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Phytoplankton community response to extreme climate variability has an important effect on the regional marine ecosystem. The 2015–2016 El Niño event has been the strongest El Niño event on record of the century, which has a high chance of impacting the most productive ecological zone like the Arabian Sea. In this study, we investigated the influence of the 2015–2016 El Niño on the seasonal pattern and its impact on the phytoplankton size class (PSC) distribution in the Arabian Sea based on monthly satellite PSC observations and physical observations from various sources from 2003 to 2019. Results showed that the 2015–2016 El Niño event influenced the physical drivers and consequently weakened the monsoonal pattern that led to the enhancement or inhibition of specific PSC in the Arabian Sea. During the 2015 summer monsoon, the intensity of summer upwelling was weaker than normal, which caused a reduction in the abundance of microplankton to ~8% and an increase in the abundance of picoplankton to ~12%. Similarly, during the 2016 winter monsoon, the weakening of convective mixing and the formation of warm core eddy led to a reduction in the abundance of microplankton to ~6% and an increase in the abundance of picoplankton to ~9%. However, the nanoplankton distribution was observed stable with no significant response to the extreme physical condition due to its ubiquitous nature. The effect of the 2015–2016 El Niño event altered the physical conditions and associated specific PSC distribution in the Arabian Sea. Notably, the distinct response of each PSC to the changing climate and their environmental adaptation was identified in this study. Our results indicated that the phytoplankton community structure and associated marine ecosystem are sensitive to the effects of climate change. [ABSTRACT FROM AUTHOR]

Short-term events like tropical cyclones often have a strong impact on the upper ocean, causing transient changes in the physical and chemical properties of the sea. These changes could potentially affect the distribution pattern of the phytoplankton community. The present study aims to investigate the impact of the distribution of phytoplankton size classes (PSCs) caused by the tropical cyclonic event Ockhi in the Arabian Sea (AS) in 2017. Multiple datasets, including satellite remote sensing and reanalysis data, were utilized to observe the biogeochemical variability and the corresponding changes in PSCs distribution caused by the Ockhi cyclone event. One of the predominant issues in using satellite remote sensing data is the loss of data due to overcast conditions, which was resolved by reconstructing the chl-a and SST using an empirical orthogonal function. The results show a short-term shift in the PSCs pattern from the pico and nano-rich region to the micro-dominated environment in the cyclone passage region during the post-cyclone period. When comparing the results with the physical and biochemical parameters, it was found that the cyclonic event triggered the deep-seated nutrients to the surface, which promoted favourable conditions for microorganisms to proliferate for a short span in the cyclone passage region. The overall study highlights the impact of the Ockhi cyclone on biogeochemical changes and corresponding shifts in the phytoplankton size distribution along the cyclone path. [ABSTRACT FROM AUTHOR]

The present study has been carried out to analyze the seasonal variation of the Urban Heat and Cool Island over the nine developing cities of India. The magnitude of urban heat/cool island and vegetation gradient (∆NDVI) were measured from the daytime satellite datasets. Results of this study show that during the pre-monsoon (March to May) season, the maximum magnitude of the Surface Urban Heat Island (SUHI) was experienced over Kolhapur city, whereas, in the winter, the highest intensity of SUHI was noticed over Pune city. Subsequently, outcomes also depict that the changes in ∆NDVI restrain the pre-monsoon means and the seasonal alterations in SUHI magnitude. However, during the winter (November to February) season, it is controlled by the temperature–vegetation conditions of the rural areas. For pre-monsoon and seasonal changes in SUHI, with the aid of ∆NDVI and the surface temperature of the urban area, regression equations were fitted for pre-monsoon and seasonal changes in SUHI, which explains nearly 90% of SUHI variation. Similarly, the variation of SUHI has been modeled for winter, which elucidates up to 84% of SUHI discrepancy. The study reveals that, on a seasonal scale, a decrement of 0.1 in seasonal ∆NDVI leads to an increase in the seasonal intensity of SUHI by 1.74 °C, which is quite a significant augmentation. [ABSTRACT FROM AUTHOR]

