A Novel Model for Soil Organic Matter and Total Nitrogen Detection Based on Visible/Shortwave Near-Infrared Spectroscopy

Land, Journal Year: 2025, Volume and Issue: 14(2), P. 329 - 329

Published: Feb. 6, 2025

Soil organic matter (SOM) and total nitrogen (TN) are critical indicators for assessing soil fertility. Although laboratory chemical analysis methods can accurately measure their contents, these techniques time-consuming labor-intensive. Spectral technology, characterized by its high sensitivity convenience, has been increasingly integrated with machine learning algorithms nutrient monitoring. However, the process of spectral data remains complex requires further optimization simplicity efficiency to improve prediction accuracy. This study proposes a novel model enhance accuracy SOM TN predictions in northeast China’s black soil. Visible/Shortwave Near-Infrared Spectroscopy (Vis/SW-NIRS) within 350–1070 nm range were collected, preprocessed, dimensionality-reduced. The scores first nine principal components after partial least squares (PLS) dimensionality reduction selected as inputs, measured contents used outputs build back-propagation neural network (BPNN) model. results show that processed combination standard normal variate (SNV) multiple scattering correction (MSC) have best modeling performance. To stability this model, three named random search (RS), grid (GS), Bayesian (BO) introduced. demonstrate Vis/SW-NIRS provides reliable PLS-RS-BPNN achieving performance (R2 = 0.980 0.972, RMSE 1.004 0.006 TN, respectively). Compared traditional models such forests (RF), one-dimensional convolutional networks (1D-CNNs), extreme gradient boosting (XGBoost), proposed improves R2 0.164–0.344 predicting 0.257–0.314 respectively. These findings confirm potential technology effective tools prediction, offering valuable insights application sensing information.

Language: Английский

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Assessing the potential of multi-source remote sensing data for cropland soil organic matter mapping in hilly and mountainous areas

CATENA, Journal Year: 2024, Volume and Issue: 245, P. 108312 - 108312

Published: Aug. 12, 2024

Language: Английский

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The validity domain of sensor fusion in sensing soil quality indicators

Geoderma, Journal Year: 2023, Volume and Issue: 438, P. 116657 - 116657

Published: Sept. 4, 2023

Soil health has gained increasing attention under the rapid development of industrialization and requirement for green agriculture. Therefore, up-to-date soil information related to is urgently needed ensure food security biodiversity protection. Previous studies have shown potential proximal sensing in measuring information, while it remains challenging get cost-efficient robust estimates multiple indicators simultaneously via sensor fusion. In this study, we investigated visible near-infrared (vis-NIR), mid-infrared (MIR) spectroscopy as well three model averaging methods predicting properties, including organic matter (SOM), pH, cation exchange capacity (CEC). The are not only used fusion but also high-level fusion, which include Granger-Ramanathan (GR), Bayesian Model Averaging Spectral-Guided Ensemble Modelling (S-GEM). Here, S-GEM a recently proposed algorithm that can improve spectroscopic prediction by spectral ensemble modelling. Four widely models were evaluated, partial least square regression, Cubist, memory based learning convolutional neural network. For SOM, on algorithms was comparable Sensorsingle + Modelmultiple (MIR singly S-GEM) with R2 0.86. However, MIR performed best among all (LCCC 0.92, RMSE 3.66 g kg−1 RPIQ 3.68). 10-fold cross-validation results indicated 0.84, LCCC 0.90, 0.45 3.65. CEC, Sensormultiple GR 0.66, 0.80, 3.48 cmol 2.22. Our showed failed when performance sensors differed lot (△R2 > 0.2), use single therefore suggested case. When close < recommended. outcome study provide reference determining validity domain improving accuracy prediction.

