Soil moisture estimation in South Asia via assimilation of SMAP retrievals
نویسندگان
چکیده
Abstract. A soil moisture retrieval assimilation framework is implemented across South Asia in an attempt to improve regional estimation as well provide a consistent dataset. This study aims the spatiotemporal variability of estimates by assimilating Soil Moisture Active Passive (SMAP) near-surface retrievals into land surface model. The Noah-MP (v4.0.1) model run within NASA Land Information System software processes. Modern-Era Retrospective Analysis for Research and Applications (MERRA2) Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals (IMERG) meteorological boundary conditions Assimilation carried out using both cumulative distribution function (CDF)-corrected (DA-CDF) uncorrected SMAP (DA-NoCDF). CDF matching applied correct statistical moments relative Comparison assimilated model-only with publicly available situ measurements highlights improvement retrievals. Across Tibetan Plateau, DA-NoCDF reduced mean bias RMSE 8.4 % 9.4 %, even though only occurred during less than 10 period due frozen (or partially frozen) conditions. best goodness-of-fit statistics were achieved IMERG experiment. general lack irrigated areas limited domain-wide direct validation. However, comparison irrigation patterns suggested correction biases associated unmodeled hydrologic phenomenon (i.e., anthropogenic influence via irrigation) result assimilation. greatest sensitivity was observed cropland areas. Improvements potentially translate improved modeled evapotranspiration, although from on processes carbon cycle such gross primary production. Improvement fine-scale coarse-scale potential this approach over data-scarce regions.
منابع مشابه
Towards the estimation root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals
The upcoming deployment of satellite-based microwave sensors designed specifically to retrieve surface soil moisture represents an important milestone in efforts to develop hydrologic applications for remote sensing observations. However, typical measurement depths of microwave-based soil moisture retrievals are generally considered too shallow (top 2–5 cm of the soil column) for many important...
متن کاملData Assimilation to Extract Soil Moisture Information from SMAP Observations
This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NASA) Catchment model over the contiguous United States for April 2015 to March 2017. By constructio...
متن کاملThe Compact Polarimetry alternative for Soil Moisture Estimation using SMAP
In this paper, we investigate the potential of the compact polarimetry mode at longer wavelengths from space for soil moisture estimation using SMAP data. Compact polarimetry consists of transmitting a single polarization while receiving two polarizations. At longer wavelengths, one of the main challenges associated with compact polarimetry from space is Faraday rotation estimation and correcti...
متن کاملSoil Moisture Active Passive (SMAP) Algorithm Theoretical Basis Document SMAP L2 & L3 Radar Soil Moisture (Active) Data Products
متن کامل
CMIP 5 Projected Soil Moisture Changes over South Asia
Soil moisture is the vital component of the hydrological cycle and its variability is largely uncertain in the upcoming decades. In this paper the future projections of soil moisture changes over South Asia have been analyzed on both annual and seasonal basis from 2020-2050. The comparison of 24 CMIP5 (Coupled Model Inter comparison Project Phase 5) models with GLDAS (Global Lad Data Assimilati...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Hydrology and Earth System Sciences
سال: 2022
ISSN: ['1607-7938', '1027-5606']
DOI: https://doi.org/10.5194/hess-26-2221-2022