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Rationale for Surface Observations

In remote sensing, ground truth is just a jargon term for near-surface observations. As applied to a planetary body, this refers to gathering reference data on-site and deriving information therefrom that properly characterizes states, conditions, and parameters associated with the surface. With appropriate sensors, we can derive aspects of the subsurface and any gaseous envelope (atmosphere) above it, as well. The purpose in acquiring ground truth is ultimately to aid in calibrating and interpreting remotely recorded surveys by checking realities within the scene. Since human interpreters normally experience Earth as ground dwellers, their view of the world from a horizontal or low-angle panorama is the customary frame of reference. In fact, the remote sensing specialist and the novice should retain a surface-based perspective during all phases of data collection, analysis, and applications, because they will implement most interpretations and decisions dealing with natural resources and land use at the ground level.

Among many ground-oriented data sources are field observations, in situ spectral measurements, aerial reconnaissance and photography, descriptive reports and inventory tallies, and maps. Table 13-1 summarizes the types of tasks and operations associated with obtaining and using ground truth data:

Table 13-1

Role of Ground and Aircraft Observations in Supporting

Satellite Remote Sensing

Correlate surface features and localities as known from familiar ground perspectives with their expression in satellite imagery

  • Provide input and control during the first stages of planning for analysis, interpreting, and applying remote sensing data (e.g., identifying landmarks, logistics of access. etc.)

  • Reduce data and sampling requirements (e.g., areas of needed coverage) for exploring, monitoring, and inventory activities

  • Select test areas for aircraft and other multistage support missions (e.g., underflights simultaneous with spacecraft passes)

  • Identify classes established by unsupervised classification

  • Select and categorize training sites for supervised classification

  • Verify accuracy of classification (error types and rates) by using quantitative statistical techniques

  • Obtain quantitative estimates relevant to class distributions (e.g. field size; forest acreage)

  • Collect physical samples for laboratory analysis of phenomena detected from remote sensing data (e.g., water quality, rock types, and insect-induced disease)

  • Acquire supplementary (ancillary) non-remote sensing data for interpretive model analysis or for integration into Geographic Information Systems

  • Develop standard sets of spectral signatures by using ground-based instruments

  • Measure spectral and other physical properties needed to stipulate characteristics and parameters pertinent to designing new sensor systems


    13-1: Assume you are working on a Landsat scene that is close enough for you to actually go into the field to examine firsthand the features contained within it. What, in your opinion, would be the most important task to carry out 1) before the satellite takes its image and 2) after you receive and process this image? ANSWER

    Examples of typical observations and measurements conducted in the field, commonly as the remote sensing platform passes over, or shortly thereafter, include these:

    1. Meteorological conditions (air temperature, wind velocity, humidity, etc.)
    2. Insolation (solar irradiance)
    3. On-site calibration of reflectance
    4. Soil moisture
    5. Water levels (stream gauge data)
    6. Snow thickness
    7. Siltation in lakes and rivers
    8. Growth stages of vegetation
    9. Distribution of urban subclasses
    10. Soil and rock types

    13-2: Field work is expensive, and often inconvenient. Yet some kinds of data are needed in near real time. Mention three in this category from the above list. How would you go about getting these critical data values if field work is not an option? ANSWER

    Ground truth activities are an integral part of the "multi" approach. Thus, we should procure data whenever possible from different platforms (multistage), at various altitudes (multilevel). This gives rise to multiscaled images or classification maps. We should employ multisensor systems simultaneously to provide data over various regions of the spectrum (multispectral). Often, we obtain data at different times (mutitemporal), whenever seasonal effects or illumination differences are factors or change detection is the objective. Supporting ground observations should come from many relevant, but not necessarily interrelated, sources (multisource). Some types of surface data may correlate with one another and with other types of remote sensing data (multiphase).

    13-3: Using your imagination and growing experience, design an experiment using as many of the above "multi's" as seems sensible. ANSWER

    Probably the most common reasons for conducting field activities are to select training sites prior to supervised classification or to identify key classes after unsupervised classification. The best way to collect field data, if feasible, is simply to spend a few days in the field, examining the terrain for the classification. Obviously, the scale of this effort depends on the size of the area we want to classify. One or more full Landsat scenes may require considerable travel and field time, whereas we can often examine a typical subscene (such as 512 x 512 pixels) in a day or two. If logistics or circumstances (e.g., an inaccessible foreign area or during an off-season such as winter) limit field operations, then we may use instead aerial photography, maps, literature research, interviews with residents (perhaps over the Internet), etc. In practice, to specify training sites generally means integrating the following sources of information: direct observations, photo documentation, a variety of maps, personal familiarity, and others.

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    Primary Author: Nicholas M. Short, Sr. email:

    Collaborators: Code 935 NASA GSFC, GST, USAF Academy
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