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Article

A Novel Approach to Interpret Soil Moisture Content for Economical Monitoring of Urban Landscape

by
Junwei Liu
1,*,
Vinay Kumar Gadi
2,
Ankit Garg
3,
Suriya Prakash Ganesan
1 and
Anasua GuhaRay
4
1
School of Civil Engineering, Qingdao University of Technology, Qingdao 266000, China
2
Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati 781030, India
3
Department of Civil and Environmental Engineering, Shantou University, Shantou 515000, China
4
Department of Civil Engineering, BITS-Pilani Hyderabad Campus, Hyderabad 500078, India
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(20), 5609; https://doi.org/10.3390/su11205609
Submission received: 17 September 2019 / Revised: 30 September 2019 / Accepted: 1 October 2019 / Published: 12 October 2019
(This article belongs to the Section Sustainable Engineering and Science)
Browse Figures
Versions Notes

Abstract

:
Preservation of green infrastructure (GI) needs continuous monitoring of soil moisture. Moisture content in soil is generally interpreted on the basis electrical conductivity (EC), soil temperature and relative humidity (RH). However, validity of previous approaches to interpret moisture content in urban landscape was rarely investigated. There is a need to relate the moisture content with other parameters (EC, temperature and RH) to economize the sensor installation. This study aims to quantify the dynamics of the above-mentioned parameters in an urban green space, and to further develop correlations between moisture content and other parameters (EC, temperature and RH). An integrated field monitoring and statistical modelling approach were adopted to achieve the objective. Four distinct sites comprising treed (younger and mature tree), grassed and bare soil were selected for investigation. Field monitoring was conducted for two months to measure four parameters. This was followed by statistical modelling by artificial neural networks (ANN). Correlations were developed for estimating soil moisture as a function of other parameters for the selected sites. Irrespective of the type of site, EC was found to be the most significant parameter affecting soil moisture, followed by RH and soil temperature. This correlation with EC is found to be stronger in vegetated soil as compared to that without vegetation. The correlations of soil temperature with water content do not have a conclusive trend. A considerable increase in temperature was not found due to the subsequent drying of soil after rainfall. A normal distribution function was found from the uncertainty analysis of soil moisture in the case of treed soil, whereas soil moisture was observed to follow a skewed distribution in the bare and grassed soils.

