MEASUREMENT OF NEAR-SURFACE SOIL MOISTURE WITH A HYDROGENOUSLY SHIELDED NEUTRON PROBE

1988 ◽  
Vol 68 (1) ◽  
pp. 171-176 ◽  
Author(s):  
D. S. CHANASYK ◽  
M. A. NAETH
Keyword(s):  
Soil Moisture ◽  
Key Words ◽  
Surface Soil ◽  
Ground Surface ◽  
Soil Surface ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Surface Moisture ◽  
Neutron Probe

Numerous techniques for measuring near-surface soil moisture with a neutron probe have been reported. Placing the neutron probe horizontally on the ground surface within a hydrogenous shield has been discussed theoretically, but field adaptations have not. Such a shield was designed and used, yielding measurements of near-surface soil moisture quickly and accurately. Key words: Neutron probe, soil surface moisture, calibration

High-resolution near-surface soil moisture through the combination of Sentinel-1 and Cosmic-Ray Neutron Probe in a Mediterranean agroforestry

2021 ◽  
Author(s):  
Aida Taghavi Bayat ◽  
Sarah Schönbrodt-Stitt ◽  
Paolo Nasta ◽  
Nima Ahmadian ◽  
Christopher Conrad ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Cosmic Ray ◽  
Small Scale ◽  
Surface Soil Moisture ◽  
Field Scale ◽  
Point Scale ◽  
Near Surface ◽  
Neutron Probe ◽  
Topographic Attributes

<p>The precise estimation and mapping of the near-surface soil moisture (~5cm, SM<sub>5cm</sub>) is key to supporting sustainable water management plans in Mediterranean agroforestry environments. In the past few years, time series of Synthetic Aperture Radar (SAR) data retrieved from Sentinel-1 (S1) enable the estimation of SM<sub>5cm</sub> at relatively high spatial and temporal resolutions. The present study focuses on developing a reliable and flexible framework to map SM<sub>5cm</sub> in a small-scale agroforestry experimental site (~30 ha) in southern Italy over the period from November 2018 to March 2019. Initially, different SAR-based polarimetric parameters from S1 (in total 62 parameters) and hydrologically meaningful topographic attributes from a 5-m Digital Elevation Model (DEM) were derived. These SAR and DEM-based parameters, and two supporting point-scale estimates of SM<sub>5cm</sub> were used to parametrize a Random Forest (RF) model. The inverse modeling module of the Hydrus-1D model enabled to simulate two  supporting estimates of SM<sub>5cm</sub> by using i) sparse soil moisture data at the soil depths of 15 cm and 30 cm acquired over 20 locations comprised in a SoilNet wireless sensor network (SoilNet-based approach), and ii) field-scale soil moisture monitored by a Cosmic-Ray Neutron Probe (CRNP-based approach). In the CRNP-based approach, the field-scale SM<sub>5cm</sub> was further downscaled to obtain point-scale supporting SM<sub>5cm</sub> data over the same 20 positions by using the physical-empirical Equilibrium Moisture from Topography (EMT) model. Our results show that the CRNP-based approach can provide reasonable SM<sub>5cm</sub> retrievals with RMSE values ranging from 0.034 to 0.050 cm³ cm<sup>-3</sup> similar to the ones based on the SoilNet approach ranging from 0.029 to 0.054 cm³ cm<sup>-3</sup>. This study highlights the effectiveness of integrating S1 SAR-based measurements, topographic attributes, and CRNP data for mapping SM<sub>5cm</sub> at the small agroforestry scale with the advantage of being non-invasive and easy to maintain.</p><p> </p>

Mapping of tank silt application using Sentinel-2 images over the Berambadi catchment (India).

2021 ◽  
Author(s):  
Cécile Gomez ◽  
Dharumarajan Subramanian ◽  
Philippe Lagacherie ◽  
Jean Riotte ◽  
Sylvain Ferrant ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Properties ◽  
South India ◽  
Surface Soil ◽  
Soil Surface ◽  
Calibration Data ◽  
Surface Soil Moisture ◽  
Soil Color ◽  
Color Changes ◽  
Sentinel 2

<p>Mapping soil properties is becoming more and more challenging due to the increase in anthropogenic modification of the landscape, calling for new methods to identify these changes. A striking example of anthropogenic modifications of soil properties is the widespread practice in South India of applying large quantities of silt from dry river dams (or “tanks”) to agricultural fields. Whereas several studies have demonstrated the interest of tank silt for soil fertility, no assessment of the actual extent of this age-old traditional practice exists. Over pedological contexts characterized by Vertisol, Ferralsols and Chromic Luvisols in sub-humid and semi-arid Tropical climate, this practice is characterized by an application of black-colored tank silt providing from Vertisol, to red-colored soils such as Ferralsols. The objective of this work was to evaluate the usefulness of Sentinel-2 images for mapping tank silt applications, hypothesizing that observed changes in soil surface color can be a proxy for tank silt application.</p><p>We used data collected in a cultivated watershed (Berambadi, Karnataka state, South India) including 217 soil surface samples characterized in terms of Munsell color. We used two Sentinel-2 images acquired on February 2017 and April 2017. The surface soil color over each Sentinel-2 image was classified into two-class (“Black” and “Red” soils). A change of soil color from “Red” in February 2017 to “Black” in April 2017 was attributed to tank silt application. Soil color changes were analyzed accounting for possible surface soil moisture changes. The proposed methodology was based on a well-balanced Calibration data created from the initial imbalanced Calibration dataset thanks to the Synthetic Minority Over-sampling Technique (SMOTE) methodology, coupled to the Cost-Sensitive Classification And Regression Trees (Cost-Sensitive CART) algorithm. To estimate the uncertainties of i) the two-class classification at each date and ii) the change of soil color from “Red” to “Black”, a bootstrap procedure was used providing fifty two-class classifications for each Sentinel-2 image.</p><p>The results showed that 1) the CART method allowed to classify the “Red” and “Black” soil with overall accuracy around 0.81 and 0.76 from the Sentinel-2 image acquired on February and April 2017, respectively, 2) a tank silt application was identified over 97 fields with high confidence and over 107 fields with medium confidence, based on the bootstrap results and 3) the identified soil color changes are not related to a surface soil moisture change between both dates. With the actual availability of the Sentinel-2 and the past availability of the LANDSAT satellite imageries, this study may open a way toward a simple and accurate method for delivering tank silt application mapping and so to study and possibly quantify retroactively this farmer practice.</p>

