Unmixing-based radiometric and spectral harmonization for consistency of multi-sensor reflectance time-series data

We developed a new algorithm for computing radiometrically and spectrally consistent surface reflectances from multiple sensors. The algorithm approximates surface reflectances of reference sensors directly from top-of-atmosphere (TOA) reflectances of sensors-to-be-transformed. A unique characteristic of the algorithm is that coefficients in the algorithm are computed independently using statistics of time-series reflectance data for each sensor; thus, no regressions or optimizations using pairs of data from different sensors are required. This characteristic can lead to a substantial reduction in the number of computational tasks required for calibrating models when numerous satellite sensors or datasets are used. First, a system of equations relating TOA reflectances of one sensor and surface reflectances of another sensor in the red and near-infrared bands was analytically approximated using a linear mixture model of three land-cover types and radiative transfer in the atmosphere. The equations were subsequently used to develop an unmixing-based algorithm for radiometric corrections and spectral transformations. The algorithm was evaluated using synchronous observation data and long-term time-series data with middle spatial resolution, which were obtained from the Landsat 4–5 Multispectral Scanner (MSS) and Thematic Mapper (TM) sensors. Results obtained using contemporaneous data from the two sensors indicated that cross-sensor differences in reflectances and in a spectral index, the normalized difference vegetation index (NDVI), between the MSS and TM sensors were reduced to reasonable levels after the algorithm was applied; the magnitudes of remaining biases were less than 0.005 in reflectance units and less than 0.03 in NDVI units. Results obtained using time-series data for four regions of interest with different land-cover types indicated that the transformed MSS time-series data well synchronized with the TM data used as a reference. Reflectance differences remaining after implementation of the algorithm were possibly due to instability of the algorithm for computing parameters, sensor-dependent quality assurance (QA) data and QA accuracy, and geolocation errors, among others. The concept of the developed algorithm might be applicable universally to various combinations of spectral bands and sensors/missions, which should be further evaluated for cross-sensor radiometric and spectral harmonization with the aim of multi-sensor analysis.