Estimating of soil texture using landsat imagery: A case study of Thatta Tehsil, Sindh

Khalil, Rao Muhammad Zahid; Khalid, Wajiha; Akram, Mehwish

doi:10.1109/igarss.2016.7729804

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…Indeed, Coleman et al [7] started with intermediate performances, reaching an R 2 of 0.4 for clay mapping using Landsat. Khalil et al [71] reached an R 2 of 0.4 for the same attribute using a single image. Furthermore, the study by Shabou et al [24] was based on 100 soil samples and a dataset of four Landsat TM scenes, reporting an R 2 of 0.65 and an RMSE of 100 g kg −1 for clay.…”

Section: Performance Of Clay and Sand Content Predictionsmentioning

confidence: 96%

Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images

et al. 2018

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Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0–20 cm depth, 919 points) from an area of 14,614 km2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R2 = 0.83; RMSE = 65.0 g kg−1) and sand (R2 = 0.86; RMSE = 79.9 g kg−1). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.

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Section: Performance Of Clay and Sand Content Predictionsmentioning

confidence: 96%

Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images

et al. 2018

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“…To evaluate the relationship between the reflectance values of Landsat8 OLI images and soil variables, the Ordinary Least Squares (OLS) regression analyses were used. The results indicated a significant correlation (P< 0.01) between the amount of silt with B2 and B5 bands (R 2 = 0.52), and the amount of clay with B4 and B6 bands (R 2 = 0.40) (Khalil et al, 2016). In estimating spatial variations in soil properties, accuracy enhancement would be feasible by simultaneous use of a combination of fuzzy algorithms, geostatistical methods, and remote sensing systems.…”

Section: Introductionmentioning

confidence: 98%

Improving Soil Texture Digital Mapping Using Landsat 8 Satellite Imageries in Calcareous Soils of Southern Iran

Shirazi

Abtahi

Nejad

et al. 2023

JAST

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Over the last three decades, there has been a general tendency for changing research methods on soil resource management from conventional and mainly qualitative methods to quantitative ones based on spatial correlation models, which are called Digital Soil Mapping (DSM). The present study was carried out in Shabankareh Plain (15,000 ha) with different physiographic units, in Bushehr Province, southern Iran. The target sites (172 points) were selected for soil sampling at depths of 0-30 cm. Soil texture classes DSM was produced by two methods. The first method was Conventional DSM, in which data of soil particles was obtained from laboratory analysis for each sampling point along with their geographical location. Also, the study area boundary was added to ArcGIS software in UTM format and was analyzed by operating Kriging or IDW estimators. The map produced by this method was a low quality digital map containing extra and scattered texture classes with unrealistically sharp boundaries. The second method used CoKriging of L8 multispectral imagery data (OLI bands) and soil samples analysis was operated. Results showed that using B1 band (0.433-0.453 µm) of Landsat 8 satellite imageries of the study area in April 2020 produced high quality digital maps. In this method, soil textures were the same as the ones in the study area. Salt accumulation and water content of surface soil were possible reasons indicating why satellite imageries in the other periods of year were not suitable for DSM. The highest and the lowest ranges of influence among soil texture parameters were 684 meters and 388 meter for clay and sand particles, respectively.

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“…These problems are directly related to soil functions particularly to agricultural productivity, loss of biodiversity, and provision of water [3]. Producing accurate and reliable soil maps is indispensable in watershed management [4,5], rangeland management, and landscape ecology [6].…”

Section: Introductionmentioning

confidence: 99%

Estimating topsoil texture fractions by digital soil mapping - a response to the long outdated soil map in the Philippines

Mondejar

Tongco

2019

Sustain Environ Res

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Digital soil mapping for soil texture is mostly an understanding of how soil texture fractions vary in space as influenced by environmental variables mainly derived from the digital elevation model (DEM). In this study, topsoil texture models were generated and evaluated by multiple linear regression (MLR), ordinary kriging (OK), simple kriging (SK) and universal kriging (UK) using free and open-source R, System for Automated Geoscientific Analyses, and QGIS software. Comparing these models is the main objective of the study. The study site covers an area of 124 km 2 of the Municipality of Barili, Cebu. A total of 177 soil samples were gathered and analyzed from irregular sample points. DEM derivatives and remote sensing data (Landsat 8) were used as environmental variables. Exploratory analyses revealed no outlier in the data. Skewness and kurtosis values of the untransformed data vary greatly between -3.85 to 7.20 and 1.8 to 70.7, respectively; an indication that variables are highly skewed with heavy tails. Thus, Tukey's ladder of powers transformation was applied that resulted to normal or nearly normal distribution having skewness values close to zero and kurtosis values have lighter tails. All data analysis from MLR modeling, variography, kriging, and crossvalidations of models were implemented using the transformed data. Forward selection, backward elimination, and stepwise selection methods were adapted for predictors selection in MLR. The MLR, OK, SK, and UK were applied and cross validated for topsoil texture prediction. Likewise, exponential, Gaussian, and spherical models were fitted for the experimental variograms. Backward elimination method for clay, sand, and silt have the lowest MAE and highest R 2 in MLR. The UK fitted with exponential variogram model has the highest R 2 of 0.878, 0.821, and 0.893 for clay, sand, and silt, respectively. These models can be adapted as a decision support for agricultural land use planning and crop suitability development in the area.

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Estimating of soil texture using landsat imagery: A case study of Thatta Tehsil, Sindh

Cited by 5 publications

References 10 publications

Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images

Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images

Improving Soil Texture Digital Mapping Using Landsat 8 Satellite Imageries in Calcareous Soils of Southern Iran

Estimating topsoil texture fractions by digital soil mapping - a response to the long outdated soil map in the Philippines

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