A Review: Soil Moisture Estimation Using Different Techniques

Pandey, Jitender; Chamoli, Vivek; Prakash, Rishi

doi:10.1007/978-981-13-8618-3_12

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Another measuring technique, gamma-ray attenuation, is based on radio signals and measures soil water content with a high resolution up to a soil depth of 25 mm (Figure 3) or less in field and laboratory research. Additionally, its non-destructive nature enables repeated assessments of the physical soil parameters in the same site at various periods [86,87]. This technique is sensitive to the soil bulk density changes and SSM.…”

Section: Gamma Attenuationmentioning

confidence: 99%

Soil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review

et al. 2022

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The amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land surface processes. It affects critical land–atmospheric phenomena, including the division of energy and water (infiltration, runoff, and evaporation), that impacts the effectiveness of agricultural output (sensible and latent heat fluxes and surface air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, and interpreting SSM at high spatial and temporal resolutions. The current study critically reviews the methods and procedures for calculating SSM and the variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions. For laboratory and field measurements, this study divides SSM estimate strategies into (i) direct and (ii) indirect procedures. The accuracy and applicability of a technique depends on the environment and the resources at hand. Comparative research is geographically restricted, although precise and economical—direct measuring techniques like the gravimetric method are time-consuming and destructive. In contrast, indirect methods are more expensive and do not produce measurements at the spatial scale but produce precise data on a temporal scale. While measuring SSM across more significant regions, ground-penetrating radar and remote sensing methods are susceptible to errors caused by overlapping data and atmospheric factors. On the other hand, soft computing techniques like machine/deep learning are quite handy for estimating SSM without any technical or laborious procedures. We determine that factors, e.g., topography, soil type, vegetation, climate change, groundwater level, depth of soil, etc., primarily influence the SSM measurements. Different techniques have been put into practice for various practical situations, although comparisons between them are not available frequently in publications. Each method offers a unique set of potential advantages and disadvantages. The most accurate way of identifying the best soil moisture technique is the value selection method (VSM). The neutron probe is preferable to the FDR or TDR sensor for measuring soil moisture. Remote sensing techniques have filled the need for large-scale, highly spatiotemporal soil moisture monitoring. Through self-learning capabilities in data-scarce areas, machine/deep learning approaches facilitate soil moisture measurement and prediction.

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Section: Gamma Attenuationmentioning

confidence: 99%

Soil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review

et al. 2022

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“…to determine the relationship between NavIC signal intensity and angle of elevation. These mathematical models can be used in a variety of applications, including soil moisture estimation [8][9][10][11][12][13][14]. The measurement of soil moisture content is important in the fields of agriculture, climate monitoring, climate change, soil erosion, etc.…”

Section: In This Study Mathematical Model For Both Direct and Multipa...mentioning

confidence: 99%

Mathematical Model Development for Navigation with Indian Constellation (NavIC) L-Band Geo Synchronous Satellite's Direct Signal and Multipath Signals

Chamoli

Prakash²,

Vidyarthi³

2022

Wireless Pers Commun

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NavIC L-band satellite signal that travels from the satellite to the receiver undergoes mainly shadowing and multipath effects. In this research paper, NavIC satellites were observing and analyzing signal efficiency for both the direct signal and multipath signal over the Dehradun area. Two open space data series (Direct signal and Multipath signal) for Dehradun were obtained in this analysis by the satellite receiver. The methodology includes evaluation, testing, and analysis of data in both cases. Based on collected data, a general mathematical model has been developed, describing the signal intensity in the open space context. The both NavIC Mathematical model was validated with the experimental data. The average R 2 and RMSE values developed for the direct signal mathematical model were 0.89 and 1.08% respectively which show a good prediction of NavIC C/N0 value with the developed model. For the multipath mathematical model, four cases have been evaluated. Comparing the graphical view of all four cases for C/No multipath signal concerning raw NavIC C/No multipath signal has been done to select the best mathematical model.

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“…Soil moisture (SM) is defined as retained water within the unsaturated soil zone [1] which serves as an essential variable for hydrological management and understanding the energy balance of terrestrial water [2]. It makes up 0.15% of the global fresh water [3] and is affected by factors such as precipitation, temperature, and other soil characteristics [4].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Based Soil Moisture Estimation for Agricultural Productivity: A note from Lake Tana Sub Basin, NW Ethiopia

Gela¹,

Mengistu²,

Derseh³

2024

New Insights in Soil-Water Relationship

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Soil moisture availability affects agricultural productivity and in turn food security. Estimating the moisture content of soil is imperative for proper water resource management and agricultural productivity. However, field based method is expensive and covers limited spatial variation. The advancement of remote sensing technology eases the soil moisture estimation over large geographic area. Hence, this study intended to apply the optical and thermal remote sensing data for estimating SM in the Lake Tana sub basin. Temperature vegetation dryness index (TVDI) model which is used in this study to estimate soil moisture is derived from the wet and dry edge of the LST-NDVI triangular scatterplot. The finding revealed that NDVI and LST have inverse relationship where LST decrease with increasing NDVI. Spatially, northern and north western part has experienced high LST. The estimated soil moisture result ranging from 0 to 1 where the soil moisture is higher in areas with TVDI value is near 1. Thus, soil moisture is higher in the east, and northeast part of the sub basin whereas the central, western and northwest part experienced low soil moisture. Therefore, applying remote sensing enables estimation of soil moisture across large geographical area with scarcity of field data (in-situ observations).

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A Review: Soil Moisture Estimation Using Different Techniques

Cited by 5 publications

References 8 publications

Soil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review

Soil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review

Mathematical Model Development for Navigation with Indian Constellation (NavIC) L-Band Geo Synchronous Satellite's Direct Signal and Multipath Signals

Remote Sensing Based Soil Moisture Estimation for Agricultural Productivity: A note from Lake Tana Sub Basin, NW Ethiopia

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