Ph. D. (Pursuing)
M.Tech., Water Resources & Environmental Engineering
B.Tech., Civil Engineering
Remote sensing, Hydrology, Multi-scale data fusion, Non-stationarity, Spatio-temporal statistics for massive datasets
The global burgeoning of environmental remote sensing datasets in the past decade holds a signiﬁcant potential in improving our understanding of multi-scale hydrological dynamics. The primary issues that hinder the fusion of different data platforms are
- Massive size of datasets on a continental scale,
- Different spatial resolutions of the data platforms,
- Inherent spatial variability in environmental variables caused due to atmospheric and land surface controls and
- Measurement errors caused due to imperfect retrievals of remote sensing platforms.
I have recently developed a novel data fusion scheme which takes all the above factors into account using a spatial hierarchical model (SHM). An SHM enables coherent integration of data, science and uncertainties to make optimal predictions at unobserved locations using an underlying non-stationary geostatistical model.
The applicability of the hierarchical approach, however, is severely limited by huge datasets. To account for the massive size of the datasets at a continental scale, I am currently working on a novel extension of a likelihood approximation in a multi-scale multi-platform setting and its subsequent application to fuse in-situ soil moisture observations from SCAN and USCRN with satellite-derived soil moisture products from SMOS and SMAP for Contiguous USA (CONUS). The fusion scheme will be further extended to a multivariate setting to combine different components of the water cycle such as evapotranspiration, rainfall, soil moisture, and ground-water.
Awards, scholarships, & fellowships
- (2019) Bill and Rita Stout International Graduate Student Achievement Award
List of select publications:
- Kathuria, D., Mohanty, B. P. and Katzfuss, M. (2019). A nonstationary geostatistical framework for soil moisture prediction in the presence of surface heterogeneity. Water Resources Research. doi: 10.1029/2018WR023505
- Kathuria, D., Mohanty, B. P. and Katzfuss, M. (2018+). Multiscale data fusion for soil moisture estimation: a spatial hierarchical approach (under revision in Water Resources Research, 2018WR024581).
- Mao, H., Kathuria, D., Duﬃeld, N and Mohanty, B. P. (2019+). Gap Filling of High-Resolution Soil Moisture for SMAP/Sentinel-1: A Two-layer Machine Learning-based Framework with Spatial/Temporal Transfer Learning (under revision in Water Resources Research, 2019WR024902, preprint: doi: 10.31223/osf.io/ce865)