On the use of AMSU-based products for the description of soil water content at basin scale

Abstract. Characterizing the dynamics of soil moisture fields is a key issue in hydrology, offering a strategy to improve our understanding of complex climate-soil-vegetation interactions. Besides in-situ measurements and hydrological models, soil moisture dynamics can be inferred by analyzing data acquired by sensors on board of airborne and/or satellite platforms. In this work, we investigated the use of the National Oceanic and Atmospheric Administration – Advanced Microwave Sounding Unit-A (NOAA-AMSU-A) radiometer for the remote characterization of soil water content. To this aim, a field measurement campaign, lasted about three months (3 March 2010–18 May 2010), was carried out using a portable time-domain reflectometer (TDR) to get soil water content measures over five different locations within an experimental basin of 32.5 km2, located in the South of Italy. In detail, soil moisture measurements were carried out systematically at the times of satellite overpasses, over two square areas of 400 m2, a triangular area of 200 m2 and two transects of 60 and 170 m, respectively. Each monitored site is characterized by different land covers and soil textures, to account for spatial heterogeneity of land surface. Afterwards, a more extensive comparison (i.e. analyzing a 5 yr data time series) was made using soil moisture simulated by a hydrological model. Measured and modeled soil moisture data were compared with two AMSU-based indices: the Surface Wetness Index (SWI) and the Soil Wetness Variation Index (SWVI). Both time series of indices have been filtered by means of an exponential filter to account for the fact that microwave sensors only provide information at the skin surface. This allowed to understand the ability of each satellite-based index to account for soil moisture dynamics and to understand its performances under different conditions. As a general remark, the comparison shows a higher ability of the filtered SWI to describe the general trend of soil moisture, while the SWVI can capture soil moisture variations with a precision that increases at the higher values of SWVI.

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On the use of AMSU-based products for the description of soil water content at basin scale

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-5319-2011 ◽

2011 ◽

Vol 8 (3) ◽

pp. 5319-5353

Author(s):

S. Manfreda ◽

T. Lacava ◽

B. Onorati ◽

N. Pergola ◽

M. Di Leo ◽

...

Keyword(s):

Soil Moisture ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Land Surface ◽

Soil Depth ◽

Soil Moisture Dynamics ◽

Basin Scale ◽

Triangular Area ◽

Moisture Dynamics

Abstract. Characterizing the dynamics of soil moisture fields is a key issue in hydrology, offering a strategy to improve our understanding of complex climate-soil-vegetation interactions. Apart from in-situ measurements and hydrological models, soil moisture dynamics can be inferred by analyzing data acquired by sensors aboard satellite platforms. In this work, we investigated the use of the National Oceanic and Atmospheric Administration – Advanced Microwave Sounding Unit (NOAA-AMSU) radiometer for the remote characterization of soil water content. To this aim, a field measurement campaign, lasted about three months, was carried out using a portable time-domain reflectometer (TDR) to get soil water content measures over five different locations within an experimental basin of 32.5 km2, located in the South of Italy. In detail, soil moisture measurements have been carried out systematically at the times of satellite overpasses, over two square areas of 400 m2, a triangular area of 200 m2 and two transects of 60 and 170 m, respectively. Each monitored site is characterized by different land covers and soil textures, to account for spatial heterogeneity of land surface. Afterwards, a more extensive comparison (i.e. analyzing a 5-yr data time series) has been made using soil moisture simulated by a hydrological model. Achieved measured and modeled soil moisture data were compared with two AMSU-based indices: the Surface Wetness Index (SWI) and the Soil Wetness Variation Index (SWVI). Both indices have been filtered to account for soil depth by means of an exponential filter. This allowed to understand the ability of each satellite-based index to account for soil moisture dynamics and to understand its performances under different conditions. As a general remark, the comparison shows a higher ability of the filtered SWI to describe the state of the soil, while the SWVI can capture soil moisture variations with a precision that increases at the higher values of SWVI and it may represent a useful and reliable tool to frequently monitor the soil moisture state for flood forecasting purposes.

