Development and Demonstration of a Method for GEO-to-LEO NDVI Transformation

Abstract

This study presents a new method that mitigates biases between the normalized difference vegetation index (NDVI) from geostationary (GEO) and low Earth orbit (LEO) satellites for Earth observation. The method geometrically and spectrally transforms GEO NDVI into LEO-compatible GEO NDVI, in which GEO’s off-nadir view is adjusted to a near-nadir view. First, a GEO-to-LEO NDVI transformation equation is derived using a linear mixture model of anisotropic vegetation and nonvegetation endmember spectra. The coefficients of the derived equation are a function of the endmember spectra of two sensors. The resultant equation is used to develop an NDVI transformation method in which endmember spectra are automatically computed from each sensor’s data independently and are combined to compute the coefficients. Importantly, this method does not require regression analysis using two-sensor NDVI data. The method is demonstrated using Himawari 8 Advanced Himawari Imager (AHI) data at off-nadir view and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at near-nadir view in middle latitude. The results show that the magnitudes of the averaged NDVI biases between AHI and MODIS for five test sites (0.016–0.026) were reduced after the transformation (<0.01). These findings indicate that the proposed method facilitates the combination of GEO and LEO NDVIs to provide NDVIs with smaller differences, except for cases in which the fraction of vegetation cover (FVC) depends on the view angle. Further investigations should be conducted to reduce the remaining errors in the transformation and to explore the feasibility of using the proposed method to predict near-real-time and near-nadir LEO vegetation index time series using GEO data.

Orthorectification of Data from the AHI Aboard the Himawari-8 Geostationary Satellite

“…Moreover, because this direction (eastward or westward) differs substantially from the displacement direction of the GEO satellites (the same direction of the pixel position from the subsatellite point), data from the LEO satellites can compensate for the occlusion pixels of the GEO satellite images, which would be a great advantage of combining the data from both satellites. To facilitate such a compensation algorithm, techniques for comparing the data between GEO and LEO sensors need to be established, especially for targets located in middle-latitude mountainous regions, which is considered a challenging task [48][49][50]. Comparisons between GEO and LEO sensors are a far more complicated issue than are comparisons between orthorectified data and original (before rectification) data.…”

A Sensor Bias Correction Method for Reducing the Uncertainty in the Spatiotemporal Fusion of Remote Sensing Images

“…The multi-sensor NDVI inconsistencies are mainly from the differences in the following: orbital overpass times [35], geometric, spectral, and radiometric calibration errors [36][37][38][39], and directional sampling and scanning systems [40]. Satellite-based NDVI may be more complicated due to the varying sun-target sensor geometries [41,42]. The difference in the relative spectral response functions of the different sensors (such as Landsat-TM and Terra-MODIS) can cause the inconsistency in NDVI [43].…”

“…The effect is comparable in magnitude to the uncertainties caused by sensor calibration, atmospheric, and angular correction and can lead to systematic biases if neglected [44]. To reduce these differences, Obata et al (2021) [41] developed an NDVI transformation method based on a linear mixture model of anisotropic vegetation and non-vegetation endmember spectra, which can reduce the effects of surface anisotropy caused by viewing angle differences and spectral response function differences at the scene level. Wang and Huang (2017) [45] constructed a linear model to correct the temporal change in coarse images.…”