Incorporation of Fused Remote Sensing Imagery to Enhance Soil Organic Carbon Spatial Prediction in an Agricultural Area in Yellow River Basin, China
<p>Soil sampling sites in the study area.</p> "> Figure 2
<p>Importance scores of Landsat-8-based spectral indices identified as predictors in (<b>A</b>) Landsat-8 model; (<b>B</b>) Brovey Landsat-8 model; (<b>C</b>) IHS Landsat-8 model; (<b>D</b>) GS Landsat-8 model.</p> "> Figure 3
<p>Importance score of Sentinel-2-based spectral indices identified as predictors in (<b>A</b>) Sentinel-2 model; (<b>B</b>) Brovey fused Sentinel-2-Landsat-8 model; (<b>C</b>) IHS fused Sentinel-2-Landsat-8 model; (<b>D</b>) GS fused Sentinel-2-Landsat-8 model.</p> "> Figure 3 Cont.
<p>Importance score of Sentinel-2-based spectral indices identified as predictors in (<b>A</b>) Sentinel-2 model; (<b>B</b>) Brovey fused Sentinel-2-Landsat-8 model; (<b>C</b>) IHS fused Sentinel-2-Landsat-8 model; (<b>D</b>) GS fused Sentinel-2-Landsat-8 model.</p> "> Figure 4
<p>Spatial pattern of SOC in the study area based on (<b>A</b>) the LT model; (<b>B</b>) the LTb model; (<b>C</b>) the LTi model; (<b>D</b>) the LTg model.</p> "> Figure 5
<p>Spatial pattern of SOC in Land Parcel A based on (<b>A</b>) the LT model; (<b>B</b>) the LTb model; (<b>C</b>) the LTi model; (<b>D</b>) the LTg model.</p> "> Figure 6
<p>Spatial pattern of SOC in the study area based on (<b>A</b>) the ST model; (<b>B</b>) the STb model; (<b>C</b>) the STi model; (<b>D</b>) the STg model.</p> "> Figure 7
<p>Spatial pattern of SOC in Land Parcel A based on (<b>A</b>) the ST model; (<b>B</b>) the STb model; (<b>C</b>) the STi model; (<b>D</b>) the STg model.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area Description and Soil Sampling
2.2. Image Fusion
2.3. Environmental Data Extraction
2.4. Model Calibration and Validation
2.4.1. Model Calibration
2.4.2. Model Validation
3. Results
3.1. Statistical Summary of SOC Concentration
3.2. Relationships between SOC and Remote Sensing Predictors
3.2.1. Relationships between SOC and L8-Based Spectral Indices
3.2.2. Relationships between SOC and S2-Based Spectral Indices
3.3. Spatial Prediction of SOC
3.3.1. Spatial Prediction of SOC Based on MS and PAN L8 Imagery
3.3.2. Spatial Prediction of SOC Based on MS S2 and Fused S2–L8 Imagery
3.4. Comparison of Different SOC Models
4. Discussion
4.1. Controlling Factors of SOC
4.2. Effect of Remote Sensing Fusion on DSM
4.3. Prospect of Soil Spatial Prediction Models in Developing Countries
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Payen, F.T.; Sykes, A.; Aitkenhead, M.; Alexander, P.; Moran, D.; MacLeod, M. Soil Organic Carbon Sequestration Rates in Vineyard Agroecosystems under Different Soil Management Practices: A Meta-Analysis. J. Clean. Prod. 2021, 290, 125736. [Google Scholar] [CrossRef]
- Liang, Z.; Chen, S.; Yang, Y.; Zhou, Y.; Shi, Z. High-Resolution Three-Dimensional Mapping of Soil Organic Carbon in China: Effects of SoilGrids Products on National Modeling. Sci. Total Environ. 2019, 685, 480–489. [Google Scholar] [CrossRef] [PubMed]