Language: Английский

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Improving soil organic carbon mapping in farmlands using machine learning models and complex cropping system information

Environmental Sciences Europe, Journal Year: 2024, Volume and Issue: 36(1)

Published: April 21, 2024

Abstract Obtaining accurate spatial maps of soil organic carbon (SOC) in farmlands is crucial for assessing quality and achieving precision agriculture. The cropping system an important factor that affects the cycle farmlands, different agricultural managements under systems lead to heterogeneity SOC. However, current research often ignores differences main controlling factors SOC systems, especially when pattern complex, which not conducive farmland zoning management. This study aims (i) obtain distribution map six by using multi-phase HJ-CCD satellite images; (ii) explore stratified heterogeneous relationship between environmental variables Cubist model; (iii) predict Xiantao, Tianmen, Qianjiang cities, are core areas Jianghan Plain, were selected as area. Results showed content rice–wheat rotation was highest among systems. model outperformed random forest, ordinary kriging, multiple linear regression mapping. results system, climate, attributes, vegetation index influencing farmlands. different. Specifically, summer crop types had a greater influence on variations than winter crops. Paddy–upland more affected river distance NDVI, while upland–upland irrigation-related factors. work highlights differentiated provides data support can improve prediction accuracy complex

Language: Английский

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Preliminary Results in Innovative Solutions for Soil Carbon Estimation: Integrating Remote Sensing, Machine Learning, and Proximal Sensing Spectroscopy

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(23), P. 5571 - 5571

Published: Nov. 30, 2023

This paper explores the application and advantages of remote sensing, machine learning, mid-infrared spectroscopy (MIR) as a popular proximal sensing tool in estimation soil organic carbon (SOC). It underscores practical implications benefits integrated approach combining for SOC prediction across range applications, including comprehensive health mapping credit assessment. These advanced technologies offer promising pathway, reducing costs resource utilization while improving precision estimation. We conducted comparative analysis between MIR-predicted values laboratory-measured using 36 samples. The results demonstrate strong fit (R² = 0.83), underscoring potential this approach. While acknowledging that our is based on limited sample size, these initial findings promise serve foundation future research. will be providing updates when we obtain more data. Furthermore, commercialising Australia, with aim helping farmers harness markets. Based study’s findings, coupled insights from existing literature, suggest adopting measurement could significantly benefit local economies, enhance farmers’ ability to monitor changes health, promote sustainable agricultural practices. outcomes align global climate change mitigation efforts. approach, supported by other research, offers template regions worldwide seeking similar solutions.

Language: Английский

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Applicability of three remote sensing based soil moisture variables for mapping soil organic matter in areas with different vegetation densities

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132980 - 132980

Published: Feb. 1, 2025

Language: Английский

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Vis-NIR soil spectral library of the Hungarian Soil Degradation Observation System

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: March 1, 2025

Abstract Since soil spectroscopy is considered to be a fast, simple, accurate and non-destructive analytical method, its application can integrated with wet analysis as an alternative. Therefore, development of national-level spectral libraries containing information about all types represented in country continuously increasing serve basis for calibrated predictive models capable assessing physical chemical parameters soils at multiple spatial scales. In this article, we present database laboratory visible-near infrared data legacy samples from the Hungarian Soil Degradation Observation System (HSDOS). The published set includes following measured 5,490 samples: pH KCl , organic matter (SOM), calcium carbonate (CaCO 3 ), total salt content (TSC), nitrogen (TN), soluble phosphorus (P 2 O 5 -AL), potassium (K O-AL), plasticity index according standard (PLI), profile depth reflectance between 350 2,500 nm wavelength. presented complement further related research on continental, national or regional scales support sustainable management.

Language: Английский

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Continental-scale mapping of soil pH with SAR-optical fusion based on long-term earth observation data in google earth engine

Ecological Indicators, Journal Year: 2024, Volume and Issue: 165, P. 112246 - 112246

Published: June 14, 2024

Language: Английский

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Improved soil organic matter monitoring by using cumulative crop residue indices derived from time-series remote sensing images in the central black soil region of China

Soil and Tillage Research, Journal Year: 2024, Volume and Issue: 246, P. 106357 - 106357

Published: Nov. 13, 2024

Language: Английский

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Design and experimentation of soil organic matter content detection system based on high-temperature excitation principle

Computers and Electronics in Agriculture, Journal Year: 2023, Volume and Issue: 214, P. 108325 - 108325

Published: Oct. 14, 2023

Language: Английский

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