Applying imaging spectroscopy techniques to map saline soils with ASTER images

Melendez-Pastor, Ignacio; Pedreño, José Navarro; Koch, Magaly; Gómez, Ignacio

doi:10.1016/j.geoderma.2010.02.015

Cited by 36 publications

(12 citation statements)

References 31 publications

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“…Moreover, regression analyses, including multiple regression analysis and partial least‐squares regression, are the most popular data‐analysis techniques for relating soil properties to reflectance records (Ge et al, 2011; Gomez et al, 2012). Further soil properties estimated by multi‐ and hyperspectral remote sensing are calcium carbonate content (Lagacherie et al, 2008), salinity (Melendez‐Pastor et al, 2010; Ghosh et al, 2012), and texture (Casa et al, 2013). The enhanced combination of soil spectral libraries (Brown, 2007) and hyperspectral remote sensing may in the future lead to improved maps of soil properties and may be able to monitor and automate updates of changes in soil properties.…”

Section: Modern Sources Of Spatial and Temporal Data For Soil Modelingmentioning

confidence: 99%

Modeling Soil Processes: Review, Key Challenges, and New Perspectives

Vereecken

Schnepf

Hopmans

et al. 2016

Vadose Zone Journal

541

394

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The remarkable complexity of soil and its importance to a wide range of ecosystem services presents major challenges to the modeling of soil processes. Although major progress in soil models has occurred in the last decades, models of soil processes remain disjointed between disciplines or ecosystem services, with considerable uncertainty remaining in the quality of predictions and several challenges that remain yet to be addressed. First, there is a need to improve exchange of knowledge and experience among the different disciplines in soil science and to reach out to other Earth science communities. Second, the community needs to develop a new generation of soil models based on a systemic approach comprising relevant physical, chemical, and biological processes to address critical knowledge gaps in our understanding of soil processes and their interactions. Overcoming these challenges will facilitate exchanges between soil modeling and climate, plant, and social science modeling communities. It will allow us to contribute to preserve and improve our assessment of ecosystem services and advance our understanding of climate-change feedback mechanisms, among others, thereby facilitating and strengthening communication among scientific disciplines and society. We review the role of modeling soil processes in quantifying key soil processes that shape ecosystem services, with a focus on provisioning and regulating services. We then identify key challenges in modeling soil processes, including the systematic incorporation of heterogeneity and uncertainty, the integration of data and models, and strategies for effective integration of knowledge on physical, chemical, and biological soil processes. We discuss how the soil modeling community could best interface with modern modeling activities in other disciplines, such as climate, ecology, and plant research, and how to weave novel observation and measurement techniques into soil models. We propose the establishment of an international soil modeling consortium to coherently advance soil modeling activities and foster communication with other Earth science disciplines. Such a consortium should promote soil modeling platforms and data repository for model development, calibration and intercomparison essential for addressing contemporary challenges.

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Section: Modern Sources Of Spatial and Temporal Data For Soil Modelingmentioning

confidence: 99%

Modeling Soil Processes: Review, Key Challenges, and New Perspectives

Vereecken

Schnepf

Hopmans

et al. 2016

Vadose Zone Journal

541

394

View full text Add to dashboard Cite

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“…Using remote sensing on a local scale (b10 4 km) broad salinity classes can be mapped with ASTER (Melendez-Pastor et al, 2010), Hymap (Dehaan and Taylor, 2003), Landsat TM and ALI imagery -the latter two using the Salinity Index (SI) and the Normalized Salinity Index (NSI) (Bannari et al, 2008;Jabbar and Chen, 2008;Odeh and Onus, 2008). Weng et al (2008) were able to discriminate 5 classes of saline soils with Hyperion data for an area of about 1200 km 2 .…”

Section: Soil Salinitymentioning

confidence: 99%

The use of remote sensing in soil and terrain mapping — A review

et al. 2011

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“…As a result, some sensitive spectral features and spectral bands have been defined. Most mentioned among these are the near-infrared (NIR) and short-wave IR bands of Landsat and other multispectral satellites (Yu et al, 2010;Setia et al, 2013;Sidike et al, 2014) and the narrower absorption features of the same (NIR and short-wave IR) spectral region (Howari, 2003;Bannari et al, 2007;Farifteh et al, 2008;Melendez-Pastor et al, 2010;Mashimbye et al, 2012;Wang et al, 2012). Some authors have proposed use of visible spectra in addition to IR (Noroozi et al, 2012;Zhang et al, 2012;Setia et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Satellite Thermography for Soil Salinity Assessment of Cropped Areas in Uzbekistan

et al. 2017

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A change of canopy temperature can indicate stress in vegetation. Use of canopy temperature to assess salt stress in specific plant species has been well studied in laboratory and greenhouse experiments, but its potential for use in landscape-level studies using remote sensing techniques has not yet been explored. Our study investigated the application of satellite thermography to assess soil salinity of cropped areas at the landscape level. The study region was Syrdarya Province, a salt-affected, irrigated semi-arid province of Uzbekistan planted mainly to cotton and wheat. We used moderate-resolution imaging spectroradiometer satellite images as an indicator for canopy temperature and the provincial soil salinity map as a ground truth dataset. Using analysis of variance, we examined relations among the soil salinity map and canopy temperature, normalized difference vegetation index, enhanced vegetation index, and digital elevation model. The results showed significant correlations between soil salinity and canopy temperature, but the strength of the relation varied over the year. The strongest relation was observed for cotton in September. The calculated F values were higher for canopy temperature than for the other indicators investigated. Our results suggest that satellite thermography is a valuable landscape-level approach for detecting soil salinity in areas under agricultural crops.

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Applying imaging spectroscopy techniques to map saline soils with ASTER images

Cited by 36 publications

References 31 publications

Modeling Soil Processes: Review, Key Challenges, and New Perspectives

Modeling Soil Processes: Review, Key Challenges, and New Perspectives

The use of remote sensing in soil and terrain mapping — A review

Satellite Thermography for Soil Salinity Assessment of Cropped Areas in Uzbekistan

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