Soil moisture retrieval improvement over agricultural fields by adding entropy–alpha dual-polarimetric decomposition features

Akhavan, Zeinab; Hasanlou, Mahdi; Hosseini, Mehdi; Becker‐Reshef, Inbal

doi:10.1117/1.jrs.15.034516

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…As an activation function, the rectified linear unit (ReLU) function [77], which was developed to solve the gradient vanishing problem in the sigmoid and hyperbolic tangent, was applied in previous works [4,[78][79][80]. After the proposal of ReLU, new activation functions that improved ReLU, such as LeakyReLU [81], exponential linear units (ELU) [82], and scaled exponential linear unit (SELU) [83], were developed.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Soil Moisture Content Estimation Based on Sentinel-1 SAR Imagery Using an Artificial Neural Network and Hydrological Components

Lee

Kim

Jung³

et al. 2022

Remote Sensing

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this study estimates soil moisture content (SMC) using Sentinel-1A/B C-band synthetic aperture radar (SAR) images and an artificial neural network (ANN) over a 40 × 50-km2 area located in the Geum River basin in South Korea. The hydrological components characterized by the antecedent precipitation index (API) and dry days were used as input data as well as SAR (cross-polarization (VH) and copolarization (VV) backscattering coefficients and local incidence angle), topographic (elevation and slope), and soil (percentage of clay and sand)-related data in the ANN simulations. A simple logarithmic transformation was useful in establishing the linear relationship between the observed SMC and the API. In the dry period without rainfall, API did not decrease below 0, thus the Dry days were applied to express the decreasing SMC. The optimal ANN architecture was constructed in terms of the number of hidden layers, hidden neurons, and activation function. The comparison of the estimated SMC with the observed SMC showed that the Pearson’s correlation coefficient (R) and the root mean square error (RMSE) were 0.85 and 4.59%, respectively.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Soil Moisture Content Estimation Based on Sentinel-1 SAR Imagery Using an Artificial Neural Network and Hydrological Components

Lee

Kim

Jung³

et al. 2022

Remote Sensing

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“…GLCM texture feature extraction occurs when analyzing the local features of an image for pattern recognition, image classification, and image segmentation [38][39][40][41][42][43][44][45]. This approach has been rarely used in the field of soil science; however, some studies tested the feasibility of image texture features from GLCM to determine the correlation between soil moisture conditions and the intensity of the pixel in laboratory conditions [46] while, in the open field, they tested image texture parameters from Sentinel-1 for soil moisture retrieval [47]. Recently, Zhao et al [48] used the GLCM texture analysis to describe the surface cracking conditions of soda saline-alkali soil and quantitatively studied the responses of GLCM texture features to soil salinity.…”

Section: Introductionmentioning

confidence: 99%

Using Image Texture Analysis to Evaluate Soil–Compost Mechanical Mixing in Organic Farms

et al. 2023

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Soil amendments (e.g., compost) require uniform incorporation in the soil profile to benefit plants. However, machines may not mix them uniformly throughout the upper soil layer commonly explored by plant roots. The study focuses on using image texture analysis to determine the level of mixing uniformity in the soil following the passage of two kinds of harrows. A 12.3-megapixel DX-format digital camera acquired images of soil/expanded polystyrene (in the laboratory) and soil/compost mixtures (in field conditions). In the laboratory, pictures captured the soil before and during the simulated progressive mixing of expanded polystyrene particles. In field conditions, images captured the exposed superficial horizons of compost-amended soil after the passage of a combined spike-tooth–disc harrow and a disc harrow. Image texture analysis based on the gray-level co-occurrence matrix calculated the sums of dissimilarity, contrast, entropy, and uniformity metrics. In the laboratory conditions, the progressive mixing resulted in increased image dissimilarity (from 1.15 ± 0.74 × 106 to 1.65 ± 0.52 × 106) and contrast values (from 2.69 ± 2.06 × 106 to 5.67 ± × 1.93 106), almost constant entropy (3.50 ± 0.25 × 106), and decreased image uniformity (from 6.65 ± 0.31 × 105 to 4.49 ± 1.36 × 105). Using a tooth-disc harrow in the open field resulted in higher dissimilarity, contrast, entropy (+73.3%, +62.8%, +16.3%), and lower image uniformity (−50.6%) than the disc harrow, suggesting enhanced mixing in the superficial layer.

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“…The moisture content of the soil plays a crucial role in a broad contexts, such as irrigation management, crop growth study, and climate change analysis (Thi et al 2019;Ranjbar, Zarei, et al 2021) . In the last few decades, the agricultural industry has widely used new technologies, including remote sensing tools and techniques, to boost agricultural productivity because of today's growing demand (Akhavan, Hasanlou, Hosseini, and Becker-Reshef 2021;Reisi-Gahrouei et al 2019). Among remote sensing sensors, active microwave sensors, specifically Synthetic Aperture Radar (SAR) are greatly affected by the characteristics of the target, including the surface roughness and vegetation coverage, making the SAR-based soil moisture retrieval a challenging process (Hajj et al 2017;Paloscia et al 2013;.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Tree-Based Regression Models for Soil Moisture Estimation Using Sar Data

Akhavan

Hasanlou

Hosseini

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Soil moisture content plays a pivotal role in biomass development of vegetation coverage at various growth stages. Moisture content of the soil is considered as a crucial parameter for agricultural studies which directly leads to higher fertility rate. Remote sensing techniques, specifically Synthetic Aperture Radar (SAR) sensors, provides suitable opportunity for continuous soil moisture monitoring at various spatial and temporal resolutions. In this study, field campaigns conducted to measure soil surface parameters, including soil moisture and roughness, synchronized with Sentinel-1 pass over an agricultural region near Mohammadshahr, Iran. Fieldwork for soil moisture sampling have done during plants’ (canola and winter wheat) growth stages. The Gradient Boosted Regression Tree (GBRT), eXtreme Gradient Boosted (XGB), and Random Forest (RF) machine learning algorithms were employed to model the relationship between the ground measured soil moisture and polarimetric SAR derived features from Sentinel-1 imageries. The results showed promising results obtained for soil moisture estimation using the dual-polarized SAR dataset over crop-covered agricultural fields with R2 = 0.95 and RMSE = 0.023 m3 m−3 using the GBRT regression model.

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Soil moisture retrieval improvement over agricultural fields by adding entropy–alpha dual-polarimetric decomposition features

Cited by 8 publications

References 25 publications

Soil Moisture Content Estimation Based on Sentinel-1 SAR Imagery Using an Artificial Neural Network and Hydrological Components

Soil Moisture Content Estimation Based on Sentinel-1 SAR Imagery Using an Artificial Neural Network and Hydrological Components

Using Image Texture Analysis to Evaluate Soil–Compost Mechanical Mixing in Organic Farms

A Comparison of Tree-Based Regression Models for Soil Moisture Estimation Using Sar Data

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