1. Introduction

Green infrastructure (GI), such as green roofs, lawns, parks and biofiltration units, are an economic, environmentally friendly and resilient approach to managing storm water [1,2,3]. Preservation of GI is essential for ensuring its serviceability [4,5]. Among all different types of GI, urban lawns and gardens are the most common among both developing and developed worlds. GI mainly utilizes vegetated soil to reinstate biological processes that are required for its preservation [6,7,8,9].
The soil–plant–atmosphere interaction is the most important process that is frequently investigated by scientific communities around the world in GI [10,11,12,13,14,15,16]. The soil–plant–water–atmospheric interaction was widely studied in terms of the hydrological effects of vegetation. A few studies [17,18,19,20] report that water-holding capacity increases due to the presence of roots [18]. The reason for this may be blockage of pores due to root permeation. However, some other studies [21,22,23] show that water-holding capacity decreases due to the decay and shrinkage of roots. Accurate measurements of moisture content in GI is vital to analyse evapotranspiration, groundwater recharge and cloud formation (i.e., soil–plant–atmosphere interaction [24,25])
A large number of approaches were developed previously to interpret moisture content in soil on the basis of EC [26,27,28,29,30], temperature [31,32,33,34] and RH [35]. Even though interpretation from the above-mentioned approaches have been successfully used in various types of soils vegetated with crop species [19,36], they may not be applicable for the case of vegetated soil in urban space [16,37]. This can be attributed to a difference between frequencies adopted in urban green space and agricultural fields [38,39,40]. In urban green space, the density of soil would be higher than that in agricultural soils [41]. In addition, plants are not irrigated and harvested as frequently as those crop lands [42]. Unlike agricultural lands, urban space is usually vegetated for aesthetic purposes [16,23].
It is evident that various types of vegetation may alter dynamics of water content [19,23]. However, the validity of the previous approaches developed to interpret moisture content was rarely investigated in urban landscapes. Therefore, relationships between water content and other parameters (EC, temperature and RH) need to be investigated. There could be uncertainties in moisture content that can arise due to climatic variation. It is known that long-term field monitoring is usually conducted in urban landscapes by installing moisture content sensors. It is evident that salt is applied to remove and manage snow on pavements and parking lots [43,44]. Stormwater in urban areas is generally contaminated by salts from pavements and parking lots [45]. These salts adversely affect the plant growth in urban green spaces [46]. The salinity effect is commonly expressed in terms of osmatic potential. Hoffman et al. [47] shows that the area covered by vegetation can be increased up to 130% due to a 460 kPa increase in osmatic potential. This change in surface area alter the transpiration rate by 46% [47]. In addition, EC sensors are also used to interpret topsoil depth and clay content [48,49,50]. Furthermore, few studies also reported that soil salinity affect evapotranspiration [51,52,53].
Evapotranspiration is a key phenomenon of water balance and it induces suction in vegetated soil [24,54,55]. Further, evapotranspiration-induced suction affects the infiltration through the vegetated soil [56,57]. An accurate estimation of evapotranspiration is important to model the water balance. RH is one of the important parameters that govern evapotranspiration [58]. This can be observed from the Penman–Monteith equation [51,59]. In addition, RH significantly affects plant growth. Hoffman et al. [47] have reported that the canopy area of cotton increases by 34% due to a 50% rise in relative humidity. This change in surface area causes a 57% difference in evapotranspiration [47]. Installation of a large number of sensors (especially soil moisture probes) would not be economically feasible in developing countries. In addition, these relationships are important in critical times (especially during severe weather conditions), where malfunctioning of sensors may occur. In such cases, moisture content can be approximated based on EC, temperature and RH. Hence, the above-mentioned relationships can also help to minimize the cost of installing additional moisture sensors that are usually required in a relatively large area of urban landscape in developing countries.
The main objective of this study was to relate the water content of soil with various parameters (EC, RH and soil water content) for various types of covers, such as bare, grassed and treed. A holistic long-term monitoring and artificial neural network (ANN) approach is adopted to measure the properties of soil and develop various models, respectively. The prominence of properties of soil and uncertainty in water content distribution was evaluated after developing the model. This research helps to infer soil water content in a usual spacious GI site from various other parameters. Furthermore, it is helpful to comprehend the reliability of GI. The generated dataset could be useful to calibrate and develop theoretical models in soil–vegetation–atmosphere interactions.

2. Materials and Methods

2.1. Field Monitoring Programme

2.1.1. Site Details

The test site is located in Guangdong Province. A schematic view of the instrumentation in the test site is shown in Figure 1. The selected site has two areas of 64 square meters, of which one part is covered with trees and the other is covered with grass. The depth of the groundwater table was found at 2.9 m. Field monitoring was conducted from 12 July to 14 September 2017. The main objective of this field monitoring was air temperature, RH and solar radiation. The ranges of these parameters were found to be 27–36 °C, 49%–100% and 44–906 W/m2, respectively. Bare soil and three distinct types of vegetated soils were selected. Those were soils vegetated with Chinese banyan (Ficus microcarpa), Foxtail palm (Wodyetia bifurcate) and Cynodon dactylon. The age of the Chinese banyan (Tree 1) and Foxtail palm (Tree 2) were found to be 30 years and 10 years from the rings on stems, respectively. This suggests that they are at a mature stage. These trees were selected based on their widespread presence in subtropical regions.