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Climatic expression of rainfall on soil moisture dynamics in drylands

10.5194/hess-2021-48 ◽

2021 ◽

Author(s):

Isaac Kipkemoi ◽

Katerina Michaelides ◽

Rafael Rosolem ◽

Michael Bliss Singer

Keyword(s):

Climate Change ◽

Soil Moisture ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Water Scarcity ◽

Rainfall Intensity ◽

Precipitation Data ◽

Soil Moisture Dynamics ◽

Moisture Dynamics

Abstract. In drylands, characterised by water scarcity and frequent meteorological droughts, knowledge of soil moisture dynamics and its drivers (evapotranspiration, soil physical properties and the timing and sequencing of precipitation events) is fundamental to understanding changes in water availability to plants and human society, especially under a nonstationary climate. Given the episodic and stochastic nature of rainfall in drylands and the limited availability of data in these regions, we sought to explore what effects the temporal resolution of precipitation data has on soil moisture and how soil moisture distributions might evolve under different scenarios of climate change. Such information is critical for anticipating the impact of a changing climate on dryland communities across the globe, especially those that depend on rainfed agriculture and groundwater wells for drinking water for humans and livestock. A major challenge to understanding soil moisture in response to climate is the availability of precipitation datasets for dryland regions across the globe. Gridded precipitation data may only be available for daily or weekly time periods, even though rainstorms in drylands often occur on much shorter time scales, but it is currently unknown how this timescale mismatch might affect our understanding of soil moisture. Numerical modelling enables retrodiction or prediction of how climate translates into dynamically evolving moisture within the soil profile. It can be used to explore how climate data at different temporal resolutions affect these soil moisture dynamics, as well as to explore the influence of shifts in rainfall characteristics (e.g., storm intensity) under potential scenarios of climate change. This study uses Hydrus 1-D, to investigate the dynamics of soil moisture over a period of decades in response to the same underlying rainfall data resolved at hourly, daily, and weekly resolutions, as well as to step changes in rainfall delivery, which is expected under a warming atmosphere. We parameterised the model using rainfall, evaporative demand, and soils data from the semi-arid Walnut Gulch Experimental Watershed (WGEW) in SE Arizona, but we present the results as a generalized study of how rainfall resolution and shifts in rainfall intensity may affect dryland soil moisture at different depths. Our results indicate that hourly or better rainfall resolution captures the dynamics of soil moisture in drylands, and that critical information on soil water content, moisture availability to vegetation, actual evapotranspiration, and deep percolation of infiltrated water is lost when soil moisture modelling is driven by rainfall data at coarser temporal resolutions (daily, weekly). We further show that modest changes in rainfall intensity dramatically shift soil water content and the overall water balance. These findings are relevant to the prediction of soil moisture for crop yield forecasts, for adaptation to climate-related risks, and for anticipating the challenges of water scarcity and food insecurity in dryland communities around the globe, where available datasets are of low spatial and temporal resolution.

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Use of cosmic ray neutron sensors for soil moisture monitoring in forests

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-9813-2015 ◽

2015 ◽

Vol 12 (9) ◽

pp. 9813-9864 ◽

Cited By ~ 2

Author(s):

I. Heidbüchel ◽

A. Güntner ◽

T. Blume

Keyword(s):