scholarly journals The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

2015 ◽  
Vol 19 (12) ◽  
pp. 4831-4844 ◽  
Author(s):  
C. Draper ◽  
R. Reichle
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
In Situ Observations ◽  
The Impact

Abstract. A 9 year record of Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SMshort), mean seasonal (SMseas), and inter-annual (SMlong) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10−3 m3 m−3)2, of which 19 % was in SMlong, 26 % in SMseas, and 55 % in SMshort. The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SMlong, 24 % in SMseas, and 38 % in SMshort. For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SMshort, assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SMseas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

scholarly journals A Data-Driven Approach for Daily Real-Time Estimates and Forecasts of Near-Surface Soil Moisture

2017 ◽  
Vol 18 (3) ◽  
pp. 837-843 ◽  
Author(s):  
Randal D. Koster ◽  
Rolf H. Reichle ◽  
Sarith P. P. Mahanama
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Real Time ◽  
Surface Soil ◽  
Data Driven ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Time Estimates ◽  
Data Driven Approach ◽  
Global Surface

Abstract NASA’s Soil Moisture Active Passive (SMAP) mission provides global surface soil moisture retrievals with a revisit time of 2–3 days and a latency of 24 h. Here, to enhance the utility of the SMAP data, an approach is presented for improving real-time soil moisture estimates (nowcasts) and for forecasting soil moisture several days into the future. The approach, which involves using an estimate of loss processes (evaporation and drainage) and precipitation to evolve the most recent SMAP retrieval forward in time, is evaluated against subsequent SMAP retrievals themselves. The nowcast accuracy over the continental United States is shown to be markedly higher than that achieved with the simple yet common persistence approach. The accuracy of soil moisture forecasts, which rely on precipitation forecasts rather than on precipitation measurements, is reduced relative to nowcast accuracy but is still significantly higher than that obtained through persistence.

scholarly journals Influence of cracking clays on satellite estimated and model simulated soil moisture

2010 ◽  
Vol 14 (6) ◽  
pp. 979-990 ◽  
Author(s):  
Y. Y. Liu ◽  
J. P. Evans ◽  
M. F. McCabe ◽  
R. A. M. de Jeu ◽  
A. I. J. M. van Dijk ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Soil Properties ◽  
Surface Soil ◽  
Clay Soils ◽  
Soil Types ◽  
Soil Porosity ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Community Land Model

Abstract. Vertisols are clay soils that are common in the monsoonal and dry warm regions of the world. One of the characteristics of these soil types is to form deep cracks during periods of extended dry, resulting in significant variation of the soil and hydrologic properties. Understanding the influence of these varying soil properties on the hydrological behavior of the system is of considerable interest, particularly in the retrieval or simulation of soil moisture. In this study we compare surface soil moisture (θ in m3 m−3) retrievals from AMSR-E using the VUA-NASA (Vrije Universiteit Amsterdam in collaboration with NASA) algorithm with simulations from the Community Land Model (CLM) over vertisol regions of mainland Australia. For the three-year period examined here (2003–2005), both products display reasonable agreement during wet periods. During dry periods however, AMSR-E retrieved near surface soil moisture falls below values for surrounding non-clay soils, while CLM simulations are higher. CLM θ are also higher than AMSR-E and their difference keeps increasing throughout these dry periods. To identify the possible causes for these discrepancies, the impacts of land use, topography, soil properties and surface temperature used in the AMSR-E algorithm, together with vegetation density and rainfall patterns, were investigated. However these do not explain the observed θ responses. Qualitative analysis of the retrieval model suggests that the most likely reason for the low AMSR-E θ is the increase in soil porosity and surface roughness resulting from cracking of the soil. To quantitatively identify the role of each factor, more in situ measurements of soil properties that can represent different stages of cracking need to be collected. CLM does not simulate the behavior of cracking soils, including the additional loss of moisture from the soil continuum during drying and the infiltration into cracks during rainfall events, which results in overestimated θ when cracks are present. The hydrological influence of soil physical changes are expected to propagate through the modeled system, such that modeled infiltration, evaporation, surface temperature, surface runoff and groundwater recharge should be interpreted with caution over these soil types when cracks might be present. Introducing temporally dynamic roughness and soil porosity into retrieval algorithms and adding a "cracking clay" module into models are expected to improve the representation of vertisol hydrology.

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