Abstract
Background and aims
Solar-induced chlorophyll fluorescence (SIF) is closely related to vegetation photosynthesis and can sensitively reflect the growth and health of vegetation. Using the advantages of SIF in photosynthetic physiological diagnosis, this study carried out a collaborative study of SIF, land attributes and image reflectance spectra to estimate soil organic carbon (SOC) content in typical agricultural areas of the Qinghai-Tibet plateau (QTP).
Methods
The spectral reflectance (R), first derivative of reflectance (FDR), second derivative of reflectance (SDR) of spectral band of Landsat 8 Operational Land Imager (OLI) data were selected together with land attributes (i.e. elevation, slope, soil temperature, and soil moisture content) and SIF index and vegetation indices to establish the SOC content estimation models using the random forest (RF), back propagation neural network (BPNN) and partial least squares regression (PLSR), respectively.
Results
SIF index can significantly improve the SOC content estimation compared to the vegetation indices. The accuracy of the BPNN model established by combining SIF index with the FDR of Landsat 8 OLI data and land attributes was the highest (R2 = 0.977, RMSEC = 2.069 g·kg− 1, MAE = 0.945 g·kg− 1, RPD = 3.970, d-factor = 0.010).
Conclusion
This study confirmed the good effect of BPNN model driven by SIF index, land attributes, and Landsat 8 OLI data on the estimation of SOC content, which can provide a new way for the accurate estimation of the soil internal components in the agricultural areas.
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Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Abbreviations
- ANN:
-
Artificial neural network
- BPNN:
-
Back propagation neural network
- ESA:
-
European Space Agency
- EVI:
-
Enhanced vegetation index
- FDR:
-
First derivative of reflectance
- GOME:
-
Global ozone monitoring experiment
- GOSIF:
-
Global ‘OCO-2’ SIF
- IDL:
-
Interactive data language
- MAE:
-
Mean absolute error
- MODIS:
-
Moderate resolution imaging spectroradiometer
- NASA:
-
National aeronautics and space administration
- NDVI:
-
Normalized difference vegetation index
- NPQ:
-
Non-photochemical quenching
- OCO:
-
Orbiting carbon observatory
- OLI:
-
Operational land imager
- PLSR:
-
Partial least squares regression
- QTP:
-
Qinghai-Tibet plateau
- R:
-
Spectral reflectance
- RF:
-
Random forest
- RMSEC:
-
Root mean square error of calibration
- RMSEV:
-
Root mean square error of validation
- RPD:
-
Residual prediction deviation
- RSIF:
-
Reconstructed SIF
- SDR:
-
Second derivative of reflectance
- SIF:
-
Solar-induced chlorophyll fluorescence
- SMC:
-
Soil moisture content
- SOC:
-
Soil organic carbon
- SOM:
-
Soil organic matter
- ST:
-
Soil temperature
References
Ayala Izurieta JE, Jara Santillán CA, Márquez CO et al (2022) Improving the remote estimation of soil organic carbon in complex ecosystems with Sentinel-2 and GIS using gaussian processes regression. Plant Soil 479(1–2):159–183. https://doi.org/10.1007/s11104-022-05506-1
Ben-Dor E, Inbar Y, Chen Y et al (1997) The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400–2500 nm) during a controlled decomposition process. Remote Sens Environ 61:1–15. https://doi.org/10.1016/S0034-4257(96)00120-4
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Callies J, Corpaccioli E, Eisinger M (2000) GOME-2-Metop’s second-generation sensor for operational ozone monitoring. ESA Bull 102:28–37
Chen D, Chang N, Xiao J et al (2019) Mapping dynamics of soil organic matter in croplands with MODIS data and machine learning algorithms. Sci Total Environ 669:844–855. https://doi.org/10.1016/j.scitotenv.2019.03.151