In recent years, semi-arid regions of India, especially Marathwada, have been continuously under the grip of drought. Increasing water scarcity and depleting ground water levels have accentuated the agrarian crisis with an increased number of farmers committing suicide in this region. To understand this issue, the present paper deals with the roots of the drought severities concerning the summer monsoon rainfall and changing crop types over the districts of the Marathwada region, India, from 1996 to 2018. In this study, drought severities were quantified using station-based rainfall, groundwater level, and crop data (10 most cultivated crops) collected from various national agencies. The increasing rainfall trends over the Latur, Beed, and Aurangabad districts depict positive signs for agriculture. In contrast, other districts were under decreasing rainfall trends, but these declining rates were not statistically significant. The alarming fall of ground water level from 4 to 5 m during the considered period was noticed over most of the region, which is a cause for concern. The significant changes in agricultural practices from low-water-requirement crops such millet (bajra), sorghum (jowar), and wheat to high-water-requirement crops such as sugarcane and cotton were observed over Beed, Latur, Osmanabad, and Parbhani. An increase in the yield of cash crops demands an augmented water supply, which is becoming responsible for the rapidly declining ground water level. Therefore, this study claims that more than rainfall vagaries, the severe impact of droughts is a reflection of changing agricultural practices and poor management of water resources. [ABSTRACT FROM AUTHOR]

Haze, an atmospheric condition that degrades many remote sensing images, is part of the atmospheric correction. In this study, a new method is proposed to reduce the effect of haze on hyperspectral imagery over inland water bodies using the spectral unmixing approach. If haze is present in the image, spectral unmixing can result an end member corresponding to haze, and a corresponding haze abundance map (HAM) can be generated. The spectrally and spatially varying haze contribution to reflectance spectra, referred to as hyperspectral haze reflectance cube, is estimated using the HAM and the reflectance spectra extracted from haze affected areas of the hyperspectral image. Actual reflectance from water bodies can thus be obtained by subtracting this cube from the observed hyperspectral reflectance cube. Two hyperspectral images acquired with Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor over the Ujani reservoir of Maharashtra, India, are used to develop and validate the proposed hyperspectral haze correction method. We demonstrate the successful removal of haze effects by validating with field measured reflectance spectra, visual, spectral and statistical interpretation of the haze corrected AVIRIS-NG imagery. The proposed methodology can be readily implemented for existing and future hyperspectral imagery from NASA's Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission. [ABSTRACT FROM AUTHOR]

Changes in the environmental condition associated with climatic events could potentially influence the PSC dynamics of the regional marine ecosystem. The Indian Ocean dipole (IOD) is one of the critical ocean–atmosphere interactions that affects the climate of the Arabian Sea, and it could be a potential factor influencing the regional PSC distribution. However, the relationship between PSC and IOD remains unclear and less explored. In this study, using the in-situ database acquired from the Arabian Sea, we reparametrized the three−component abundance−based phytoplankton size class model and applied it to reconstructed satellite−derived chlorophyll−a concentration to extract the fractional contribution of phytoplankton size classes to chlorophyll−a concentration. Further, we investigated the influence of IOD on the changes in the biological–physical properties in the Arabian Sea. The results showed that the biological–physical processes in the Arabian Sea are interlinked and the changes in the IOD mode control the physical variables like sea surface temperature (SST), sea surface height (SSH), and mixed layer depth (MLD), which influence the specific PSC abundance. Unprecedented changes in the PSC distribution and physical properties were observed during the extreme positive and negative IOD events, which clearly indicated the potential role of IOD in altering the PSC distribution in the Arabian Sea. This study highlights the impact of extreme climate events on PSC distribution and the need for a better understanding of the associated physical–biological–climate interactions. [ABSTRACT FROM AUTHOR]

Dense stereo image matching in remotely sensed images is a challenging problem, though it has been studied for more than two decades, due to occlusions, discontinuities, geometric, and radiometric distortions. A novel multiobjective fitness function-based dense stereo matching approach using genetic algorithms (GAs) is proposed in this paper. The proposed method is useful for estimating dense disparity map with an improved number of inliers for a stereo image pair, despite the constraint of finding correct disparity at depth discontinuities. In this paper, the steps of GA, such as initialization of the population, fitness function, and crossover and mutation operation, are designed and implemented to effectively deal with the problem of dense stereo image matching. To initialize the population, a Scale Invariant Feature Transform (SIFT) descriptor is computed for each pixel and multiple-size window-based matching is performed, using the similarity measures: 1) Euclidean distance and 2) spectral angle mapper. The generated disparity maps are pruned to choose a suitable subset using the designed fitness functions, considering the constraints related to stereo image pair, such as epipolar constraint, which encodes the epipolar geometry and the similarity measure that is useful to decide accuracy of the correspondences. The two objective functions are the number of inliers computed using the fundamental matrix and an energy minimization function, considering discontinuities and occlusions. The usefulness of this approach for remotely sensed stereo image pairs is demonstrated by improving the number of inliers and favorably comparing with state-of-the-art dense stereo image matching methods. [ABSTRACT FROM AUTHOR]

