la estación fija S03 la abundancia de T. thiebautii variaba desde 1.14×103 a 2060×103 tricomas m–2, con una media anual de 273×103 tricomas m–2. El promedio de la tasa de fijación de nitrógeno por colonia de T. thiebautii era de 0.27 nmol N h-1 colonia y no mostraba una clara variación estacional. La fijación de nitrógeno por las cianobacterias planctónicas era superior en las estaciones de fuera de la Bahía, donde la cantidad estimada de nitrógeno nuevo introducido por Trichodesmium contribuía del 0.03 al 1.63% del total de la producción primaria y hasta el 11.64% de la producción nueva. Estadísticamente los resultados mostraban que las variaciones espaciales y estacionales significativas de las tasas de fijación de nitrógeno fueron encontradas entre las comunidades intermareales. Las principales cianobacterias bentónicas fijadoras de nitrógeno fueron identificadas como miembros de los géneros Anabaena, Calothrix, Lyngbya, Nostoc y Oscillatoria. La tasa de fijación de nitrógeno más alta fue encontrada en los tapetes microbianos y las más bajas en los arrecifes y sedimentos rocosos. Todas las comunidades bentónicas estudiadas presentaban la mayor actividad de fijación de nitrógeno en verano, con un promedio de tasas de fijación de 33.31 ?mol N h-1 m-2, mientras que la menor actividad de fijación de nitrógeno fue detectada en invierno, con un promedio de 5.66 ?m N h-1 m-2. Análisis de correlación (Pearson) indicaban que las tasas de fijación de nitrógeno en los tres tipos de comunidades intermareales estaban significativamente correlacionados con la temperatura del agua (P&lt;0.05). Mientras que la tasa de fijación de nitrógeno de las comunidades de los arrecifes y sedimentos rocosos estaban correlacionadas significativamente con la salinidad del agua de mar (P&lt;0.05)

XRoute Environment: A Novel Reinforcement Learning Environment for Routing

Routing is a crucial and time-consuming stage in modern design automation flow for advanced technology nodes. Great progress in the field of reinforcement learning makes it possible to use those approaches to improve the routing quality and efficiency. However, the scale of the routing problems solved by reinforcement learning-based methods in recent studies is too small for these methods to be used in commercial EDA tools. We introduce the XRoute Environment, a new reinforcement learning environment where agents are trained to select and route nets in an advanced, end-to-end routing framework. Novel algorithms and ideas can be quickly tested in a safe and reproducible manner in it. The resulting environment is challenging, easy to use, customize and add additional scenarios, and it is available under a permissive open-source license. In addition, it provides support for distributed deployment and multi-instance experiments. We propose two tasks for learning and build a full-chip test bed with routing benchmarks of various region sizes. We also pre-define several static routing regions with different pin density and number of nets for easier learning and testing. For net ordering task, we report baseline results for two widely used reinforcement learning algorithms (PPO and DQN) and one searching-based algorithm (TritonRoute). The XRoute Environment will be available at https://github.com/xplanlab/xroute_env.Comment: arXiv admin note: text overlap with arXiv:1907.11180 by other author

Landsat 5 Thematic Mapper Reflectance and NDVI 27-year Time Series Inconsistencies Due to Satellite Orbit Change

The Landsat 5 Thematic Mapper (TM) sensor provided the longest single mission terrestrial remote sensing data record but temporally sparse station keeping maneuvers meant that the Landsat 5 orbit changed over the 27 year mission life. Long-term Landsat 5 TM reflectance inconsistencies may be introduced by orbit change induced solar zenith variations combined with surface reflectance anisotropy, commonly described by the Bi-directional Reflectance Distribution Function (BRDF). This study quantifies the local overpass time and observed solar zenith angle changes for all the Landsat 5 TM images available at two latitudinally separated locations along the same north-south Landsat path (27) in Minnesota (row 26) and Texas (row 42). Over the 27 years the Landsat 5 orbit changed by nearly 1 h and resulted in changes in the Landsat 5 observed solar zenith angle of N10°. The Landsat 5 orbit was relatively stable from 1984 to 1994 and from 2007 to 2011, but changed rapidly from 1995 to 2000, and from 2003 to 2007. Rather than directly examine Landsat 5 TM reflectance time series a modelling approach was used. This was necessary because unambiguous separation of orbit change induced Landsat reflectance variations from other temporal variations is non-trivial. The impact of Landsat 5 orbit induced observed solar zenith angle variations on the red and near-infrared reflectance and derived normalized difference vegetation index (NDVI) values were modelled with respect to different Moderate-Resolution Imaging Spectroradiometer (MODIS) BRDF land cover types. Synthetic nadir BRDF-adjusted reflectance (NBAR) for the Landsat 5 TM observed and a modelled reference year 2011 solar zenith were compared over the 27 years of acquisitions. Ordinary least squares linear regression fits of the NBAR difference values as a function of the acquisition date indicated an increasing trend in red and near-infrared NBAR and a decreasing trend in NDVI NBAR due to orbit changes. The trends are statistically significant but small, no more than 0.0006 NDVI/year. Comparison of certain years of Landsat 5 data may result in significant reflectance and NDVI differences due only to Landsat 5 orbit changes and cause spurious detection of “browning” vegetation events and underestimation of greening trends. The greatest differences will occur when 1995 Landsat 5 TM data are compared with 2007 to 2011 data; NDVI values could be up to 0.11 greater in 1995 than in 2011 for anisotropic land cover types and up to 0.05 greater for average CONUS land cover types. A smaller number of Landsat 5 TM images were also examined and provide support for the modelled based findings. The paper concludes with a discussion of the implications of the research findings for Landsat 5 TM time series analyses

