Optimizing Soil Moisture Station Networks for Future Climates

Bessenbacher, Verena; Gudmundsson, Lukas; Seneviratne, Sonia I.

doi:10.1029/2022gl101667

Cited by 2 publications

(2 citation statements)

References 69 publications

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“…Soil water content (SWC) plays a vital role in the water and energy balance by partitioning the mass and energy fluxes between the land surface and atmosphere [1][2][3], making it an essential state variable for climate and meteorological monitoring and forecasting [4]. SWC data are needed for numerical watershed and land surface models as well as validation of satellite products [5,6].…”

Section: Introduction 1applications Requiring Local Scale Soil Water ...mentioning

confidence: 99%

Field Testing of Gamma-Spectroscopy Method for Soil Water Content Estimation in an Agricultural Field

Becker,

Franz,

Morris

et al. 2024

Sensors

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Gamma-ray spectroscopy (GRS) enables continuous estimation of soil water content (SWC) at the subfield scale with a noninvasive sensor. Hydrological applications, including hyper-resolution land surface models and precision agricultural decision making, could benefit greatly from such SWC information, but a gap exists between established theory and accurate estimation of SWC from GRS in the field. In response, we conducted a robust three-year field validation study at a well-instrumented agricultural site in Nebraska, United States. The study involved 27 gravimetric water content sampling campaigns in maize and soybean and 40K specific activity (Bq kg−1) measurements from a stationary GRS sensor. Our analysis showed that the current method for biomass water content correction is appropriate for our maize and soybean field but that the ratio of soil mass attenuation to water mass attenuation used in the theoretical equation must be adjusted to satisfactorily describe the field data. We propose a calibration equation with two free parameters: the theoretical 40K intensity in dry soil and a, which creates an “effective” mass attenuation ratio. Based on statistical analyses of our data set, we recommend calibrating the GRS sensor for SWC estimation using 10 profiles within the footprint and 5 calibration sampling campaigns to achieve a cross-validation root mean square error below 0.035 g g−1.

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Section: Introduction 1applications Requiring Local Scale Soil Water ...mentioning

confidence: 99%

Field Testing of Gamma-Spectroscopy Method for Soil Water Content Estimation in an Agricultural Field

Becker,

Franz,

Morris

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Satellite observations often have ample missing values due to retrieval limitations or flight geometry (Bessenbacher et al., 2022). Ground observations are sparse and have insufficient coverage in many regions in the world (Bessenbacher et al., 2023). Therefore, combining two or more incomplete observations remains difficult, as the different patterns of missing values (also referred to as “missingness”) often do not overlap; therefore, only a small fraction of points can be retrieved where both variables are observed.…”

Section: Introductionmentioning

confidence: 99%

Gap‐Filled Multivariate Observations of Global Land–Climate Interactions

Bessenbacher,

Schumacher,

Hirschi

et al. 2023

JGR Atmospheres

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The volume of Earth system observations has grown massively in recent decades. However, multivariate or multisource analyses at the interface of atmosphere and land are still hampered by the sparsity of ground measurements and the abundance of missing values in satellite observations. This can hinder robust multivariate analysis and introduce biases in trends. Nevertheless, gap‐filling is often done univariately, which can obscure physical dependencies. Here, we apply the new multivariate gap‐filling framework CLIMate data gapFILL (CLIMFILL). CLIMFILL combines state‐of‐the‐art spatial interpolation with an iterative approach accounting for dependencies across multiple incomplete variables. CLIMFILL is applied to a set of remotely sensed and in situ observations over land that are central to observing land–atmosphere interactions and extreme events. The resulting gridded monthly time series covers 1995–2020 globally with gap‐free maps of nine variables: surface layer soil moisture from European Space Agency (ESA)‐Climate Change Initiative (CCI), land surface temperature and diurnal temperature range from Moderate‐resolution Imaging Spectroradiometer, precipitation from GPM, terrestrial water storage from GRACE, ESA‐CCI burned area, and snow cover fraction as well as 2‐m temperature and precipitation from CRU. Time series of anomalies are reconstructed better compared to state‐of‐the‐art interpolation. The gap‐filled data set shows high correlations with ERA5‐Land, and soil moisture estimates compare favorably to in situ observations from the International Soil Moisture Network. Soil moisture drying trends in ESA‐CCI only agree with the reanalysis product ERA5‐Land trends after gap‐filling. We furthermore showcase that key features of droughts and heatwaves in major fire seasons are well represented. The data set can serve as a step toward the fusion of multivariate multisource observations.

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Optimizing Soil Moisture Station Networks for Future Climates

Abstract: Soil moisture plays a central role in the local climate on land. As a water reservoir, it provides persistence and predictability to the land climate by storing precipitation via the soil moisture memory effect (Z.

Cited by 2 publications

References 69 publications

Field Testing of Gamma-Spectroscopy Method for Soil Water Content Estimation in an Agricultural Field

Field Testing of Gamma-Spectroscopy Method for Soil Water Content Estimation in an Agricultural Field

Gap‐Filled Multivariate Observations of Global Land–Climate Interactions

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