- Goldman, M.A.; Needelman, B.A.; Rabenhorst, M.C.; Lang, M.W.; McCarty, G.W.; King, P. Digital Soil Mapping in a Low-Relief Landscape to Support Wetland Restoration Decisions. Geoderma 2020, 373, 114420. [Google Scholar] [CrossRef]
- Mansuy, N.; Valeria, O.; Laamrani, A.; Fenton, N.; Guindon, L.; Bergeron, Y.; Beaudoin, A.; Légaré, S. Digital Mapping of Paludification in Soils under Black Spruce Forests of Eastern Canada. Geoderma Reg. 2018, 15, e00194. [Google Scholar] [CrossRef]
- Wang, S.; Fan, J.; Zhong, H.; Li, Y.; Zhu, H.; Qiao, Y.; Zhang, H. A Multi-Factor Weighted Regression Approach for Estimating the Spatial Distribution of Soil Organic Carbon in Grasslands. Catena 2019, 174, 248–258. [Google Scholar] [CrossRef]
- Bogunovic, I.; Pereira, P.; Brevik, E.C. Spatial Distribution of Soil Chemical Properties in an Organic Farm in Croatia. Sci. Total Environ. 2017, 584–585, 535–545. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Xu, Y.; Smith, S.E.; Grunwald, S.; Abd-Elrahman, A.; Wani, S.P.; Nair, V.D. Estimating Soil Total Nitrogen in Smallholder Farm Settings Using Remote Sensing Spectral Indices and Regression Kriging. Catena 2018, 163, 111–122. [Google Scholar] [CrossRef] [Green Version]
- Chen, S.; Arrouays, D.; Mulder, V.L.; Poggio, L.; Minasny, B.; Roudier, P.; Libohova, Z.; Lagacherie, P.; Shi, Z.; Hannam, J.; et al. Digital Mapping of GlobalSoilMap Soil Properties at a Broad Scale: A Review. Geoderma 2022, 409, 115567. [Google Scholar] [CrossRef]
- Van Shi, Z.; van Groenigen, K.J.; Osenberg, C.W.; Andresen, L.C.; Dukes, J.S.; Hovenden, M.J.; Luo, Y.; Michelsen, A.; Pendall, E. Predicting Soil Carbon Loss with Warming. Nature 2018, 554, E4–E5. [Google Scholar] [CrossRef]
- Padarian, J.; Minasny, B.; McBratney, A.B. Chile and the Chilean Soil Grid: A Contribution to GlobalSoilMap. Geoderma Reg. 2017, 9, 17–28. [Google Scholar] [CrossRef]
- Xu, Y.; Smith, S.E.; Grunwald, S.; Abd-Elrahman, A.; Wani, S.P. Incorporation of Satellite Remote Sensing Pan-Sharpened Imagery into Digital Soil Prediction and Mapping Models to Characterize Soil Property Variability in Small Agricultural Fields. ISPRS J. Photogramm. Remote Sens. 2017, 123, 1–19. [Google Scholar] [CrossRef] [Green Version]
- Wieland, M.; Martinis, S.; Kiefl, R.; Gstaiger, V. Semantic Segmentation of Water Bodies in Very High-Resolution Satellite and Aerial Images. Remote Sens. Environ. 2023, 287, 113452. [Google Scholar] [CrossRef]
- Abowarda, A.S.; Bai, L.; Zhang, C.; Long, D.; Li, X.; Huang, Q.; Sun, Z. Generating Surface Soil Moisture at 30 m Spatial Resolution Using Both Data Fusion and Machine Learning toward Better Water Resources Management at the Field Scale. Remote Sens. Environ. 2021, 255, 112301. [Google Scholar] [CrossRef]
- Ghassemian, H. A Review of Remote Sensing Image Fusion Methods. Inf. Fusion 2016, 32, 75–89. [Google Scholar] [CrossRef]
