Deep learning approaches in remote sensing of soil organic carbon: a review of utility, challenges, and prospects

Odebiri, Omosalewa; Mutanga, Onisimo; Odindi, John; Naicker, Rowan; Masemola, Cecilia; Sibanda, Mbulisi

doi:10.1007/s10661-021-09561-6

Cited by 18 publications

(8 citation statements)

References 112 publications

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“…The multiple, spatially extensive spectral data from remote sensing serve as input to the machine learning models, leading to model the complex relationships for SOC estimations [24]. This approach can reduce the cost of measuring SOC by reducing the number of sampling profiles required for estimation of SOC, therefore providing a more cost-effective and scalable solution for large-scale SOC mapping [17,18,51,54].…”

Section: Integration Of Remote Sensing and Machine Learningmentioning

confidence: 99%

“…MIR can provide highly accurate, local-scale SOC predictions [30], while remote sensing data can provide broader, landscape-scale information [17]. Machine learning techniques, on the other hand, can integrate these different types of data and handle their complex relationships to provide more accurate and spatially comprehensive SOC predictions [54]. Overall, the approach may facilitate a speedy, precise, and low-resource base approach.…”

Section: Integration Of Mir Remote Sensing and Machine Learningmentioning

confidence: 99%

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

Li,

Xia,

McLaren

et al. 2023

Remote Sensing

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

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Section: Integration Of Remote Sensing and Machine Learningmentioning

confidence: 99%

Section: Integration Of Mir Remote Sensing and Machine Learningmentioning

confidence: 99%

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

Li,

Xia,

McLaren

et al. 2023

Remote Sensing

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“…As a result of its ability to rely primarily on data for image recognition, deep learning is considered to be a powerful tool (Buxbaum et al, 2022). Furthermore, Pullanagari et al (2021), Odebiri et al (2021), andYuan et al (2020) have reported the superiority of DL over ML and geostatistical methods. For example, using a large field spectroscopy database, Pullanagari et al ( 2021) compared the accuracy of onedimensional convolutional neural network (1D-CNN) with PLSR and gaussian process regression (GPR) in estimating canopy nitrogen in grassland.…”

Section: Regression and Machine Learning Algorithms Utilized In Estim...mentioning

confidence: 99%

The role of remote sensing in tropical grassland nutrient estimation: a review

Arogoundade

Mutanga

Odindi

et al. 2023

Environ Monit Assess

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The carbon (C) and nitrogen (N) ratio is a key indicator of nutrient utilization and limitations in rangelands. To understand the distribution of herbivores and grazing patterns, information on grass quality and quantity is important. In heterogeneous environments, remote sensing offers a timely, economical, and effective method for assessing foliar biochemical ratios at varying spatial and temporal scales. Hence, this study provides a synopsis of the advancement in remote sensing technology, limitations, and emerging opportunities in mapping the C:N ratio in rangelands. Specifically, the paper focuses on multispectral and hyperspectral sensors and investigates their properties, absorption features, empirical and physical methods, and algorithms in predicting the C:N ratio in grasslands. Literature shows that the determination of the C:N ratio in grasslands is not in line with developments in remote sensing technologies. Thus, the use of advanced and freely available sensors with improved spectral and spatial properties such as Sentinel 2 and Landsat 8/9 with sophisticated algorithms may provide new opportunities to estimate C:N ratio in grasslands at regional scales, especially in developing countries. Spectral bands in the near-infrared, shortwave infrared, red, and red edge were identified to predict the C:N ratio in plants. New indices developed from recent multispectral satellite imagery, for example, Sentinel 2 aided by cutting-edge algorithms, can improve the estimation of foliar biochemical ratios. Therefore, this study recommends that future research should adopt new satellite technologies with recent development in machine learning algorithms for improved mapping of the C:N ratio in grasslands.

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“…At present, the means of obtaining agricultural soil information is relatively simple. The information technology gap for the rapid detection of the soil component content is the bottleneck of modern precision agriculture [ 8 ]. There is an urgent need for a rapid, easy-to-operate, non-polluting, and convenient soil composition detection method.…”

Section: Introductionmentioning

confidence: 99%

Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy

Tan

You

Tian

et al. 2022

Sensors

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Traditional soil nitrogen detection methods have the characteristics of being time-consuming and having an environmental pollution effect. We urgently need a rapid, easy-to-operate, and non-polluting soil nitrogen detection technology. In order to quickly measure the nitrogen content in soil, a new method for detecting the nitrogen content in soil is presented by using a near-infrared spectrum technique and random forest regression (RF). Firstly, the experiment took the soil by the Xunsi River in the area of Hubei University of Technology as the research object, and a total of 143 soil samples were collected. Secondly, NIR spectral data from 143 soil samples were acquired, and chemical and physical methods were used to determine the content of nitrogen in the soil. Thirdly, the raw spectral data of soil samples were denoised by preprocessing. Finally, a forecast model for the soil nitrogen content was developed by using the measured values of components and modeling algorithms. The model was optimized by adjusting the changes in the model parameters and Gini coefficient (∆Gini), and the model was compared with the back propagation (BP) and support vector machine (SVM) models. The results show that: the RF model modeling set prediction R2C is 0.921, the RMSEC is 0.115, the test set R2P is 0.83, and the RMSEP is 0.141; the detection of the soil nitrogen content can be realized by using a near-infrared spectrum technique and random forest algorithm, and its prediction accuracy is better than that of the BP and SVM models; using ∆ Gini to optimize the RF modeling data, the spectral information of the soil nitrogen content can be extracted, and the data redundancy can be reduced effectively.

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Deep learning approaches in remote sensing of soil organic carbon: a review of utility, challenges, and prospects

Cited by 18 publications

References 112 publications

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

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

The role of remote sensing in tropical grassland nutrient estimation: a review

Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy

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