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Research Article

Using SAR-based products to calculate potato carbon uptake in a tropical Andean region

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Article: 2339272 | Received 17 Jun 2023, Accepted 29 Mar 2024, Published online: 14 Apr 2024

References

  • Anav, A., Friedlingstein, P., Beer, C., Ciais, P., Harper, A., Jones, C., Murray-Tortarolo, G., Papale, D., Parazoo, N. C., Peylin, P., Piao, S., Sitch, S., Viovy, N., Wiltshire, A., & Zhao, M. (2015). Reviews of geophysics primary production: A review. Reviews of Geophysics, 1–21. https://doi.org/10.1002/2015RG000483.
  • Badgley, G., Field, C. B., & Berry, J. A. (2017). Canopy near-infrared reflectance and terrestrial photosynthesis. Science Advances, 3(3), 1–6. https://doi.org/10.1126/sciadv.1602244
  • Barbouchi, M., Lhissou, R., Abdelfattah, R., El Alem, A., Chokmani, K., Ben Aissa, N., M’hamed, H. C., Annabi, M., & Bahri, H. (2022). The potential of using radarsat-2 Satellite image for modeling and mapping wheat yield in a semiarid environment. Agriculture (Switzerland), 12(3), 315. https://doi.org/10.3390/agriculture12030315
  • Bauer-Marschallinger, B., Cao, S., Navacchi, C., Freeman, V., Reuß, F., Geudtner, D., Rommen, B., Vega, F. C., Snoeij, P., Attema, E., Reimer, C., & Wagner, W. (2021). The normalised sentinel-1 global backscatter model, mapping earth’s land surface with C-band microwaves. Scientific Data, 8(1), 1–18. https://doi.org/10.1038/s41597-021-01059-7
  • Box, E. O., Holben, B. N., & Kalb, V. (1989). Accuracy of the AVHRR vegetation index as a predictor of biomass, primary productivity and net CO2 flux. Vegetatio, 80(2), 71–89. https://doi.org/10.1007/BF00048034
  • Castaño-Marin, A. M., Sanchez-Vivas, D. F., Duarte-Carvajalino, J. M., Goez-Vinasco, G. A., & Araujo-Carrillo, G. A. (2023). Estimating carrot gross primary production using UAV-based multispectral imagery. AgriEngineering, 5(1), 325–337. https://doi.org/10.3390/agriengineering5010021
  • Cawley, G. C., & Talbot, N. L. C. (2010). On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research, 11, 2079–2107. https://doi.org/10.5555/1756006.1859921
  • Chen, B., Black, T. A., Coops, N. C., Hilker, T., Trofymow, J. A., & Morgenstern, K. (2009). Assessing tower flux footprint climatology and scaling between remotely sensed and eddy covariance measurements. Boundary-Layer Meteorology, 130(2), 137–167. https://doi.org/10.1007/s10546-008-9339-1
  • Chi, J., Waldo, S., Pressley, S., O’Keeffe, P., Huggins, D., Stöckle, C., Pan, W. L., Brooks, E., & Lamb, B. (2016). Assessing carbon and water dynamics of no-till and conventional tillage cropping systems in the inland Pacific Northwest US using the eddy covariance method. Agricultural and Forest Meteorology, 218–219, 37–49. https://doi.org/10.1016/j.agrformet.2015.11.019
  • CIP. (2023). Potato Facts and Figures. International Potato Center. https://cipotato.org/potato/potato-facts-and-figures/
  • Davi, H., Dufrêne, E., Francois, C., Le Maire, G., Loustau, D., Bosc, A., Rambal, S., Granier, A., & Moors, E. (2006). Sensitivity of water and carbon fluxes to climate changes from 1960 to 2100 in European forest ecosystems. Agricultural and Forest Meteorology, 141(1), 35–56. https://doi.org/10.1016/j.agrformet.2006.09.003
  • Devaux, A., Goffart, J. P., Kromann, P., Andrade Piedra, J., Polar, V., & Hareau, G. (2021). The potato of the future: Opportunities and challenges in sustainable agri-food systems. Potato Research, 64(4), 681–720. https://doi.org/10.1007/s11540-021-09501-4