Time Series ◽

Soil Moisture ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Cosmic Ray ◽

Soil Samples ◽

Calibration Function ◽

Neutron Sensors ◽

Hydrogen Mass

Abstract. Cosmic ray neutron sensors (CRS) are a promising technique to measure soil moisture at intermediate scales. To convert neutron counts to average volumetric soil water content a simple calibration function can be used (the N0-calibration of Desilets et al., 2010). This calibration function is based on soil water content derived directly from soil samples taken within the footprint of the sensor. We installed a CRS in a mixed forest in the lowlands of north-eastern Germany and calibrated it 10 times throughout one calendar year. Each calibration with the N0-calibration function resulted in a different CRS soil moisture time series, with deviations of up to 0.12 m3 m-3 for individual values of soil water content. Also, many of the calibration efforts resulted in time series that could not be matched with independent in situ measurements of soil water content. We therefore suggest a new calibration function with a different shape that can vary from one location to another. A two-point calibration proved to be adequate to correctly define the shape of the new calibration function if the calibration points were taken during both dry and wet conditions covering at least 50 % of the total range of soil moisture. The best results were obtained when the soil samples used for calibration were linearly weighted as a function of depth in the soil profile and non-linearly weighted as a function of distance from the CRS, and when the depth-specific amount of soil organic matter and lattice water content was explicitly considered. The annual cycle of tree foliation was found to be a negligible factor for calibration because the variable hydrogen mass in the leaves was small compared to the hydrogen mass changes by soil moisture variations. Finally, we provide a best practice calibration guide for CRS in forested environments.

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Use of cosmic-ray neutron sensors for soil moisture monitoring in forests

Hydrology and Earth System Sciences ◽

10.5194/hess-20-1269-2016 ◽

2016 ◽

Vol 20 (3) ◽

pp. 1269-1288 ◽

Cited By ~ 31

Author(s):

Ingo Heidbüchel ◽

Andreas Güntner ◽

Theresa Blume

Keyword(s):

Time Series ◽

Soil Moisture ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Cosmic Ray ◽

Soil Samples ◽

Calibration Function ◽

Total Range ◽

Hydrogen Mass

Abstract. Measuring soil moisture with cosmic-ray neutrons is a promising technique for intermediate spatial scales. To convert neutron counts to average volumetric soil water content a simple calibration function can be used (the N0-calibration of Desilets et al., 2010). The calibration is based on soil water content derived directly from soil samples taken within the footprint of the sensor. We installed a cosmic-ray neutron sensor (CRS) in a mixed forest in the lowlands of north-eastern Germany and calibrated it 10 times throughout one calendar year. Each calibration with the N0-calibration function resulted in a different CRS soil moisture time series, with deviations of up to 0.1 m3 m−3 (24 % of the total range) for individual values of soil water content. Also, many of the calibration efforts resulted in time series that could not be matched with independent in situ measurements of soil water content. We therefore suggest a modified calibration function with a different shape that can vary from one location to another. A two-point calibration was found to effectively define the shape of the modified calibration function if the calibration points were taken during both dry and wet conditions spanning at least half of the total range of soil moisture. The best results were obtained when the soil samples used for calibration were linearly weighted as a function of depth in the soil profile and nonlinearly weighted as a function of distance from the CRS, and when the depth-specific amount of soil organic matter and lattice water content was explicitly considered. The annual cycle of tree foliation was found to be a negligible factor for calibration because the variable hydrogen mass in the leaves was small compared to the hydrogen mass changes by soil moisture variations. As a final point, we provide a calibration guide for a CRS in forested environments.

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Estimating Soil Water Retention Curve by Inverse Modelling from Combination of In Situ Dynamic Soil Water Content and Soil Potential Data

Soil Systems ◽

10.3390/soilsystems2040055 ◽

2018 ◽

Vol 2 (4) ◽

pp. 55 ◽

Cited By ~ 2

Author(s):

Pinnara Ket ◽

Chantha Oeurng ◽

Aurore Degré

Keyword(s):

Soil Moisture ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Water Retention ◽

