Soil erosion topographic factor (LS): Accuracy calculated from different data sources

Lu, Shaojuan; Liu, Baoyuan; Hu, Yaxian; Fu, Suhua; Cao, Qi; Shi, Yandong; Huang, Taiwen

doi:10.1016/j.catena.2019.104334

Cited by 34 publications

(19 citation statements)

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“…In more general terms, the greater emissions from the UAVderived DTM illustrate that when the raster cells in DTMs were greater than the actual topographic features, it would potentially lead to significant errors of agricultural land CO 2 emissions. Such errors are dependent on how close the pixel size is to the microreliefs and would thus be particularly pronounced in landscapes with short slopes and contrasting erosion and deposition-induced soil development along them (e.g., Van Oost et al, 2009;Greenwood et al, 2015;Hu et al, 2016;Lu et al, 2020). However, such discrepancy induced by using improper pixel size to model slope dependent impacts on surface processes or biogeochemistry is not uncommon in recent global and regional-scale studies addressing erosion and climate change (e.g., 250 m × 250 m in Borrelli et al, 2017; 1 km × 1 km in Lugato et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

“…A substantial number of studies have utilized a combination of field data and remote sensing to assess the potential impacts of soil erosion on atmospheric CO 2 at various spatiotemporal scales (Nadeu et al, 2012;Yue et al, 2016;Borrelli et al, 2017;Wilken et al, 2017;Lugato et al, 2018). One of the common complications in this research is the compatibility between the resolution of digital terrain models (DTMs) and the field topographic features and processes to be represented (Lu et al, 2020). Presumably, large scale topographic maps (e.g., 1:10,000) can produce DTMs of more desirable accuracy, but highly accurate topographic data is not always available, processable by available computers, or is often too expensive to be acquired.…”

Section: Introductionmentioning

confidence: 99%

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Capturing the Scale Dependency of Erosion-Induced Variation in CO2 Emissions on Terraced Slopes

Schneider

Kuhn

et al. 2021

Front. Environ. Sci.

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Net soil CO2 emissions are not independent of topography but tend to decline with increasing slope gradients. Such decline has been attributed to increased runoff and greater soil loss on steep slopes, leaving the soil less habitable for microorganisms. However, the specific variations of slope gradients and thus the associated soil properties relevant for CO2 emissions, especially from terraced slopes, are often disguised by the coarse resolution of digital terrain models (DTMs) based on commonly available open-source data. Such misrepresentation of the relationship between topography and soil CO2 emissions carries the risk of a wrong assessment of soil-atmosphere interaction. By applying a slope dependent soil CO2 emission model developed from erosion plots to nearby sloping and partially terraced cropland using two DTMs of different spatial resolutions, this study tested the significance of these resolution-induced errors on CO2 emission estimates. The results show that the coarser-resolution Shuttle Radar Topography Mission (SRTM) underestimated CO2-C emission by 27% compared to the higher-resolution DTM derived from Unmanned Aerial Vehicles (UAV) imagery. Such difference can be mostly attributed to a better representation of the proportion of flat slopes in the high-resolution DTM. Although the observations from erosion plots cannot be directly extrapolated to a larger scale, the 27% underestimation using the coarser-resolution SRTM DTM emphasizes that it is essential to represent microreliefs and their impact on runoff and erosion-induced soil heterogeneity at an appropriate scale. The widespread impact of topography on erosion and deposition on cropland, and the associated slope-dependent heterogeneity of soil properties, may lead to even greater differences than those observed in this study. The greatly improved estimation on CO2 emissions by the UAV-derived DTM also demonstrates that UAVs have a great potential to fill the gap between conventional field investigations and commonly applied coarse-resolution remote sensing when assessing the impact of soil erosion on global soil-atmosphere interaction.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Capturing the Scale Dependency of Erosion-Induced Variation in CO2 Emissions on Terraced Slopes

Schneider

Kuhn

et al. 2021

Front. Environ. Sci.

