A temperature compensated soil specific calibration approach for frequency domain soil moisture sensors for in-situ agricultural applications

Abstract Soil moisture affects the spatial variation of land–atmosphere interactions through its influence on the balance of latent and sensible heat fluxes. Wetter soils are more prone to flooding because a smaller fraction of rainfall can infiltrate into the soil. The Soil Moisture Ocean Salinity (SMOS) satellite carries a remote sensing instrument able to make estimates of near-surface soil moisture on a global scale. One way to validate satellite observations is by comparing them with observations made with sparse networks of in situ soil moisture sensors that match the extent of satellite footprints. The rate of soil drying after significant rainfall observed by SMOS is found to be higher than the rate observed by a U.S. Department of Agriculture (USDA) soil moisture network in the watershed of the South Fork Iowa River. This leads to the conclusion that SMOS and the network observe different layers of the soil: SMOS observes a layer of soil at the soil surface that is a few centimeters thick, while the network observes a deeper soil layer centered at the depth at which the in situ soil moisture sensors are buried. It is also found that SMOS near-surface soil moisture is drier than the South Fork network soil moisture, on average. The conclusion that SMOS and the network observe different layers of the soil, and therefore different soil moisture dynamics, cannot explain the dry bias. However, it can account for some of the root-mean-square error in the relationship. In addition, SMOS observations are noisier than the network observations.

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Approaches to calibrate in-situ capacitance soil moisture sensors and some of their implications

10.5194/soil-2016-40 ◽

2016 ◽

Cited By ~ 2

Author(s):

N. A. L. Archer ◽

B. R. Rawlins ◽

B. P. Machant ◽

J. D. Mackay ◽

P. I. Meldrum

Keyword(s):

Soil Moisture ◽

Field Measurements ◽

Soil Cores ◽

Undisturbed Soil ◽

Soil Moisture Sensors ◽

Soil Sensors ◽

Laboratory Calibration ◽

Rain Events ◽

Undisturbed Soil Cores

Abstract. Capacitance probes are increasingly being used to monitor volumetric water content (VWC) in field conditions and are provided with in-built factory calibrations so they can be deployed at a field site without the requirement for local calibration. These calibrations may not always have acceptable accuracy and therefore to improve the accuracy of such calibrations soil-specific laboratory or field calibrations are required. In some cases, manufacturers suggest calibration is undertaken on soil in which the structure has been removed (through sieving or grinding), whilst in other cases manufacturers suggest structure may be retained. The objectives of this investigation were to (i) demonstrate the differences in laboratory calibration of the sensors using both structured and unstructured soils, (ii) compare moisture contents at a range of suctions with those predicted from soil moisture release curves for their texture classes (iii) compare the magnitude of errors for field measurements of soil moisture based on the original factory calibrations and the laboratory-based calibrations using structured soil. Grinding and sieving clay soils to 50 % water to the ground and sieved soil samples, dielectric values to VWC > 50 % were observed to be significantly lower than using undisturbed soil cores taken from the field and therefore undisturbed soil cores were considered to be better to calibrate capacitance probes. Generic factory calibrations for most soil sensors have a range of measurement from 0 to 50 %, which is not appropriate for the studied clay-rich soil, where ponding can occur during persistent rain events, which are common in temperate regions.

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Combining satellite observations to develop a daily global soil moisture product for a wide range of applications

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-11549-2015 ◽

2015 ◽

Vol 12 (11) ◽

pp. 11549-11589 ◽

Cited By ~ 5

Author(s):

M. Enenkel ◽

C. Reimer ◽

W. Dorigo ◽

W. Wagner ◽

I. Pfeil ◽

...

Keyword(s):

Soil Moisture ◽

Weather Forecasting ◽

European Space Agency ◽

Space Agency ◽

Global Correlation ◽

Wide Range ◽

Global Soil ◽

Agricultural Applications ◽

The Individual

Abstract. The soil moisture dataset that is generated via the Climate Change Initiative (CCI) of the European Space Agency (ESA) (ESA CCI SM) is a popular research product. It is composed of observations from nine different satellites and aims to exploit the individual strengths of active (radar) and passive (radiometer) sensors, thereby providing surface soil moisture estimates at a spatial resolution of 0.25°. However, the annual updating cycle limits the use of the ESA CCI SM dataset for operational applications. Therefore, this study proposes an adaptation of the ESA CCI processing chain for daily global updates via satellite-derived near real-time (NRT) soil moisture observations. In order to extend the ESA CCI SM dataset from 1978 to present we use NRT observations from the Advanced SCATterometer on-board the MetOp satellites and the Advanced Microwave Scanning Radiometer 2 on-board GCOM-W. Since these NRT observations do not incorporate the latest algorithmic updates, parameter databases, and intercalibration efforts, by nature they offer a lower quality than reprocessed offline datasets. Our findings indicate that, despite issues in arid regions, the new "CCI NRT" dataset shows a good correlation with ESA CCI SM. The average global correlation coefficient between CCI NRT and ESA CCI SM (Pearson's R) is 0.8. An initial validation with 40 in-situ observations in France, Kenya, Senegal and Kenya yields an average R of 0.58 and 0.49 for ESA CCI SM and CCI NRT respectively. In summary, the CCI NRT dataset is getting ready for operational use, supporting applications such as drought and flood monitoring, weather forecasting or agricultural applications.