Inverse Modelling ◽

Soil Water Retention ◽

Moisture Dynamics ◽

Hydrus 1D

Soil water retention curves (SWRCs) are crucial for characterizing soil moisture dynamics, and are particularly relevant in the context of irrigation management. Inverse modelling is one of the methods used to parameterize models representing these curves, which are closest to the field reality. The objective of this study is to estimate the soil hydraulic properties through inverse modelling using the HYDRUS-1D code based on soil moisture and potential data acquired in the field. The in situ SWRCs acquired every 30 min are based on simultaneous soil water content and soil water potential measurements with 10HS and MPS-2 sensors, respectively, in five experimental fields. The fields were planted with drip-irrigated lettuces from February to March 2016 in the Chrey Bak catchment located in the Tonlé Sap Lake region, Cambodia. After calibration of the van Genuchten soil water retention model parameters, we used them to evaluate the performance of HYDRUS-1D to predict soil moisture dynamics in the studied fields. Water flow was reasonably well reproduced in all sites covering a range of soil types (loamy sand and loamy soil) with root mean square errors ranging from 0.02 to 0.03 cm3 cm−3.

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Soil Water Content Diachronic Mapping: An FFT Frequency Analysis of a Temperature–Vegetation Index

Geosciences ◽

10.3390/geosciences10010023 ◽

2020 ◽

Vol 10 (1) ◽

pp. 23 ◽

Cited By ~ 1

Author(s):

Fulvio Capodici ◽

Carmelo Cammalleri ◽

Antonio Francipane ◽

Giuseppe Ciraolo ◽

Goffredo La Loggia ◽

...

Keyword(s):

Time Series ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Land Surface ◽

Vegetation Index ◽

Root Zone ◽

Simple Relationship ◽

Meteorological Forcing ◽

Triangle Method

Among the indirect estimation approaches of soil water content in the upper layer of the soil, the “triangle method” is one of the most common that relies on the simple relationship between the optical and thermal features sensed via Earth Observation. These features are controlled by water content at the surface and within the root zone but also by meteorological forcing including air temperature and humidity, as well as solar radiation. Night- and day-time MODIS composites of land-surface temperature (LST) allowed applying a version of the triangle method that takes into account the temporal admittance of the soil. In this study, it has been applied to a long time-series of pair images to analyze the seasonal influence of the meteorological forcing on a triangle method index (or temperature–vegetation index, TVX), as well as to discuss extra challenges of the diachronic approach including seasonality effects and the variability of environmental forcing. The Imera Meridionale basin (Sicily, Italy) has been chosen to analyze the method over a time-series of 12 years. The analysis reveals that, under these specific environmental and climatic conditions (strong seasonality and rainfall out of phase with vegetation growth), Normalized Difference Vegetation Index (NDVI) and LST pairs move circularly in time within the optical vs. thermal feature space. Concordantly, the boundaries of the triangle move during the seasons. Results showed a strong correlation between TVX and rainfall normalized amplitudes of the power spectra (r2 ~0.8) over the range of frequencies of the main harmonics.

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Evaluation of Soil Water Content Using SWAT for Southern Saskatchewan, Canada

Water ◽

10.3390/w14020249 ◽

2022 ◽

Vol 14 (2) ◽

pp. 249

Author(s):

Mohammad Zare ◽

Shahid Azam ◽

David Sauchyn

Keyword(s):

Soil Moisture ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Land Surface ◽