- Fletcher, R.J., Jr.; Fletcher, T.J.; Robertson, E.P.; Zuckerberg, B.; McCleery, R.A.; Dorazio, R.M. A Practical Guide for Combining Data to Model Species Distributions. Ecology 2019, 100, e02710. [Google Scholar] [CrossRef]
- Peters, D.P.C. Accessible Ecology: Synthesis of the Long, Deep, and Broad. Trends Ecol. Evol. 2010, 25, 592–601. [Google Scholar] [CrossRef]
- Möller, M.; Gerstmann, H.; Gao, F.; Dahms, T.C.; Förster, M. Coupling of Phenological Information and Simulated Vegetation Index Time Series: Limitations and Potentials for the Assessment and Monitoring of Soil Erosion Risk. Catena 2017, 150, 192–205. [Google Scholar] [CrossRef]
- Kereszturi, G.; Schaefer, L.N.; Schleiffarth, W.K.; Procter, J.; Pullanagari, R.R.; Mead, S.; Kennedy, B. Integrating Airborne Hyperspectral Imagery and LiDAR for Volcano Mapping and Monitoring through Image Classification. Int. J. Appl. Earth Obs. Geoinformation 2018, 73, 323–339. [Google Scholar] [CrossRef]
- Adrian, J.; Sagan, V.; Maimaitijiang, M. Sentinel SAR-Optical Fusion for Crop Type Mapping Using Deep Learning and Google Earth Engine. ISPRS J. Photogramm. Remote Sens. 2021, 175, 215–235. [Google Scholar] [CrossRef]
- Loiseau, T.; Chen, S.; Mulder, V.L.; Román Dobarco, M.; Richer-de-Forges, A.C.; Lehmann, S.; Bourennane, H.; Saby, N.P.A.; Martin, M.P.; Vaudour, E.; et al. Satellite Data Integration for Soil Clay Content Modelling at a National Scale. Int. J. Appl. Earth Obs. Geoinf. 2019, 82, 101905. [Google Scholar] [CrossRef]
- Ciampalini, A.; André, F.; Garfagnoli, F.; Grandjean, G.; Lambot, S.; Chiarantini, L.; Moretti, S. Improved Estimation of Soil Clay Content by the Fusion of Remote Hyperspectral and Proximal Geophysical Sensing. J. Appl. Geophys. 2015, 116, 135–145. [Google Scholar] [CrossRef]
- Xu, Y.; Smith, S.E.; Grunwald, S.; Abd-Elrahman, A.; Wani, S.P. Effects of Image Pansharpening on Soil Total Nitrogen Prediction Models in South India. Geoderma 2018, 320, 52–66. [Google Scholar] [CrossRef]
- van Dijk, M.; Morley, T.; Rau, M.L.; Saghai, Y. A Meta-Analysis of Projected Global Food Demand and Population at Risk of Hunger for the Period 2010–2050. Nat. Food 2021, 2, 494–501. [Google Scholar] [CrossRef]
- Di Curzio, D.; Castrignanò, A.; Fountas, S.; Romić, M.; Viscarra Rossel, R.A. Multi-Source Data Fusion of Big Spatial-Temporal Data in Soil, Geo-Engineering and Environmental Studies. Sci. Total Environ. 2021, 788, 147842. [Google Scholar] [CrossRef]
- Walkley, A.; Black, I.A. An examination of the degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Sci. 1934, 37, 29. [Google Scholar] [CrossRef]
- Ehlers, M.; Klonus, S.; Åstrand, P.J.; Rosso, P. Multi-Sensor Image Fusion for Pansharpening in Remote Sensing. Int. J. Image Data Fusion 2010, 1, 25–45. [Google Scholar] [CrossRef]
- Daneshvar, S.; Ghassemian, H. MRI and PET Image Fusion by Combining IHS and Retina-Inspired Models. Inf. Fusion 2010, 11, 114–123. [Google Scholar] [CrossRef]
