Soil salinity assessment through satellite thermography for different irrigated and rainfed crops

Ivushkin, Konstantin; Bartholomeus, Harm; Bregt, A.K.; Pulatov, Alim; Bui, Elisabeth N.; Wilford, John

doi:10.1016/j.jag.2018.02.004

Cited by 28 publications

(24 citation statements)

References 21 publications

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“…Crops: This group of articles focused not merely in soil but in its interaction within the soil-plant continuum. Water and nutrient availability in order to assure crop yields is a key component of this topic (Karandish and Šimȗnek, 2016;Ivushkin et al, 2018;Khanal et al, 2018;Leenaars et al, 2018).…”

Section: Methods (Nn Svm)mentioning

confidence: 99%

Machine learning and soil sciences: A review aided by machine learning tools

Padarian¹,

Minasny²,

McBratney³

2019

Preprint

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Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last ten years. The increasing availability of soil data that can be efficiently acquired remotely and proximally, and freely available open-source algorithms, have led to an accelerated adoption of ML techniques to analyse soil data. Given the large number of publications, it is an impossible task to manually review all papers on the application of ML in soil science without narrowing down a narrative of ML application in a specific research question. This paper aims to provide a comprehensive review of the application of ML techniques in soil science aided by a ML algorithm (Latent Dirichlet Allocation) to find patterns in a large collection of text corpus. The objective is to gain insight into publications of ML applications in soil science and to discuss the research gaps in this topic. We found that: a) there is an increasing usage of ML methods in soil sciences, mostly concentrated in developed countries, b) the reviewed publication can be grouped into 12 topics, namely remote sensing, soil organic carbon, water, contamination, methods (ensembles), erosion and parent material, methods (NN, SVM), spectroscopy, modelling (classes), crops, physical and modelling (continuous), c) advanced ML methods usually perform better than simpler approaches thanks to their capability to capture non-linear relationships. From these findings, we found research gaps, in particular: about the precautions that should be taken (parsimony) to avoid overfitting, and that the interpretability of the ML models is an important aspect to consider when applying advanced ML methods in order to improve our knowledge and understanding of soil. We foresee that a large number of studies will focus on the latter topic.

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Section: Methods (Nn Svm)mentioning

confidence: 99%

Machine learning and soil sciences: A review aided by machine learning tools

Padarian¹,

Minasny²,

McBratney³

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…This group of articles focused not merely on soil, but also on its interaction within the soil-plant continuum. Water and nutrient availability in order to ensure crop yields is a key component of this topic (Karandish and Šimȗnek, 2016;Ivushkin et al, 2018;Khanal et al, 2018;Leenaars et al, 2018).…”

Section: Main Topicsmentioning

confidence: 99%

Machine learning and soil sciences: a review aided by machine learning tools

Padarian

Minasny

McBratney

2020

SOIL

236

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Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last 10 years. The increasing availability of soil data that can be efficiently acquired remotely and proximally, and freely available open-source algorithms, have led to an accelerated adoption of ML techniques to analyse soil data. Given the large number of publications, it is an impossible task to manually review all papers on the application of ML in soil science without narrowing down a narrative of ML application in a specific research question. This paper aims to provide a comprehensive review of the application of ML techniques in soil science aided by a ML algorithm (latent Dirichlet allocation) to find patterns in a large collection of text corpora. The objective is to gain insight into publications of ML applications in soil science and to discuss the research gaps in this topic. We found that (a) there is an increasing usage of ML methods in soil sciences, mostly concentrated in developed countries, (b) the reviewed publications can be grouped into 12 topics, namely remote sensing, soil organic carbon, water, contamination, methods (ensembles), erosion and parent material, methods (NN, neural networks, SVM, support vector machines), spectroscopy, modelling (classes), crops, physical, and modelling (continuous), and (c) advanced ML methods usually perform better than simpler approaches thanks to their capability to capture non-linear relationships. From these findings, we found research gaps, in particular, about the precautions that should be taken (parsimony) to avoid overfitting, and that the interpretability of the ML models is an important aspect to consider when applying advanced ML methods in order to improve our knowledge and understanding of soil. We foresee that a large number of studies will focus on the latter topic.

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“…Saline soils, that is, soils with electrical conductivity equal or exceeding 4 deci Siemens per metre (dS m −1 ) at 25 °C (Shahid et al 2018) affect crops through crop water stress, thereby influencing crop growth (for less salt-tolerant crops) and raising crop canopy temperature (Ivushkin et al 2018). Soil salinization, that is, "salt accumulation in the root zone", can reduce yields by up to 30% (Cherlet et al 2018;Shahid et al 2018).…”

Section: Salt-affected Agricultural Soilsmentioning

confidence: 99%

Early Warning from Space for a Few Key Tipping Points in Physical, Biological, and Social-Ecological Systems

et al. 2020

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In this review paper, we explore latest results concerning a few key tipping elements of the Earth system in the ocean, cryosphere, and land realms, namely the Atlantic overturning circulation and the subpolar gyre system, the marine ecosystems, the permafrost, the Greenland and Antarctic ice sheets, and in terrestrial resource use systems. All these different tipping elements share common characteristics related to their nonlinear nature. They can also interact with each other leading to synergies that can lead to cascading tipping points. Even if the probability of each tipping event is low, they can happen relatively rapidly, involve multiple variables, and have large societal impacts. Therefore, adaptation measures and management in general should extend their focus beyond slow and continuous changes, into abrupt, nonlinear, possibly cascading, high impact phenomena. Remote sensing observations are found to be decisive in the understanding and determination of early warning signals of many tipping elements. Nevertheless, considerable research still remains to properly incorporate these data in the current generation of coupled Earth system models. This is a key prerequisite to correctly develop robust decadal prediction systems that may help to assess the risk of crossing thresholds potentially crucial for society. The prediction of tipping points remains difficult, notably due to stochastic resonance, i.e. the interaction between natural variability and anthropogenic forcing, asking for large ensembles of predictions to correctly assess the risks. Furthermore, evaluating the proximity to crucial thresholds using process-based understanding of each system remains a key aspect to be developed for an improved assessment of such risks. This paper finally proposes a few research avenues concerning the use of remote sensing data and the need for combining different sources of data, and having long and precise-enough time series of the key variables needed to monitor Earth system tipping elements.

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Soil salinity assessment through satellite thermography for different irrigated and rainfed crops

Cited by 28 publications

References 21 publications

Machine learning and soil sciences: A review aided by machine learning tools

Machine learning and soil sciences: A review aided by machine learning tools

Machine learning and soil sciences: a review aided by machine learning tools

Early Warning from Space for a Few Key Tipping Points in Physical, Biological, and Social-Ecological Systems

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