Strategies for the efficient estimation of soil organic matter in salt-affected soils through Vis-NIR spectroscopy: Optimal band combination algorithm and spectral degradation

Geoderma ◽  
2021 ◽  
Vol 382 ◽  
pp. 114729
Author(s):  
Zipeng Zhang ◽  
Jianli Ding ◽  
Chuanmei Zhu ◽  
Jingzhe Wang ◽  
Guolin Ma ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Nir Spectroscopy ◽  
Efficient Estimation ◽  
Band Combination ◽  
Spectral Degradation ◽  
Salt Affected Soils

Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

2021 ◽  
Author(s):  
Iva Hrelja ◽  
Ivana Šestak ◽  
Igor Bogunović
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Data ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Least Squares Regression ◽  
Macro Scale ◽  
Sentinel 2

<p>Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.</p><p>The study is performed in Mediterranean Croatia (44° 05’ N; 15° 22’ E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed <em>Quercus ssp.</em> and <em>Juniperus ssp.</em> forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22<sup>nd</sup> 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31<sup>st</sup> and September 5<sup>th </sup>2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSE<sub>p</sub>) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.</p><p>The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSE<sub>p </sub>(Spectroscopy dataset RMSE<sub>p</sub> = 1.91; Sentinel-2 dataset RMSE<sub>p</sub> = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.</p><p><strong>Keywords:</strong> remote sensing, soil spectroscopy, wildfires, soil organic matter</p><p><strong>Acknowledgment: </strong>This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Perčin is acknowledged for her cooperation during the laboratory work.</p>

scholarly journals Exploring Appropriate Preprocessing Techniques for Hyperspectral Soil Organic Matter Content Estimation in Black Soil Area

2020 ◽  
Vol 12 (22) ◽  
pp. 3765
Author(s):  
Xitong Xu ◽  
Shengbo Chen ◽  
Zhengyuan Xu ◽  
Yan Yu ◽  
Sen Zhang ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Dimensionality Reduction ◽  
Near Infrared ◽  
Organic Matter Content ◽  
Nir Spectroscopy ◽  
Data Transformation ◽  
Black Soil ◽  
Locally Linear Embedding ◽  
Soil Area

Black soil in northeast China is gradually degraded and soil organic matter (SOM) content decreases at a rate of 0.5% per year because of the long-term cultivation. SOM content can be obtained rapidly by visible and near-infrared (Vis–NIR) spectroscopy. It is critical to select appropriate preprocessing techniques for SOM content estimation through Vis–NIR spectroscopy. This study explored three categories of preprocessing techniques to improve the accuracy of SOM content estimation in black soil area, and a total of 496 ground samples were collected from the typical black soil area at 0–15 cm in Hai Lun City, Heilongjiang Province, northeast of China. Three categories of preprocessing include denoising, data transformation and dimensionality reduction. For denoising, Svitzky-Golay filter (SGF), wavelet packet transform (WPT), multiplicative scatter correction (MSC), and none (N) were applied to spectrum of ground samples. For data transformation, fractional derivatives were allowed to vary from 0 to 2 with an increment of 0.2 at each step. For dimensionality reduction, multidimensional scaling (MDS) and locally linear embedding (LLE) were introduced and compared with principal component analysis (PCA), which was commonly used for dimensionality reduction of soil spectrum. After spectral pretreatments, a total of 132 partial least squares regression (PLSR) models were constructed for SOM content estimation. Results showed that SGF performed better than the other three denoising methods. Low-order derivatives can accentuate spectral features of soil for SOM content estimation; as the order increases from 0.8, the spectrum were more susceptible to spectral noise interferences. In most cases, 0.2–0.8 order derivatives exhibited the best estimation performance. Furthermore, PCA yielded the optimal predictability, the mean residual predictive deviation (RPD) and maximum RPD of the models using PCA were 1.79 and 2.60, respectively. The application of appropriate preprocessing techniques could improve the efficiency and accuracy of SOM content estimation, which is important for the protection of ecological and agricultural environment in black soil area.

Soil Organic Matter Content Estimation Based on Soil Covariate and VIS-NIR Spectroscopy

2020 ◽  
Vol 57 (19) ◽  
pp. 192801
Author(s):  
马国林 Ma Guolin ◽  
丁建丽 Ding Jianli ◽  
张子鹏 Zhang Zipeng
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Organic Matter Content ◽  
Nir Spectroscopy ◽  
Matter Content ◽  
Soil Organic Matter Content

Estimation Method of VIS-NIR Spectroscopy for Soil Organic Matter Based on Sparse Networks

2020 ◽  
Vol 57 (24) ◽  
pp. 242803
Author(s):  
冉思 Ran Si ◽  
丁建丽 Ding Jianli ◽  
葛翔宇 Ge Xiangyu ◽  
刘博华 Liu Bohua ◽  
张钧泳 Zhang Junyong
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Estimation Method ◽  
Nir Spectroscopy ◽  
Sparse Networks

scholarly journals Assessing Soil Organic Matter Content in a Coal Mining Area through Spectral Variables of Different Numbers of Dimensions

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1795 ◽  
Author(s):  
Chuanmei Zhu ◽  
Zipeng Zhang ◽  
Hongwei Wang ◽  
Jingzhe Wang ◽  
Shengtian Yang
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Coal Mining ◽  
Spectral Response ◽  
Spectral Feature ◽  
Organic Matter Content ◽  
Mining Area ◽  
Coal Mining Area ◽  
Band Combination ◽  
First Choice

Soil organic matter (SOM) is a crucial indicator for evaluating soil quality and an important component of soil carbon pools, which play a vital role in terrestrial ecosystems. Rapid, non-destructive and accurate monitoring of SOM content is of great significance for the environmental management and ecological restoration of mining areas. Visible-near-infrared (Vis-NIR) spectroscopy has proven its applicability in estimating SOM over the years. In this study, 168 soil samples were collected from the Zhundong coal field of Xinjiang Province, Northwest China. The SOM content (g kg−1) was determined by the potassium dichromate external heating method and the soil reflectance spectra were measured by the spectrometer. Two spectral feature extraction strategies, namely, principal component analysis (PCA) and the optimal band combination algorithm, were introduced to choose spectral variables. Linear models and random forests (RF) were used for predictive models. The coefficient of determination (R2), root mean square error (RMSE), and the ratio of the performance to the interquartile distance (RPIQ) were used to evaluate the predictive performance of the model. The results indicated that the variables (2DI and 3DI) derived from the optimal band combination algorithm outperformed the PCA variables (1DV) regardless of whether linear or RF models were used. An inherent gap exists between 2DI and 3DI, and the performance of 2DI is significantly poorer than that of 3DI. The accuracy of the prediction model increases with the increasing number of spectral variable dimensions (in the following order: 1DV < 2DI < 3DI). This study proves that the 3DI is the first choice for the optimal band combination algorithm to derive sensitive parameters related to SOM in the coal mining area. Furthermore, the optimal band combination algorithm can be applied to hyperspectral or multispectral images and to convert the spectral response into image pixels, which may be helpful for a soil property spatial distribution map.

Susceptible soil organic matter, SOM, fractions to agricultural management practices in salt-affected soils

Geoderma ◽  
2020 ◽  
Vol 366 ◽  
pp. 114257
Author(s):  
Mohamed Emran ◽  
Serena Doni ◽  
Cristina Macci ◽  
Grazia Masciandaro ◽  
Mohamed Rashad ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Management Practices ◽  
Agricultural Management ◽  
Salt Affected Soils
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