2.1.2. Soil Properties and Details of Vegetation

The samples were collected and transported to the laboratory under the provisions of ASTM codes [60,61,62]. Grain size analysis [62] revealed that the average contents of gravel (particle size ≥ 2 mm), sand (0.063 mm ≥ particle size ≤ 2 mm) and, silt and clay (particle size ≤ 0.063 mm) of treed soil are 3%, 89% and 8%, respectively. These contents are 2%, 91% and 7% in the bare soil. Plastic limit (PL) and liquid limit (LL) were determined under the provisions of ASTM D4318-93 [63]. The LL of vegetated soil and bare soil was found to be 28 ± 3% and 28 ± 5%, respectively. PL of bare soil and vegetated soil was found to be 9% and 7%, respectively. The soils in the selected site are categorized as well-graded sand (SW), based on the Unified Soil Classification System (USCS [62]). Specific gravity of the soil in the test sites was determined to be 2.66 under the provisions of ASTM [64]. In situ dry density was observed to vary between 1702 kg/m3 and 1731 kg/m3. The saturated hydraulic conductivity of bare soil and vegetated soil was determined to be 4.9 ± 0.23 × 10−4 m/s and 3.9 ± 0.4 × 10−5 m/s from the falling head method [65].

2.1.3. Experimental Setup and Instrumentation

Four groups of soil moisture sensors (EC5 [66]) and soil EC sensors (5TE [67]) were installed at the depths of 0.1 m at selected locations. Soil temperature was also monitored using 5TE. The distance between different sensors were set as 0.3 m so as to avoid any interference between sensors. Corresponding to the bare ground, the same sensors in the tree-covered ground were set. The distance between EC5s/5TEs and the tree trunk were 0.5 m, 1 m, 1.5 m and 2 m successively. The EC5 sensor interprets the volumetric water content from the dielectric permittivity of the soil medium [66]. Dielectric permittivity is measured using the electromagnetic field. An oscillating wave is supplied to the sensor tines after installation. The sensor quantifies the dielectric constant from the charge stored in the tines. An alternating current is applied to two tines of 5TE to measure the electrical resistance. Electric conductivity is derived from the measured electrical resistance [67]. The temperature is measured by the thermistor in 5TE [67]. The operational range of the selected sensors was −40–50 °C. Furthermore, the ground temperature distribution map was protracted by a thermal imager. The relative humidity and rainfall intensity were monitored using a commercially available weather monitoring unit (Vantage Pro2). The automatic weather station was placed at 2 m above the ground.

2.2. Development of Model

ANN is widely used as a modeling tool due to its potential to learn proficiently from the provided data. ANN comprises of three different kinds of layers, i.e., (i) input layer, (ii) output layer and (iii) hidden layer. Generally, the first layer takes variables while the second layer has a single neuron which is similar to the number of output variables. Second and third layers consist of few neurons that utilizes a nonlinear function. Neurons of one layer are connected to those of the pre or after layer using weighted links. Each neuron of the second layer and third layer is offset by a value [25,68]. In this study, models were developed using ANN to estimate soil water content from soil temperature, soil EC and RH. The parameters were normalized with respect to the highest values of those. In addition, the collected data were split into three intervals. This helps to evaluate the effect of changes in atmospheric parameters on the monitored parameters. The ANN model was formulated using 69 mix compositions to improve accuracy and robustness.
A software JMP was used to implement the three-layer feed forward neural network. Table 1 presents parameter settings used in the present study. The data of training, testing and validation were set at 70%, 15% and 15% to search for a suitable model. Efficiency of the selected models is discussed in following paragraph.

3. Results and Discussion

Changes in Soil Moisture Content, Electrical Conductivity and Temperature in bare soil and soils vegetated with Trees are discussed in the following sections.

3.1. Mean Soil Water Content

Figure 2 shows the changes in soil moisture content at a depth of 0.1 m for bare soil and soil with trees. Soil water content increases with a rise in rainfall intensity in both bare and vegetated soils. Peak water content near the saturation state between the two grounds are fairly close. A decrease in water content can be attributed to evaporation and evapotranspiration. The rate of decrease in moisture content in bare soil is found to be higher than that in soil vegetated with trees. Moisture content in soil vegetated with trees is observed to be higher (up to 155%) than that in the bare ground on 15 August. Unlike data in [69,70,71], which consistently shows that due to evapotranspiration, moisture content is higher when vegetation is absent.
Soil moisture measurements were carried out at shallow depth from the surface (0.1 m). Moisture status depends on physical evaporation [16] at such shallow depth. It is determined by the heating of the surface and turbulent gas exchange, which in the open area is much more than under the forest, judging by the constant temperature difference of 5 °C or more (Figure 3; [52,72,73,74]). Volumetric water content near to saturation state (33%) as well as dry state (i.e., 2%) were found to occur during the monitoring period. As the objective of this study is to relate the water content with other parameters, the considered monitoring period is sufficient.

