Since the launch of the Landsat-8 (L8) Operational Land Imager (OLI) on 11 February 2013, there has been a continuous effort to produce reliable ocean color products by taking the advantages of its medium spatial resolution (30 m) and higher Signal to Noise Ratio (SNR). A Provisional Aquatic Reflectance product for the L8 OLI (L8PAR) has been recently released to the public to explore its potential for ocean color applications. This study used a six-year data record of L8 for development of a regionally tuned algorithm (RTA20) for estimating Chlorophyll-a (Chl-a) concentrations around the complex coastal environment of Hong Kong, and is the first to report the usability of the L8PAR product for coastal areas. Furthermore, this study validated three previously developed algorithms, namely RTA16, RTA17 and RTA19, and two ocean color algorithms (OC2 and OC3) modified for L8 OLI by NASA’s Ocean Color group. Results indicate that the newly released L8PAR product has a high potential for estimating the coastal water Chl-a concentrations with higher detail and higher accuracy than previously. The RTA20 algorithm developed in this study outperformed the previous algorithms (RTA16, RTA17, RTA19, OC2 and OC3), e.g., with lower values for Root Mean Square Error (RMSE; 0.92 mg/m³), bias (−0.26 mg/m³) and mean ratio (1.29). Although inferior to the RTA20, the OC2 algorithm also performed well in terms of Pearson’s correlation coefficient (r; 0.84), slope (6.87) and intercept (−8.44) while for RTA20 the values for r, slope and intercept were 0.96, 0.77 and 0.27, respectively. This preliminary evaluation reveals that the OC2 algorithm can be used as an operational algorithm for L8 Chl-a product generation for global coastal areas while RTA20 can be used as a regional algorithm for the routine monitoring of Chl-a concentrations around the coastal areas of Hong Kong or for coastal areas with similar water quality elsewhere in the world.

The Aerosol Optical Depth (AOD) from several to tens of kilometres coarse resolution satellite images such as MODIS and VIIRS sensors is a standard geophysical product that has gained significant importance for atmospheric pollution and climate studies. In regional studies, coarser-resolution limits the application of these products. AOD retrieval is highly challenging due to subjected high complex surface characteristics and dynamics aerosol properties. Many methods have been developed based on the state-of-the-art radiative transfer (RT) model or the Look-Up-Table (LUT) approach, which is very time-consuming. Therefore, in this paper, we proposed the integration of two simplified algorithms for retrieving AOD from Landsat 8 Images; one is Simplified and Robust Surface Reflectance Estimation Method (SREM), and the second is Simplified Aerosol Retrieval Algorithm (SARA). SREM has been used for the estimation of LSR from top-of-atmospheric reflectance (TOA) with support of geolocation information, which is one of the key input in SARA for AOD retrievals from TOA. Both simplified algorithms are developed based on the RT equation without using a LUT approach, which makes them fast and robust in their inherent retrieval processes. The method is validated using Aerosol Robotic Network (AERONET) measurements over two distinct locations, Beijing (China) and Indo Gangetic Plain (IGP) (India, Bangladesh and Nepal foothills). For cross-evaluation of SREM LSR in AOD retrieval, available LSR products LaSRC (Landsat 8 Surface Reflectivity Code) have been taken as input to SARA. Also, the results of the SREM-SARA algorithm have been evaluated with a collection of 6 (C6) MODIS MOD04_3k products at 3 km spatial resolution. The performance of this algorithm is evaluated with four statistical metrics: correlation coefficient (R2), root means square error (RMSE), mean absolute error (MAE) and expected error (EE). The 30 m AOD retrieved from the SREM-SARA algorithm showed high consistency with AERONET AOD measurements, with R2  ~ 0.98, and that approximately 97.44% of the retrievals fall within the EE with a low RMSE of 0.072 and MAE of 0.037 over the Beijing area. However, in the IGP region, surface features distributed consist of various land covers with high reflective surfaces and complex aerosol type distribution; SREM-SARA-derived AOD showed relatively high agreement with AERONET measurements, with an R2~ 0.90, RMSE~0.168, and MAE~0.141 compared to the LaSRC corrected LSR, where LaSRC-SARA showed R2 ~ 0.61, RMSE~0.298, and MAE~0.250. Comparison of SREM-SARA retrieved AOD with MOD04_3k revealed that the retrieved AOD agree quite well with MODIS C6 products (spatial  R2<= 0.80). In terms of spatial coverage, the MODIS product has not as good as the SREM-SARA AOD. These results suggested the robustness of the combination of SREM-SARA and the potential for effective in retrieving AOD at the finer scale resolution that holds the impression of the localized process in the atmospheric pollution and thereby lays out a way to study the atmosphere and climate interaction at the finer scale. 

The problem of particulate matter concentration, especially PM2.5 and PM10, is a major concern for all million-plus cities worldwide. Depending upon the scale of implementation of the available technologies and emission reduction policies, the levels of these particulate matter vary over time and space. In this study, we evaluated the effect of wetlands in reducing the concentration of PM2.5 in the air over one of the highest polluted cities in the world, New Delhi, in the Indian region. The PM2.5 was modeled considering the distance-to-wetlands - from the in-situ pollution monitoring stations - as an influential factor, including other determining environmental covariates such as meteorological parameters, atmospheric optical parameters, surface greenness, and land use and land cover (LULC) type (Experiment Set 1). We also conducted a similar experimental setup to build a predictive model excluding the variable distance-to-wetlands (Experiment Set 2). The data of PM2.5 from 21 monitoring stations and all other covariates corresponding to these stations were collected at a daily temporal scale from January 2016 to August 2019. Due to the complexity of the relationships as well as the distribution patterns of all independent variables, a series of machine learning (ML) and artificial intelligence (AI) based analytics, such as random forest (RF), gradient boosting (GB), support vector machine (SVM), and artificial neural net (ANN) regression, were used to model the PM2.5 at monthly and seasonal time scale spatially. All these AI/ML models were trained on 70% of the observations through a random selection. The remaining 30% of the data was used for evaluating the models’ performance. The performances of the models were then compared for both sets of experimental setups. The statistics for model performance diagnostic shows a higher R2 for RF-regression than other AI/ML regression models at the training stage under both sets of experimental setups (R2 ≥ 0.69 for Experiment Set 1; R2 ≤ 0.66 for Experiment Set 2 under RF regression; R2 ≤ 0.64 for other models under both experimental setups). The variable influence score (VIS) under RF-regression manifests that the proximity of wetlands is important than the variation of precipitation and LULC type (VIS: 4.12% for distance-to-wetlands, VIS: 2.61%, and 0.24% for precipitation and LULC type, respectively). The predictability of the RF-regression model, while evaluated with the test data, shows R2 ~ 0.66, with RMSE of 80.4 µg m-3 for the Experimental Set 1, and R2 ~ 0.63 with RMSE of 83.7 µg m-3 for the Experimental Set 2. It was noticed from the analysis that within a 1000 m buffer distance from the wetlands, the concentration of PM2.5 remains relatively lower than a distance greater than 1000 m. Such difference is benign, yet the key factor behind such a benign effect is related to the variation in surface area of the wetlands. Larger wetlands may have a distant impact on keeping the PM2.5 low. The study, thus, concludes that restoring the wetlands might be one of the practical solutions to keep the PM level within the ambit of NAAQ standards.