Modeling And Mapping of Soil Salinity And Alkalinity Using Remote Sensing Data And Topographic Factors

2021 ◽  
Author(s):  
Elham Shahrayini ◽  
Ali Akbar Noroozi
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Crop Production ◽  
Coefficient Of Determination ◽  
Soil Productivity ◽  
Landsat 8 ◽  
Topographic Wetness Index ◽  
Channel Network ◽  
Auxiliary Data ◽  
Terrain Attributes

Abstract Soil salinity and alkalinity seriously threaten crop production, soil productivity and sustainable agriculture, especially in arid and semi-arid areas, leading to land degradation, therefore, spatial distribution of these parameters are really important for successful management of such areas. The surface soil salinity and sodium adsorption ratio (SAR) have been modeled in this article. Auxiliary data were terrain attributes derived from digital elevation model (DEM), remote sensing spectral bands, and indices of vegetation and salinity derived from Landsat 8 OLI satellite. In total, 118 soil samples were collected from depth of 0-15 cm in homogenous units at Doviraj plain in the southern part of Ilam province, western Iran. Saturated electrical conductivity (ECe), SAR and other soil properties were analyzed and calculated. To model ECe and SAR parameters with the auxiliary data, stepwise multi linear regression (SMLR) and random forest (RF) regression were applied. The highest accuracy were obtained through RF model with validation coefficient of determination (R2val) =0.82 and 0.83 and validation root mean square error (RMSEval)=7.40 dS/m and 11.20 for ECe and SAR respectively. Furthermore, results indicated that strongest influence on the prediction of soil salinity followed by Band10, principal component analysis (PC3), Vertical Distance to Channel Network (VDCN) and Analytical Hill Shading (AH). Also, Band10, Band11, Flow Accumulation (FA) and Topographic Wetness Index (TWI) were the important covariate in alkalinity prediction through RF model. Finally, it is suggested that similar techniques can be used to map and monitor soil salinity and alkalinity in other parts of arid regions.

scholarly journals Multi-Temporal Mapping of Soil Total Nitrogen Using Google Earth Engine across the Shandong Province of China

2020 ◽  
Vol 12 (24) ◽  
pp. 10274
Author(s):  
Wu Xiao ◽  
Wenqi Chen ◽  
Tingting He ◽  
Linlin Ruan ◽  
Jiwang Guo
Keyword(s):  
Remote Sensing ◽  
Total Nitrogen ◽  
Model Building ◽  
Google Earth ◽  
Shandong Province ◽  
Coefficient Of Determination ◽  
Soil Productivity ◽  
Soil Total Nitrogen ◽  
Wide Range ◽  
Google Earth Engine

Nitrogen plays an important role in improving soil productivity and maintaining ecosystem stability. Mapping and monitoring the soil total nitrogen (STN) content is the basis for modern soil management. The Google Earth Engine (GEE) platform covers a wide range of available satellite remote sensing datasets and can process massive data calculations. We collected 6823 soil samples in Shandong Province, China. The random forest (RF) algorithm predicted the STN content in croplands from 2002 to 2016 in Shandong Province, China on the GEE platform. Our results showed that RF had the coefficient of determination (R2) (0.57), which can predict the spatial distribution of the STN and analyze the trend of STN changes. The remote sensing spectral reflectance is more important in model building according to the variable importance analysis. From 2002 to 2016, the STN content of cropland in the province had an upward trend of 35.6%, which increased before 2010 and then decreased slightly. The GEE platform provides an opportunity to map dynamic changes of the STN content effectively, which can be used to evaluate soil properties in the future long-term agricultural management.

Developing Logistic Regression Models to Identify Salt-Affected Soils Using Optical Remote Sensing

2019 ◽  
pp. 233-256
Author(s):  
Nirmal Kumar ◽  
S. K. Singh ◽  
G. P. Obi Reddy ◽  
R. K. Naitam
Keyword(s):  
Remote Sensing ◽  
Logistic Regression ◽  
Spatial Distribution ◽  
Soil Salinity ◽  
Regression Models ◽  
Landsat 8 ◽  
Gangetic Plain ◽  
Logistic Regression Models ◽  
Salt Affected Soils ◽  
Indo Gangetic Plain