3.2. Temperature of the Soil

Changes in the soil temperature during the monitoring period at a depth of 0.1 m is shown in Figure 3. Changes in soil temperature can be seen corresponding to variation in air temperature. The temperature of bare soil is observed to be greater than that of air when soil moisture content is relatively low (Figure 1 and Figure 2). This can be attributed to relatively less specific heat of the dry soil as compared to that of wet soil. Relatively less specific heat implies rapid exchange of heat between air and soil. Therefore, soil temperature increases rapidly in the dry soil [75]. Temperature of air and bare soil is found to be up to 30% greater than that in soil vegetated with trees during the monitoring period. Fluctuation was found to be more significant in the temperature of bare soil (27.1–38.2 °C) as compared to that of the treed soil (26–31.9 °C). As heat exchange between the soil surface and atmosphere was mainly driven by radiation [76], the tree canopy intercepted radiation and hence strongly dampened soil temperature variations in the soil [77].

3.3. Mean Soil EC

Changes in EC at 0.1 m depth in soil with and without vegetation are shown in Figure 4. Maximum EC (147.5 microS/cm) of soil vegetated with trees was found to occur right after a heavy precipitation on 4 September. The EC of the vegetated soil was found to be greater than that in bare soil. The magnitude of change in EC of bare soil (6.6–80.9 microS/cm) was observed to be 24% smaller that of the treed soil (47.83–146.8 microS/cm). Trend of variation of EC was observed to be dissimilar from the moisture and temperature of soil. Changes in the magnitude of moisture content and temperature of soil was observed to be lower in vegetated soil as compared to that in bare soil. However, any difference between saturated water contents of bare and vegetated soil was not found due to heavy rainfall, while soil EC in the tree ground was about 88% and 100% higher than that in the bare ground. It can be seen from Figure 2, Figure 3 and Figure 4 that EC increases with a rise in moisture content and a decrease in temperature of the soil. Comparing Figure 2, Figure 3 and Figure 4, soil EC is observed to increase with an increase in moisture content and a decrease in temperature, irrespective of soil cover. It must be noted that soil composition affects the association between EC and moisture content [78]. It is evident that the LL and plasticity index (PI) varies with change in composition [79]. Previous researchers [79] found the threshold values of LL, PI and clay content beyond which the trend of variation of EC may not be consistent. Those threshold values of LL, PI and clay content were 49%, 34% and 41%, respectively. LL, PI and clay content in the selected site are less than these threshold values. Therefore, the measurements of EC in the site could be adopted to interpret the water content.

4. Development of Model to Compute Soil EC

ANN is applied using an in-house established program for developing models to compute moisture as a function of other parameters. The following sections discuss the efficiency and insights of the developed models.

4.1. Performance of the Developed Models for Various Soil Covers

Measured and computed data from the developed models to predict soil moisture from EC, RH and temperature is shown in Figure 5. Reasonably good agreement between measured and computed moisture content of moisture content can be observed from Figure 5. In all the cases, R2 varies generally between 0.85 and 0.97. This indicates that the newly developed ANN-based models are accurate.
Figure 6 shows the three-dimensional plots demonstrating the reliance of moisture content on other parameters (data measured at 2:00 PM). The non-linear relationship is observed using these plots. The biomass of tree roots is usually larger than grass roots as was also observed in [39]. This might have caused higher root water uptake and also influence on soil electrical conductivity. It can be observed that normalized water content is directly proportional to both soil EC and RH. The correlations observed in three-dimensional plots are dissimilar to that found by previous researchers [36,80]. However, for a large range of soil moisture, in [6,7,8,81,82] the experimental nonlinear dependence of osmotic pressure and EC from the water content with a single-vertex extremum was obtained, and a theoretical explanation with a three-parameter statistical model of this phenomenon based on the lognormal distribution was given.
The non-linear relation was found to be more significant when temperature was one of the dependent parameters. This can be attributed to variation in soil temperature, which is affected by (i) heat flow due to a temperature gradient and (ii) water flow. The effect of heat flow is dominant during drying, whereas the effect of water flow is dominant during wetting. In addition, there is a difference in specific heat capacities of moisture and soil. Hence, heat absorption/dissipation (variation in temperature) of pore water is not proportional to each other.

