In remote sensing, ground truth is just a jargon term for near surface observations. As applied to a planetary body, this refers to the on-site gathering of reference data and information derived therefrom that properly characterize states, conditions, and parameters associated with the surface (and with appropriate sensors, aspects of the subsurface) and any gaseous envelope (the atmosphere) above it. The purpose in acquiring ground truth is ultimately to aid in the calibration and interpretation of remotely recorded surveys by checking out realities from within the scene. Since human interpreters normally experience the Earth as ground dwellers, their view of the world from a horizontal or low-angle panorama is the customary frame of reference. In fact, both the remote sensing specialist and the novice should retain a surface-based perspective during all phases of data collection, analysis, and applications inasmuch as most interpretations and decisions dealing with natural resources and land use will eventually be implemented 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. The types of tasks and operations associated with obtaining and utilizing ground truth are summarized in Table 13-1:

• 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, interpretation, and application of remote sensing data (landmark identification, logistics of access. etc.)
• Reduce data and sampling requirements (e.g., areas of needed coverage) for exploration, 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; 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

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

Probably the most common reasons for conducting field activities lie either in the necessity of selecting training sites prior to supervised classification or identification of key classes after unsupervised classification. The best way to do this, if feasible, is simply to spend a few days in the field examining the terrain for which a classification is to be prepared. Obviously, the scale of this effort depends on the areal extent to be classified: one or more full Landsat scenes may require considerable travel and field time whereas examination of a typical subscene (such as 512 x 512 pixels) can often be accomplished in a day or two. If field operations are limited by logistics or circumstances (e.g., in an inaccessible foreign area or during an off-season such as winter), then one may fall back instead on aerial photography, maps, literature research, interviews with residents (perhaps over the Internet), etc. In practice, specification of training sites generally involves integration of these several sources of information -- direct observations, photo documentation, a variety of maps, personal familiarity, etc.

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