A major part of Indo-Gangetic plain is affected with soil salinity/alkalinity. Information on spatial distribution of soil salinity is important for planning management practices for its restoration. Remote sensing has proven to be a powerful tool in quantifying and monitoring the development of soil salinity. The chapter aims to develop logistic regression models, using Landsat 8 data, to identify salt affected soils in Indo-Gangetic plain. Logistic regression models based on Landsat 8 bands and several salinity indices were developed, individually and in combination. The bands capable of differentiating salt affected soils from other features were identified as green, red, and SWIR1. The logistic regression model developed in the study area was found to be 81% accurate in identifying salt-affected soils. A total area of 34558.49 ha accounting to ~10% of the total geographic area of the district was found affected with salinity/alkalinity. The spatial distribution of salt-affected soils in the district showed an association of shallow ground water depth with salinity.

scholarly journals Using remote sensing for monitoring the dynamic of soil organic carbon concentration in Lake Valencia basin, Venezuela, based on Landsat 8 data

2020 ◽  
Vol 169 ◽  
pp. 01021
Author(s):  
Israel Jose Arias Govín ◽  
Elena V. Stanis ◽  
Elena N. Latushkina ◽  
Aigul Ospanova
Keyword(s):  
Remote Sensing ◽  
Predictive Models ◽  
Significant Variation ◽  
Soil Productivity ◽  
Landsat 8 ◽  
Spectral Features ◽  
Landsat 8 Oli ◽  
Spectral Signatures ◽  
Organic Carbon Concentration ◽  
Bare Soils

Maintaining or increasing SOC concentration is fundamental for reducing the effects of global warming and increasing soil productivity. In this paper, a method based on Landsat 8 OLI products was developed for qualitatively monitoring in the Lake Valencia basin (Venezuela) the dynamic of SOC concentration between the years 2013 to 2018. The developed method uses the Green (B3), NIR (B5) and SW1 (B6) bands of Landsat 8 OLI sensor for detecting changes in the spectral signatures of bare soils that indicate possible variations in their concentrations of SOC. It was found that for the study period, the Lake Valencia basin soils do not present spectral features of significant variation in SOC concentration. An area of 8.61Km2 (0.3% of the study area) was identified as a zone with a possible reduction of SOC concentration. In case of insufficient data for developing remote sensing based predictive models, the proposed method allows qualitatively monitoring and categorizing the dynamic of SOC concentration and identifying areas with spectral features of a possible variation in SOC concentration.

scholarly journals Soil salinity mapping and hydrological drought indices assessment in arid environments based on remote sensing techniques

2017 ◽  
Vol 6 (1) ◽  
pp. 149-158 ◽  
Author(s):  
Mohamed Elhag ◽  
Jarbou A. Bahrawi
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Hydrological Drought ◽  
Drought Indices ◽  
Landsat 8 ◽  
Arid Environments ◽  
Crop Water ◽  
Sensing Applications

Abstract. Vegetation indices are mostly described as crop water derivatives. The normalized difference vegetation index (NDVI) is one of the oldest remote sensing applications that is widely used to evaluate crop vigor directly and crop water relationships indirectly. Recently, several NDVI derivatives were exclusively used to assess crop water relationships. Four hydrological drought indices are examined in the current research study. The water supply vegetation index (WSVI), the soil-adjusted vegetation index (SAVI), the moisture stress index (MSI) and the normalized difference infrared index (NDII) are implemented in the current study as an indirect tool to map the effect of different soil salinity levels on crop water stress in arid environments. In arid environments, such as Saudi Arabia, water resources are under pressure, especially groundwater levels. Groundwater wells are rapidly depleted due to the heavy abstraction of the reserved water. Heavy abstractions of groundwater, which exceed crop water requirements in most of the cases, are powered by high evaporation rates in the designated study area because of the long days of extremely hot summer. Landsat 8 OLI data were extensively used in the current research to obtain several vegetation indices in response to soil salinity in Wadi ad-Dawasir. Principal component analyses (PCA) and artificial neural network (ANN) analyses are complementary tools used to understand the regression pattern of the hydrological drought indices in the designated study area.

scholarly journals Development of multivariate integrated drought monitoring index (MIDMI) for Warangal region of Telangana, India

2021 ◽  
Author(s):  
Mahesh R. Tapas ◽  
Uday Kumar ◽  
Sudhakar Mogili ◽  
K. V. Jayakumar
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Crop Production ◽  
Continuous Monitoring ◽  
Irrigation Management ◽  
Weighted Average ◽  
Agricultural Drought ◽  
Drought Indices ◽  
Landsat 8 ◽  
Percentage Area