Croft H, Chen JM, Luo X et al (2017) Leaf chlorophyll content as a proxy for leaf photosynthetic capacity. Glob Chang Biol 23:3513–3524. https://doi.org/10.1111/gcb.13599
Croft H, Kuhn NJ, Anderson K (2012) On the use of remote sensing techniques for monitoring spatio-temporal soil organic carbon dynamics in agricultural systems. Catena 94:64–74. https://doi.org/10.1016/j.catena.2012.01.001
Dai F, Zhou Q, Lv Z et al (2014) Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecol Indic 45:184–194. https://doi.org/10.1016/j.ecolind.2014.04.003
Damm A, Guanter L, Paul-Limoges E et al (2015) Far-red sun-induced chlorophyll fluorescence shows ecosystem-specific relationships to gross primary production: an assessment based on observational and modeling approaches. Remote Sens Environ 166:91–105. https://doi.org/10.1016/j.rse.2015.06.004
Dechant B, Ryu Y, Badgley G et al (2020) Canopy structure explains the relationship between photosynthesis and sun-induced chlorophyll fluorescence in crops. Remote Sens Environ 241:111733. https://doi.org/10.1016/j.rse.2020.111733
Dogan HM, Kılıç OM (2013) Modelling and mapping some soil surface properties of Central Kelkit Basin in Turkey by using Landsat-7 ETM + images. Int J Remote Sens 34:5623–5640. https://doi.org/10.1080/01431161.2013.796097
Dotto A, Dalmolin R, Caten A et al (2018) A systematic study on the application of scatter-corrective and spectral-derivative preprocessing for multivariate prediction of soil organic carbon by Vis-NIR spectra. Geoderma 314:262–274. https://doi.org/10.1016/j.geoderma.2017.11.006
Doughty R, Kurosu TP, Parazoo N et al (2022) Global GOSAT, OCO-2, and OCO-3 solar-induced chlorophyll fluorescence datasets. Earth Syst Sci Data 14:1513–1529. https://doi.org/10.5194/essd-14-1513-2022
Du S, Liu L, Liu X et al (2018) Retrieval of global terrestrial solar-induced chlorophyll fluorescence from TanSat satellite. Sci Bull 63:1502–1512. https://doi.org/10.1016/j.scib.2018.10.003
Duveiller G, Cescatti A (2016) Spatially downscaling sun-induced chlorophyll fluorescence leads to an improved temporal correlation with gross primary productivity. Remote Sens Environ 182:72–89. https://doi.org/10.1016/j.rse.2016.04.027
Fang H, Ji B, Deng X et al (2018) Effects of topographic factors and aboveground vegetation carbon stocks on soil organic carbon in Moso bamboo forests. Plant Soil 433:363–376. https://doi.org/10.1007/s11104-018-3847-7
Feng W, Lu H, Yao T et al (2020) Drought characteristics and its elevation dependence in the Qinghai–Tibet plateau during the last half-century. Sci Rep 10:14323. https://doi.org/10.1038/s41598-020-71295-1
Frankenberg C, Fisher JB, Worden J et al (2011) New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophys Res Lett 38:L17706. https://doi.org/10.1029/2011gl048738
Gentine P, Alemohammad SH (2018) Reconstructed Solar-Induced fluorescence: a machine learning vegetation product based on MODIS Surface Reflectance to Reproduce GOME-2 Solar-Induced fluorescence. Geophys Res Lett 45:3136–3146. https://doi.org/10.1002/2017GL076294
Grunwald D, Kaiser M, Junker S et al (2017) Influence of elevated soil temperature and biochar application on organic matter associated with aggregate-size and density fractions in an arable soil. Agric Ecosyst Environ 241:79–87. https://doi.org/10.1016/j.agee.2017.02.029
Guanter L, Frankenberg C, Dudhia A et al (2012) Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sens Environ 121:236–251. https://doi.org/10.1016/j.rse.2012.02.006
Guan Y, Cui W, Liu J et al (2021) Observed changes of Köppen Climate Zones based on high-resolution data sets in the Qinghai-Tibet Plateau. Geophys Res Lett 48:e2021GL096159. https://doi.org/10.1029/2021GL096159
