Developing a soil spectral library using a low-cost NIR spectrometer for precision fertilization in Indonesia

Ng, Wartini; Husnain,; Anggria, Linca; Siregar, Adha Fatmah; Hartatik, Wiwik; Sulaeman, Yiyi; Jones, E.V.; Minasny, Budiman

doi:10.1016/j.geodrs.2020.e00319

Cited by 31 publications

(21 citation statements)

References 31 publications

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“…Further, different depth ranges were investigated and the study areas were covering much bigger scales than our study. In a study with focus on precision fertilization in Indonesia [ 28 ] reported

= 0.57 for SOC and

= 0.52 for total N content, which is within the same range as our regional Ah results. In this case, samples from 0 to 25 cm depth were used.…”

Section: Discussionsupporting

confidence: 88%

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Application of Low-Cost MEMS Spectrometers for Forest Topsoil Properties Prediction

Thomas

Petzold²,

Becker³

et al. 2021

Sensors

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Increasing temperatures and drought occurrences recently led to soil moisture depletion and increasing tree mortality. In the interest of sustainable forest management, the monitoring of forest soil properties will be of increasing importance in the future. Vis-NIR spectroscopy can be used as fast, non-destructive and cost-efficient method for soil parameter estimations. Microelectromechanical system devices (MEMS) have become available that are suitable for many application fields due to their low cost as well as their small size and weight. We investigated the performance of MEMS spectrometers in the visual and NIR range to estimate forest soil samples total C and N content of Ah and Oh horizons at the lab. The results were compared to a full-range device using PLSR and Cubist regression models at local (2.3 ha, n: Ah = 60, Oh = 50) and regional scale (State of Saxony, Germany, 184,000 km2, n: Ah = 186 and Oh = 176). For each sample, spectral reflectance was collected using MEMS spectrometer in the visual (Hamamatsu C12880MA) and NIR (NeoSpetrac SWS62231) range and using a conventional full range device (Veris Spectrophotometer). Both data sets were split into a calibration (70%) and a validation set (30%) to evaluate prediction power. Models were calibrated for Oh and Ah horizon separately for both data sets. Using the regional data, we also used a combination of both horizons. Our results show that MEMS devices are suitable for C and N prediction of forest topsoil on regional scale. On local scale, only models for the Ah horizon yielded sufficient results. We found moderate and good model results using MEMS devices for Ah horizons at local scale (R2≥ 0.71, RPIQ ≥ 2.41) using Cubist regression. At regional scale, we achieved moderate results for C and N content using data from MEMS devices in Oh (R2≥ 0.57, RPIQ ≥ 2.42) and Ah horizon (R2≥ 0.54, RPIQ ≥2.15). When combining Oh and Ah horizons, we achieved good prediction results using the MEMS sensors and Cubist (R2≥ 0.85, RPIQ ≥ 4.69). For the regional data, models using data derived by the Hamamatsu device in the visual range only were least precise. Combining visual and NIR data derived from MEMS spectrometers did in most cases improve the prediction accuracy. We directly compared our results to models based on data from a conventional full range device. Our results showed that the combination of both MEMS devices can compete with models based on full range spectrometers. MEMS approaches reached between 68% and 105% of the corresponding full ranges devices R2 values. Local models tended to be more accurate than regional approaches for the Ah horizon. Our results suggest that MEMS spectrometers are suitable for forest soil C and N content estimation. They can contribute to improved monitoring in the future as their small size and weight could make in situ measurements feasible.

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= 0.57 for SOC and

= 0.52 for total N content, which is within the same range as our regional Ah results. In this case, samples from 0 to 25 cm depth were used.…”

Section: Discussionsupporting

confidence: 88%

“…[ 26 , 27 ] report model performances comparable to standard full-range devices. Their applicability for fertilizer recommendations are investigated as well [ 28 ]. However, there are so far no investigations evaluating their performance on forest soil humus and topsoil layers.…”

Section: Introductionmentioning

confidence: 99%

Application of Low-Cost MEMS Spectrometers for Forest Topsoil Properties Prediction