- Dadrass Javan, F.; Samadzadegan, F.; Mehravar, S.; Toosi, A.; Khatami, R.; Stein, A. A Review of Image Fusion Techniques for Pan-Sharpening of High-Resolution Satellite Imagery. ISPRS J. Photogramm. Remote Sens. 2021, 171, 101–117. [Google Scholar] [CrossRef]
- Laben, C.A.; Brower, B.V. Process for Enhancing the Spatial Resolution of Multispectral Imagery Using Pan-Sharpening. U.S. Patent No. 6,011,875, 4 January 2000. [Google Scholar]
- Thomas, C.; Ranchin, T.; Wald, L.; Chanussot, J. Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics. IEEE Trans. Geosci. Remote Sens. 2008, 46, 1301–1312. [Google Scholar] [CrossRef] [Green Version]
- Du, Q.; Younan, N.H.; King, R.; Shah, V.P. On the Performance Evaluation of Pan-Sharpening Techniques. IEEE Geosci. Remote Sens. Lett. 2007, 4, 518–522. [Google Scholar] [CrossRef]
- Neeti, N.; Ronald Eastman, J. Novel Approaches in Extended Principal Component Analysis to Compare Spatio-Temporal Patterns among Multiple Image Time Series. Remote Sens. Environ. 2014, 148, 84–96. [Google Scholar] [CrossRef]
- Rudnicki, W.; Kursa, M. Feature Selection with the Boruta Package. J. Stat. Softw. 2010, 36, 1–13. [Google Scholar]
- Amiri, M.; Pourghasemi, H.R.; Ghanbarian, G.A.; Afzali, S.F. Assessment of the Importance of Gully Erosion Effective Factors Using Boruta Algorithm and Its Spatial Modeling and Mapping Using Three Machine Learning Algorithms. Geoderma 2019, 340, 55–69. [Google Scholar] [CrossRef]
- Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef] [Green Version]
- Song, X.-D.; Wu, H.-Y.; Liu, F.; Tian, J.; Cao, Q.; Yang, S.-H.; Peng, X.-H.; Zhang, G.-L. Three-Dimensional Mapping of Organic Carbon Using Piecewise Depth Functions in the Red Soil Critical Zone Observatory. Soil Sci. Soc. Am. J. 2019, 83, 687–696. [Google Scholar] [CrossRef]
- Panigrahi, N.; Das, B.S. Canopy Spectral Reflectance as a Predictor of Soil Water Potential in Rice. Water Resour. Res. 2018, 54, 2544–2560. [Google Scholar] [CrossRef]
- Roosjen, P.P.J.; Bartholomeus, H.M.; Clevers, J.G.P.W. Effects of Soil Moisture Content on Reflectance Anisotropy—Laboratory Goniometer Measurements and RPV Model Inversions. Remote Sens. Environ. 2015, 170, 229–238. [Google Scholar] [CrossRef]
- Sothe, C.; Gonsamo, A.; Arabian, J.; Snider, J. Large Scale Mapping of Soil Organic Carbon Concentration with 3D Machine Learning and Satellite Observations. Geoderma 2022, 405, 115402. [Google Scholar] [CrossRef]
- de Almeida Minhoni, R.T.; Scudiero, E.; Zaccaria, D.; Saad, J.C.C. Multitemporal Satellite Imagery Analysis for Soil Organic Carbon Assessment in an Agricultural Farm in Southeastern Brazil. Sci. Total Environ. 2021, 784, 147216. [Google Scholar] [CrossRef]
- Achat, D.L.; Fortin, M.; Landmann, G.; Ringeval, B.; Augusto, L. Forest Soil Carbon Is Threatened by Intensive Biomass Harvesting. Sci. Rep. 2015, 5, 15991. [Google Scholar] [CrossRef] [Green Version]