Guo N, Shi X, Zhao Y et al (2017) Environmental and anthropogenic factors driving changes in paddy soil organic matter: a case study in the middle and lower Yangtze River Plain of China. Pedosphere 27(5):926–937. https://doi.org/10.1016/s1002-0160(17)60383-7
Hao D, Asrar GR, Zeng Y et al (2021) Potential of hotspot solar-induced chlorophyll fluorescence for better tracking terrestrial photosynthesis. Glob Chang Biol 27:2144–2158. https://doi.org/10.1111/gcb.15554
Helland IS (1990) Partial least squares regression and statistical models. Scand J Stat 17:97–114. https://doi.org/10.2307/4616159
Hutchinson JJ, Campbell CA, Desjardins RL (2007) Some perspectives on carbon sequestration in agriculture. Agric For Meteorol 142:288–302. https://doi.org/10.1016/j.agrformet.2006.03.030
Jaber SM, Al-Qinna MI (2017) TM-Based SOC models augmented by auxiliary data for carbon crediting programs in semi-arid environments. Photogramm Eng Remote Sens 83:447–457. https://doi.org/10.14358/pers.83.6.447
Joiner J, Guanter L, Lindstrot R et al (2013) Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos Meas Tech 6:2803–2823. https://doi.org/10.5194/amt-6-2803-2013
Joiner J, Yoshida Y, Vasilkov AP et al (2011) First observations of global and seasonal terrestrial chlorophyll fluorescence from space. Biogeosciences 8:637–651. https://doi.org/10.5194/bg-8-637-2011
Kusumo BH, Hedley MJ, Hedley CB et al (2011) Measuring carbon dynamics in field soils using soil spectral reflectance: prediction of maize root density, soil organic carbon and nitrogen content. Plant Soil 338:233–245. https://doi.org/10.1007/s11104-010-0501-4
Köhler P, Guanter L, Joiner J (2015) A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data. Atmos Meas Tech 8:2589–2608. https://doi.org/10.5194/amt-8-2589-2015
Leifeld J, Bassin S, Fuhrer J (2005) Carbon stocks in swiss agricultural soils predicted by land-use, soil characteristics, and altitude. Agric Ecosyst Environ 105:255–266. https://doi.org/10.1016/j.agee.2004.03.006
Li X, Xiao J (2019) A global, 0.05-degree product of solar-induced chlorophyll fluorescence derived from OCO-2, MODIS, and reanalysis data. Remote Sens 11:517. https://doi.org/10.3390/rs11050517
Liu K, Huang J, Li D et al (2019) Comparison of carbon sequestration efficiency in soil aggregates between upland and paddy soils in a red soil region of China. J Integr Agric 18:1348–1359. https://doi.org/10.1016/S2095-3119(18)62076-3
Liu L, Teng Y, Wu J et al (2020) Soil water deficit promotes the effect of atmospheric water deficit on solar-induced chlorophyll fluorescence. Sci Total Environ 720:137408. https://doi.org/10.1016/j.scitotenv.2020.137408
Li S, Zhou S, Chen S et al (2015) In situ measurements of organic carbon in soil profiles using vis-NIR spectroscopy on the Qinghai-Tibet plateau. Environ Sci Technol 49:4980–4987. https://doi.org/10.1021/es504272x
Lu W, Lu D, Wang G et al (2018) Examining soil organic carbon distribution and dynamic change in a hickory plantation region with Landsat and ancillary data. Catena 165:576–589. https://doi.org/10.1016/j.catena.2018.03.007
Martin MP, Orton TG, Lacarce E et al (2014) Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale. Geoderma 223–225:97–107. https://doi.org/10.1016/j.geoderma.2014.01.005
Meroni M, Rossini M, Guanter L et al (2009) Remote sensing of solar-induced chlorophyll fluorescence: review of methods and applications. Remote Sens Environ 113:2037–2051. https://doi.org/10.1016/j.rse.2009.05.003
Mirzaee S, Ghorbani-Dashtaki S, Mohammadi J et al (2016) Spatial variability of soil organic matter using remote sensing data. Catena 145:118–127. https://doi.org/10.1016/j.catena.2016.05.023