  • dos Santos, E. P., Da Silva, D. D., & Do Amaral, C. H. (2021). Vegetation cover monitoring in tropical regions using SAR-C dual-polarization index: Seasonal and spatial influences. International Journal of Remote Sensing, 42(19), 7581–7609. https://doi.org/10.1080/01431161.2021.1959955
  • European Space Agency. (2022). ESA unclassified-for public release Sentinel-1B in-flight anomaly. Summary report. https://sentinel.esa.int/documents/247904/4819394/Sentinel-1B+In-Flight+Anomaly+Summary+Report.pdf
  • European Space Agency. (2023a). Copernicus Hub. https://scihub.copernicus.eu/dhus/#/home
  • European Space Agency. (2023b). Level-1 Products. https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-1-sar/products-algorithms/level-1-algorithms/products
  • European Space Agency. (2023c). SNAP toolbox. https://step.esa.int/main/toolboxes/snap/
  • Farr, T., Rosen, P., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., & Alsdorf, D. (2007). The shuttle radar topography mission. Reviews of geophysics, 45(2), 33. https://doi.org/10.1029/2005RG000183
  • Filipponi, F. (2019). Sentinel-1 GRD preprocessing workflow. Proceedings MDPI, 18(11), 4. https://doi.org/10.3390/ecrs-3-06201
  • Fu, Z., Ciais, P., Bastos, A., Stoy, P. C., Yang, H., Green, J. K., Wang, B., Yu, K., Huang, Y., Knohl, A., Šigut, L., Gharun, M., Cuntz, M., Arriga, N., Roland, M., Peichl, M., Migliavacca, M., Cremonese, E., Varlagin, A. … Koebsch, F. (2020). Sensitivity of gross primary productivity to climatic drivers during the summer drought of 2018 in Europe: Sensitivity of GPP to climate drivers. Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1810), 20190747. https://doi.org/10.1098/rstb.2019.0747
  • Gao, Y., Yu, G., Yan, H., Zhu, X., Li, S., Wang, Q., Zhang, J., Wang, Y., Li, Y., Zhao, L., & Shi, P. (2014). A MODIS-based photosynthetic capacity model to estimate gross primary production in Northern China and the Tibetan Plateau. Remote Sensing of Environment, 148, 108–118. https://doi.org/10.1016/j.rse.2014.03.006
  • Georgiana, M., Ranta, O., Molnar, A., & Păcurar, A. (2019). Analysis of synthetic aperture radar for agriculture applications: A review. ProEvironment, 12, 240–245. http://journals.usamvcluj.ro/index.php/promediu
  • Gitelson, A. A., Viña, A., Masek, J. G., Verma, S. B., & Suyker, A. E. (2008). Synoptic monitoring of gross primary productivity of maize using landsat data. IEEE Geoscience and Remote Sensing Letters, 5(2), 133–137. https://doi.org/10.1109/LGRS.2008.915598
  • Google Earth Engine. (2023). Sentinel-1 Algorithms. Google for Developers. https://developers.google.com/earth-engine/guides/sentinel1
  • Harfenmeister, K., Spengler, D., & Weltzien, C. (2019). Analyzing temporal and spatial characteristics of crop parameters using Sentinel-1 backscatter data. Remote Sensing, 11(13), 1569. https://doi.org/10.3390/rs11131569
  • Huang, X., Xiao, J., & Ma, M. (2019). Evaluating the performance of satellite-derived vegetation indices for estimating gross primary productivity using FLUXNET observations across the globe. Remote Sensing, 11(15), 1823. https://doi.org/10.3390/rs11151823
  • IGAC. (2013). Levantamiento detallado de suelos en las áreas planas de los municipios de Cogua, El Rosal, Nemocón, Subachoque, Suesca, Zipacón y Zipaquirá. Departamento de Cundinamarca. Escala 1:10.000. Instituto Geográfico Agustín Codazzi - IGAC. https://sie.car.gov.co/handle/20.500.11786/37174
