Landscape-scale exploratory radiometric mapping using proximal soil sensing

Stockmann, Uta; Malone, Brendan; McBratney, Alex B.; Minasny, Budiman

doi:10.1016/j.geoderma.2014.10.005

Cited by 19 publications

(3 citation statements)

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“…For example, it can cost up to 272,000 to 408,000 USD to fly a 1:250,000 map sheet as of 2016 (Rouze et al, in press). While recent methods have sought to use optimized PGR sampling strategies across large areas (Stockmann et al, 2015), we recommend that it is worthwhile to first investigate the quality of legacy AGR surveys, and we have demonstrated that such information has inherent value for soil mapping purposes. Future studies could address the value of legacy AGR by identifying sites with multiple AGR flight lines within parent materials not present in this study.…”

Section: Sources Of Error In Aerial Gamma Radiometrics and Need For Smentioning

confidence: 99%

“…Aerial gamma radiometrics (AGR) are an environmental covariate that have been proposed to assist in characterizing soil variability not only at the local (meter) scale but also at the regional (1,000 to 10,000 kilometer) scale because such information represents the state of the parent material in terms of its weathering intensity (Stockmann et al, 2015). Thus, AGR have a potential advantage over stationary soil sensors such as XRF and vis-NIR spectroscopy and mobile sensors limited in spatial extent such as electromagnetic induction (Viscarra-Rossel et al, 2011;Söderström et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Understanding the utility of aerial gamma radiometrics for mapping soil properties through proximal gamma surveys

2017

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Section: Sources Of Error In Aerial Gamma Radiometrics and Need For Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding the utility of aerial gamma radiometrics for mapping soil properties through proximal gamma surveys

2017

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“…In the development process of soil mapping, with the development of remote sensing technology and spatial information technology, it has experienced a rapid development process from the initial prediction of distribution through a simple regression model [9,22,23] to the analysis of spatial difference [24,25] to the current digital spatial mapping through machine learning [26,27]. In the development of digital mapping technology, machine learning is a common mapping method.…”

Section: Introductionmentioning

confidence: 99%

Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang

Ding

Liu

et al. 2021

Remote Sensing

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As an important evaluation index of soil quality, soil organic carbon (SOC) plays an important role in soil health, ecological security, soil material cycle and global climate cycle. The use of multi-source remote sensing on soil organic carbon distribution has a certain auxiliary effect on the study of soil organic carbon storage and the regional ecological cycle. However, the study on SOC distribution in Ebinur Lake Basin in arid and semi-arid regions is limited to the mapping of measured data, and the soil mapping of SOC using remote sensing data needs to be studied. Whether different machine learning methods can improve prediction accuracy in mapping process is less studied in arid areas. Based on that, combined with the proposed problems, this study selected the typical area of the Ebinur Lake Basin in the arid region as the study area, took the sentinel data as the main data source, and used the Sentinel-1A (radar data), the Sentinel-2A and the Sentinel-3A (multispectral data), combined with 16 kinds of DEM derivatives and climate data (annual average temperature MAT, annual average precipitation MAP) as analysis. The five different types of data are reconstructed by spatial data and divided into four spatial resolutions (10, 100, 300, and 500 m). Seven models are constructed and predicted by machine learning methods RF and Cubist. The results show that the prediction accuracy of RF model is better than that of Cubist model, indicating that RF model is more suitable for small areas in arid areas. Among the three data sources, Sentinel-1A has the highest SOC prediction accuracy of 0.391 at 10 m resolution under the RF model. The results of the importance of environmental variables show that the importance of Flow Accumulation is higher in the RF model and the importance of SLOP in the DEM derivative is higher in the Cubist model. In the prediction results, SOC is mainly distributed in oasis and regions with more human activities, while SOC is less distributed in other regions. This study provides a certain reference value for the prediction of small-scale soil organic carbon spatial distribution by means of remote sensing and environmental factors.

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