Assessment Tool ◽

Swat Model ◽

Warm Season ◽

Decisive Role ◽

R Value

Soil water content (SWC) is one of the most important hydrologic variables; it plays a decisive role in the control of various land surface processes. We simulated SWC using a Soil and Water Assessment Tool (SWAT) model in southern Saskatchewan. SWC was calibrated using measured data and Soil Moisture Active Passive (SMAP) Level-4 for the surface (0–5 cm) SWC for hydrological response units (HRU) at daily and monthly (warm season) intervals for the years 2015 to 2020. We used the SUFI-2 technique in SWAT-CUP, and observed daily instrumented streamflow records, for calibration (1995 to 2004) and validation (2005–2010). The results reveal that the SWAT model performs well with a monthly PBIAS < 10% and Nash–Sutcliffe efficiency (NS) and R2 ≥ 0.8 for calibration and validation. The correlation coefficient between ground measurement with SMAP and SWAT products are 0.698 and 0.633, respectively. Moreover, SMAP data of surface SWC coincides well with measurements in terms of both amount and trend compared with the SWAT product. The highest r value occurred in July when the mean r value in SWAT and SMAP were 0.87 to 0.84, and then in June for r value of 0.75. In contrast, the lowest values were in April and May (0.07 and 0.04, respectively) at the beginning of the growing season in southern Saskatchewan. Furthermore, calibration in the SWAT model is based on a batch form whereby parameters are adjusted to corresponding input by modifying simulations with observations. SWAT underestimates the abrupt increase in streamflow during the snowmelt months (April and May). This study achieved the objective of developing a SWAT model that simulates SWC in a prairie watershed, and, therefore, can be used in a subsequent phase of research to estimate future soil moisture conditions under projected climate changes.

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Estimating in-situ soil moisture dynamics from soil thermal dependencies

10.5194/egusphere-egu2020-13852 ◽

2020 ◽

Author(s):

Svenja Hoffmeister ◽

Sibylle Haßler ◽

Mirko Mälicke ◽

Erwin Zehe

Keyword(s):

Time Series ◽

Soil Moisture ◽

Phase Shift ◽

Soil Temperature ◽

Soil Water ◽

Coupled Model ◽

Soil Moisture Dynamics ◽

Amplitude Attenuation ◽

Moisture Dynamics

<p>Soil moisture plays an important role for the understanding of hydrological processes due to its influence on water and energy fluxes between the soil surface and the atmosphere. Knowledge of soil water dynamics is especially critical in water-scarce areas. In agroforestry systems, for instance, excessive competition for water between the trees and crops might outweigh the benefits of the system, thus preventing a successful implementation.<br>Several techniques exist for measuring soil moisture and commercial devices vary widely in cost, reliability and efficiency. An alternative approach could be to estimate soil moisture dynamics from soil thermal dependencies. Similar approaches are already being used in remote sensing, as soil moisture influences the soil thermal properties and thus the surface energy balance and soil heat transfer. However, few studies have tested the feasibility of estimating in-situ soil moisture dynamics from soil temperature dynamics within a soil profile. Temperature sensors are cheaper, smaller and technically robust and could thus provide an interesting alternative to available commercial soil moisture sensors.<br>In this study, we quantify the effect of soil moisture on phase shift and amplitude attenuation of soil temperature to estimate soil moisture content. We investigate these relationships from two different angles. Firstly, we use virtual measurements in coupled model simulations of soil water and soil heat dynamics to infer the general feasibility and precision of the method in an idealized error-free world. A sensitivity analysis can give insights on how the parametrization of the thermal diffusivity affects the precision and feasibility. Secondly, we compare findings from these simulations to results from analyzing time series of both soil moisture and soil temperature measured in an agroforestry field site in South Africa. A tentative analysis of these time series reveals that the amplitude attenuation and phase shift in the daily temperature signal is clearly sensitivity to changes in soil moisture. Finally, we aim to setup a coupled model for the study site based on the available soil hydraulic and textural data and compare simulated with observed phase shifts and attenuations at different depths.</p>

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Probabilistic inference of ecohydrological parameters using observations from point to satellite scales

Hydrology and Earth System Sciences ◽

10.5194/hess-22-3229-2018 ◽

2018 ◽

Vol 22 (6) ◽

pp. 3229-3243 ◽

Cited By ~ 3

Author(s):

Maoya Bassiouni ◽

Chad W. Higgins ◽

Christopher J. Still ◽

Stephen P. Good

Keyword(s):

