Novel Proximal Sensing for Monitoring Soil Organic C Stocks and Condition

Rossel, Raphael A. Viscarra; Lobsey, C.; Sharman, Chris; Flick, Paul; McLachlan, Gordon

doi:10.1021/acs.est.7b00889

Cited by 83 publications

(44 citation statements)

References 52 publications

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“…In addition, PXRF and LIBS have also been used under field conditions (Peinado et al, 2010;Bricklemyer et al, 2011). With the development of sensors, there is an increasing number of soil sensing platforms (Viscarra Rossel et al, 2017). The field use of the single instrument and various integrated platforms provides the possibility of sensor fusion directly in the field, but there are more uncertainties for field measurement because of the complex nature of soil.…”

Section: Discussionmentioning

confidence: 99%

Multi‐sensor fusion for the determination of several soil properties in the Yangtze River Delta, China

Zhao

et al. 2018

European J Soil Science

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Summary Soil organic matter (SOM), total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), available potassium (AK) and pH are key chemical properties for evaluating soil fertility and quality. This study involved the integration of four soil sensors, visible near‐infrared (vis–NIR) spectrometer, mid‐infrared (mid‐IR) spectrometer, portable X‐ray fluorescence (PXRF) analyser and laser‐induced breakdown spectroscopy (LIBS), to achieve rapid measurement of these soil properties. A genetic algorithm and partial least‐squares regression (GA–PLSR) were used to select characteristic bands to reduce data redundancy. We then calibrated models from three aspects: models using partial least‐squares regression (PLSR) based on single sensor data, models using PLSR based on fused sensor data, involving data combined from the four sensors into a new dataset to create a data fusion (DF) model, and models with Bayesian model averaging (BMA) based on prediction results of fused sensor data, involving prediction results combined from the four sensors into a new dataset to form the BMA model. The results showed the following. (i) For the single sensor, the predictive performance decreased as follows: mid‐IR > vis–NIR > LIBS > PXRF. (ii) Compared with the single sensor approach, the DF approach slightly improved or even reduced prediction accuracy and caused a large amount of redundancy. We suggest that this approach is not able to improve predictive ability. (iii) The BMA approach achieved the best prediction for the six soil properties. Our findings suggest that model averaging of vis–NIR, mid‐IR and LIBS could be a reliable and stable approach for the fast measurement of soil properties. Highlights We used four proximal soil sensors to evaluate six key properties for evaluating soil fertility and quality. GA–PLSR was used to select characteristic bands. We compared predictions of six soil properties from single sensor, DF and BMA approaches. BMA predictions were more accurate than predictions from single and fused sensor data.

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Section: Discussionmentioning

confidence: 99%

Multi‐sensor fusion for the determination of several soil properties in the Yangtze River Delta, China

Zhao

et al. 2018

European J Soil Science

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“…The spectroscopic methods include soil visible, near‐ and mid‐infrared reflectance spectroscopy (McCarty, Reeves, Reeves, Follett, & Kimble, ), laser‐induced breakdown spectroscopy (Ebinger et al, ), and inelastic neutron scattering (Wielopolski et al, ). Recently, a proximal sensing method was described, which combines a self‐acting sensing system for soil cores with statistical analyses and modeling to characterize SOC stocks at small depth increments and across land surfaces (Viscarra Rossel, Lobsey, Sharman, Flick, & McLachlan, ). However, there are numerous uncertainties about their general use for in situ measurements of SOC stock and its dynamics.…”

Section: Availability Of Soc Stock Datamentioning

confidence: 99%

Soil organic carbon stock as an indicator for monitoring land and soil degradation in relation to United Nations' Sustainable Development Goals