- Zhang, X.; Jia, J.; Chen, L.; Chu, H.; He, J.-S.; Zhang, Y.; Feng, X. Aridity and NPP Constrain Contribution of Microbial Necromass to Soil Organic Carbon in the Qinghai-Tibet Alpine Grasslands. Soil Biol. Biochem. 2021, 156, 108213. [Google Scholar] [CrossRef]
- Frampton, W.J.; Dash, J.; Watmough, G.; Milton, E.J. Evaluating the Capabilities of Sentinel-2 for Quantitative Estimation of Biophysical Variables in Vegetation. ISPRS J. Photogramm. Remote Sens. 2013, 82, 83–92. [Google Scholar] [CrossRef] [Green Version]
- Guo, Y.; Ren, H. Remote Sensing Monitoring of Maize and Paddy Rice Planting Area Using GF-6 WFV Red Edge Features. Comput. Electron. Agric. 2023, 207, 107714. [Google Scholar] [CrossRef]
- Dvorakova, K.; Heiden, U.; Pepers, K.; Staats, G.; van Os, G.; van Wesemael, B. Improving Soil Organic Carbon Predictions from a Sentinel–2 Soil Composite by Assessing Surface Conditions and Uncertainties. Geoderma 2023, 429, 116128. [Google Scholar] [CrossRef]
- Guo, L.; Fu, P.; Shi, T.; Chen, Y.; Zeng, C.; Zhang, H.; Wang, S. Exploring Influence Factors in Mapping Soil Organic Carbon on Low-Relief Agricultural Lands Using Time Series of Remote Sensing Data. Soil Tillage Res. 2021, 210, 104982. [Google Scholar] [CrossRef]
- Aksoy, S.; Yildirim, A.; Gorji, T.; Hamzehpour, N.; Tanik, A.; Sertel, E. Assessing the Performance of Machine Learning Algorithms for Soil Salinity Mapping in Google Earth Engine Platform Using Sentinel-2A and Landsat-8 OLI Data. Adv. Space Res. 2022, 69, 1072–1086. [Google Scholar] [CrossRef]
- Xu, Y.; Smith, S.E.; Grunwald, S.; Abd-Elrahman, A.; Wani, S.P. Evaluating the Effect of Remote Sensing Image Spatial Resolution on Soil Exchangeable Potassium Prediction Models in Smallholder Farm Settings. J. Environ. Manage. 2017, 200, 423–433. [Google Scholar] [CrossRef]
- Nguyen, T.T.; Pham, T.D.; Nguyen, C.T.; Delfos, J.; Archibald, R.; Dang, K.B.; Hoang, N.B.; Guo, W.; Ngo, H.H. A Novel Intelligence Approach Based Active and Ensemble Learning for Agricultural Soil Organic Carbon Prediction Using Multispectral and SAR Data Fusion. Sci. Total Environ. 2022, 804, 150187. [Google Scholar] [CrossRef]
- Duan, M.; Song, X.; Liu, X.; Cui, D.; Zhang, X. Mapping the Soil Types Combining Multi-Temporal Remote Sensing Data with Texture Features. Comput. Electron. Agric. 2022, 200, 107230. [Google Scholar] [CrossRef]
- Ma, L.; Liu, Y.; Zhang, X.; Ye, Y.; Yin, G.; Johnson, B.A. Deep Learning in Remote Sensing Applications: A Meta-Analysis and Review. ISPRS J. Photogramm. Remote Sens. 2019, 152, 166–177. [Google Scholar] [CrossRef]
- Sanchez, P.A.; Ahamed, S.; Carré, F.; Hartemink, A.E.; Hempel, J.; Huising, J.; Lagacherie, P.; McBratney, A.B.; McKenzie, N.J.; de Mendonça-Santos, M.L.; et al. Digital Soil Map of the World. Science 2009, 325, 680–681. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dharumarajan, S.; Kalaiselvi, B.; Suputhra, A.; Lalitha, M.; Vasundhara, R.; Kumar, K.S.A.; Nair, K.M.; Hegde, R.; Singh, S.K.; Lagacherie, P. Digital Soil Mapping of Soil Organic Carbon Stocks in Western Ghats, South India. Geoderma Reg. 2021, 25, e00387. [Google Scholar] [CrossRef]