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Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures

Remote Sensing ◽

10.3390/rs11060656 ◽

2019 ◽

Vol 11 (6) ◽

pp. 656 ◽

Cited By ~ 4

Author(s):

Lei Fan ◽

A. Al-Yaari ◽

Frédéric Frappart ◽

Jennifer Swenson ◽

Qing Xiao ◽

...

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Land Surface ◽

In Situ Measurements ◽

Mountainous Regions ◽

Elevation Data ◽

Agricultural Applications ◽

Moderate Resolution Imaging Spectroradiometer ◽

Topography Data

Hydro-agricultural applications often require surface soil moisture (SM) information at high spatial resolutions. In this study, daily spatial patterns of SM at a spatial resolution of 1 km over the Babao River Basin in northwestern China were mapped using a Bayesian-based upscaling algorithm, which upscaled point-scale measurements to the grid-scale (1 km) by retrieving SM information using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived land surface temperature (LST) and topography data (including aspect and elevation data) and in situ measurements from a wireless sensor network (WSN). First, the time series of pixel-scale (1 km) representative SM information was retrieved from in situ measurements of SM, topography data, and LST. Second, Bayesian linear regression was used to calibrate the relationship between the representative SM and the WSN measurements. Last, the calibrated relationship was used to upscale a network of in situ measured SM to map spatially continuous SM at a high resolution. The upscaled SM data were evaluated against ground-based SM measurements with satisfactory accuracy—the overall correlation coefficient (r), slope, and unbiased root mean square difference (ubRMSD) values were 0.82, 0.61, and 0.025 m3/m3, respectively. Moreover, when accounting for topography, the proposed upscaling algorithm outperformed the algorithm based only on SM derived from LST (r = 0.80, slope = 0.31, and ubRMSD = 0.033 m3/m3). Notably, the proposed upscaling algorithm was able to capture the dynamics of SM under extreme dry and wet conditions. In conclusion, the proposed upscaled method can provide accurate high-resolution SM estimates for hydro-agricultural applications.

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Standardization of Frequency Domain Reflectometry and Watermark Sensors for Soil Moisture Measurement at Field Level

Journal of AgriSearch ◽

10.21921/jas.v6i04.16899 ◽

2019 ◽

Vol 6 (04) ◽

Author(s):

ARTI Kumari ◽

NEELAM PATEL ◽

AKRAM AHMED

Keyword(s):

Soil Moisture ◽

Frequency Domain ◽

Soil Depth ◽

Daily Basis ◽

Coefficient Of Determination ◽

Soil Moisture Sensors ◽

Soil Water Tension ◽

Dry Conditions ◽

Frequency Domain Reflectometry ◽

Water Tension

A field experiment with spilt plot design was carried out for standardization of Frequency Domain Reflectometry (FDR) and Watermark sensors in drip irrigated broccoli(Brassica oleracea var. italica). The experiment included three levels of irrigation frequencies: N1 (once every day), N2 (once every 2 days) and N3 (once every 3 days) with three irrigation regimes of 100, 80 and 60 % of crop evapotranspiration (ETc). For evaluating the performance of different soil moisture sensors, the sensors’ readings were taken from the plot on a daily basis and these readings were compared with gravimetric methods (standard). It was observed that the calibration of two sensors (FDR and Watermark) give a similar calibration equation with lowRMSE after field calibration. The value of coefficient of determination (R2) for FDR was observed 0.85, 0.86, 0.89 and 0.86 for 0-15, 15-30, 30-45 and 45-60 cm soil depth whereas, the value of coefficient of determination (R2) for Watermark sensor was observed as 0.76, 0.83, 0.84 and 0.85 for 0-15, 15-30, 30-45 and 45-60 cm soil depth, respectively. The Watermark sensorscurves observed less sensitive at low soil water tension,whereas FDR sensors performed better in wet as well as dry conditions. That field calibrationof soil moisture sensors isa prerequisite to measure soil moisture content in the soil.

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GNSS-based remote sensing: Innovative observation of key hydrological parameters in the Central Andes

10.5194/egusphere-egu2020-11586 ◽

2020 ◽

Author(s):

