On the darkest pixel atmospheric correction algorithm: a revised procedure applied over satellite remotely sensed images intended for environmental applications

Atmospheric correction is an essential part of the pre-processing of satellite remote sensing data. Several atmospheric correction approaches can be found in the literature ranging from simple to sophisticated methods. The sophisticated methods require auxiliary data, however the simple methods are based only on the image itself and are served to be suitable for operational use. One of the most widely used and well-known simple atmospheric correction methods is the darkest pixel (DP). Despite of its simplicity, the user must be aware of several key points in order to avoid any erroneous results. Indeed, this paper addresses a new strategy for selecting the suitable dark object based on the proposed analysis of digital number histograms and image examination. Several case studies, in which satellite remotely sensed image data intended for environmental applications have been atmospherically corrected using the DP method, are presented in this article

Comparison of GPM IMERG and TRMM 3B43 Products over Cyprus

Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) high-resolution product and Tropical Rainfall Measuring Mission (TRMM) 3B43 product are validated against rain gauges over the island of Cyprus for the period from April 2014 to June 2018. The comparison performed is twofold: firstly, the Satellite Precipitation (SP) estimates are compared with the gauge stations’ records on a monthly basis and, secondly, on an annual basis. The validation is based on ground data from a dense and well-maintained network of rain gauges, available in high temporal (hourly) resolution. The results show high correlation coefficient values, on average reaching 0.92 and 0.91 for monthly 3B43 and IMERG estimates, respectively, although both IMERG and TRMM tend to underestimate precipitation (Bias values of −1.6 and −3.0, respectively), especially during the rainy season. On an annual basis, both SP estimates are underestimating precipitation, although IMERG estimates records (R = 0.82) are slightly closer to that of the corresponding gauge station records than those of 3B43 (R = 0.81). Finally, the influence of elevation of both SP estimates was considered by grouping rain gauge stations in three categories, with respect to their elevation. Results indicated that both SP estimates underestimate precipitation with increasing elevation and overestimate it at lower elevations

Accuracy assessment of atmospheric correction algorithms using sunphotometers (AERONET), LIDAR system and in-situ spectroradiometers

Atmospheric correction is still considered as the most important part of pre-processing of satellite remotely sensed images. The accuracy assessment of the existing atmospheric correction must be monitored on a systematic basis since the user must be aware about the effectiveness of each algorithm intended for specific application. Indeed this study integrates the following measurements coincided with the satellite overpass (ASTER and Landsat TM/ETM+) in order to assess the accuracy of the most widely used atmospheric correction algorithms (such as darkest pixel, atmospheric modelling, ATCOR, 6S code etc.): spectroradiometric measurements of suitable calibration targets using GER1500 or SVC HR-1024 field spectro-radiometers, MICROTOPS hand held sun-photometers, LIDAR backscattering system, CIMEL sun photometer (Cyprus University of Technology recently joined with AERONET

Particulate matter estimation from photochemistry: a modelling approach using neural networks and synoptic clustering

We report on the development and validation of a neural network (NN) model of PM10 concentrations in terms of photochemical measurements of NO, NO_2 and O_3 and temporal parameters that include the day of the week and the day of the year with its sinusoidal variation. A long-term record (≈10 yr) from 2001–2012 (inclusive) assembled from measurements taken at 10 station nodes in the air quality monitoring network of the Greater Athens Area in Greece has been used. Eight synoptic categorizations of the circulation at 850 hPa were used to partition the data record, and to train individual NNs with Bayesian regularization using 90% of available data for different atmospheric conditions. The time series of PM10 estimates was then reconstructed from the partitioned output. As a control, a NN without synoptic clustering was trained on the same data. The remaining 10% of the data was used for testing the simulation performance. NN models with synoptic clustering achieved an average root mean square error (RMSE) ≈ 16 µg/m^3 across the station nodes with an average index of agreement (IA) of 0.71 (somewhat better than the control network whose performance statistics were RMSE ≈ 17 µg/m^3 and IA = 0.61, respectively). For routine measurements below the EU Air Quality Directive limit value of 50 µg/m^3, the average error is as low as RMSE ≈ 11 µg/m^3 across the station nodes. NN models were found to strongly outperform analogous MLR models over all station nodes