Atmospheric correction of HJ-1 A/B CCD over land: Land surface reflectance calculation for geographical information product

Abstract

This paper proposed a method to retrieve the land surface reflectance from the HJ-1A/B CCD data. The aerosol optical depth (AOD), the most important factor affecting the atmospheric correction of CCD images at all bands, is proposed to retrieve from the CCD imagery by the approach of dense dark vegetation (DDV) method. A look-up table in terms of the transmittances, the path radiances and the atmospheric spherical albedo as functions of the AOD was established for a variety of sun-sensor geometry and aerosol loadings. The atmospheric correction is then achieved with the look-up table and the MODIS surface reflectance output (MOD09) as the priori datasets. Based on the retrieved AOD and the look-up table of atmospheric correction coefficients, the land surface reflectance was retrieved for the HJ-1A/B data according to the atmospheric radiative transfer equation. Some in-situ measurement Data for Yanzhou of Shandong province in East China and MODIS land surface reflectance products MOD09 are used to preliminarily validate the proposed method. The results show that the proposed method can remove effectively the atmospheric contributions, and the overall accuracy of the retrieval land surface reflectance can be improved substantially.

Improved Understanding of How Catchment Properties Control Hydrological Partitioning Through Machine Learning

“…The long‐term soil water storage changes are considered negligible in water and energy balances (Sinha et al., 2018). Based on this “limit” concept, the formulation proposed by Fu (1981) was adopted in this study as follows: $$$\frac{{E}_{a}}{P}=1+\frac{{E}_{P}}{P}-{\left[1+{\left(\frac{{E}_{P}}{P}\right)}^{\omega }\right]}^{1/\omega }$$$ where E a is actual evapotranspiration; E P is potential evapotranspiration; P is precipitation; ω is a model parameter determined by catchment attributes. The parameter ω ranges from 1 to ∞.…”

“…The long-term soil water storage changes are considered negligible in water and energy balances (Sinha et al, 2018). Based on this "limit" concept, the formulation proposed by Fu (1981) was adopted in this study as follows:…”

“…The Budyko framework has been widely used to establish the relationship between evaporative ratio (i.e., E a / P ) and relative availability of water and energy (i.e., aridity index (AI), the ratio between long‐term E P and P , AI = E p / P ) (Budyko, 1974; Cheng et al., 2011). In the widely used Budyko framework proposed by Fu (1981) (i.e., Fu's equation), the controls of other secondary factors on partitioning are lumped into a landscape parameter ( ω ) that includes intra‐annual climate variability, soil, vegetation, and topography (Cheng et al., 2021; Fu, 1981; Zhang et al., 2004). Note that Fu's equation is further explained in Section 2.1.…”

“…The BRT “black box” is further interpreted by methods of feature importance, ALE, and LIME. The relationship between the ω parameter of Fu's equation (Fu, 1981) and catchment properties was investigated using observed hydroclimatic data and 15 properties of 443 catchments across Australia. The primary objectives of this study were to: (a) Identify which of 15 catchment properties were the key factors controlling ω ; (b) determine how the key factors control ω and vary across different regions; and (c) improve the performance of Fu's equation across Australia by using ω parameterization by the BRT model.…”

A Modified Aerosol Free Vegetation Index Algorithm for Aerosol Optical Depth Retrieval Using GOSAT TANSO-CAI Data

“…Atmospheric correction primarily eliminates the impact of molecular and aerosol scattering and absorption by gases, such as water vapor, ozone, oxygen, and aerosols [53][54][55][56]. Molecular scattering and absorption by ozone and oxygen, which have relatively stable atmospheric concentrations, are not difficult to subtract.…”