Abstract Agricultural drought is one of the most frequent natural disasters in India's southern part. Remote sensing-based drought indices give advantages in terms of continuous monitoring of land surface. The crop production in the Warangal region in India's southern part is adversely affected due to insufficient rainfall and poor irrigation management. This study aims to develop a multivariate remote sensing-based composite drought index (CDI) to monitor the agricultural drought. Landsat-8 satellite data for all the 11 subregions of Warangal urban and 15 subregions of the rural district of Telangana from 2013 to 2020 for the month of May is used to obtain drought indices. The drought indices are used in this study to develop MIDMI and are compared according to the percentage area of the Warangal region under five different drought categories. In this study, the MIDMI is computed by a weighted average of five vegetation drought indices for the Warangal region as per the method developed by Iyengar and Sudarshan for the multivariate data. MIDMI for all the 26 subregions of the Warangal rural and Warangal Urban Districts is between 0.4 and 0.6, which makes the Warangal region moderately vulnerable to agricultural drought.

scholarly journals Potential of Landsat 8 OLI for mapping and monitoring of soil salinity in an arid region: A case study in Dushak, Turkmenistan

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259695
Author(s):  
Elif Günal ◽  
Xiukang Wang ◽  
Orhan Mete Kılıc ◽  
Mesut Budak ◽  
Sami Al Obaid ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Regression Model ◽  
Soil Salinity ◽  
Multivariate Regression ◽  
Regression Models ◽  
Satellite Images ◽  
Remotely Sensed ◽  
Small Scale ◽  
Landsat 8 ◽  
Landsat 8 Oli

Soil salinity is the most common land degradation agent that impairs soil functions, ecosystem services and negatively affects agricultural production in arid and semi-arid regions of the world. Therefore, reliable methods are needed to estimate spatial distribution of soil salinity for the management, remediation, monitoring and utilization of saline soils. This study investigated the potential of Landsat 8 OLI satellite data and vegetation, soil salinity and moisture indices in estimating surface salinity of 1014.6 ha agricultural land located in Dushak, Turkmenistan. Linear regression model was developed between land measurements and remotely sensed indicators. A systematic regular grid-sampling method was used to collect 50 soil samples from 0–20 cm depth. Sixteen indices were extracted from Landsat-8 OLI satellite images. Simple and multivariate regression models were developed between the measured electrical conductivity values and the remotely sensed indicators. The highest correlation between remote sensing indicators and soil EC values in determining soil salinity was calculated in SAVI index (r = 0.54). The reliability indicated by R2 value (0.29) of regression model developed with the SAVI index was low. Therefore, new model was developed by selecting the indicators that can be included in the multiple regression model from the remote sensing indicators. A significant (r = 0.74) correlation was obtained between the multivariate regression model and soil EC values, and salinity was successfully mapped at a moderate level (R2: 0.55). The classification of the salinity map showed that 21.71% of the field was non-saline, 29.78% slightly saline, 31.40% moderately saline, 15.25% strongly saline and 1.44% very strongly. The results revealed that multivariate regression models with the help of Landsat 8 OLI satellite images and indices obtained from the images can be used for modeling and mapping soil salinity of small-scale lands.

scholarly journals Remote Sensing of Turbidity in the Tennessee River Using Landsat 8 Satellite

2021 ◽  
Vol 13 (18) ◽  
pp. 3785
Author(s):  
A. K. M. Azad Hossain ◽  
Caleb Mathias ◽  
Richard Blanton
Keyword(s):  
Remote Sensing ◽  
Accuracy Assessment ◽  
The United States ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Water Runoff ◽  
Estimation Model ◽  
Tennessee River ◽  
The World ◽  
Remote Estimation

The Tennessee River in the United States is one of the most ecologically distinct rivers in the world and serves as a great resource for local residents. However, it is also one of the most polluted rivers in the world, and a leading cause of this pollution is storm water runoff. Satellite remote sensing technology, which has been used successfully to study surface water quality parameters for many years, could be very useful to study and monitor the quality of water in the Tennessee River. This study developed a numerical turbidity estimation model for the Tennessee River and its tributaries in Southeast Tennessee using Landsat 8 satellite imagery coupled with near real-time in situ measurements. The obtained results suggest that a nonlinear regression-based numerical model can be developed using Band 4 (red) surface reflectance values of the Landsat 8 OLI sensor to estimate turbidity in these water bodies with the potential of high accuracy. The accuracy assessment of the estimated turbidity achieved a coefficient of determination (R2) value and root mean square error (RMSE) as high as 0.97 and 1.41 NTU, respectively. The model was also tested on imagery acquired on a different date to assess its potential for routine remote estimation of turbidity and produced encouraging results with R2 value of 0.94 and relatively high RMSE.