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“…The LS-factor consists of a slope length (L) factor and a slope steepness (S) factor, which are calculated by DEM data in GIS-based RUSLE model (Wischmeier and Smith, 1978;Renard et al, 1991). The resolution of the DEM data influences the accuracy of the simulation of the LS-factor, as it has significant scaling effect (Panagos et al, 2015a;Lu et al, 2020;Wang et al, 2020).…”

Section: Calculation Of the Ls-and K-factormentioning

confidence: 99%

Long-time Series Dataset of Soil Conservation Capacity Preventing Water Erosion in China (1992–2019)

Chen

et al. 2022

Preprint

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Abstract. Soil conservation capacity (SC) is defined as the ability of the ecosystem to control soil erosion and protect soil function. A long-term and high-resolution estimation of soil conservation is urgent for ecological assessment and land management on a large scale. Here, a 300-m resolution SC dataset in China is established from 1992 to 2019 based on the Revised Universal Soil Loss Equation (RUSLE) model. The RUSLE modelling was conducted based on five key parameters, including the rainfall erosivity (interpolation of daily rainfall), land cover management (provincial data), conservation practices (weighted by terrain and crop types), topography (30 m), and soil properties (250 m). The dataset agrees with previous measurements (R2 > 0.5 in all the basins) and other regional simulations. The results show that China's SC decreased before 2003 and then increased up to now. The SC change exhibits the ecological effects of soil and water conservation policies in China, such as the Conversion of Farmland to Forests and Grass (Grain-for-Green), which unfolded many movements after 2000. Compared with current studies, the dataset has long-term, large-scale, and relatively high-resolution characteristics. This dataset will serve as a base to open out the mechanism of SC variations in China and could help assess the ecological effects of land management policies. This dataset is available at https://doi.org/10.11888/Terre.tpdc.272668 (Li et al., 2022).

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“…Also, due to the strong elevation gradients, land-use changes and soil erosion is considered an important factor for the SOC variability, as changes in the soil erosion can alter long-term stored SOC in the top soil layers and lead loss of SOC (Olson et al 2016 ; Shi et al 2020 ). The LS factor (Desmet and Govers 1996 ; Panagos et al 2015 ; Lu et al 2020 ) is one of the most important controlling factors of soil characteristics and geomorphic processes such as soil erosion (Khanifar and Khademalrasoul 2020 ). It is the product between the L and S factors (Eqs.…”

Section: Methodsmentioning

confidence: 99%

Improving the remote estimation of soil organic carbon in complex ecosystems with Sentinel-2 and GIS using Gaussian processes regression

et al. 2022

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Background and aims The quantitative retrieval of soil organic carbon (SOC) storage, particularly for soils with a large potential for carbon sequestration, is of global interest due to its link with the carbon cycle and the mitigation of climate change. However, complex ecosystems with good soil qualities for SOC storage are poorly studied. Methods The interrelation between SOC and various vegetation remote sensing drivers is understood to demonstrate the link between the carbon stored in the vegetation layer and SOC of the top soil layers. Based on the mapping of SOC in two horizons (0–30 cm and 30–60 cm) we predict SOC with high accuracy in the complex and mountainous heterogeneous páramo system in Ecuador. A large SOC database (in weight % and in Mg/ha) of 493 and 494 SOC sampling data points from 0–30 cm and 30–60 cm soil profiles, respectively, were used to calibrate GPR models using Sentinel-2 and GIS predictors (i.e., Temperature, Elevation, Soil Taxonomy, Geological Unit, Slope Length and Steepness (LS Factor), Orientation and Precipitation). Results In the 0–30 cm soil profile, the models achieved a R2 of 0.85 (SOC%) and a R2 of 0.79 (SOC Mg/ha). In the 30–60 cm soil profile, models achieved a R2 of 0.86 (SOC%), and a R2 of 0.79 (SOC Mg/ha). Conclusions The used Sentinel-2 variables (FVC, CWC, LCC/Cab, band 5 (705 nm) and SeLI index) were able to improve the estimation accuracy between 3–21% compared to previous results of the same study area. CWC emerged as the most relevant biophysical variable for SOC prediction.

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Soil erosion topographic factor (LS): Accuracy calculated from different data sources

Cited by 34 publications

References 34 publications

Capturing the Scale Dependency of Erosion-Induced Variation in CO2 Emissions on Terraced Slopes

Capturing the Scale Dependency of Erosion-Induced Variation in CO2 Emissions on Terraced Slopes

Long-time Series Dataset of Soil Conservation Capacity Preventing Water Erosion in China (1992–2019)

Improving the remote estimation of soil organic carbon in complex ecosystems with Sentinel-2 and GIS using Gaussian processes regression

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