Temporal transferability of soil moisture calibration equations

Berg, Aaron; Bullock, Paul; Hanis-Gervais, Krista; Ojo, E. RoTimi; Cosh, Michael H.; Powers, Jarrett; McNairn, Heather

doi:10.1016/j.jhydrol.2017.11.023

Cited by 11 publications

(5 citation statements)

References 24 publications

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“…The stations were programmed to capture hourly soil moisture and temperature readings throughout the campaign. Field calibration equations (Rowlandson et al, 2018) derived from soil core data were used to convert real dielectric constant values to volumetric water content. During the campaign, a total of 90,000 hourly measurements were recorded by these temporary stations.…”

Section: Site Description and Data Collectionmentioning

confidence: 99%

“…Volumetric water content is a linear function of the square root of real dielectric permittivity. As per Rowlandson et al (2013, 2018), linear regression analysis was used to determine the equation of best fit between the volumetric moisture content (θ v ) and the square root of the dielectric permittivity (ε TC ) from the POGO as

{normalθ}_{normalv} = a {({normalε}_{TC})}^{0.5} + b

…”

Section: Site Description and Data Collectionmentioning

confidence: 99%

“…Given the success of the specific calibration approach, SMAPVEX16-MB calibration functions were developed in a similar manner for this study (Rowlandson et al, 2018). To increase the volume of calibration data points for 2016, two core samples (volume of 80 cm 3 ) were collected from each field on every sampling date.…”

Section: Soil Moisture Data Collected By Field Crews During Intensive...mentioning

confidence: 99%

See 2 more Smart Citations

Assessing SMAP Soil Moisture Scaling and Retrieval in the Carman (Canada) Study Site

Bhuiyan

McNairn

Powers

et al. 2018

Vadose Zone Journal

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Section: Site Description and Data Collectionmentioning

confidence: 99%

{normalθ}_{normalv} = a {({normalε}_{TC})}^{0.5} + b

…”

Section: Site Description and Data Collectionmentioning

confidence: 99%

Section: Soil Moisture Data Collected By Field Crews During Intensive...mentioning

confidence: 99%

See 1 more Smart Citation

Assessing SMAP Soil Moisture Scaling and Retrieval in the Carman (Canada) Study Site

Bhuiyan

McNairn

Powers

et al. 2018

Vadose Zone Journal

Self Cite

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“…At the plot to the field-scale, the direct thermo-gravimetric method provides reliable in-situ measurements of θ . Alternatively, indirect methods through the use of portable devices or stationary sensors (e.g., Time Domain Reflectometry) measure the soil dielectric properties from which the soil moisture is inferred through semi-empirical equations (e.g., Rowlandson et al, 2018;Babaeian et al, 2019). However, their applications are rather unfeasible for large-scale operations due to excessive demand for time (e.g., maintenance) and costs (e.g., labor) for surveying (e.g., Santi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem

et al. 2021

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Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability of agroforestry systems of Mediterranean environments to climate change. We propose a simple empirical model by integrating dual-polarimetric Sentinel-1 (S1) Synthetic Aperture Radar (SAR) C-band single-look complex data and topographic information together with in-situ measurements of θ into a random forest (RF) regression approach (10-fold cross-validation). Firstly, we compare two RF models' estimation performances using either 43 SAR parameters (θNovSAR) or the combination of 43 SAR and 10 terrain parameters (θNovSAR+Terrain). Secondly, we analyze the essential parameters in estimating and mapping θ for S1 overpasses twice a day (at 5 a.m. and 5 p.m.) in a high spatiotemporal (17 × 17 m; 6 days) resolution. The developed site-specific calibration-dependent model was tested for a short period in November 2018 in a field-scale agroforestry environment belonging to the “Alento” hydrological observatory in southern Italy. Our results show that the combined SAR + terrain model slightly outperforms the SAR-based model (θNovSAR+Terrain with 0.025 and 0.020 m3 m−3, and 89% compared to θNovSAR with 0.028 and 0.022 m3 m−3, and 86% in terms of RMSE, MAE, and R2). The higher explanatory power for θNovSAR+Terrain is assessed with time-variant SAR phase information-dependent elements of the C2 covariance and Kennaugh matrix (i.e., K1, K6, and K1S) and with local (e.g., altitude above channel network) and compound topographic attributes (e.g., wetness index). Our proposed methodological approach constitutes a simple empirical model aiming at estimating θ for rapid surveys with high accuracy. It emphasizes potentials for further improvement (e.g., higher spatiotemporal coverage of ground-truthing) by identifying differences of SAR measurements between S1 overpasses in the morning and afternoon.

