Using Near-Surface Photogrammetry Assessment of Surface Roughness (NSPAS) to assess the effectiveness of erosion control treatments applied to slope forming materials from a mine site in West Africa

Campbell, Stephanie; Simmons, Robert W.; Rickson, R. J.; Waine, Toby; Simms, Daniel M.

doi:10.1016/j.geomorph.2018.08.027

Cited by 11 publications

(5 citation statements)

References 25 publications

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“…The VWC and roughness are computed from NDVI and slope with empirical relations as follows (Jackson et al, 1999 ; Campbell et al, 2018 ; Eroglu et al, 2019 ),…”

Section: Methodsmentioning

confidence: 99%

Daily Spatial Complete Soil Moisture Mapping Over Southeast China Using CYGNSS and MODIS Data

Yang¹,

Sun²,

Jundong³

et al. 2022

Front. Big Data

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Daily spatial complete soil moisture (SM) mapping is important for climatic, hydrological, and agricultural applications. The Cyclone Global Navigation Satellite System (CYGNSS) is the first constellation that utilizes the L band signal transmitted by the Global Navigation Satellite System (GNSS) satellites to measure SM. Since the CYGNSS points are discontinuously distributed with a relativity low density, limiting it to map continuous SM distributions with high accuracy. The Moderate-Resolution Imaging Spectroradiometer (MODIS) product (i.e., vegetation index [VI] and land surface temperature [LST]) provides more surface SM information than other optical remote sensing data with a relatively high spatial resolution. This study proposes a point-surface fusion method to fuse the CYGNSS and MODIS data for daily spatial complete SM retrieval. First, for CYGNSS data, the surface reflectivity (SR) is proposed as a proxy to evaluate its ability to estimate daily SM. Second, the LST output from the China Meteorological Administration Land Data Assimilation System (CLDAS, 0.0625° × 0.0625°) and MODIS LST (1 × 1 km) are fused to generate spatial complete and temporally continuous LST maps. An Enhanced Normalized Vegetation Supply Water Index (E-NVSWI) model is proposed to estimate SM derived from MODIS data at high spatial resolution. Finally, the final SM estimation model is constructed from the back-propagation artificial neural network (BP-ANN) fusing the CYGNSS point, E-NWSVI data, and ancillary data, and applied to get the daily continuous SM result over southeast China. The results show that the estimation SM are comparable and promising (R = 0.723, root mean squared error [RMSE] = 0.062 m3 m−3, and MAE = 0.040 m3 m−3 vs. in situ, R = 0.714, RMSE = 0.057 m3 m−3, and MAE = 0.039 m3 m−3 vs. CLDAS). The proposed algorithm contributes from two aspects: (1) validates the CYGNSS derived SM by taking advantage of the dense in situ networks over Southeast China; (2) provides a point-surface fusion model to combine the usage of CYGNSS and MODIS to generate the temporal and spatial complete SM. The proposed approach reveals significant potential to map daily spatial complete SM using CYGNSS and MODIS data at a regional scale.

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“…The VWC and roughness are computed from NDVI and slope with empirical relations as follows (Jackson et al, 1999 ; Campbell et al, 2018 ; Eroglu et al, 2019 ),…”

Section: Methodsmentioning

confidence: 99%

Daily Spatial Complete Soil Moisture Mapping Over Southeast China Using CYGNSS and MODIS Data

Yang¹,

Sun²,

Jundong³

et al. 2022

Front. Big Data

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“…During erosion, the corresponding covering effects of hard‐to‐transport coarse rock fragments are gradually exerted over time (Figure 4a), making the slope gradually approach the rock cover pattern, which differs significantly from the rock cover directly applied (Cochrane et al, 2019; Ni et al, 2020; Ni, Wen, et al, 2022; Sharmeen & Willgoose, 2007). Accumulated rock fragments could directly increase surface roughness and shield the soil surface by pedestalling or protruding (Campbell et al, 2018; Mombini et al, 2021; Poesen et al, 1998; Rieke‐Zapp et al, 2007; van Wesemael et al, 1996; Wang et al, 2012). The increase of surface roughness gradually enhances greater resistance to flow velocity, impeding runoff detachment and transport processes, thereby reducing the erosion potential and altering the erosion patterns (Li, Nearing, Polyakov, Nichols, & Cavanaugh, 2020; Nearing et al, 1999, 2017; Ni, Wen, et al, 2022).…”

