A Nonstationary Geostatistical Framework for Soil Moisture Prediction in the Presence of Surface Heterogeneity

Kathuria, Dhruva; Mohanty, Binayak P.; Katzfuß, Matthias

doi:10.1029/2018wr023505

Cited by 18 publications

(14 citation statements)

References 83 publications

(117 reference statements)

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“…Gaur and Mohanty (2019) showed that SM drydowns at these scales can be modeled as quantitative functions of subgrid‐scale land surface heterogeneity (soil, vegetation, and topography) depending on the hydroclimate. Several other studies have also shown that the effective SM dynamics at the RS‐footprint scales are moderated by subgrid‐scale variability in the soil and land‐surface characteristics like soil texture, soil composition, topography and slope (Gaur & Mohanty, 2013, 2016), vegetation characteristics (pattern, type, and growth) (Ivanov et al., 2010), spatial distribution of precipitation and drainage/runoff patterns, and so on (Kathuria et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Global Surface Soil Moisture Drydown Patterns

Sehgal

Mohanty

2021

Water Resources Research

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Soil moisture (SM) accounts for a small fraction of the total global freshwater yet exerts a large influence on the global water cycle (McColl, Alemohammad, et al., 2017). At fine spatial scale, soil governs plant growth, geochemical processes, and groundwater recharge. However at large spatial scales (several hundred meters to several kilometers), SM plays a crucial role in precipitation recycling (Baudena et al., 2008), cloud formation (Fast et al., 2019), land-atmospheric coupling (Schwingshackl et al., 2018), and numerous other critical processes for global energy, water, and carbon cycle. Global SM status and the characteristic soil water retention functions are needed as inputs to the large-scale Earth system models (Bonan & Doney, 2018; Dunne et al., 2012; Flato, 2011; Hurrell et al., 2013). However, the mechanistic understanding (and modeling) of SM dynamics for large-spatial scales is mainly driven by a Darcy-scale perspective, largely due to the unavailability of long-term observed data at large spatial scales. Hence, there is a need to critically reevaluate the process understanding and interpretations about the governing mechanisms of SM, dynamics specific to large spatial scales. The general trajectory of SM drydowns (period of sustained loss of SM) finds its origin in fine-scale studies (Guswa et al., 2002; Rodriguez-Iturbe, 2000; Rodriguez-Iturbe et al., 1999). Laio et al. (2001) proposed that mapping the trajectory of the rate of loss of the observed SM with decreasing SM can provide information about the dominant soil hydrologic regimes and the soil water retention properties like field capacity and wilting point. The pathway of SM drydown was assumed to be a piecewise-linear function where each limb represented a unique soil hydrologic regime. Moreover, the rate of drainage loss and the strength of the land-atmospheric interaction may also be inferred from the observed SM drydown curves. Operating in the L-band microwave frequency, the Soil Moisture Active Passive (SMAP) radiometer provides SM estimates for the top 5 cm of soil with a high retrieval accuracy at a spatial support scale of 36 km. The spatial support (36 km) and global extent make SMAP observations ideal for a global evaluation of SM drydowns at large spatial scales.

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Section: Introductionmentioning

confidence: 99%

Global Surface Soil Moisture Drydown Patterns

Sehgal

Mohanty

2021

Water Resources Research

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“…where H denotes the feature set including leaf area index, land surface temperature, soil properties, elevation, slope, land cover, and precipitation and have been shown to be the major drivers of soil-moisture variability in past studies [69,70,71,72,73]. In this paper, we call this framework as "one-layer".…”

Section: Traditional One-layer Machine Learning Frameworkmentioning

confidence: 99%

Gap Filling of High-Resolution Soil Moisture for SMAP/Sentinel-1: A Two-Layer Machine Learning-Based Framework

