LANDSAT MID‐INFRARED DATA AND GIS IN REGIONAL SURFACE SOIL‐MOISTURE ASSESSMENT<sup>1</sup>

Shih, S. F.; Jordan, Jonathan D.

doi:10.1111/j.1752-1688.1992.tb01493.x

Cited by 19 publications

(11 citation statements)

References 14 publications

(13 reference statements)

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“…We used blue (TM Band 1, B), green (TM Band 2, G), red (TM Band 3, R), near-infrared (TM Band 4, NIR), two mid-infrared (TM Band 5, MIR1; and TM Band 7, MIR2), and thermal infrared (TM Band 6, TIR) bands from all image dates. We included NIR, MIR1, MIR2, and TIR because of their suitability for land cover mapping and detecting water content in plants and soil [58,59]. Though multi-temporal and multi-platform data were used, the acquired satellite data were not atmospherically corrected and the data remained in digital number format.…”

Section: Optical Input Datamentioning

confidence: 99%

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

2013

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Abstract:Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource OPEN ACCESS Remote Sens. 2013, 5 3213 managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

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Section: Optical Input Datamentioning

confidence: 99%

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

2013

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“…Although there is some evidence for the persistence in time of spatial structure of soil moisture characteristics (Munoz-Pardo et d., 1990;Comegna and Basile, 1994;McBratney, 1992), the type of experiment reported in this paper needs to be repeated on a range of times with differing soil moisture states.…”

Section: Discussion Of Results From Geostatistical Analysismentioning

confidence: 98%

“…According to Lillesand and Kiefer (1987), wavebands 2 to 8 are most sensitive to vegetation and waveband 11 shows variation in thermally related phenomena including soil moisture. Shih et al (1990) give the results of a case study designed to estimate soil water content using infrared thermometry. They used a portable infrared thermometer with a spectral waveband of 7-16 pm.…”

Section: Imagerymentioning

confidence: 99%

“…They obtained coefficients of determination between temperature and soil moisture ranging from 0.64 to 0.80. Shih and Jordan (1992) provide a case study of using shortwave (1.45-2.35 pm) Landsat data to provide regional assessments of soil moisture. They found that results from waveband 2.08-2.35pm accorded with land cover classes which were considered to have contrasting soil moisture conditions.…”

Section: Imagerymentioning

confidence: 99%

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Spatial variability in soil moisture as predicted from airborne thematic mapper (ATM) data

Davidson¹,

Watson²

1995

Earth Surf Processes Landf

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In the first part of this project, the extent to which moisture content of alluvial soils could be predicted from imagery derived from an airborne thematic mapper (ATM) was investigated. From sampling done on the same day as the flight, it was found that digital numbers derived from the thermal channel (waveband 11) were strongly correlated with gravimetric moisture content. From sampling three fields of contrasting land cover, the relationship between waveband I 1 values and moisture content was found to be independent of land cover type. Spatial va-iation in waveband 11 values and thus moisture content were related to palaeochannel patterns on the alluvial land. This was investigated by deriving variograms for long transects from each of the three investigated fields. The range and sills of the variograms are shown to express the nature and pattern of palaeochannels. By the application of such geostatistical techniques, high resolution imagery can thus be used to quantify palaeochannel characteristics on alluvial land.

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“…Shih and Jordan (1992) integrated the MIR data of the Landsat TM band 7 with GIS to assess regional soil moisture conditions. They found that the agricultural/irrigated and urban/clearings land-use categories had generally higher MIR response than did the forest/wetland land use, while water had the lowest MIR response.…”

Section: Mid-infrared Methodsmentioning

confidence: 99%

Integration of Remote Sensing and GIS for Hydrologic Studies

Shih¹

1996

Geographical Information Systems in Hydrology

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Abstract. Recently, the importance of integration of remote sensing and geographic information system (GIS) to study hydrologic processes has been realized by water-resources workers. However, the interface between remotely sensed data and GIS is still weak and many problems must be solved before it becomes widely available. In the meantime, the public use of satellite data to manage water resources is still in its infancy, and more application techniques are urgently in need of development. Thus this chapter is separated into two major parts. The first part is to introduce general information about remote sensing systems, GIS, and the global positioning system (GPS). The second part is to exemplify successful applications of the integration of remote sensing and GIS in hydrologic studies such as land use/land cover classification, precipitation, soil moisture, evapotranspiration, water extent, groundwater, water quality, and runoff. IntroductionThe endless recirculation of water in the atmosphere-hydrosphere-lithosphere is known as the hydrologic cycle. The cycle can be studied according to the particular scale of reference i.e., global scale, basin scale, etc. From the hydrological point of view, the basin scale cycle is emphasized. The basin-scale cycle is considered to be a continuous circulation of water from water vapor to precipitation, stream flow, lakes, reservoirs, soil moisture, groundwater, and out of basin transfer, to the return to water vapor through evaporation and transpiration. Within a basin, the dynamics of the hydrologic processes are governed partially by the temporal and spatial characteristics of inputs and outputs and the land use/land cover conditions. Currently, little is known about the accuracy of the estimation of hydrologic parameters in a given area, primarily because of the wide variation of the distribution in

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LANDSAT MID‐INFRARED DATA AND GIS IN REGIONAL SURFACE SOIL‐MOISTURE ASSESSMENT¹

Cited by 19 publications

References 14 publications

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

Spatial variability in soil moisture as predicted from airborne thematic mapper (ATM) data

Integration of Remote Sensing and GIS for Hydrologic Studies

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LANDSAT MID‐INFRARED DATA AND GIS IN REGIONAL SURFACE SOIL‐MOISTURE ASSESSMENT1

Cited by 19 publications

References 14 publications

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

Spatial variability in soil moisture as predicted from airborne thematic mapper (ATM) data

Integration of Remote Sensing and GIS for Hydrologic Studies

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Product

Resources

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LANDSAT MID‐INFRARED DATA AND GIS IN REGIONAL SURFACE SOIL‐MOISTURE ASSESSMENT¹