Soil Moisture ◽

Water Balance ◽

Soil Water ◽

Dry Season ◽

Soil Water Balance ◽

Rainfall Pattern ◽

Site Specific ◽

Soil Moisture Dynamics ◽

Soil Saturation ◽

Moisture Dynamics

Abstract. Vegetation controls on soil moisture dynamics are challenging to measure and translate into scale- and site-specific ecohydrological parameters for simple soil water balance models. We hypothesize that empirical probability density functions (pdfs) of relative soil moisture or soil saturation encode sufficient information to determine these ecohydrological parameters. Further, these parameters can be estimated through inverse modeling of the analytical equation for soil saturation pdfs, derived from the commonly used stochastic soil water balance framework. We developed a generalizable Bayesian inference framework to estimate ecohydrological parameters consistent with empirical soil saturation pdfs derived from observations at point, footprint, and satellite scales. We applied the inference method to four sites with different land cover and climate assuming (i) an annual rainfall pattern and (ii) a wet season rainfall pattern with a dry season of negligible rainfall. The Nash–Sutcliffe efficiencies of the analytical model's fit to soil observations ranged from 0.89 to 0.99. The coefficient of variation of posterior parameter distributions ranged from < 1 to 15 %. The parameter identifiability was not significantly improved in the more complex seasonal model; however, small differences in parameter values indicate that the annual model may have absorbed dry season dynamics. Parameter estimates were most constrained for scales and locations at which soil water dynamics are more sensitive to the fitted ecohydrological parameters of interest. In these cases, model inversion converged more slowly but ultimately provided better goodness of fit and lower uncertainty. Results were robust using as few as 100 daily observations randomly sampled from the full records, demonstrating the advantage of analyzing soil saturation pdfs instead of time series to estimate ecohydrological parameters from sparse records. Our work combines modeling and empirical approaches in ecohydrology and provides a simple framework to obtain scale- and site-specific analytical descriptions of soil moisture dynamics consistent with soil moisture observations.

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Effects of Irrigation and Cassava Peel Mulch on Soil Water Content and Pineapple Growth

Jurnal Irigasi ◽

10.31028/ji.v8.i2.99-114 ◽

2013 ◽

Vol 8 (2) ◽

pp. 99

Author(s):

Ali Rahmat ◽

Afandi ◽

Tumiar K Manik ◽

Priyo Cahyono

Keyword(s):

Time Series ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Cassava Peel

Irigasi pada tanaman nanas sangat penting karena mempengaruhi pertumbuhan dan produksi namun biayanya sangat mahal. Penelitian ini bertujuan untuk mengetahui pengaruh irigasi dan mulsa organik pada kadar air tanah dan pertumbuhan nanas. Penelitian ini dilakukan menggunakan perlakuan faktorial (5 x 2) dalam rancangan acak kelompok dengan tiga ulangan. Faktor pertama adalah panjang waktu irigasi (I), yang terdiri dari 5 waktu yaitu tanpa irigasi (I0), irigasi 1 bulan (I1), irigasi 2 bulan (I2), irigasi 3 bulan (I3), dan irigasi 4 bulan (I4). Faktor kedua adalah dosis kulit singkong (mulsa organik) terdiri dari 2 level 0 ton/ha (M0) dan 50 ton/ha (M1). Kadar air tanah diukur menggunakan Diviner 2000. Data kadar air tanah dianalisis dengan time series. Pertumbuhan tanaman dianalisis keragamannya dan diuji BNT pada taraf 5 %. Hasil penelitian menunjukkan kulit singkong 50 ton/ha pada umumnya hanya bertahan 2,5 bulan untuk mempertahankan kadar air. Mulsa kulit singkong lebih berperan ketika tanah mulai mengering. Pemberian mulsa kulit singkong berpengaruh terhadap tinggi dan berat basah tanaman sedangkan perlakuan, irigasi secara terpisah hanya berpengaruh terhadap berat basah tanaman. Interaksi antara irigasi dan kulit singkong berpengaruh terhadap berat basah tanaman. Meskipun kadar air tanah tersedia cukup saat memasuki musim hujan, namun tidak efektif dalam memulihkan keragaan tanaman nanas. Pemulihan terjadi setelah memasuk musim hujan dimana kadar air tanah tinggi.

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