A diagnostic study on the energetics aspects of hiatus in the advance of southwest monsoon

Advance of southwest monsoon, after its onset, often gets stalled for a week or more causing concern to the farmers and other community whose activities are weather dependent. The present study on the energetics aspect of hiatus in the advance of southwest monsoon over India aims at understanding the dynamical reasons for this. Nine cases of hiatus of duration more than 10 days during 1982-2006 have been selected. For each hiatus case, different energy terms, their generation and conversion among different terms have been computed during the hiatus period and also during the pre-hiatus pentad over a limited region between 65° E to 90° E, 5° N to 30° N. These computations are based on NCEP 2.5° × 2.5° re-analysed daily composite data during different hiatus period and during corresponding pre-hiatus pentad. From this study it is found that : (i) In most of the cases there is a reduction in the generation of zonal available potential energy [G(AZ)] during hiatus period compared to pre-hiatus pentad. (ii) Drop in the conversion from zonal available potential energy to zonal kinetic energy [C(AZ, KZ)] during hiatus period has been observed in most of the cases. (iii) In most of the cases there is a reduction in zonal kinetic energy (KZ) and in eddy kinetic energy (KE) during hiatus period compared to pre-hiatus pentad.

Dimensionality reduction and data integration for scRNA-seq data based on integrative hierarchical Poisson factorisation

Single-cell RNA sequencing (scRNA-seq) data sets consist of high-dimensional, sparse and noisy feature vectors, and pose a challenge for classic methods for dimensionality reduction. We show that application of Hierarchical Poisson Factorisation (HPF) to scRNA-seq data produces robust factors, and outperforms other popular methods. To account for batch variability in composite data sets, we introduce Integrative Hierarchical Poisson Factorisation (IHPF), an extension of HPF that makes use of a noise ratio hyper-parameter to tune the variability attributed to technical (batches) vs. biological (cell phenotypes) sources. We exemplify the advantageous application of IHPF under data integration scenarios with varying alignments of technical noise and cell diversity, and show that IHPF produces latent factors with a dual block structure in both cell and gene spaces for enhanced biological interpretability.

A Novel Strategy to Reconstruct NDVI Time-Series with High Temporal Resolution from MODIS Multi-Temporal Composite Products

Vegetation indices (VIs) data derived from satellite imageries play a vital role in land surface vegetation and dynamic monitoring. Due to the excessive noises (e.g., cloud cover, atmospheric contamination) in daily VI data, temporal compositing methods are commonly used to produce composite data to minimize the negative influence of noise over a given compositing time interval. However, VI time series with high temporal resolution were preferred by many applications such as vegetation phenology and land change detections. This study presents a novel strategy named DAVIR-MUTCOP (DAily Vegetation Index Reconstruction based on MUlti-Temporal COmposite Products) method for normalized difference vegetation index (NDVI) time-series reconstruction with high temporal resolution. The core of the DAVIR-MUTCOP method is a combination of the advantages of both original daily and temporally composite products, and selecting more daily observations with high quality through the temporal variation of temporally corrected composite data. The DAVIR-MUTCOP method was applied to reconstruct high-quality NDVI time-series using MODIS multi-temporal products in two study areas in the continental United States (CONUS), i.e., three field experimental sites near Mead, Nebraska from 2001 to 2012 and forty-six AmeriFlux sites evenly distributed across CONUS from 2006 to 2010. In these two study areas, the DAVIR-MUTCOP method was also compared to several commonly used methods, i.e., the Harmonic Analysis of Time-Series (HANTS) method using original daily observations, Savitzky–Golay (SG) filtering using daily observations with cloud mask products as auxiliary data, and SG filtering using temporally corrected composite data. The results showed that the DAVIR-MUTCOP method significantly improved the temporal resolution of the reconstructed NDVI time series. It performed the best in reconstructing NDVI time-series across time and space (coefficient of determination (R2 = 0.93~0.94) between reconstructed NDVI and ground-observed LAI). DAVIR-MUTCOP method presented the highest robustness and accuracy with the change of the filtering parameter (R2 = 0.99~1.00, bias = 0.001, root mean square error (RMSE) = 0.020). Only MODIS data were used in this study; nevertheless, the DAVIR-MUTCOP method proposed a universal and potential way to reconstruct daily time series of other VIs or from other operational sensors, e.g., AVHRR and VIIRS.

Accuracy assessment of bare soil map of Hungary based on Sentinel satellite data

<p>As Earth observation (EO) data is increasing in volume, fast and reliable data-processing tools are also required especially for analyzing large areas with high spatial resolution. Google Earth Engine (GEE) platform provides wide sets of EO imagery and elevation data in a cloud-based processing environment. This research focused on i) the generation of bare soil map of Hungary and ii) the accuracy assessment of created soil maps representing soil texture (clay, sand, silt) and soil chemical parameters (SOC, pH and CaCO<sub>3</sub>).</p><p>In this study Copernicus Sentinel-1 SAR and Sentinel-2 optical images acquired on a mid-term time period between 2017 April and 2020 December were used to generate a median composite. Optical images were filtered for cloud coverage less than 50% and a cloud mask was also implemented on all remaining images. The threshold values for Normalized Difference Vegetation Index and Normalized Burn Ratio indices were 0.55 and 0.35 respectively to differentiate bare soil pixels.</p><p>We tested the prediction accuracy of bare soil composite supplemented by various environmental datasets as additional predictor variables in different scenarios: (i) using solely bare soil composite data (ii) composite data, elevation and its derived parameters (e.g. slope, aspect) (iii) composite data and spectral indices and (iv) all aforementioned data in fusion.</p><p>For validation two types of datasets were used: i) the reference points of the Hungarian Soil Information and Monitoring System with a five-fold cross-validation method and ii) the recently compiled national maps for soil texture and soil chemical parameters.</p><p><strong>Acknowledgment:</strong> Our research was supported by the Hungarian National Research, Development and Innovation Office (NKFIH; K-131820 and K-124290) and by the Scholarship of Human Resource Supporter (NTP-NFTÖ-20-B-0022).</p>