2019

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Seventeen Sustainable Development Goals (SDGs) adopted in September 2015 aim to end hunger and poverty, to protect the Planet, and to ensure peace and prosperity for all. The soil organic carbon (SOC) stock is a major planetary resource supporting many critically important ecosystem services (ESs) and underpins realization of some of the SDGs at the national level. Thus, decrease in the SOC stock is among the significant universal indicators for land and soil degradation and compromises efforts to achieve the SDGs especially those with reference to food, health, water, climate, and land management. However, there is currently no well‐established relationship (i.e., quantitative evidence) between the SOC stock and the level of ESs attributable to it. Further, basic soil data and monitoring systems including those of SOC stock and its changes are not available for many regions and nations. This uncertainty affects the suitability of using the SOC stock as absolute indicator to monitor changes in land and soil degradation and, particularly, in relation to the SDG monitoring framework. Thus, although the SOC stock is arguably an important indicator for land and soil degradation among others, more research and data on a national level are needed to establish the relationship between the SOC stock and the targets to monitor progress towards achieving the SDGs with reference to food, health, water, climate, and land management. To the best of our knowledge, this is the first review on the suitability of the SOC stock as an indicator for monitoring land and soil degradation with regard to the SDG framework.

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“…In this context, our results here are significant because we show that, compared with the more conventional method based on composite sampling, new integrated technologies that use new proximal sensing and data analytics (Viscarra Rossel et al ) can provide a more accurate, reliable, and cost‐efficient method for measuring soil organic C stocks and for monitoring and verifying its change. We also show that with composite sampling all the spatial information is lost, unlike with sensing.…”

Section: Discussionmentioning

confidence: 80%

“…Stockmann et al, 2013). Sensing can rapidly and simultaneously measure other soil properties that interact with and affect organic C, such as water, nutrients, clay content, pH, and the different forms of C (see Viscarra Rossel et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…The soil cores were measured at field condition using the core sensing system in the SCANS (Viscarra Rossel et al ), equipped with a gamma‐ray attenuation densitometer (model LB444, Berthold Technologies, Germany) and a visiblie–near infrared spectrometer Labspec

®

(PANalytical, Boulder, Colorado, USA). For details on the SCANS approach and the proximal sensing of soil core samples, the reader is directed to Viscarra Rossel et al ().…”

Section: Methodsmentioning

confidence: 99%

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The cost‐efficiency and reliability of two methods for soil organic C accounting

Rossel

Brus

2018

Land Degrad Dev

Self Cite

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Sequestering organic carbon (C) in soil can help to combat land degradation, improve food security, and mitigate greenhouse gas emissions and climate change. But we need reliable, cost‐efficient methods to assess, monitor, and verify the change. Here, we compared two methods for the direct measurement of soil organic C stocks and for monitoring the change. Our aims were to quantify the soil organic C stock in two carbon estimation areas, under cropping and grazing, using composite sampling with two designs and proximal sensing. We compared the two schemes in terms of the (a) accuracy of the estimated C stocks, the total cost, and the cost‐efficiency, calculated as the ratio of the accuracy of the estimate and the total cost, and (b) uncertainty of the estimated standard error of the estimated C stocks. We found that compositing was cheaper but more inaccurate than sensing. Sensing was 1.2 to 2.1 times more cost‐efficient than compositing. We also found that the uncertainty of the estimated standard errors from compositing was large and unreliable, which can hinder the quantification of a minimum detectable difference in organic C stocks. We show that the sensor‐derived spatially explicit data can also be used to map the C stocks, which can help to optimise the sampling design in subsequent monitoring rounds. Our findings have important implications for the development of C measurement and monitoring methodologies. Visible–near infrared and gamma attenuation sensing can accurately, cost‐efficiently, and reliably monitor and verify changes in soil C stocks.

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Novel Proximal Sensing for Monitoring Soil Organic C Stocks and Condition

Cited by 83 publications

References 52 publications

Multi‐sensor fusion for the determination of several soil properties in the Yangtze River Delta, China

Multi‐sensor fusion for the determination of several soil properties in the Yangtze River Delta, China

Soil organic carbon stock as an indicator for monitoring land and soil degradation in relation to United Nations' Sustainable Development Goals

The cost‐efficiency and reliability of two methods for soil organic C accounting

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