scholarly journals Multi-Dimensional Remote Sensing Analysis Documents Beaver-Induced Permafrost Degradation, Seward Peninsula, Alaska

2021 ◽  
Vol 13 (23) ◽  
pp. 4863
Author(s):  
Benjamin M. Jones ◽  
Ken D. Tape ◽  
Jason A. Clark ◽  
Allen C. Bondurant ◽  
Melissa K. Ward Jones ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Unmanned Aircraft ◽  
The Arctic ◽  
Series Data ◽  
Landsat 8 ◽  
Permafrost Degradation ◽  
Channel Network ◽  
Seward Peninsula ◽  
Sentinel 2

Beavers have established themselves as a key component of low arctic ecosystems over the past several decades. Beavers are widely recognized as ecosystem engineers, but their effects on permafrost-dominated landscapes in the Arctic remain unclear. In this study, we document the occurrence, reconstruct the timing, and highlight the effects of beaver activity on a small creek valley confined by ice-rich permafrost on the Seward Peninsula, Alaska using multi-dimensional remote sensing analysis of satellite (Landsat-8, Sentinel-2, Planet CubeSat, and DigitalGlobe Inc./MAXAR) and unmanned aircraft systems (UAS) imagery. Beaver activity along the study reach of Swan Lake Creek appeared between 2006 and 2011 with the construction of three dams. Between 2011 and 2017, beaver dam numbers increased, with the peak occurring in 2017 (n = 9). Between 2017 and 2019, the number of dams decreased (n = 6), while the average length of the dams increased from 20 to 33 m. Between 4 and 20 August 2019, following a nine-day period of record rainfall (>125 mm), the well-established dam system failed, triggering the formation of a beaver-induced permafrost degradation feature. During the decade of beaver occupation between 2011 and 2021, the creek valley widened from 33 to 180 m (~450% increase) and the length of the stream channel network increased from ~0.6 km to more than 1.9 km (220% increase) as a result of beaver engineering and beaver-induced permafrost degradation. Comparing vegetation (NDVI) and snow (NDSI) derived indices from Sentinel-2 time-series data acquired between 2017 and 2021 for the beaver-induced permafrost degradation feature and a nearby unaffected control site, showed that peak growing season NDVI was lowered by 23% and that it extended the length of the snow-cover period by 19 days following the permafrost disturbance. Our analysis of multi-dimensional remote sensing data highlights several unique aspects of beaver engineering impacts on ice-rich permafrost landscapes. Our detailed reconstruction of the beaver-induced permafrost degradation event may also prove useful for identifying degradation of ice-rich permafrost in optical time-series datasets across regional scales. Future field- and remote sensing-based observations of this site, and others like it, will provide valuable information for the NSF-funded Arctic Beaver Observation Network (A-BON) and the third phase of the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) Field Campaign.

scholarly journals Evaluation of Surface Reflectance Products Based on Optimized 6S Model Using Synchronous In Situ Measurements

2021 ◽  
Vol 14 (1) ◽  
pp. 83
Author(s):  
Xiaocheng Zhou ◽  
Xueping Liu ◽  
Xiaoqin Wang ◽  
Guojin He ◽  
Youshui Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Solar Spectrum ◽  
In Situ Measurements ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Landsat 8 Oli ◽  
Auxiliary Data ◽  
Sentinel 2

