Efficiency of Spathiphyllum spp. as a plant-microbial fuel cell

The purpose of this study was to investigate the utility of an ornamental plant, Spathiphyllum spp., as a plant-microbial fuel cell (Plant MFC) to produce voltage and current. This study also evaluated the effect of the Plant MFC on water use efficiency and plant growth. The experiment used four experimental groups: used MFC without plant (Soil MFC), used MFC with plant (Plant MFC), unused MFC without plant (Soil Pot), and unused MFC with plant (Plant Pot). Plant MFC generated higher voltage and current levels than Soil MFC. The average voltage of Plant MFC and Soil MFC was 0.475 V and 0.375 V, respectively, and the average current was 0.110 mA and 0.030 mA, respectively. Plant MFC using Spathiphyllum spp. produced a constant voltage output, with a deviation of 0.027 V during the four-month indoor experiment. The difference between the maximum and minimum voltage during the day was as small as 0.015 V, which supports the utility of Plant MFC as a stable power source. Volumetric soil moisture content, chlorophyll fluorescence (Fv/Fm), photosynthesis rate, leaf area, fresh weight, and dry weight of Plant MFC and Plant Pot were measured. There was no significant difference in any values, and volumetric soil moisture and plant growth were not affected by the utilization of Plant MFC. Thus, a Plant-MFC using Spathiphyllum spp. can play the same ornamental role as conventional plants and at the same time be used as a sustainable bioelectricity source.

Water balance for determination of excess water in soybean cultivated in lowland soils

The aim of this study was to derive a methodology for calculating a sequential water balance that accurately estimates the occurrence of excess water in soybeans cultivated in lowlands. We tested four calculation strategies of water balance associated with the simulation of soybean development, which differed on the calculation of rainfall and time of water drainage from the soil macropores. Data of volumetric moisture monitored in three soil layers throughout the soybean cycle in the 2014/15 agricultural year were used as a reference. Microporosity was used as a lower limit for the occurrence of excess water in the area. Excess water was considered to be whenever the daily volumetric soil moisture in the 0-100 mm layer was greater than 0.39 mm3 mm-3. Over the 111 days of measurement, soil moisture indicated the presence of excess water in 38 days. The traditional calculation strategy of water balance underestimated the occurrence of excess water, as well as the other strategies that considered effective precipitation in their formulas. The calculation strategy that considers that all the rainfall infiltrates in the soil and that the water from macropores is removed only by crop evapotranspiration exhibited good performance and indicated 35 days of excess water, being the most appropriate and recommended for determining excess water in lowland soybeans.

SOURCES OF GLOBAL SCALE SOIL MOISTURE MONITORING DATA BY SATELLITE BASED REMOTE SENSING OF EARTH’S SURFACE

A review of modern tools of global monitoring of soil moisture by means of remote sensing of the Earth’s surface is presented. The characteristic features of the use of orbital radiometers and radars of C, X and L microwave bands for estimating the volumetric soil moisture at a depth of 5 cm and the root layer of vegetation are considered. A review of the capabilities of satellite gravimetry to assess the land water equivalent thickness is made. A number of sources have been proposed for obtaining estimates of soil water content from satellite based radiometric devices and orbital gravimetric systems. Based on the analysis of scientific research papers, the complexity of monitoring the level of fire danger indices in forests is shown, and the prospects of assessing soil moisture in agricultural regions using microwave orbital instruments are demonstrated, and the adequacy of calculating the moisture content in soil at a depth of up to one meter using satellite gravimetry is described.

Estimation of Soil Moisture Applying Modified Dubois Model to Sentinel-1; A Regional Study from Central India

