Linear Kernel-driven Model Research Articles

A new computationally efficient algorithm to generate global fractional vegetation cover from Sentinel-2 imagery at 10 m resolution

ABSTRACT The multi-angle method in the pixel dichotomy model (PDM) is used to produce fine spatial resolution (FSR) products of fractional vegetation cover (FVC), which involves numerous iterative computations. This phenomenon causes a low computational efficiency for global FVC. Here, we established the multi-angle equations, and simplified the two-stream approximation method to obtain multi-angle reflectance, which calculated the KDM parameters possible. Moreover, to accelerate the calculation of parameters in the multi-angle equations, land cover data were utilized as priority knowledge, which aims to decrease iterative computations to speed up the computational speed. Finally, we proposed an accelerated pixel dichotomy coupled linear kernel-driven model (PDKDM-A). By validating PDKDM-A using field measurements and FVC products, we found that the global FVC computed for various land covers exhibited strong agreement with the field measurements. The root mean square errors (RMSEs) were consistently below 0.150, except for grasslands, croplands, and cropland/natural vegetation mosaics. When comparing FVC products on the same scale, we discovered that the RMSE was less than 0.103, except for deciduous broadleaf forests. Our study demonstrates that PDKDM-A is an efficient algorithm, with the potential to quickly generate accurate global FVC products with fine spatial resolution.

A Pixel Dichotomy Coupled Linear Kernel-Driven Model for Estimating Fractional Vegetation Cover in Arid Areas From High-Spatial-Resolution Images

A Pixel Dichotomy Coupled Linear Kernel-Driven Model for Estimating Fractional Vegetation Cover in Arid Areas From High-Spatial-Resolution Images

Information flow and controlling in regularization inversion of quantitative remote sensing

In order to minimize uncertainty of the inversed parameters to the largest extent by making full use of the limited information in remote sensing data, it is necessary to understand what the information flow in quantitative remote sensing model inversion is, thus control the information flow. Aiming at this, the paper takes the linear kernel-driven model inversion as an example. At first, the information flow in different inversion methods is calculated and analyzed, then the effect of information flow controlled by multi-stage inversion strategy is studied, finally, an information matrix based on USM is defined to control information flow in inversion. It shows that using Shannon entropy decrease of the inversed parameters can express information flow more properly. Changing the weight of a priori knowledge in inversion or fixing parameters and partitioning datasets in multi-stage inversion strategy can control information flow. In regularization inversion of remote sensing, information matrix based on USM may be a better tool for quantitatively controlling information flow.

Estimation of the parameter error propagation in inversion based BRDF observations at single sun position

In multiangular remote sensing observations, the variable range of the solar zenith angle (SZN) is narrow. We takes the linear kernel-driven model as an example to analyze the parameter error propagation in inversion by using the observations at single sun position and to show that in such case it is unreliable to invert the BRDF tendency with SZN. To improve the algorithm, we suggest adding certain constraint for the changing trend of albedo with SZN in the model inversion.

Estimating land surface albedo in the HAPEX-Sahel southern super-site: inversion of two BRDF models against multiple angle ASAS images

The provision of spatially and temporally disaggregated values of albedo is critical to an improved understanding of energy interactions at the Earth surface. Remotely sensed estimates of land surface albedo can best be obtained by inverting models of the Bidirectional Reflectance Distribution Function (BRDF) against bidirectional reflectance factor measurements sampled at different sensor view angles and solar illumination angles. This paper describes the preliminary results obtained using such an approach over the HAPEX-Sahel (Hydrological and Atmospheric Pilot Experiment) southern super-site in Niger. Two BRDF models, one empirical (modified-Walthall) and one semi-empirical (a linear kernel-driven model, employing isotropic, geometric and volume scattering kernels), are inverted analytically against each pixel in a set of co-registered multispectral images acquired by NASA's Advanced Solid-state Array Spectroradiometer (ASAS). The paper describes the methods used to register these images to sub-pixel accuracy, to perform radiometric and first-order atmospheric correction of the data, and to invert the BRDF model against the pre-processed image data to yield spatially referenced estimates of the model parameters and angularly integrated terms related to albedo. Spatial patterns, closely related to variations in land cover type, are clearly evident in these data. It is shown that, in this instance, the simple empirical model provides a better fit to the measured data, particularly at red and near-infrared wavelengths. The poorer performance of the semi-empirical model at this particular study site is discussed in terms of the assumptions that the model makes about energy interaction with the land surface. The impacts of changes in the projected instantaneous field-of-view as a function of sensor view angle and of residual image-to-image mis-registration on the derived BRDF model parameters and estimated albedo values are also examined.