Nikolaos Antonoglou ◽

Bodo Bookhagen ◽

Danilo Dadamia ◽

Alejandro de la Torre ◽

Jens Wickert

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Central Andes ◽

Time Domain Reflectometry ◽

Precipitation Event ◽

Hydrological Parameters ◽

Soil Moisture Sensors ◽

Gnss Receivers ◽

Sar Data

The Central Andes are characterized by a steep climatic and environmental gradient with large spatial and temporal variations of associated hydrological parameters. In this region, important hydrological components are integrated water vapor (IWV) and soil moisture. Both parameters can be monitored in parallel by using Global Navigation Satellite System - Reflectometry (GNSS-R) techniques. Soil moisture can furthermore be estimated using Synthetic Aperture Radar (SAR) data.As part of International Research Training Group-StRATEGy project, our research aims at monitoring IWV and soil moisture with new station data in the Central Andes. According to the needs of the research, four independent GNSS ground stations and in-situ soil-moisture sensors were installed in spring 2019. Each station is located at different altitude along the climatic gradient and contains various quality GNSS receivers. It has been shown that high-quality receivers provide precise measurements, while low-quality receivers have not been widely tested for these applications. A goal of this project is the direct comparison of data quality from each site and receiver type. Additionally, soil moisture sensors were installed at each site. This set-up will help to evaluate the quality of the GNSS receivers. Moreover, the GNSS-based remote sensing approaches are directly compared to traditional Time-Domain Reflectometry (TDR) techniques. Meteorological data are used for studying the relation between the magnitude of precipitation events and soil moisture, as well as the time needed to spot a significant change in soil moisture after a precipitation event.GNSS-R soil moisture estimations and in-situ measurements were compared with estimations derived from SAR data. More specifically, we used data from Sentinel-1 and Sat&#233;lite Argentino de Observaci&#243;n COn Microondas (SAOCOM) missions. Sentinel-1 is a fully operational mission that uses C-band wavelengths, while SAOCOM relies on L-band wavelength, but is still in a calibration phase. We analyze both wavelengths and estimate the potential for soil-moisture measurements in the Argentinean Andes.

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A daily/25 km short-latency rainfall product for data scarce regions based on the integration of the GPM IMERG Early Run with multiple satellite soil moisture products

10.5194/hess-2019-387 ◽

2019 ◽

Cited By ~ 1

Author(s):

Christian Massari ◽

Luca Brocca ◽

Thierry Pellarin ◽

Gab Abramowitz ◽

Paolo Filippucci ◽

...

Keyword(s):

Soil Moisture ◽

Resource Management ◽

Water Resource ◽

Water Resource Management ◽

Short Latency ◽

In Situ Data ◽

Long Latency ◽

Agricultural Applications ◽

Unique Source

Abstract. Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative are satellite-based rainfall estimates, yet comparisons with in-situ data suggest they're often not optimal. In this study, we developed a short-latency (i.e., 2–3 days) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM early run (IMERG-ER) with multiple satellite soil moisture-based rainfall products derived from ASCAT, SMOS and SMAP L3 satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high quality ground-based rainfall datasets (India, Conterminous United States, Australia and Europe) and over data scarce regions in Africa and South America by using Triple Collocation analysis (TC). We found the integration of satellite SM observations with in-situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long latency datasets. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.

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The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields

10.5194/hess-2018-94 ◽

2018 ◽

Author(s):

Mireia Fontanet ◽

Daniel Fernández-Garcia ◽

Francesc Ferrer

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Spatial Scales ◽

Irrigation Management ◽

Irrigation Scheduling ◽

Rainfall Events ◽

Soil Moisture Sensors ◽

Gravimetric Measurements ◽

Agricultural Applications ◽

Remote Sensing Techniques

Abstract. Soil moisture measurements are needed in a large number of applications such as climate change, watershed water balance and irrigation management. One of the main characteristics of this property is that soil moisture is highly variable with both space and time, hindering the estimation of a representative value. Deciding how to measure soil moisture before undertaking any type of study is therefore an important issue that needs to be addressed correctly. Nowadays, different kinds of methodologies exist for measuring soil moisture; Remote Sensing, soil moisture sensors or gravimetric measurements. This work is focused on how to measure soil moisture for irrigation scheduling, where soil moisture sensors are the main methodology for monitoring soil moisture. One of its disadvantages, however, is that soil moisture sensors measure a small volume of soil, and do not take into account the existing variability in the field. In contrast, Remote Sensing techniques are able to estimate soil moisture with a low spatial resolution, and thus it is not possible to apply these estimations to agricultural applications. In order to solve this problem, different kinds of algorithms have been developed for downscaling these estimations from low to high resolution. The DISPATCH algorithm downscales soil moisture estimations from 40 km to 1 km resolution using SMOS satellite soil moisture, NDVI and LST from MODIS sensor estimations. In this work, DISPATCH estimations are compared with soil moisture sensors and gravimetric measurements to validate the DISPATCH algorithm in two different hydrologic scenarios; (1) when wet conditions are maintained around the field for rainfall events, and (2) when it is local irrigation that maintains wet conditions. Results show that the DISPATCH algorithm is sensitive when soil moisture is homogenized during general rainfall events, but not when local irrigation generates occasional heterogeneity. In order to explain these different behaviours, we have examined the spatial variability scales of NDVI and LST data, which are the variables involved in the downscaling process provided by the MODIS sensor. Sample variograms show that the spatial scales associated with the NDVI and LST properties are too large to represent the variations of the average water content at the site, and this could be a reason for why the DISPATCH algorithm is unable to detect soil moisture increments caused by local irrigation.

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