Thomas

Petzold²,

Becker³

et al. 2021

Sensors

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show abstract

“…Alternatively, there are 195 three different options to determine SOC contents with low cost. One is the Walkley-Black method (Walkley and Black, 1934), another is the loss-on-ignition method (Wang et al, 2013), the other is near-and mid-infrared reflectance method (Ewing et al, 2021;Tang et al, 2020;Ng et al, 2020). These methods can produce reliable SOC content data (Ewing et al, 2021;Gessesse and Khamzina, 2018;Apesteguia et al, 2018;Nóbrega et al, 2015).…”

Section: Biomass and Soil Carbon Pool And Dynamicsmentioning

confidence: 99%

Ideas and Perspectives: Enhancing research and monitoring of carbon pools and land-to-atmosphere greenhouse gases exchange in developing countries

Kim

Bond‐Lamberty

Ryu

et al. 2021

Preprint

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Abstract. Carbon (C) and greenhouse gas (GHG) research has traditionally required data collection and analysis using advanced and often expensive instruments, complex and proprietary software, and skilled technicians. Partly as a result, relatively little C and GHG research has been conducted in resource-constrained developing countries. At the same time, these are the same countries and regions in which climate-change impacts will likely be strongest, and in which major science uncertainties are centred, given the importance of dryland and tropical systems to the global C cycle. Increasingly, scientific communities have adopted appropriate technology and approach (AT&A) for C and GHG research, which focuses on low-cost and low-technology instruments, open source software and data, and participatory and networking-based research approaches. Adopting AT&A can mean acquiring data with fewer technical constraints and lower economic burden and is thus a strategy for enhancing C and GHG research in developing countries. However, AT&A can be characterized by higher uncertainties; these can often be mitigated by carefully designing experiments, providing clear protocols for data collection, and monitoring and validating the quality of obtained data. For implementing this approach in developing countries, it is first necessary to recognize the scientific and moral importance of AT&A. At the same time, new AT&A techniques should be identified and further developed. All these processes should be promoted in collaboration with local researchers and through training local staff and encouraged for wide use and further innovation in developing countries.

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“…Intuitively, it is expected that the prediction accuracy for a category with two levels will be more accurate than with five levels. Researchers mainly divide fertility levels into three main categories: below optimum, optimum, and above optimum [ 23 , 24 , 25 , 26 , 27 , 28 ], where the “low” or “high” category can increase or decrease the fertilizer recommendation by 25% or 30% of a general recommendation. Heckman J. R. et al [ 25 ] further divided into subcategories: very low, low and medium.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method

ml.¹,

Chambers²

2021

Sensors

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The research presented in this paper is based on the hypothesis that the machine learning approach improves the accuracy of soil properties prediction. The correlations obtained in this research are important for understanding the overall strategy for soil properties prediction using optical spectroscopy sensors. Several research results have been stated and investigated. A comparison is made between six commonly used techniques: Random Forest, Decision Tree, Naïve Bayes, Support Vector Machine, Least-Square Support Vector Machine and Artificial Neural Network, showing that the best prediction accuracy cannot always be achieved by the most common and complicated method. The influence of the chosen category for nutrient characterization was investigated, indicating better prediction when a multi-component strategy was used. In contrast, the prediction of single-component soil properties was less accurate. In addition, the influence of category levels was not as significant as expected when choosing between 3-level, 5-level or 13-level nutrient characterization for some nutrients, which can be used for a more precise nutrient characterization strategy. A comparative analysis was performed between soil from a local farm with similar texture and soils collected from different locations in Slovenia, which gave a better prediction for a local farm. Finally, the influence of principal component analysis was validated using 5, 10, 20 and 50 first principal components, indicating the better performance of machine learning when using the 50 principal components.

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Developing a soil spectral library using a low-cost NIR spectrometer for precision fertilization in Indonesia

Cited by 31 publications

References 31 publications

Application of Low-Cost MEMS Spectrometers for Forest Topsoil Properties Prediction

Application of Low-Cost MEMS Spectrometers for Forest Topsoil Properties Prediction

Ideas and Perspectives: Enhancing research and monitoring of carbon pools and land-to-atmosphere greenhouse gases exchange in developing countries

Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method

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