Surface soil moisture has a wide application in climate change, agronomy, water resources, and in many other domain of science and engineering. Measurement of soil moisture at high spatial and temporal resolution at regional and global scale is needed for the prediction of flood, drought, planning and management of agricultural productivity to ensure food security. Recent advancement in microwave remote sensing, especially after the launch of Sentinel operational satellites has enabled the scientific community to estimate soil moisture at higher spatial and temporal resolution with greater accuracy. This study evaluates the potential of Sentinel-1A satellite images to estimate soil moisture in a semi-arid region. Exactly at the time when satellite passes over the study area, we have collected soil samples at 37 different locations and measured the soil moisture from 5 cm below the ground surface using ML3 theta probe. We processed the soil samples in laboratory to obtain volumetric soil moisture using the oven dry method. We found soil moisture measured from calibrated theta probe and oven dry method are in good agreement with Root Mean Square Error (RMSE) 0.025 m 3 /m 3 and coefficient of determination (R 2 ) 0.85. We then processed Sentinel-1A images and applied modified Dubois model to calculate relative permittivity of the soil from the backscatter values ( σ ∘ ). The volumetric soil moisture at each pixel is then calculated by applying the universal Topp’s model. Finally, we masked the pixels whose Normalised Difference Vegetation Index (NDVI) value is greater than 0.4 to generate soil moisture map as per the Dubois NDVI criterion. Our modelled soil moisture accord with the measured values with RMSE = 0.035 and R 2 = 0.75. We found a small bias in the modelled soil moisture ( 0.02 m 3 / m 3 ). However, this has reduced significantly ( 0.001 m 3 / m 3 ) after applying a bias correction based on Cumulative Distribution Function (CDF) matching. Our approach provides a first-order estimate of soil moisture from Sentinel-1A images in sparsely vegetated agricultural land.

Accuracies of Soil Moisture Estimations Using a Semi-Empirical Model over Bare Soil Agricultural Croplands from Sentinel-1 SAR Data

This study describes a semi-empirical model developed to estimate volumetric soil moisture ( ϑ v ) in bare soils during the dry season (March–May) using C-band (5.42 GHz) synthetic aperture radar (SAR) imagery acquired from the Sentinel-1 European satellite platform at a 20 m spatial resolution. The semi-empirical model was developed using backscatter coefficient ( σ ° dB ) and in situ soil moisture collected from Siruguppa taluk (sub-district) in the Karnataka state of India. The backscatter coefficients σ V V 0 and σ V H 0 were extracted from SAR images at 62 geo-referenced locations where ground sampling and volumetric soil moisture were measured at a 10 cm (0–10 cm) depth using a soil core sampler and a standard gravimetric method during the dry months (March–May) of 2017 and 2018. A linear equation was proposed by combining σ V V 0 and σ V H 0 to estimate soil moisture. Both localized and generalized linear models were derived. Thirty-nine localized linear models were obtained using the 13 Sentinel-1 images used in this study, considering each polarimetric channel Co-Polarization (VV) and Cross-Polarization (VH) separately, and also their linear combination of VV + VH. Furthermore, nine generalized linear models were derived using all the Sentinel-1 images acquired in 2017 and 2018; three generalized models were derived by combining the two years (2017 and 2018) for each polarimetric channel; and three more models were derived for the linear combination of σ V V 0 and σ V H 0 . The above set of equations were validated and the Root Mean Square Error (RMSE) was 0.030 and 0.030 for 2017 and 2018, respectively, and 0.02 for the combined years of 2017 and 2018. Both localized and generalized models were compared with in situ data. Both kind of models revealed that the linear combination of σ V V 0 + σ V H 0 showed a significantly higher R2 than the individual polarimetric channels.

Evaluating the potential of Sentinel-1 images for the estimation of soil moisture on an alluvial Fan

<p>We evaluate the potential of Sentinel-1A & 1B satellite images to estimate the volumetric soil moisture content over an alluvial fan of the Kosi River in the North Bihar, India. Over this region, only dual polarised images (VH and VV) are available. However, the existing backscattering models (i.e., Dubois, Oh and IEM models) uses quad polarised (VV, VH, HH and HV) images for the estimation of soil permittivity and surface roughness over the bareland. To overcome the constraint of dual polarised data, we eliminated one of the unknown (i.e. surface roughness) by developing a regression model between the in-situ measured surface roughness and the ratio of backscatter values (VH/VV) in dB.  In a field campaign in the Kosi Fan from December 10-21, 2019, we have measured surface roughness, soil temperature, soil pH and soil moisture at 78 different location using the pin-meter, soil survey instrument (soil temperature and pH), and Time Domain Reflectometer (TDR) respectively. The average surface roughness and soil moisture varies between (0.61 - 5.45) cm and (0.12-0.53) m<sup>3</sup>/m<sup>3</sup> respectively in the study area.</p><p>Further, using the surface roughness we modify the Dubois, Oh and IEM models. This reduces the number of unknowns in the models from two to one; the soil permittivity. We compute the soil permittivity from the inversion of the existing backscattering models. Finally, we use the permittivity values in the Top’s model to estimate the volumetric soil moisture in the study area. Our initial results exhibit a good correlation (R<sup>2</sup> = 0.85) to the in-situ measured soil moisture.</p>