- Dong, W.; Wu, T.; Luo, J.; Sun, Y.; Xia, L. Land Parcel-Based Digital Soil Mapping of Soil Nutrient Properties in an Alluvial-Diluvia Plain Agricultural Area in China. Geoderma 2019, 340, 234–248. [Google Scholar] [CrossRef]
- Rouse, J.W. Monitoring Vegetation Systems in the Great Plains with ERTS. NASA Spec. Publ. 1974, 351, 309. [Google Scholar]
- Xu, Y.; Wang, X.; Bai, J.; Wang, D.; Wang, W.; Guan, Y. Estimating the Spatial Distribution of Soil Total Nitrogen and Available Potassium in Coastal Wetland Soils in the Yellow River Delta by Incorporating Multi-Source Data. Ecol. Indic. 2020, 111, 106002. [Google Scholar] [CrossRef]
- Gitelson, A.A.; Viña, A.; Ciganda, V.; Rundquist, D.C.; Arkebauer, T.J. Remote Estimation of Canopy Chlorophyll Content in Crops. Geophys. Res. Lett. 2005, 32, e022688. [Google Scholar] [CrossRef] [Green Version]
- Daughtry, C.S.T.; Walthall, C.L.; Kim, M.S.; de Colstoun, E.B.; McMurtrey, J.E. Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sens. Environ. 2000, 74, 229–239. [Google Scholar] [CrossRef]
- Haboudane, D.; Miller, J.R.; Tremblay, N.; Zarco-Tejada, P.J.; Dextraze, L. Integrated Narrow-Band Vegetation Indices for Prediction of Crop Chlorophyll Content for Application to Precision Agriculture. Remote Sens. Environ. 2002, 81, 416–426. [Google Scholar] [CrossRef]
- Rogers, A.S.; Kearney, M.S. Reducing Signature Variability in Unmixing Coastal Marsh Thematic Mapper Scenes Using Spectral Indices. Int. J. Remote Sens. 2004, 25, 2317–2335. [Google Scholar] [CrossRef]
- Gao, B. NDWI—A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sens. Environ. 1996, 58, 257–266. [Google Scholar] [CrossRef]
- Rock, B.N.; Vogelmann, J.E.; Williams, D.L.; Vogelmann, A.F.; Hoshizaki, T. Remote Detection of Forest DamagePlant Responses to Stress May Have Spectral “Signatures” That Could Be Used to Map, Monitor, and Measure Forest Damage. BioScience 1986, 36, 439–445. [Google Scholar] [CrossRef]
- Musick, H.B.; Pelletier, R.E. Response to Soil Moisture of Spectral Indexes Derived from Bidirectional Reflectance in Thematic Mapper Wavebands. Remote Sens. Environ. 1988, 25, 167–184. [Google Scholar] [CrossRef]
- Allbed, A.; Kumar, L.; Aldakheel, Y.Y. Assessing Soil Salinity Using Soil Salinity and Vegetation Indices Derived from IKONOS High-Spatial Resolution Imageries: Applications in a Date Palm Dominated Region. Geoderma 2014, 230–231, 1–8. [Google Scholar] [CrossRef]
- Gorji, T.; Sertel, E.; Tanik, A. Monitoring Soil Salinity via Remote Sensing Technology under Data Scarce Conditions: A Case Study from Turkey. Ecol. Indic. 2017, 74, 384–391. [Google Scholar] [CrossRef]
Data Type | N | Mean | Median | SD | Min | Max | Range | Skew | CV |
---|---|---|---|---|---|---|---|---|---|
Total | 155 | 11.15 | 10.03 | 5.83 | 2.03 | 26.28 | 24.25 | 0.37 | 0.52 |
Calibration | 109 | 11.10 | 10.03 | 5.75 | 2.03 | 26.28 | 24.25 | 0.38 | 0.52 |
Validation | 46 | 11.27 | 10.09 | 6.1 | 2.61 | 23.26 | 20.65 | 0.33 | 0.54 |
