Hyperspectral Estimation Model of Forest Soil Organic Matter in Northwest Yunnan Province, China

Abstract

Soil organic matter (SOM) is an important index to evaluate soil fertility and soil quality, while playing an important role in the terrestrial carbon cycle. The technology of hyperspectral remote sensing is an important method to estimate SOM content efficiently and accurately. This study researched the best hyperspectral estimation model for SOM content in Shangri-La forest soil. The spectral reflectance of soils with sizes of 2 mm, 1 mm, 0.50 mm, and 0.25 mm were measured indoors. After smoothing and de-noising, the reciprocal reflectance (RR), logarithmic reflectance (LR), first-derivative reflectance (FR), reciprocal first-derivative reflectance (RFR), logarithmic first-derivative reflectance (LFR), and mathematical transformations of the original spectral reflectance (REF) were carried out to analyze the relevance of spectral reflectance and SOM content and extract the characteristic bands. Finally the simple linear regression (SLR), multiple stepwise linear regression (SMLR), and partial least squares regression (PLSR) models for SOM content estimation were established. The results showed that: (1) With the decrease of soil particle size, the spectral reflectance increased. The smaller the soil particle sizes, the more obvious was the increase in spectral reflectance. (2) The sensitive bands of SOM were mainly in the 580–690 nm range (correlation coefficient (R) > 0.6, p-value (p) < 0.01), and the spectral information of SOM could be significantly enhanced by first-order differential transformation. (3) Comparing the three models, PLSR had better estimation ability than SMLR and SLR. The precision of the 0.25 mm soil particle size and the LFR index in the PLSR estimation model of SOM content was the best (coefficient of determination of validation (Rv2) = 0.91, root mean square error of validation (RMSEv) = 13.41, the ratio of percent deviation (RPD) = 3.33). The results provide a basis for monitoring SOM content rapidly in the forests of Northwest Yunnan, and provide a reference for forest SOM estimation in other areas.

Using InVEST to evaluate water yield services in Shangri-La, Northwestern Yunnan, China

“…The average surface water and groundwater resources in the study area over many years are 5 billion m 3 and 2 billion m 3 , respectively ( Li & Wang, 2016 ). Shangri-La City is rich in soil resources, mainly dark brown soil, brown soil, red soil, alpine shrub meadow soil, brown dark coniferous forest soil, alpine cold desert soil, and alluvial soil ( Chen et al, 2019 ). Most areas of Shangri-La City are subalpine and alpine landforms with elevations exceeding 3,000 m and the region contains 43 species of major forest trees, including 10 species of coniferous forests and 33 species of broadleaf forests ( Liu et al, 2018 ; Yu et al, 2019 ; Zhang, Wang & Liu, 2020 ).…”

Synchronized dewatering measured-value fuzzification improves the partial least squares regression method for the hyperspectral analysis of soil organic matter

“…16 For example, Yu et al 17 used SMLR and PLSR to estimate the content of several soil elements, including SOM. Chen et al 18 estimated the SOM content using PLSR, SMLR, simple linear regression models, and PLSR showed higher accuracy and better predictive ability. Vohland et al 19 investigated the effect of spectral variable selection and used PLSR to build SOM models.…”

“…used SMLR and PLSR to estimate the content of several soil elements, including SOM. Chen et al 18 . estimated the SOM content using PLSR, SMLR, simple linear regression models, and PLSR showed higher accuracy and better predictive ability.…”

Hyperspectral Estimation Model of Organic Matter Content in Farmland Soil in the Arid Zone

“…Hyperspectral remote sensing technology has the advantages of rapid and accurate monitoring and plays a critical role in the detection of soil properties, including soil moisture [13], salinity [14], electrical conductivity (EC) [15], heavy metals [16], trace elements [17], organic carbon [18], etc. Recently, with the promotion of hyperspectral resolution, the application of hyperspectral remote sensing technology to SOM estimating has also developed rapidly [19,20]. The spectrum of soil is highly correlated with SOM in the visible bands, especially in the red bands, with the most sensitive bands being the near-infrared spectrum [20,21].…”