Section: Relationship Between Rock Fragments and Slope Erosionmentioning

confidence: 99%

A review of impacts of soil rock fragments on slope erosion: Mechanism and processes

Ni,

Shi,

Zhao

et al. 2024

Land Degrad Dev

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Rock fragments play a pivotal role in influencing slope erosion processes, attracting increasing scientific interest and growing concerns regarding their potential impacts on erosion. This paper reviews the studies published in recent decades concerning the various impacts of rock fragments on soil erosion processes by water, highlighting the research gaps and outstanding challenges in exploring the erosion evolution process involving rock fragments. Systematic studies and illustrative examples have shown that the impacts of rock fragments characteristics (such as coverage, content, size, position, shape, and spatial heterogeneity) on erosion processes are complex and inconsistent (positive/negative). This uncertainty can be attributed to the intricate erosion process and the diverse rock fragment characteristics, complicating the development of accurate erosion prediction models. Current models predominantly focus on rock cover and often ignore the essential factors including rock size, position, and shape, thus impeding the precise understanding of how rock fragment characteristics specifically affect soil erosion within existing erosion models. Furthermore, this review discusses the perspectives for enhancing the research on rock fragment impacts on soil erosion processes and provides insights into potential research requirements and solutions.

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“…All data sets of the input and output layers were averaged to obtain monthly values. The VWC and roughness were computed from NDVI and slope with empirical relations [39,40], respectively.…”

Section: Monthly Sm Estimation Using Neural Networkmentioning

confidence: 99%

Comprehensive Evaluation of Using TechDemoSat-1 and CYGNSS Data to Estimate Soil Moisture over Mainland China

et al. 2020

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Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) provides a new opportunity for land observation. This study is the first to compare and evaluate the performance of the only two spaceborne GNSS-R satellite missions whose data are publicly available, i.e., the UK’s TechdemoSat-1 (TDS-1) and the US’s Cyclone Global Navigation Satellite System (CYGNSS), for sensitivity analysis with SMAP SM on a daily basis and soil moisture (SM) estimates on a monthly basis over Mainland China. For daily sensitivity analysis, the two data were matched up and compared for the period (i.e., May 2017 through April 2018) when they coexisted (R = 0.561 vs R = 0.613). For monthly SM estimates, a back-propagation artificial neural network (BP-ANN) was used to construct a model using data from more than two years. The model was subsequently used to derive long-term and continuous SM maps over Mainland China. The results showed that TDS-1 and CYGNSS agree and correlate very well with the SMAP SM in Mainland China (R = 0.676, MAE = 0.052 m3m-3, and ubRMSE = 0.060 m3m-3 for TDS-1; R = 0.798, MAE = 0.040 m3m-3, and ubRMSE = 0.062 m3m-3 for CYGNSS). The retrieved results were further validated using monthly in situ SM data from dense sites across Mainland China. It was found that the SM derived from the TDS-1/ CYGNSS also correlated well with in situ SM (R = 0.687, MAE = 0.066 m3m-3, and ubRMSE = 0.056 m3m-3 for TDS-1; R = 0.724, MAE = 0.052 m3m-3, and ubRMSE = 0.053 m3m-3 for CYGNSS). The results in this study suggested that TDS-1/CYGNSS and the upcoming spaceborne GNSS-R mission could be new and powerful data sources to produce SM data set at a large scale and with relatively high precision.

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Using Near-Surface Photogrammetry Assessment of Surface Roughness (NSPAS) to assess the effectiveness of erosion control treatments applied to slope forming materials from a mine site in West Africa

Cited by 11 publications

References 25 publications

Daily Spatial Complete Soil Moisture Mapping Over Southeast China Using CYGNSS and MODIS Data

Daily Spatial Complete Soil Moisture Mapping Over Southeast China Using CYGNSS and MODIS Data

A review of impacts of soil rock fragments on slope erosion: Mechanism and processes

Comprehensive Evaluation of Using TechDemoSat-1 and CYGNSS Data to Estimate Soil Moisture over Mainland China

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