Surface reflectance (SR) estimation is the most essential preprocessing step for multi-sensor remote sensing inversion of geophysical parameters. Therefore, accurate and stable atmospheric correction is particularly important, which is the premise and basis of the quantitative application of remote sensing. It can also be used to directly compare different images and sensors. The Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi-Spectral Instrument (MSI) surface reflectance products are publicly available and demonstrate high accuracy. However, there is not enough validation using synchronous spectral measurements over China’s land surface. In this study, we utilized Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products reconstructed by Categorical Boosting (CatBoost) and 30 m ASTER Global Digital Elevation Model (ASTER GDEM) data to adjust the relevant parameters to optimize the Second Simulation of Satellite Signal in the Solar Spectrum (6S) model. The accuracy of surface reflectance products obtained from the optimized 6S model was compared with that of the original 6S model and the most commonly used Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model. Surface reflectance products were validated and evaluated with synchronous in situ measurements from 16 sites located in five provinces of China: Fujian, Gansu, Jiangxi, Hunan, and Guangdong. Through the indirect and direct validation across two sensors and three methods, it provides evidence that the synchronous measurements have the higher and more reliable validation accuracy. The results of the validation indicated that, for Landsat-8 OLI and Sentinel-2 MSI SR products, the overall root mean square error (RMSE) calculated results of optimized 6S, original 6S and FLAASH across all spectral bands were 0.0295, 0.0378, 0.0345, and 0.0313, 0.0450, 0.0380, respectively. R2 values reached 0.9513, 0.9254, 0.9316 and 0.9377, 0.8822, 0.9122 respectively. Compared with the original 6S model and FLAASH model, the mean percent absolute error (MPAE) of the optimized 6S model was reduced by 32.20% and 15.86% for Landsat-8 OLI, respectively. On the other, for the Sentinel-2 MSI SR product, the MPAE value was reduced by 33.56% and 33.32%. For the two kinds of data, the accuracy of each band was improved to varying extents by the optimized 6S model with the auxiliary data. These findings support the hypothesis that reliable auxiliary data are helpful in reducing the influence of the atmosphere on images and restoring reality as much as is feasible.

scholarly journals SENSORIAMENTO REMOTO APLICADO A MODELAGEM DE PRODUTIVIDADE DA CULTURA DA CANA-DE-AÇÚCAR

2019 ◽  
Vol 34 (2) ◽  
pp. 263-270
Author(s):  
Victor Costa Leda ◽  
Aline Kuramoto Golçalves ◽  
Natalia da Silva Lima
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Sao Paulo ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
São Paulo ◽  
Vegetation Indexes ◽  
Production Area ◽  
Remote Sensing Techniques ◽  
Sugarcane Productivity

SENSORIAMENTO REMOTO APLICADO A MODELAGEM DE PRODUTIVIDADE DA CULTURA DA CANA-DE-AÇÚCAR   VICTOR COSTA LEDA1, ALINE KURAMOTO GOLÇALVES2, NATALIA DA SILVA LIMA3   1 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected]. 2 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected]. 3 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected].   RESUMO: O trabalho objetivou modelar as correlações de produtividade da cana-de-açúcar com índices de vegetação obtidos por meio de análise de imagens orbitais. Para análise, foram elaborados modelos matemáticos que expliquem a produtividade da cana-de-açúcar por meio das técnicas de geoprocessamento e sensoriamento remoto. O experimento foi realizado na área de produção comercial da Agrícola Rio Claro, parceira do grupo Zilor, que está localizada nos municípios de Lençóis Paulista e Pratânia, SP. A área ocupa aproximadamente 6000 ha, com altimetrias variando entre 600 e 700 m. Foi constatado que as modelagens foram satisfatórias, variando o coeficiente de determinação entre 0,15 a 0,97, sendo que, em períodos de colheita com elevados coeficientes de determinação, podem geralmente ser encontradas áreas de forma aglomerada, o que sugere uma menor incidência de variáveis. Enquanto áreas que apresentaram coeficientes de determinação baixos, podem ser explicadas devido a fatores como, dispersão dos talhões na área, classes de solo, precipitação e variedades da cultura, provavelmente distintos.   Palavras-chaves: índices de vegetação, Landsat 8, regressão linear múltipla.   REMOTE SENSING FOR THE SUGARCANE PRODUCTIVITY MODELING   ABSTRACT: The aim of this study was to model the sugarcane productivity correlations with vegetation indexes obtained through orbital image analysis. From the analysis was elaborated      mathematical models to explain sugarcane productivity through geoprocessing and remote sensing techniques. The experiment was carried out in the commercial production area of Agrícola Rio Claro, a partner of the Zilor group, located in the municipalities of Lençóis Paulista and Pratânia, SP, with approximately 6,000 hectares, with altimetry varying between 600 and 700 meters. It was verified that the modeling was satisfactory, varying the coefficient of determination between 0,15 and 0,97. Once      in periods with high determination coefficients, areas of agglomerated form can usually be found, which suggests a lower incidence of variables. While, in periods with low determination coefficients, can be explain due to listed factors that occurred as dispersion of the stands in the area, classes of soil, precipitation and probably different varieties of the crop.   Keywords: vegetation index, landsat8, multiple linear regression.

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