Modelling electrical conductivity of soil from backscattering coefficient of microwave remotely sensed data using artificial neural network

Phonphan, Walaiporn; Tripathi, Nimisha; Tipdecho, Taravudh; Eiumnoh, Apisit

doi:10.1080/10106049.2013.868040

Cited by 9 publications

(5 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is well known that EC was associated with soil salinity and soil health, which can usually be influenced by growing medium moisture content and organic materials. 20,21 TDS is a measure of the aggregate of small organic and inorganic materials in the growing medium or MSW compost dissolved in water. In general, the high microwave power level significantly increased NO 3and Ca 2+ concentrations of the growing medium and altered the growing medium chemical properties and plant growth performance.…”

Section: Resultsmentioning

confidence: 99%

Microwave power level and exposure time alteration of compost tea efficacy, and growth of Plectranthus amboinicus

Yu¹,

Iheshiulo²,

Gunupuru³

et al. 2019

HIJ

View full text Add to dashboard Cite

The unique properties of selective heating at the molecular level, and the resultant enhancement effect of microwave in agri-food sciences have been amply demonstrated. This study examined variations in microwave irradiation power level (0, 100, 200, 300 and 400 W) and exposure times (5 and 10 min) effects on the potency of municipal solid waste (MSW) compost tea and Plectranthus amboinicus plant growth and nutrient composition. A linear regression equation showed slopes of 0.160 o C/W for the 10 min exposure and 0.114 o C/W for the 5 min exposure. The turbidity of the microwaved compost tea reduced as the microwave power level and exposure increased. Electric conductivity, total dissolved solids and salinity of the MSW compost tea increased by a factor of 0.08 at the 400 W power level. Plant sap o Brix, nitrate and calcium concentrations were increased by power levels of ≥300 W. Stem diameter was increased by an average factor of 0.08 for the 5 min and 10 min exposure times following application of the 300 W microwaved MSW compost tea. Chlorophyll content rose by a factor of 0.09 at 10 mins between the 200 W and the 300 W after which it remained unchanged. On the other hand, anthocyanin content was consistently higher in plants treated with the 5 mins microwaved MSW compost tea compared to the 10 mins treatment irrespective of the microwave power level. To understand the mechanism of action of microwaved vermicast, current study is focused on microbial communities in MSW compost tea.

show abstract

Section: Resultsmentioning

confidence: 99%

Microwave power level and exposure time alteration of compost tea efficacy, and growth of Plectranthus amboinicus

Yu¹,

Iheshiulo²,

Gunupuru³

et al. 2019

HIJ

View full text Add to dashboard Cite

show abstract

“…In addition, due to the different emphasis of different studies on soil salinity, we have collected data on soil EC and salt content. This was because there is a positive correlation between them, but they cannot be accurately transformed now ( Phonphan et al., 2014 ). It is obviously soil data and meteorological factors will influence the results; the irrigation data was selected to estimate the effects of drip irrigation.…”

Section: Methodsmentioning

confidence: 99%

Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning

et al. 2023

View full text Add to dashboard Cite

Cotton is widely used in textile, decoration, and industry, but it is also threatened by soil salinization. Drip irrigation plays an important role in improving water and fertilization utilization efficiency and ensuring crop production in arid areas. Accurate prediction of soil salinity and crop evapotranspiration under drip irrigation is essential to guide water management practices in arid and saline areas. However, traditional hydrological models such as Hydrus require more variety of input parameters and user expertise, which limits its application in practice, and machine learning (ML) provides a potential alternative. Based on a global dataset collected from 134 pieces of literature, we proposed a method to comprehensively simulate soil salinity, evapotranspiration (ET) and cotton yield. Results showed that it was recommended to predict soil salinity, crop evapotranspiration and cotton yield based on soil data (bulk density), meteorological factors, irrigation data and other data. Among them, meteorological factors include annual average temperature, total precipitation, year. Irrigation data include salinity in irrigation water, soil matric potential and irrigation water volume, while other data include soil depth, distance from dripper, days after sowing (for EC and soil salinity), fertilization rate (for yield and ET). The accuracy of the model has reached a satisfactory level, R2 in 0.78-0.99. The performance of stacking ensemble ML was better than that of a single model, i.e., gradient boosting decision tree (GBDT); random forest (RF); extreme gradient boosting regression (XGBR), with R2 increased by 0.02%-19.31%. In all input combinations, other data have a greater impact on the model accuracy, while the RMSE of the S1 scenario (input without meteorological factors) without meteorological data has little difference, which is -34.22%~19.20% higher than that of full input. Given the wide application of drip irrigation in cotton, we recommend the application of ensemble ML to predict soil salinity and crop evapotranspiration, thus serving as the basis for adjusting the irrigation schedule.

