Elsevier

Pedosphere

Volume 19, Issue 4, August 2009, Pages 532-540
Pedosphere

Quantitative Analysis of Moisture Effect on Black Soil Reflectance

https://doi.org/10.1016/S1002-0160(09)60146-6Get rights and content

Abstract

Several studies have demonstrated that soil reflectance decreases with increasing soil moisture content, or increases when the soil moisture reaches a certain content; however, there are few analyses on the quantitative relationship between soil reflectance and its moisture, especially in the case of black soils in northeast China. A new moisture adjusting method was developed to obtain soil reflectance with a smaller moisture interval to describe the quantitative relationship between soil reflectance and moisture. For the soil samples with moisture contents ranging from air-dry to saturated, the changes in soil reflectance with soil moisture can be depicted using a cubic equation. Both moisture threshold (MT) and moisture inflexion (MI) of soil reflectance can also be determined by the equation. When the moisture range was smaller than MT, soil reflectance can be simulated with a linear model. However, for samples with different soil organic matter (OM), the parameters of the linear model varied regularly with the OM content. Based on their relationship, the soil moisture can be estimated from soil reflectance in the black soil region.

Cited by (43)

  • Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data

    2020, International Journal of Applied Earth Observation and Geoinformation
    Citation Excerpt :

    Hyperspectral remote sensing technology is extensively used to obtain spectral information in great detail with the aim of improving the soil property prediction accuracy; the significant advantage of this approach lies in the enormous amount of band information that can be acquired in comparison with the abilities of multispectral satellites. Taking the Landsat OLI sensor as an example, an entire OLI image contains a total of 9 bands, and thus, limiting any information may cause some prediction-related details to be ignored (Fig. 11); furthermore, the wavelength range correlated with the soil prediction is several times smaller than the 600−800 nm band, which has a high correlation with laboratory SOC predictions; thus, only 2/5 of the information is included, this band information is not sufficiently detailed, and unfortunately, different research results cannot be supported or verified with this lack of band information (Brown et al., 2006; Dalal and Henry, 1986; Daniel et al., 2004; Hummel et al., 2001; Kweon and Maxton, 2013; Liu et al., 2009a,b; Palacios-Orueta and Ustin, 1998; Rossel et al., 2006; Shepherd and Walsh, 2002; Sudduth and Hummel, 1991). In the model with the highest prediction accuracy, most of the selected bands are not in the spectral range of multispectral satellites, and the spectral differences of different SOC contents are not obvious in the multispectral data (Fig. 11).

View all citing articles on Scopus

Project supported by the National Key Technology Research and Development Program of China (Nos. 40801167 and 2006BAD05B05), the Knowledge Innovation Program of the Chinese Academy of Sciences (No. KZCX3-SW-356), and the Foundation of the Chinese Academy of Sciences for the Field Stations of Resources and Environment.

View full text