4.2. Sensitive Analysis to Explore the Relative Importance of Various Parameters on Water Content

Figure 7 presents the results of the sensitive analysis of the newly developed models to estimate normalized moisture content of soil from the other parameters. Results from the bare soil, grassed soil and treed soil (for Tree 1 and Tree 2) are shown at three different points of time. Table 2 and Table 3 summarize the sensitive analysis results.
EC is found to be the most influential parameter governing moisture in all types of soils at different points of time. Contribution of EC towards variation in moisture content is found to be highest for treed soil (Tree 2). Whereas, such contribution is observed to be lowest when vegetation cover is absent. It can be observed that soil properties of the selected sites are similar. Therefore, the variations in EC may be due to the presence of vegetation. EC of roots rise with an increase in their area and length [83]. It was demonstrated in Garg et al. [24] that root area or biomass of trees are generally relatively high as compared to that of grass. It is evident that the EC of the roots rises exponentially with an increase in the area of the individual root [84]. In agricultural science, soil EC readings and yield of the crop have been positively correlated [85]. This means at a particular moisture content, the difference between the EC of bare soil and vegetated soil can be attributed to the presence of roots. It must be noted that the root parameters can be obtained indirectly from the EC–moisture correlation.
RH is found to be the second most influential parameter. Considerable contribution of RH is observed during morning and evening. This is because, during these times soil temperature is generally lower and, hence, the effects of RH become more dominating. Sensitivity analysis shows that EC and RH are the significant predictors of moisture content.

4.3. Optimization Analysis of the Novel Model: Role of Various Parameters in Increasing and Decreasing Water Content

Optimization analyses were performed to find the combination of properties that corresponds to highest and lowest variation in moisture content. Normalized EC, RH and temperature corresponding to the highest (i.e., 1) and lowest (i.e., 0.2) normalized water contents are found. Table 4 and Table 5 summarize various combinations of parameters for different types of soil covers corresponding to the maximum and minimum normalized soil moisture, respectively. It can be observed from Table 1 that the highest values of normalized water content, EC, RH and temperature would be up to 1. It is interesting to notice that moisture should decrease with an increment in the temperature of the soil. However, in optimization analysis, despite a higher soil temperature range (0.74–1.13), the soil moisture still is maximum at 1. This is consistent with the sensitivity analysis, where we found that temperature has the least influence on soil moisture. It is also possible that the low sensitivity of soil moisture to temperature is the result of combining samples with a contrasting temperature regime (all types of soil covers), and in future studies it is necessary to analyze each place separately [9,81,82,86]. The minimum value for EC is 0.27, when RH is equivalent to 1. This is possible just at the beginning of rainfall, when a change in EC is low. Conversely, RH is observed to be minimum (0.4) at high a EC value (0.89). This is possible after the rain was stopped.
EC and RH as shown in Table 3 can be lowest (i.e., 0.01 and 0.34) when water content is minimum. This implies that the effect of EC on water content is more significant than that of RH. This agrees with observations of sensitivity analysis. Interestingly, compared to Table 2, the values of the temperature of soil are generally higher. This implies that an increase in temperature has contributed in minimizing the water content. Table 3 shows that RH or EC or both are generally significantly smaller than that in Table 2 for the corresponding lowest moisture content (0.2). For minimum soil moisture, EC is generally minimum in bare soil at the considered time as compared to vegetated soils. In summary, EC and RH was found to be most influential in increasing and decreasing water content as compared to soil temperature. This implies that changes in moisture content could be estimated from EC and RH using the newly established ANN models. It must be noted that the objective of the current study is to improve the understanding on temperature as a moisture content predictor. Therefore, the two different samples were mixed where the temperature regime is completely different. However, the significance of temperature may vary when the two samples are analyzed separately. This will be considered in future studies.