National Soil Census Office (1992) China Soil Census Technology. Agricultural Press, Beijing
Nawar S, Buddenbaum H, Hill J et al (2016) Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy. Soil Tillage Res 155:510–522. https://doi.org/10.1016/j.still.2015.07.021
Norton AJ, Rayner PJ, Koffi EN et al (2018) Assimilating solar-induced chlorophyll fluorescence into the terrestrial biosphere model BETHY-SCOPE v1.0: model description and information content. Geosci Model Dev 11:1517–1536. https://doi.org/10.5194/gmd-2017-34
Porcar-Castell A, Tyystjärvi E, Atherton J et al (2014) Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. J Exp Bot 65(15):4065–4095. https://doi.org/10.1093/jxb/eru191
Rotenberg E, Tatarinov F, Müller J et al (2018) Sun-induced fluorescence and gross primary productivity during a heat wave. Sci Rep 8:14169. https://doi.org/10.1038/s41598-018-32602-z
Pouladi N, Møller AB, Tabatabai S et al (2019) Mapping soil organic matter contents at field level with Cubist, Random Forest and kriging. Geoderma 342:85–92. https://doi.org/10.1016/j.geoderma.2019.02.019
Roberts DF, Adamchuk VI, Shanahan JF et al (2010) Estimation of surface soil organic matter using a ground-based active sensor and aerial imagery. Precis Agric 12:82–102. https://doi.org/10.1007/s11119-010-9158-5
Rumelhart DE, Hinton GE, Williams RJ (1988) Learning internal representations by error propagation. Rea Cogn Sci 323:399–421. https://doi.org/10.1016/B978-1-4832-1446-7.50035-2
Sankey J, Sankey T, Li J et al (2021) Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland. Remote Sens Environ 253:112223. https://doi.org/10.1016/j.rse.2020.112223
Schmidt MW, Torn MS, Abiven S et al (2011) Persistence of soil organic matter as an ecosystem property. Nature 478:49–56. https://doi.org/10.1038/nature10386
Schlau-Cohen G, Berry JA (2015) Photosynthetic fluorescence, from molecule to planet. Phys Today 68(9):66. https://doi.org/10.1063/PT.3.2924
Sun W, Liu S, Zhang X et al (2022) Estimation of soil organic matter content using selected spectral subset of hyperspectral data. Geoderma 409:115653. https://doi.org/10.1016/j.geoderma.2021.115653
Talebizadeh M, Moridnejad A (2011) Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models. Expert Syst Appl 38(4):4126–4135. https://doi.org/10.1016/j.eswa.2010.09.075
Terhoeven-Urselmans T, Schmidt H, Joergensen RG et al (2008) Usefulness of near-infrared spectroscopy to determine biological and chemical soil properties: importance of sample pre-treatment. Soil Biol Biochem 40:1178–1188. https://doi.org/10.1016/j.soilbio.2007.12.011
Tiessen H, Cuevas E, Chacon P (1994) The role of soil organic matter in sustaining soil fertility. Nature 371:783–785. https://doi.org/10.1038/371783a0
Udelhoven T, Emmerling C, Jarmer T (2003) Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: a feasibility study. Plant Soil 251:319–329. https://doi.org/10.1023/A:1023008322682
Wang H, Shi X, Yu D et al (2009) Factors determining soil nutrient distribution in a small-scaled watershed in the purple soil region of Sichuan Province, China. Soil Tillage Res 105:300–306. https://doi.org/10.1016/j.still.2008.08.010
Wang J, He T, Lv C et al (2010) Mapping soil organic matter based on land degradation spectral response units using Hyperion images. Int J Appl Earth Obs Geoinf 12:S171–S180. https://doi.org/10.1016/j.jag.2010.01.002
Wang X, Zhang F, Kung H et al (2018) New methods for improving the remote sensing estimation of soil organic matter content (SOMC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in northwest China. Remote Sens Environ 218:104–118. https://doi.org/10.1016/j.rse.2018.09.020
