Hyperspectral data offers a powerful tool for predicting soil heavy metal contamination due to its high spectral resolution
and many continuous bands. However, band selection is the prerequisite to accurately invert and predict soil heavy metal
concentration by hyperspectral data. In this paper, 181 soil samples were collected from the suburb of Nanjing City, and
their reflectance spectra and soil lead concentrations were measured in the laboratory. Based on these dataset, we
compare Least Angle Regression, which is a modest forward choose method, and least squares regression and partial
least squares regression based on genetic algorithm. As a result, regression with band selection has better accuracy than
those without band selection. Although both Least Angle Regression and partial least squares regression with genetic
algorithm can reach 70% training accuracy, the latter based on genetic algorithm is better, because it can reach a larger
solution space. At last, we conclude that partial least squares regression is a good choice for the soil lead content retrieval
by hyperspectral remote sensing data, and genetic algorithm can improve the retrieval by band selection promisingly.
Bands centered around 838nm,1930nm and 2148nm are sensitive for soil lead content.