5. Conclusions

The present study investigates the relationship between the water content of soil and other parameters, namely EC, temperature and RH. Water content and these other parameters were monitored for a short duration of 60 days (field monitoring). ANN was adopted as a preliminary methodology to establish models for estimating moisture content as function of the other parameters.
Based on the study, it is found that soil EC is generally higher in vegetated soils than bare soil. Among vegetated soils, treed soils are found to exhibit highest EC followed by grassed soils. The EC of the soil was found to be relatively low when the size of the canopy is large. Models developed based on ANN show that EC and RH are proportional to moisture content. However, the relationship between soil moisture and EC/RH becomes non-linear and inconclusive when soil temperature is one of the dependent parameters. EC and RH can be reasonable parameters for inferring soil moisture when monitored data is absent in GI and during malfunctioning of instrumentation in the long term. The uncertainty in the considered properties follow a normal distribution. This uncertainty plays a vital role in evaluating the reliability of urban green infrastructure (i.e., the soil water characteristic curve).
The comprehensive data generated in the current study helps to develop and calibrate theoretical models in the context of urban green space. It should be noticed that root biomass decreases due to the age effect. In addition, soil aggregation occurs due to root exudates. This change in root biomass and soil aggregation may alter the EC. Further studies need to be done to analyze biological processes in the soil–root zone (rhizosphere) for the interpretation of variation in EC. Long term (2−3 years) field monitoring can help to yield seasonal and diurnal variations in functional relationships.

Author Contributions

J.L. and A.G. (Ankit Garg) conceived the idea; A.G. (Ankit Garg), S.P.G. and A.G. (Anasua GuhaRay) designed and conducted the field monitoring programme; J.L., V.K.G. and A.G. (Ankit Garg) analyzed the results; J.L., V.K.G. and A.G. (Ankit Garg) prepared and revised the manuscript.

Funding

This research was funded by Natural Science Foundation of China (Grant number: 41772318), Qingdao Fundamental Research Project (Grant number: 16-5-1-34-jch) and Shandong Key Research and Development Plan (Grant number: 2017GSF20107). And the APC was funded by Open Fund of State Key Laboratory of Coastal and Offshore Engineering (LP1712) and Ministry of Housing and Urban-Rural Development of China (2014-K3-026).

Conflicts of Interest

The authors declare no competing conflict of interest.