Wiesmeier M, Barthold F, Blank B et al (2011) Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem. Plant Soil 340:7–24. https://doi.org/10.1007/s11104-010-0425-z
Wilding LP (1985) Spatial variability: its documentation, accommodation and implication to soil surveys. In: Nielsen DR, Bouma J (eds) Soil spatial variability. The Netherlands, pp 166–194
Winowiecki LA, Vågen TG, Boeckx P et al (2017) Landscape-scale assessments of stable carbon isotopes in soil under diverse vegetation classes in East Africa: application of near-infrared spectroscopy. Plant Soil 421:259–272. https://doi.org/10.1007/s11104-017-3418-3
Wolanin A, Rozanov VV, Dinter T et al (2015) Global retrieval of marine and terrestrial chlorophyll fluorescence at its red peak using hyperspectral top of atmosphere radiance measurements: feasibility study and first results. Remote Sens Environ 166:243–261. https://doi.org/10.1016/j.rse.2015.05.018
Xue Y, Lu H, Guan Y et al (2021) Impact of thermal condition on vegetation feedback under greening trend of China. Sci Total Environ 785:147380. https://doi.org/10.1016/j.scitotenv.2021.147380
Yu L, Wen J, Chang CY et al (2019) High-resolution global contiguous SIF of OCO‐2. Geophys Res Lett 46:1449–1458. https://doi.org/10.1029/2018gl081109
Yu Q, Lu H, Feng W et al (2021) Estimating and mapping of soil organic matter content in a typical river basin of the Qinghai Tibet Plateau. Geocarto Int 37:4088–4107. https://doi.org/10.1080/10106049.2021.1871667
Yu Q, Yao T, Lu H et al (2021) Improving estimation of soil organic matter content by combining Landsat 8 OLI images and environmental data a case study in the river valley of the southern Qinghai-Tibet Plateau. Comput Electron Agric 185:106144. https://doi.org/10.1016/j.compag.2021.106144
Yu X, Liu Q, Wang Y et al (2016) Evaluation of MLSR and PLSR for estimating soil element contents using visible/near-infrared spectroscopy in apple orchards on the Jiaodong peninsula. Catena 137:340–349. https://doi.org/10.1016/j.catena.2015.09.024
Zhang M, Liu H, Zhang M et al (2021) Mapping soil organic matter and analyzing the prediction accuracy of typical cropland soil types on the northern songnen plain. Remote Sens 13:5162. https://doi.org/10.3390/rs13245162
Zhao F, Li R, Verhoef W et al (2018) Reconstruction of the full spectrum of solar-induced chlorophyll fluorescence: Intercomparison study for a novel method. Remote Sens Environ 219:233–246. https://doi.org/10.1016/j.rse.2018.10.021
Zhao Y, Wang X, Jiang S et al (2021) Climate and geochemistry interactions at different altitudes influence soil organic carbon turnover times in alpine grasslands. Agric Ecosyst Environ 320:107591. https://doi.org/10.1016/j.agee.2021.107591
Acknowledgements
This research was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) [Grant No. 2019QZKK1003], the CAS Interdisciplinary Innovation Team [Grant No. JCTD-2019-04], and the Strategic Priority Research Program of the Chinese Academy of Sciences [Grant No. XDA20040301].
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Qing Yu: Conceptualization, Methodology, Writing - original draft preparation, Writing - review and editing; Hongwei Lu: Writing - review and editing, Funding acquisition, Supervision; Tianci Yao: Formal analysis and investigation, Supervision; Wei Feng and Yuxuan Xue: Formal analysis and investigation.
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Yu, Q., Lu, H., Yao, T. et al. An integrated solar-induced chlorophyll fluorescence model for more accurate soil organic carbon content estimation in an Alpine agricultural area. Plant Soil 486, 235–252 (2023). https://doi.org/10.1007/s11104-022-05863-x
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DOI: https://doi.org/10.1007/s11104-022-05863-x