Dielectric properties of salt-affected soils

Sreenivas, K.; Venkataratnam, L.; Rao, Preeti

doi:10.1080/01431169508954431

Cited by 78 publications

(41 citation statements)

References 4 publications

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“…Plant canopies were better for estimating salinity effects on cotton in different growth stages than direct salinity measurements. Similarly, Sreenivas et al (1995) found that differentiating saline soils from spodic soils was possible using optical and synthetic aperture radar (SAR) data. Determining soil moisture content is possible using SAR and microwave techniques (Schmugge, 1999).…”

Section: Introductionmentioning

confidence: 98%

Evaluating Remote Sensing for Determining and Classifying Soybean Anomalies

Shaw

Kelley

2005

Precision Agric

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Two soybean fields were monitored in 2001 and 2002 to determine the utility of multispectral imagery for locating and classifying crop anomalies. Crop anomalies may be due to planter problems, soil problems, weed infestations and stressed soybean plants. Three image collection dates per location for each year were used in a supervised classification analysis. In 2002, aerial images were evaluated for potential use as a directed scouting tool. Remotely sensed data as a scouting tool detected 50-100% of anomalies detected by ground truthing. The number of anomalies detected by aerial imagery decreased through the growing season, while the number of anomalies found from directed scouting remained relatively constant. Thus, agreement was higher later in the growing season, since remote sensing was detecting more anomalies than the ground truthing efforts did. Excluding bare soil and healthy soybean situations, anomalies due to stress on soybean plants in the form of iron chlorosis and stunted plants yielded highest classification accuracies, ranging from 83% to 90% both years. This is attributed to differences in coloration of soybean plants with iron chlorosis and lack of full canopy coverage of stunted soybean. Herbicide damage due to overlap of spray boom led to classification accuracies from 50% to 67%. The overlap of the spray boom was not widespread in the field; thus, fewer areas of interests could be constructed for testing purposes, which may explain the decrease in classification accuracies.

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Section: Introductionmentioning

confidence: 98%

Evaluating Remote Sensing for Determining and Classifying Soybean Anomalies

Shaw

Kelley

2005

Precision Agric

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“…However, passive optical remote sensing based approaches may be hampered over coastal areas, black-clay soils, and desert areas, due to the smoothness and the white color of the formed crust [2]. In the microwave wavelengths, the signal is sensitive to the dielectric properties of the target [17]. Studies indicate that the change of soil moisture and salinity affects soil conductivity which influences soil dielectric constant [18,19].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive Reflective and Active Microwave Remote Sensing Data

et al. 2015

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Soil salinization is one of the most widespread soil degradation processes on Earth, especially in arid and semi-arid areas. The salinized soil in arid to semi-arid Xinjiang Uyghur Autonomous Region in China accounts for 31% of the area of cultivated land, and thus it is pivotal for the sustainable agricultural development of the area to identify reliable and cost-effective methodologies to monitor the spatial and temporal variations in soil salinity. This objective was accomplished over the study area (Keriya River Basin, northwestern China) by adopting technologies that heavily rely on, and integrate information contained in, a readily available suite of remote sensing datasets. The following procedures were conducted: (1) a selective principle component analysis (S-PCA) fusion image was generated using Phased Array Type L-band SAR (PALSAR) backscattering coefficient (σ°) OPEN ACCESSRemote Sens. 2015, 7 8804 and Landsat Enhanced Thematic Mapper Plus (ETM+) multispectral image of Keriya River Basin; and (2) a support vector machines (SVM) classification method was employed to classify land cover types with a focus on mapping salinized soils; (3) a cross-validation method was adopted to identify the optimum classification parameters, and obtain an optimal SVM classification model; (4) Radarsat-2 (C band) and PALSAR polarimetric images were used to analyze polarimetric backscattering behaviors in relation to the variation in soil salinization; (5) a decision tree (DT) scheme for multi-source optical and polarimetric SAR data integration was proposed to improve the estimation and monitoring accuracies of soil salinization; and (6) detailed field observations and ground truthing were used for validation of the adopted methodology, and quantity and allocation disagreement measures were applied to assess classification outcome. Results showed that the fusion of passive reflective and active microwave remote sensing data provided an effective tool in detecting soil salinization. Overall accuracy of the adopted SVM classifier with optimal parameters for fused image of ETM+ and PALSAR data was 91.25% with a Kappa coefficient of 0.89, which was further improved by the DT data integration and classification method yielding an accuracy of 93.01% with a Kappa coefficient of 0.92 and lower disagreement of quantity and allocation.

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“…The sensitivity of higher frequency radar backscattering (C band radar) to soil types, however, has not been fully analyzed in the published literature. Several studies reported that soil moisture estimation using L-band radar should consider the impact of soil salinity in the model [36][37][38]. Since in this research the soil moisture estimation model used C band radar, the impact of both soil type and soil salinity on soil moisture estimation was investigated.…”

Section: Multiple Linear Regressionsmentioning

confidence: 99%

Soil Moisture Estimation in South-Eastern New Mexico Using High Resolution Synthetic Aperture Radar (SAR) Data

Hossain

Easson

2016

Geosciences

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Soil moisture monitoring and characterization of the spatial and temporal variability of this hydrologic parameter at scales from small catchments to large river basins continues to receive much attention, reflecting its critical role in subsurface-land surface-atmospheric interactions and its importance to drought analysis, irrigation planning, crop yield forecasting, flood protection, and forest fire prevention. Synthetic Aperture Radar (SAR) data acquired at different spatial resolutions have been successfully used to estimate soil moisture in different semi-arid areas of the world for many years. This research investigated the potential of linear multiple regressions and Artificial Neural Networks (ANN) based models that incorporate different geophysical variables with Radarsat 1 SAR fine imagery and concurrently measured soil moisture measurements to estimate surface soil moisture in Nash Draw, NM. An artificial neural network based model with vegetation density, soil type, and elevation data as input in addition to radar backscatter values was found suitable to estimate surface soil moisture in this area with reasonable accuracy. This model was applied to a time series of SAR data acquired in 2006 to produce soil moisture data covering a normal wet season in the study site.

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Dielectric properties of salt-affected soils

Cited by 78 publications

References 4 publications

Evaluating Remote Sensing for Determining and Classifying Soybean Anomalies

Evaluating Remote Sensing for Determining and Classifying Soybean Anomalies

Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive Reflective and Active Microwave Remote Sensing Data

Soil Moisture Estimation in South-Eastern New Mexico Using High Resolution Synthetic Aperture Radar (SAR) Data

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