High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia

Wang, Bin; Waters, Cathy; Orgill, Susan; Gray, Jonathan; Cowie, Annette; Clark, Anthony; Liu, De Li

doi:10.1016/j.scitotenv.2018.02.204

Cited by 164 publications

(91 citation statements)

References 59 publications

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“…Multispectral remote sensing variables were the powerful and effectual variables in the full variables model, which showed that multispectral remote sensing data can predict forest topsoil SOC stocks, which is consistent with many previous research results [23,24,[34][35][36][37][38]. In the Bale Mountains, Ethiopia, Yimer et al [24] concluded that vegetation was the main source of SOC stocks and affected its distribution, and there was a significant positive correlation between vegetation and SOC stocks, which has been certified in this study ( Table 2).…”

Section: Effect Of Multispectral Remote Sensing Variables On Soc Stockssupporting

confidence: 91%

“…As one of the five soil-forming factors [40], topographic variables as a common variable in digital soil mapping (DSM) were selected as the main prediction factor in this study. In the process of SOC stocks prediction, the relative importance of terrain related variable was lower than that of multi spectral remote sensing related variable, which was consistent with previous research results [7,8,17,34]. Yang et al [17] considered that, in the area with abundant vegetation, the influence of…”

Section: Effect Of Multispectral Remote Sensing Variables On Soc Stockssupporting

confidence: 84%

“…Our research concluded that the multispectral remote sensing data are effective to map SOC stocks in high-density vegetation coverage area. Many studies have a similar conclusion regarding the major controls of SOC distribution [7,[34][35][36][37]. For instance, in the Qilian Mountains of China, Yang et al [17] used three bands of Landsat-5 TM (B GREEN , B RED , and B NIR ) and random forest to predict SOC and obtain higher prediction accuracy in the natural field (R 2 = 0.72).…”

Section: Effect Of Multispectral Remote Sensing Variables On Soc Stocksmentioning

confidence: 94%

“…SAVI is very sensitive to the change of soil background [30]. Many studies showed that there were some potential applications of multispectral remote sensing data in SOC stocks mapping with better natural vegetation coverage areas [7,17,[34][35][36][37][38][39]. B GREEN is a better predictor than NDVI and SAVI in the full variable model, although many scholars considered NDVI was a more effective and powerful predictor [35,37].…”

Section: Effect Of Multispectral Remote Sensing Variables On Soc Stocksmentioning

confidence: 99%

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Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China

et al. 2020

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Accurately mapping the spatial distribution information of soil organic carbon (SOC) stocks is a key premise for soil resource management and environment protection. Rapid development of satellite remote sensing provides a great opportunity for monitoring SOC stocks at a large scale. In this study, based on 12 environmental variables of multispectral remote sensing, topography and climate and 236 soil sampling data, three different boosted regression tree (BRT) models were compared to obtain the most accurate map of SOC stocks covering the forest area of Lvshun District in the Northeast China. Four validation indexes, including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Lin’s concordance correlation coefficient (LCCC) were calculated to evaluate the performance of the three models. The results showed that the full variable model performed the best, except the model using multispectral remote sensing variables. In the full variable model, the regional SOC stocks are primarily determined by multispectral remote sensing variables, followed by topographic and climatic variables, with the relative importance of variables in the model being 63%, 28%, and 9%, respectively. The average prediction results of full variables model and only multispectral remote sensing variables model were 8.99 and 9.32 kg m−2, respectively. Our results indicated that there is a strong dependence of SOC stocks on multispectral remote sensing data when forest ecosystems have dense natural vegetation. Our study suggests that the multispectral remote sensing variables should be used to map SOC stocks of forest ecosystems in our study region.

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Section: Effect Of Multispectral Remote Sensing Variables On Soc Stockssupporting

confidence: 91%

Section: Effect Of Multispectral Remote Sensing Variables On Soc Stockssupporting

confidence: 84%

Section: Effect Of Multispectral Remote Sensing Variables On Soc Stocksmentioning

confidence: 94%

Section: Effect Of Multispectral Remote Sensing Variables On Soc Stocksmentioning

confidence: 99%

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Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China

et al. 2020

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“…Elevation referred to the vertical distance of a place on the ground or a geographical object above or below sea level [20,45]. In mountainous vertical zones, with increasing elevation, the temperature gradually decreases, so the SOC density was vulnerable to this vertical zonality [45][46][47]. In addition, slope gradient could significantly affect soil erosion, vegetation cover, and other factors, affecting SOC stocks.…”

Section: Controls Of Soc Stocksmentioning

confidence: 99%

Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

Wang

Zhuang

Yang

et al. 2019

Forests

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Forest soil organic carbon (SOC) accounts for a large portion of global soil carbon stocks. Accurately mapping forest SOC stocks is a necessity for quantifying forest carbon cycling and forest soil sustainable management. In this study, we used a boosted regression trees (BRT) model to predict the spatial distribution of SOC stocks during two time periods (1990 and 2015) and calculated their spatiotemporal changes during 25 years in Liaoning Province, China. A total of 367 (1990) and 539 (2015) sampling sites and 9 environmental variables (climate, topography, remote sensing) were used in the BRT model. The ten-fold cross-validation technique was used to evaluate the prediction performance and uncertainty of the BRT model in two periods. It was found that the BRT model could account for 65% and 59% of SOC stocks, respectively for the two periods. MAP and NDVI were the main environmental variables controlling the spatial variability of SOC stocks. Over the 25-year period, the average SOC stocks increased from 5.66 to 6.61 kg m −2 . In the whole study area, the SOC stocks were the highest in the northeast, followed by the southwest, and the lowest in the middle of the spatial distribution pattern in the two periods. Our accurate mapping of SOC stocks, their spatial distribution characteristics, influencing factors, and main controlling factors in forest areas will assist soil management and help assess environmental changes in the region. soil properties are affected by many environmental factors, it is difficult to predict soil properties accurately and efficiently at the regional scale [8]. As an efficient and low-cost method, digital soil mapping (DSM) technology is able to accurately describe the spatial variability of soil attributes in a region by using a limited amount of sampling data and environmental variables [9]. In order to accurately predict the SOC stocks in a region and analyze its key environmental factors, scholars have carried out a lot of researches [10][11][12]. In fact, the direct relationship between soil properties and environmental factors is non-linear and complex [13,14]. Therefore, machine learning algorithms which can effectively avoid this kind of problem are widely used in DSM mapping [11,15]. For example, on Barro Colorado Island, Panama, Grimm et al. [15] used a random forest model to simulate SOC stocks in 0-10 cm, 10-20 cm, 20-30 cm, and 30-50 cm layers, respectively. Giasson et al. [16] applied a multiple logistic regression model to predict the occurrence of soil types in southern Brazil. Dorji et al. [17] selected regression kriging and equal-area spline profile function to predict the SOC stocks at depths 0-5, 5-15, 15-30, 30-60, and 60-100 cm in montane ecosystems, Eastern Himalayas. In Denmark, Adhikari et al. [18] used regression kriging and 12 environmental variables to simulate the spatial distribution of SOC stocks at five soil layers. Were et al. [19] compared the performance of support vector regression, artificial neural network, and random forest models in predicti...

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A Review on Soil Organic Carbon Estimation via Remote Sensing Approaches

Batool¹

2022

Preprint

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High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia

Cited by 164 publications

References 59 publications

Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China

Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China

Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

A Review on Soil Organic Carbon Estimation via Remote Sensing Approaches

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