Integration MRA and MVA for cropland soil property estimation using hyperspectral reflectance

Soil chemical properties are the most important limiting factors in agricultural production. With the development of traditional agriculture towards modern precision agriculture, how to acquire soil information rapidly is a major issue for all the soil related workers. In this paper, hyperspectral technique is involved so as to discover the potential of using hyperspectrum in soil properties monitoring and estimation. First, soil properties investigation and sampling are conducted in the study area in the seedling growth stage of winter wheat. Then, soil samples were taken back to the laboratory to acquire soil chemical properties, such as soil organic matter, total N, available P and K, as well as their respective hyperspectral reflectance. The soil hyperspectral reflectance curves were filtered by wavelet transform in 6 scales using Matlab and obtain 6 low-frequency components and 6 high-frequency components, respectively. All these information are considered to estimate soil properties integrating multi-resolution analysis (MRA) and multivariate analysis (MVA). Results show that, the above methods improve the estimation accuracy obviously and could be extended to the estimation using hyperspectral images.