MS Landsat 8 Spectral Indices | MS and PAN Landsat 8 Spectral Indices | ||
---|---|---|---|
Variable | R | Variable | R |
LTSWIR2 | −0.638 | LTgS1G | −0.657 |
LTSWIR1 | −0.638 | LTgNDWI | 0.647 |
LTP1 | −0.633 | LTSWIR2 | −0.638 |
LTS2N | −0.629 | LTbSWIR2 | −0.638 |
LTMSI | −0.623 | LTiSWIR2 | −0.638 |
LTNDWI | 0.623 | LTbS1B | 0.638 |
LTNDSI | −0.623 | LTbS1G | 0.638 |
LTS1N | −0.623 | LTiSWIR1 | −0.638 |
LTBSI | −0.621 | LTgSWIR2 | −0.638 |
LTSI1 | −0.621 | LTiSI1 | −0.637 |
LTCoastal | −0.620 | LTiP1 | −0.635 |
LTRG | −0.618 | LTgBSI | 0.634 |
LTRed | −0.617 | LTgRG | −0.634 |
LTBlue | −0.616 | LTP1 | −0.633 |
LTT2 | −0.616 | LTiCoastal | −0.632 |
LTNDVI | 0.615 | LTS2N | −0.629 |
LTSR | 0.615 | LTbS2N | −0.629 |
MS Sentinel 2 Spectral Indices | MS Sentinel 2 and Fused Sentinel 2-Landsat 8 Spectral Indices | ||
---|---|---|---|
Variable | R | Variable | R |
STSWIR1 | −0.575 | STiSI2 | −0.626 |
STMSI | −0.572 | STbSI1 | −0.622 |
STNDWI | 0.572 | STiBlue | −0.611 |
STNDSI | −0.572 | STiGreen | −0.605 |
STBSI | −0.563 | STbBlue | −0.603 |
STRE1B | −0.557 | STiMCARI1 | −0.596 |
STSWIR2 | −0.548 | STiTCARI1 | −0.591 |
STP2 | 0.542 | STiARVI | −0.590 |
STRE1 | −0.534 | STiNB | 0.586 |
STRE3RE2 | 0.527 | STbNDVIg | 0.585 |
STNDVIr1 | 0.527 | STbCIg | 0.585 |
STCIr1 | 0.527 | STbNG | 0.585 |
STNRE1 | 0.527 | STgNDWI | 0.584 |
STRE4RE2 | 0.521 | STgS2REP | 0.583 |
STRE4RE1 | 0.521 | STSWIR1 | −0.575 |
STCoastal | −0.518 | STMSI | −0.572 |
STRE3RE1 | 0.518 | STNDWI | 0.572 |
Models | R2 | RMSE (g/kg) | Bias |
---|---|---|---|
LT | 0.51 | 4.20 | 1.43 |
LTb | 0.61 | 3.87 | 1.58 |
LTi | 0.64 | 3.74 | 1.64 |
LTg | 0.67 | 3.59 | 1.69 |
ST | 0.36 | 8.41 | 1.24 |
STb | 0.51 | 7.23 | 1.43 |
STi | 0.52 | 7.10 | 1.45 |
STg | 0.57 | 6.71 | 1.53 |
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Xu, Y.; Tan, Y.; Abd-Elrahman, A.; Fan, T.; Wang, Q. Incorporation of Fused Remote Sensing Imagery to Enhance Soil Organic Carbon Spatial Prediction in an Agricultural Area in Yellow River Basin, China. Remote Sens. 2023, 15, 2017. https://doi.org/10.3390/rs15082017
Xu Y, Tan Y, Abd-Elrahman A, Fan T, Wang Q. Incorporation of Fused Remote Sensing Imagery to Enhance Soil Organic Carbon Spatial Prediction in an Agricultural Area in Yellow River Basin, China. Remote Sensing. 2023; 15(8):2017. https://doi.org/10.3390/rs15082017
Chicago/Turabian StyleXu, Yiming, Youquan Tan, Amr Abd-Elrahman, Tengfei Fan, and Qingpu Wang. 2023. "Incorporation of Fused Remote Sensing Imagery to Enhance Soil Organic Carbon Spatial Prediction in an Agricultural Area in Yellow River Basin, China" Remote Sensing 15, no. 8: 2017. https://doi.org/10.3390/rs15082017
APA StyleXu, Y., Tan, Y., Abd-Elrahman, A., Fan, T., & Wang, Q. (2023). Incorporation of Fused Remote Sensing Imagery to Enhance Soil Organic Carbon Spatial Prediction in an Agricultural Area in Yellow River Basin, China. Remote Sensing, 15(8), 2017. https://doi.org/10.3390/rs15082017