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“…It is less affected by salts and temperature than TDR sensors because of the delineation of the dielectric permittivity and operational frequency at 50 MHz [17]. However, given the natural fluctuations in SM levels over time, periodic updates to the calibration equations are required to ensure accurate SM estimation [18]. These soil moisture measurement techniques provide accurate SM information for each observation point, but it is difficult and expensive to provide spatially continuous information.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Retrieval of Daily Soil Moisture Using IMERG and GK2A Satellite Images with NWP and Topographic Data: A Machine Learning Approach for South Korea

Lee,

Sohn,

Kim

et al. 2023

Remote Sensing

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Soil moisture (SM) is an indicator of the moisture status of the land surface, which is useful for monitoring extreme weather events. Representative global SM datasets include the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP), the Global Land Data Assimilation System (GLDAS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5), but due to their low spatial resolutions, none of these datasets well describe SM changes in local areas, and they tend to have a low accuracy. Machine learning (ML)-based SM predictions have demonstrated high accuracy, but obtaining semi-real-time SM information remains challenging, and the dependence of the validation accuracy on the data sampling method used, such as random or yearly sampling, has led to uncertainties. In this study, we aimed to develop an ML-based model for real-time SM estimation that can capture local-scale variabilities in SM and have reliable accuracy, regardless of the sampling method. This study was conducted in South Korea, and satellite image data, numerical weather prediction (NWP) data, and topographic data provided within one day were used as the input data. For SM modeling, 13 input variables affecting the surface SM status were selected: 10- and 20-day cumulative standardized precipitation indexes (SPI10 and SPI20), a normalized difference vegetation index (NDVI), downward shortwave radiation (DSR), air temperature (Tair), land surface temperature (LST), soil temperature (Tsoil), relative humidity (RH), latent heat flux (LE), slope, elevation, topographic ruggedness index (TRI), and aspect. Then, SM models based on random forest (RF) and automated machine learning (AutoML) were constructed, trained, and validated using random sampling and leave-one-year-out (LOYO) cross-validation. The RF- and AutoML-based SM models had significantly high accuracy rates based on comparisons with in situ SM (mean absolute error (MAE) = 2.212–4.132%; mean bias error (MBE) = −0.110–0.136%; root mean square error (RMSE) = 3.186–5.384%; correlation coefficient (CC) = 0.732–0.913), while the AutoML-based SM model tended to have a higher accuracy than the RF-based SM model, regardless of the data sampling method used. In addition, when compared to in situ SM data, the SM models demonstrated the highest accuracy, outperforming both GLDAS and ERA5 SM data and well representing changes in the dryness/wetness of the land surface according to meteorological events (heatwave, drought, and rainfall). The SM models proposed in this study can, thus, offer semi-real-time SM data, aiding in the monitoring of moisture changes in the land surface, as well as short-term meteorological disasters, like flash droughts or floods.

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Temporal transferability of soil moisture calibration equations

Cited by 11 publications

References 24 publications

Assessing SMAP Soil Moisture Scaling and Retrieval in the Carman (Canada) Study Site

Assessing SMAP Soil Moisture Scaling and Retrieval in the Carman (Canada) Study Site

Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem

Real-Time Retrieval of Daily Soil Moisture Using IMERG and GK2A Satellite Images with NWP and Topographic Data: A Machine Learning Approach for South Korea

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