Effect of Biochar on TDR-Based Volumetric Soil Moisture Measurements in a Loamy Sand Podzolic Soil

Considering the increased interests in biochar (BC) as a soil amendment and a growing media substrate in agriculture, we evaluated the effect of BC incorporation on TDR (time-domain reflectometer)-based volumetric soil moisture content (VSMC) estimations in a loamy sand podzolic soil. Two commercial BC types (powdered—BCP, and granular—BCG) were mixed in different rates (w/w) with a podzolic soil. The dielectric constants measured using a TDR cable tester (MOHR CT 100) were converted to VSMC. Three commonly used models: (i) Topp’s equation, M-1; (ii) mixing model, M-2; and (iii) the forest soil model, M-3, were used. The accuracy of the estimated VSMC using these three models was statistically compared with measured VSMC. BCP at lower rates produced very similar results to the actual VSMC with M-1 and M-2 but deviated with increasing rates. The M-3 showed a non-linear relationship with measured VSMC. In BCG treatments, all models overestimated the VSMC. BCG rates higher than 15% (w/w) resulted in highly attenuated TDR waveforms and the signal was completely dissipated when rates higher than 50% (w/w) were used (typical application for field soils is less than 5% w/w). These results showed that predictions of the soil moisture content based on the soil dielectric constant might not be feasible for tested podzolic soils amended at high BC rates.

Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

In this study, a residual soil moisture prediction model was developed using the stepwise cluster analysis (SCA) and model prediction approach in the Upper Blue Nile basin. The SCA has the advantage of capturing the nonlinear relationships between remote sensing variables and volumetric soil moisture. The principle of SCA is to generate a set of prediction cluster trees based on a series of cutting and merging process according to a given statistical criterion. The proposed model incorporates the combinations of dual-polarized Sentinel-1 SAR data, normalized difference vegetation index (NDVI), and digital elevation model as input parameters. In this regard, two separate stepwise cluster models were developed using volumetric soil moisture obtained from automatic weather stations (AWS) and Noah model simulation as response variables. The performance of the SCA models have been verified for different significance levels (i.e., α = 0.01 , α = 0.05 , and α = 0.1 ). Thus, the AWS based SCA model with α = 0.05 was found to be an optimal model for predicting volumetric residual soil moisture, with correlation coefficient (r) values of 0. 95 and 0.87 and root mean square error (RMSE) of 0.032 and 0.097 m3/m3 during the training and testing periods, respectively. While in the case of the Noah SCA model an optimal prediction performance was observed when α value was set to 0.01, with r being 0.93 and 0.87 and RMSE of 0.043 and 0.058 m3/m3 using the training and testing datasets, respectively. In addition, our result indicated that the combined use of Sentinel-SAR data and ancillary remote sensing products such as NDVI could allow for better soil moisture prediction. Compared to the support vector regression (SVR) method, SCA shows better fitting and prediction accuracy of soil moisture. Generally, this study asserts that the SCA can be used as an alternative method for remote sensing based soil moisture predictions.

Estimated soil water storage within a historical bunker during the growth period of vegetation

The objective of the study was to estimate the variation of soil water retention on the site of a historical bunker, an element of the former Wrocław Fortress in Poland. Measurements of soil moisture in the study area were taken in the period from March to September, 2017. Measurements of volumetric soil moisture were taken by means of a hand-held gauge, type FOM/mts with an FP/mts probe, operating on the basis of the reflectometric technique TDR. Soil moisture measurements realized in the vegetation period demonstrated that soil moisture resources in profiles situated in the section of the bunker varied within the range of 37–135 mm in the layer of 50 cm, and 66–203 mm in the layer of 100 cm. The maximum differences of the average value of soil moisture of the soil profiles studied in the period covered by the measurements were 31 mm and 56 mm, respectively. This indicates a significant differentiation of the retention properties of soils used for the construction of individual shelter areas.