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Figure 1. Schematic view of test site and instrumentation (after Garg et al. [54]).
Figure 1. Schematic view of test site and instrumentation (after Garg et al. [54]).
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Figure 2. Changes in water content at a depth of 0.1 m during the monitoring period.
Figure 2. Changes in water content at a depth of 0.1 m during the monitoring period.
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Figure 3. Changes in soil temperature at a depth of 0.1 m during the monitoring period.
Figure 3. Changes in soil temperature at a depth of 0.1 m during the monitoring period.
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Figure 4. Changes in EC of soil at a depth of 0.1 m during the monitoring period.
Figure 4. Changes in EC of soil at a depth of 0.1 m during the monitoring period.
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Figure 5. Performance of the considered model.
Figure 5. Performance of the considered model.
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Figure 6. Effect of temperature, EC and RH at 2:00 PM from the field on soil moisture.
Figure 6. Effect of temperature, EC and RH at 2:00 PM from the field on soil moisture.
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Figure 7. Outcomes of sensitivity analysis presenting relative contributions of various parameters to the water content of soil.
Figure 7. Outcomes of sensitivity analysis presenting relative contributions of various parameters to the water content of soil.
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Table
Table 1. Parameters for the artificial neural network (ANN).
Table 1. Parameters for the artificial neural network (ANN).
ParametersValues Assigned
Training data0.70
Testing data0.15
Validation data0.15
Number of hidden layer1
Number of neurons in hidden layer3 to 10
Target goal mean square error10−5
Minimum performance gradient10−5
Seed for sampling103
Table
Table 2. Summary of sensitivity analysis results.
Table 2. Summary of sensitivity analysis results.
Sensitivity Analysis
Samples: Train, Test, Validation
NetworksECRHSoil
Temp
Without vegetation-102.791.481.12
Without vegetation-149.085.531.62
Without vegetation-1917.8113.101.36
Grassed soil-105.691.611.48
Grassed soil-144.071.121.13
Grassed soil-193.902.371.88
Treed soil 1-103.382.571.21
Treed soil 1-148.712.512.42
Treed soil 1-195.974.722.08
Treed soil 2-107.161.120.97
Treed soil 2-148.621.171.11
Treed soil 2-199.721.461.07
Note: 10, 14 and 19 represents time in 24-hour format i.e., 10 AM, 2 PM and 7 PM.
Table
Table 3. Summary of sensitivity analysis results.
Table 3. Summary of sensitivity analysis results.
Sensitivity Analysis
Samples: Train, Test, Validation
NetworksEC (%)RH (%)Soil
Temp (%)
Without vegetation-1052.5328.1319.51
Without vegetation-1456.5433.769.68
Without vegetation-1955.3240.484.16
Grassed soil-1064.8717.9717.12
Grassed soil-1465.8617.5216.57
Grassed soil-1947.3428.7724.13
Treed soil 1-1047.9735.5216.46
Treed soil 1-1464.4318.3117.44
Treed soil 1-1947.0836.3116.64
Treed soil 2-1077.8911.3810.72
Treed soil 2-1479.7210.609.79
Treed soil 2-1978.8312.249.12
Note: 10, 14 and 19 represents time in 24-hour format i.e., 10 AM, 2 PM and 7 PM.
Table
Table 4. Optimization results of selected model by target = 1.
Table 4. Optimization results of selected model by target = 1.
Loaded XML FilesResults, Simplex Search
Dependent Name: Soil Moisture
Target = Highest = 1.0
Soil temp-X1EC-X2RH-X3Soil moisture-Y
Without vegetation-100.890.730.801.00
Without vegetation-140.760.900.391.00
Without vegetation-191.091.290.890.96
Grassed soil-100.841.161.000.93
Grassed soil-140.661.001.000.89
Grassed soil-190.770.261.001.00
Treed soil 1-100.880.970.761.00
Treed soil 1-140.790.301.000.97
Treed soil 1-190.830.970.851.00
Treed soil 2-100.730.980.731.00
Treed soil 2-140.701.000.980.89
Treed soil 2-190.801.001.000.90
Table
Table 5. Optimization results of selected model by target = 0.
Table 5. Optimization results of selected model by target = 0.
Loaded XML FilesResults, Simplex Search
Dependent Name: Soil Moisture
Target = Lowest = 0.2
Soil temp-X1EC-X2RH-X3Soil moisture-Y
Without vegetation-100.860.070.350.19
Without vegetation-140.870.080.640.19
Without vegetation-190.950.020.930.19
Grassed soil-100.83−0.290.960.25
Grassed soil-141.000.090.410.29
Grassed soil-191.000.140.700.22
Treed soil 1-101.000.290.550.46
Treed soil 1-141.000.890.390.33
Treed soil 1-190.890.290.600.31
Treed soil 2-101.000.090.800.30
Treed soil 2-140.850.110.800.18
Treed soil 2-190.930.060.860.18

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MDPI and ACS Style

Liu, J.; Gadi, V.K.; Garg, A.; Ganesan, S.P.; GuhaRay, A. A Novel Approach to Interpret Soil Moisture Content for Economical Monitoring of Urban Landscape. Sustainability 2019, 11, 5609. https://doi.org/10.3390/su11205609

AMA Style

Liu J, Gadi VK, Garg A, Ganesan SP, GuhaRay A. A Novel Approach to Interpret Soil Moisture Content for Economical Monitoring of Urban Landscape. Sustainability. 2019; 11(20):5609. https://doi.org/10.3390/su11205609

Chicago/Turabian Style

Liu, Junwei, Vinay Kumar Gadi, Ankit Garg, Suriya Prakash Ganesan, and Anasua GuhaRay. 2019. "A Novel Approach to Interpret Soil Moisture Content for Economical Monitoring of Urban Landscape" Sustainability 11, no. 20: 5609. https://doi.org/10.3390/su11205609

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