Semi-analytical algorithms (SAAs) invert spectral remote sensing reflectance (R r s (λ) ,   s r − 1 ) to Inherent Optical Properties (IOPs) of an aquatic medium (λ is the wavelength). Existing SAAs implement different methodologies with a range of spectral IOP models and inversion methods producing concentrations of non-water constituents. Absorption spectrum decomposition algorithms (ADAs) are a set of algorithms developed to partition a n w (λ) ,   m − 1 (i.e., the light absorption coefficient without pure water absorption), into absorption subcomponents of phytoplankton (a p h (λ) ,   m − 1 ) and coloured detrital matter (a d g (λ) ,   m − 1 ) . Despite significant developments in ADAs, their applicability to remote sensing applications is rarely studied. The present study formulates hybrid inversion approaches that combine SAAs and ADAs to derive absorption subcomponents from R r s (λ) and explores potential alternatives to operational SAAs. Using R r s (λ) and concurrent absorption subcomponents from four datasets covering a wide range of optical properties, three operational SAAs, i.e., Garver–Siegel–Maritorena (GSM), Quasi-Analytical Algorithm (QAA), Generalized Inherent Optical Property (GIOP) model are evaluated in deriving a n w (λ) from R r s (λ) . Among these three models, QAA and GIOP models derived a n w (λ) with lower errors. Among six distinctive ADAs tested in the study, the Generalized Stacked Constraints Model (GSCM) and Zhang's model-derived absorption subcomponents achieved lower average spectral mean absolute percentage errors (MAPE) in the range of 8–38%. Four hybrid models, GIOPGSCM, GIOPZhang, QAAGSCM and QAAZhang, formulated using the SAAs and ADAs, are compared for their absorption subcomponent retrieval performance from R r s (λ) . GIOPGSCM and GIOPZhang models derived absorption subcomponents have lower errors than GIOP and QAA. Potential uncertainties associated with datasets and dependency of algorithm performance on datasets were discussed. [ABSTRACT FROM AUTHOR]

The increasing drought severities and consequent devastating impacts on society over the Indian semi-arid regions demand better drought monitoring and early warning systems. Operational agricultural drought assessment methods in India mainly depend on a single input parameter such as precipitation and are based on a sparsely located in-situ measurements, which limits monitoring precision. The overarching objective of this study is to address this need through the development of an integrated agro-climatological drought monitoring approach, i.e., combined drought indicator for Marathwada (CDI_M), situated in the central part of Maharashtra, India. In this study, satellite and model-based input parameters (i.e., standardized precipitation index (SPI-3), land surface temperature (LST), soil moisture (SM), and normalized difference vegetation index (NDVI)) were analyzed at a monthly scale from 2001 to 2018. Two quantitative methods were tested to combine the input parameters for developing the CDI_M. These methods included an expert judgment-based weight of each parameter (Method-I) and principle component analysis (PCA)-based weighting approach (Method-II). Secondary data for major types of crop yields in Marathwada were utilized to assess the CDI_M results for the study period. CDI_M maps depict moderate to extreme drought cases in the historic drought years of 2002, 2009, and 2015–2016. This study found a significant increase in drought intensities (p ≤ 0.05) and drought frequency over the years 2001–2018, especially in the Latur, Jalna, and Parbhani districts. In comparison to Method-I (r ≥ 0.4), PCA-based (Method-II) CDI_M showed a higher correlation (r ≥ 0.60) with crop yields in both harvesting seasons (Kharif and Rabi). In particular, crop yields during the drier years showed a greater association (r > 6.5) with CDI_M over Marathwada. Hence, the present study illustrated the effectiveness of CDI_M to monitor agricultural drought in India and provide improved information to support agricultural drought management practices. [ABSTRACT FROM AUTHOR]