The study proposes Secondary Precipitation Estimate Merging using Machine Learning (SPEM2L) algorithms for merging multiple global precipitation datasets to improve the spatiotemporal rainfall characterization. SPEM2L is applied over the Krishna River Basin (KRB), India for 34 years spanning from 1985 to 2018, using daily measurements from three Secondary Precipitation Products (SPPs). Sixteen Machine Learning Algorithms (MLAs) were applied on three SPPs under four combinations to integrate and test the performance of MLAs for accurately representing the rainfall patterns. The individual SPPs and the integrated products were validated against a gauge-based gridded dataset provided by the Indian Meteorological Department. The validation was applied at different temporal scales and various climatic zones by employing continuous and categorical statistics. Multilayer Perceptron Neural Network with Bayesian Regularization (NBR) algorithm employing three SPPs integration outperformed all other Machine Learning Models (MLMs) and two dataset integration combinations. The merged NBR product exhibited improvements in terms of continuous and categorical statistics at all temporal scales as well as in all climatic zones. Our results indicate that the SPEM2L procedure could be successfully used in any other region or basin that has a poor gauging network or where a single precipitation product performance is ineffective.

The Central Indian Ocean, namely the Central Indian, Crozet, and Madagascar basins, formed by rifting and subsequent drifting of India (now Capricorn), Antarctica, and Africa (now Somalia). We gathered a comprehensive set of sea surface magnetic anomaly profiles over these basins and revisited location and identification of magnetic isochrons between C34ny (83.0 Ma) and C20ny (42.536 Ma) using the objective analytic signal technique. We present high‐resolution magnetic isochrons for 29 periods based on ~1,400 magnetic anomaly picks. From the conjugate sets of picks, we derive two‐plate finite rotation parameters for both the Capricorn‐Antarctica and Capricorn‐Somalia motions. These finite rotations are compared to three‐plate reconstructions of the plate boundaries between the Capricorn, Antarctica, and Somalia plates, constrained by the closure of the Indian Ocean Triple junction. In general, the three‐plate reconstructions slightly overrotate the reconstructed isochrons with respect to the Capricorn‐Antarctica and Capricorn‐Somalia two‐plate reconstructions. Conversely, the two‐plate reconstructions for Somalia‐Antarctica slightly underrotate the isochrons compared to the Capricorn‐Antarctica‐Somalia three‐plate reconstructions. We suspect that the discrepancies between the two‐plate and three‐plate methods result from the recent seafloor deformation in the Capricorn‐India diffuse plate boundary and/or from the contrasted nature and geometry of magnetic isochrons at different spreading rates (i.e., magnetic structure of the three spreading centers). Three‐plate reconstructions better constrain the closure of the triple junction but spread any misfit among all three plate boundaries. When enough quality data are available, two‐plate reconstructions may lead to more realistic plate motion estimates from which additional geological problems can be identified and solved.

Estimation of Chlorophyll-a (Chl-a) concentration from remote-sensing reflectance derived from satellite imagery is highly effective in monitoring the spatial and temporal optical variations in water bodies. In turbid, productive and inland waters, implementation of traditional Chl-a estimation algorithms using blue and green wavelength bands result in higher error in estimated Chl-a. To minimize the absorption and scattering effects of other water constituents like colored dissolved organic matter and particulate matter, the wavelengths bands in red and Near Infrared (NIR) are generally used for estimating Chl-a concentration in productive waters. Owing to a widely observed optical variability in natural waters, a single universal algorithm is not applicable to all waterbodies, hence, regional tuning of algorithms is necessary for estimating Chl-a in turbid and inland productive waters. Here, a 2-band based algorithm of the form $R_{rs}({\lambda }_1)/R_{rs}({\lambda }_2)$ and a 3-band algorithm of form $\left[R_{rs}^{-1}\left({\lambda }_1\right)-R_{rs}^{-1}\left({\lambda }_2\right)\right] x R_{rs}\left({\lambda }_3\right)$ were tuned for Chl-a estimation based on field measurements carried out in three water bodies, Chilika Lake of Orissa, Ujjani Reservoir of Maharashtra, Vallabh Sagar Reservoir of Gujarat, India, wherein the observed Chl-a concentration range is 5 - 33 mg/m³. Together with measured in situ reflectance spectra and concurrent Chl-a concentrations, tuning of red-NIR algorithms is performed. The optimal wavelengths to estimate Chl-a found by the proximal sensing method are ${\lambda }_1$ = 691 nm and ${\lambda }_2$ = 667 nm in case of 2-band red-NIR type algorithm. For 3-band red-NIR algorithm, the optimal wavelengths are ${\lambda }_1$ = 670 nm, ${\lambda }_2$ = 696 nm and ${\lambda }_3$ = 740 nm. The mean absolute percentage error (MAPE) and root-mean-square error (RMSE) values obtained for the estimated Chl-a using the tuned 2-band red-NIR algorithm are 29.9% and 5.35 $mg/m^3$, respectively. Similarly, RMSE and MAPE values for Chl-a concentration estimated using 3-band tuned red-NIR algorithm are 4.24 $mg/m^3$ and 22.5%, respectively. The tuned 2-band and 3-band algorithms resulted in better Chl-a estimates than fourteen blue-green and red-NIR algorithms from previous studies. The 2-band and 3-band algorithms were re-calibrated to wavelengths present in ocean land color imager (OLCI) sensor on Sentinel-3 satellite to monitor these water bodies. Spatio-temporal analysis of OLCI imagery derived Chl-a concentrations over Chilika lake (2019 and 2020) indicated seasonal variations. The tuned 3-band red-NIR algorithm exhibited robust performance in all seasons despite wide variations in colored dissolved organic matter and suspended sediments. The tuned red-NIR algorithms are useful in monitoring inland turbid productive waters with upcoming missions such as Plankton, Aerosol, Cloud Ecosystem (PACE) and Oceansat-3.