show abstract

“…The ideas of various satellite monitoring techniques are generally similar [14][15][16]. Some satellite images, or their time series, are used from various orbital platforms [17][18][19], including hyperspectral data [20,21], low spatial resolution satellite data (such as MODIS) [22,23], and radar images [24]. The spectral channels of the visible and near infrared spectral ranges are used and analyzed for the detection of soil salinity [14][15][16].…”

Section: Classical Salinity Estimation Methods Based On Spectral Datamentioning

confidence: 99%

“…In [24], the authors considered the relationship between the backscattering coefficient (BC) and EC for Maha-Sarakham district in the northeast region of Thailand. ALOS-PALSAR provides data in four polarizations, HH, HV, VH and VV, with a resolution of 12.5 m. The total for the ground measurements is about 500 points from a depth of 5 cm, and the distance between each measurement is 20 m. Each measurement is identified by GPS with an accuracy of 4 m. The electrical conductivity was evaluated in the laboratory at 25 • C (dS m −1 ).…”

Section: Machine Learning Methods In Salinity Estimation Problemsmentioning

confidence: 99%

Soil Salinity Estimation for South Kazakhstan Based on SAR Sentinel-1 and Landsat-8,9 OLI Data with Machine Learning Models

Mukhamediev,

Merembayev,

Kuchin

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Climate change, uneven distribution of water resources and anthropogenic impact have led to salinization and land degradation in the southern regions of Kazakhstan. Identification of saline lands and their mapping is a laborious process associated with a complex of ground measurements. Data from remote sensing are widely used to solve this problem. In this paper, the problem of assessing the salinity of the lands of the South Kazakhstan region using remote sensing data is considered. The aim of the study is to analyze the applicability of machine learning methods to assess the salinity of agricultural lands in southern Kazakhstan based on remote sensing. The authors present a salinity dataset obtained from field studies and containing more than 200 laboratory measurements of soil salinity. Moreover, the authors describe the results of applying several regression reconstruction algorithms (XGBoost, LightGBM, random forest, Support vector machines, Elastic net, etc.), where synthetic aperture radar (SAR) data from the Sentinel-1 satellite and optical data in the form of spectral salinity indices are used as input data. The obtained results show that, in general, these input data can be used to estimate salinity of the wetted arable land. XGBoost regressor (R2 = 0.282) showed the best results. Supplementing the radar data with the values of salinity spectral index improves the result significantly (R2 = 0.356). For the local datasets, the best result shown by the model is R2 = 0.473 (SAR) and R2 = 0.654 (SAR with spectral indexes), respectively. The study also revealed a number of problems that justify the need for a broader range of ground surveys and consideration of multi-year factors affecting soil salinity. Key results of the article: (i) a set of salinity data for different geographical zones of southern Kazakhstan is presented for the first time; (ii) a method is proposed for determining soil salinity on the basis of synthetic aperture radar supplemented with optical data, and this resulted in the improved prediction of the results for the region under consideration; (iii) a comparison of several types of machine learning models was made and it was found that boosted models give, on average, the best prediction result; (iv) a method for optimizing the number of model input parameters using explainable machine learning is proposed; (v) it is shown that the results obtained in this work are in better agreement with ground-based measurements of electrical conductivity than the results of the previously proposed global model.

show abstract

Modelling electrical conductivity of soil from backscattering coefficient of microwave remotely sensed data using artificial neural network

Cited by 9 publications

References 44 publications

Microwave power level and exposure time alteration of compost tea efficacy, and growth of Plectranthus amboinicus

Microwave power level and exposure time alteration of compost tea efficacy, and growth of Plectranthus amboinicus

Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning

Soil Salinity Estimation for South Kazakhstan Based on SAR Sentinel-1 and Landsat-8,9 OLI Data with Machine Learning Models

Contact Info

Product

Resources

About