Kathuria¹,

Duffield²,

Mohanty³

2019

Preprint

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As the most recent 3 km soil moisture product from the Soil Moisture Active Passive (SMAP) mission, the SMAP/Sentinel-1 L2_SM_SP product has a unique capability to provide global-scale 3 km soil moisture estimates through the fusion of radar and radiometer microwave observations. The spatial and temporal availability of this high-resolution soil moisture product depends on concurrent radar and radiometer observations which is significantly restricted by the narrow swath and low revisit schedule of the Sentinel-1 radars. To address this issue, this paper presents a novel two-layer machine learning-based framework which predicts the brightness temperature and subsequently the soil moisture at gap areas. The proposed method is able to gap-fill soil moisture satisfactorily at areas where the radiometer observations are available while the radar observations are missing. We find that incorporating historical radar backscatter measurements (30-day average) into the machine learning framework boosts its predictive performance. The effectiveness of the two-layer framework is validated against regional hold-out SMAP/Sentinel-1 3 km soil moisture estimates at four study areas with distinct climate regimes. Results indicate that our proposed method is able to reconstruct 3 km soil moisture at gap areas with higher Pearson correlation coefficient (47%/35%/20%/80% improvement of mean R, at Arizona/Oklahoma/Iowa/Arkansas) and lower unbiased Root Mean Square Error (20%/10%/7%/26% improvement of mean ubRMSE) when compared to the SMAP 33 km soil moisture product. Additional validation against airborne data and in-situ data from soil moisture networks is also satisfactory.

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“…Traditionally, geostatistical studies have often relied on isotropic covariance functions whose spatial structure is considered constant for the entire study domain. In areas with high geophysical heterogeneity, the stationarity assumption for SSM covariance tends to be violated (Kathuria et al, 2019).…”

Section: Process Modelmentioning

confidence: 99%

Multiscale Data Fusion for Surface Soil Moisture Estimation: A Spatial Hierarchical Approach

Kathuria

Mohanty

Katzfuß

2019

Water Resources Research

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Surface soil moisture (SSM) has been identified as a key climate variable governing hydrologic and atmospheric processes across multiple spatial scales at local, regional, and global levels. The global burgeoning of SSM datasets in the past decade holds a significant potential in improving our understanding of multiscale SSM dynamics. The primary issues that hinder the fusion of SSM data from disparate instruments are (1) different spatial resolutions of the data instruments, (2) inherent spatial variability in SSM caused due to atmospheric and land surface controls, and (3) measurement errors caused due to imperfect retrievals of instruments. We present a data fusion scheme which takes all the above three factors into account using a Bayesian spatial hierarchical model (SHM), combining a geostatistical approach with a hierarchical model. The applicability of the fusion scheme is demonstrated by fusing point, airborne, and satellite data for a watershed exhibiting high spatial variability in Manitoba, Canada. We demonstrate that the proposed data fusion scheme is adept at assimilating and predicting SSM distribution across all three scales while accounting for potential measurement errors caused due to imperfect retrievals. Further validation of the algorithm is required in different hydroclimates and surface heterogeneity as well as for other data platforms for wider applicability.Plain Language Summary Surface soil moisture (SSM) is an essential climate-variable governing land-atmosphere interactions. SSM is spatially variable in the presence of changing atmospheric factors such as rainfall and land-surface characteristics such as soil, vegetation, and topography. SSM is measured using various instruments from point to satellite resolutions (25-40 km) and each instrument is accompanied by its own set of errors. Due to the importance of SSM, it would be beneficial to combine the SSM measurements from all available instruments in a region while accounting for the spatially varying nature of SSM and the measurement errors caused due to instruments. We present a novel framework to achieve the abovementioned objective and successfully apply it to a watershed in Manitoba, Canada to combine data from point, airborne, and satellite instruments. We demonstrate that the proposed framework can be used to optimally combine and predict SSM across different spatial resolutions in the presence of uncertainty.

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A Nonstationary Geostatistical Framework for Soil Moisture Prediction in the Presence of Surface Heterogeneity

Cited by 18 publications

References 83 publications

Global Surface Soil Moisture Drydown Patterns

Global Surface Soil Moisture Drydown Patterns

Gap Filling of High-Resolution Soil Moisture for SMAP/Sentinel-1: A Two-Layer Machine Learning-Based Framework

Multiscale Data Fusion for Surface Soil Moisture Estimation: A Spatial Hierarchical Approach

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