Scaling field data to calibrate and validate moderate spatial resolution remote sensing models
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Abstract
Validation and calibration are essential components of nearly all remote sensing-based studies. In both cases, ground measurements are collected and then related to the remote sensing observations or model results. In many situations, and particularly in studies that use moderate resolution remote sensing, a mismatch exists between the sensor’s field of view and the scale at which in situ measurements are collected. The use of in situ measurements for model calibration and validation, therefore, requires a robust and defensible method to spatially aggregate ground measurements to the scale at which the remotely sensed data are acquired. This paper examines this challenge and specifically considers two different approaches for aggregating field measurements to match the spatial resolution of moderate spatial resolution remote sensing data: (a) landscape stratification; and (b) averaging of fine spatial resolution maps. The results show that an empirically estimated stratification based on a regression tree method provides a statistically defensible and operational basis for performing this type of procedure.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Scaling field data to calibrate and validate moderate spatial resolution remote sensing models |
| Series title | Photogrammetric Engineering and Remote Sensing |
| DOI | 10.14358/PERS.73.8.945 |
| Volume | 73 |
| Issue | 8 |
| Year Published | 2007 |
| Language | English |
| Publisher | ASPRS |
| Contributing office(s) | Earth Resources Observation and Science (EROS) Center |
| Description | 10 p. |
| First page | 945 |
| Last page | 954 |
| Online Only (Y/N) | N |
| Additional Online Files (Y/N) | N |
| Google Analytic Metrics | Metrics page |