Using the 500 m MODIS Land Cover Product to Derive a Consistent Continental Scale 30 m Landsat Land Cover Classification

Classification is a fundamental process in remote sensing used to relate pixel values to land cover classes present on the surface. Over large areas land cover classification is challenging particularly due to the cost and difficulty of collecting representative training data that enable classifiers to be consistent and locally reliable. A novel methodology to classify large volume Landsat data using high quality training data derived from the 500 m MODIS land cover product is demonstrated and used to generate a 30 m land cover classification for all of North America between 20°N and 50°N. Publically available 30 m global monthly Web-enabled Landsat Data (GWELD) products generated from every available Landsat 7 ETM+ and Landsat 5 TM image for a three year period, that are defined aligned to the MODIS land products and are consistently pre-processed data (cloud-screened, saturation flagged, atmospherically corrected and normalized to nadir BRDF adjusted reflectance), were classified. The MODIS 500 m land cover product was filtered judiciously, using only good quality pixels that did not change land cover class in 2009, 2010 or 2011, followed by automated selection of spatially corresponding 30 m GWELD temporal metric values, to define a large training data set sampled across North America. The training data were sampled so that the class proportions were the same as the North America MODIS land cover product class proportions and corresponded to 1% of the 500 m and b0.005% of the 30 m pixels. Thirty nine GWELD temporal metrics for every 30 m North America pixel location were classified using (a) a single random forest, and (b) a locally adaptive method with a random forest classifier derived and applied locally and the classification results spatially mosaicked together. The land cover classification results appeared geographically plausible and at synoptic scale were similar to the MODIS land cover product. Detailed visual inspection revealed that the locally adaptive random forest classifications and associated classification confidences were generally more coherent than the single random forest classification results. The level of agreement between the 30 m classifications and the MODIS land cover product derived training data was assessed by bootstrapping the random forest implementation. The locally adaptive random forest classification had higher overall agreement (95.44%, 0.9443 kappa) than the single random forest classification (93.13%, 0.9195 kappa). The paper concludes with a discussion of future research including the potential for automated global land cover classification

An Automated Approach for Sub-Pixel Registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery

Moderate spatial resolution satellite data from the Landsat-8 OLI and Sentinel-2A MSI sensors together offer 10 m to 30 m multi-spectral reflective wavelength global coverage, providing the opportunity for improved combined sensor mapping and monitoring of the Earth’s surface. However, the standard geolocated Landsat-8 OLI L1T and Sentinel-2A MSI L1C data products are currently found to be misaligned. An approach for automated registration of Landsat-8 OLI L1T and Sentinel-2A MSI L1C data is presented and demonstrated using contemporaneous sensor data. The approach is computationally efficient because it implements feature point detection across four image pyramid levels to identify a sparse set of tie-points. Area-based least squares matching around the feature points with mismatch detection across the image pyramid levels is undertaken to provide reliable tie-points. The approach was assessed by examination of extracted tie-point spatial distributions and tie-point mapping transformations (translation, affine and second order polynomial), dense-matching prediction-error assessment, and by visual registration assessment. Two test sites over Cape Town and Limpopo province in South Africa that contained cloud and shadows were selected. A Landsat-8 L1T image and two Sentinel-2A L1C images sensed 16 and 26 days later were registered (Cape Town) to examine the robustness of the algorithm to surface, atmosphere and cloud changes, in addition to the registration of a Landsat-8 L1T and Sentinel-2A L1C image pair sensed 4 days apart (Limpopo province). The automatically extracted tie-points revealed sensor misregistration greater than one 30 m Landsat-8 pixel dimension for the two Cape Town image pairs, and greater than one 10 m Sentinel-2A pixel dimension for the Limpopo image pair. Transformation fitting assessments showed that the misregistration can be effectively characterized by an affine transformation. Hundreds of automatically located tie-points were extracted and had affine-transformation root-mean-square error fits of approximately 0.3 pixels at 10 m resolution and dense-matching prediction errors of similar magnitude. These results and visual assessment of the affine transformed data indicate that the methodology provides sub-pixel registration performance required for meaningful Landsat-8 OLI and Sentinel-2A MSI data comparison and combined data applications