  • Jiang, S., Zhao, L., Liang, C., Cui, N., Gong, D., Wang, Y., Feng, Y., Hu, X., & Zou, Q. (2021). Comparison of satellite-based models for estimating gross primary productivity in agroecosystems. Agricultural and Forest Meteorology, 297(June 2020), 108253. https://doi.org/10.1016/j.agrformet.2020.108253
  • Kaplan, G., Fine, L., Lukyanov, V., Malachy, N., Tanny, J., & Rozenstein, O. (2023). Using Sentinel-1 and Sentinel-2 imagery for estimating cotton crop coefficient, height, and leaf area index. Agricultural Water Management, 276(108056), 12. https://doi.org/10.1016/j.agwat.2022.108056
  • Kaplan, G., Fine, L., Lukyanov, V., Manivasagam, V. S., Tanny, J., & Rozenstein, O. (2021). Normalizing the local incidence angle in Sentinel-1 imagery to improve leaf area index, vegetation height, and crop coefficient estimations. The Land, 10(7), 23. https://doi.org/10.3390/land10070680
  • Khabbazan, S., Vermunt, P., Steele-Dunne, S., Arntz, L. R., Marinetti, C., van der Valk, D., Iannini, L., Molijn, R., Westerdijk, K., & van der Sande, C. (2019). Crop monitoring using Sentinel-1 data: A case study from the Netherlands. Remote Sensing, 11(16), 1887. https://doi.org/10.3390/rs11161887
  • Kljun, N., Calanca, P., Rotach, M. W., & Schmid, H. P. (2004). A simple parameterisation for flux footprint predictions. Boundary-Layer Meteorology, 112(3), 503–523. https://doi.org/10.1023/B:BOUN.0000030653.71031.96
  • Kljun, N., Calanca, P., Rotach, M. W., & Schmid, H. P. (2015). A simple two-dimensional parameterisation for flux footprint prediction (FFP). Geoscientific Model Development, 8(11), 3695–3713. https://doi.org/10.5194/gmd-8-3695-2015
  • Kormann, R., & Meixner, F. X. (2001). An analytical footprint model for non-neutral stratification. Boundary-Layer Meteorology, 99(2), 207–224. https://doi.org/10.1023/A:1018991015119
  • Kumar, D. (2021). Urban objects detection from C-band synthetic aperture radar (SAR) satellite images through simulating filter properties. Scientific Reports, 11(1), 1–24. https://doi.org/10.1038/s41598-021-85121-9
  • Kumar, V., Huber, M., Rommen, B., & Steele-Dunne, S. C. (2022). Agricultural SandboxNL: A national-scale database of parcel-level processed Sentinel-1 SAR data. Scientific Data, 9(1). https://doi.org/10.1038/s41597-022-01474-4
  • Kushwaha, A., Dave, R., Kumar, G., Saha, K., & Khan, A. (2022). Assessment of rice crop biophysical parameters using Sentinel-1 C-band SAR data. Advances in Space Research, 70(12), 3833–3844. https://doi.org/10.1016/j.asr.2022.02.021
  • Liu, C., Chen, Z., Shao, Y., Chen, J. S., Tuya, H., & Pan, H. Z. (2019). Research advances of SAR remote sensing for agriculture applications: A review. Journal of Integrative Agriculture, 18(3), 506–525. https://doi.org/10.1016/S2095-3119(18)62016-7
  • Liu, S., Zhou, Z., Ding, H., Zhong, Y., & Shi, Q. (2021). Crop mapping using sentinel full-year dual-polarized SAR data and a CPU-optimized convolutional neural network with two sampling strategies. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 7017–7031. https://doi.org/10.1109/JSTARS.2021.3094973
  • Madugundu, R., Al-Gaadi, K. A., Tola, E. K., Kayad, A. G., & Jha, C. S. (2017). Estimation of gross primary production of irrigated maize using landsat-8 imagery and eddy covariance data. Saudi Journal of Biological Sciences, 24(2), 410–420. https://doi.org/10.1016/j.sjbs.2016.10.003
  • Maleki, M., Arriga, N., Roland, M., Wieneke, S., Barrios, J. M., Van Hoolst, R., Peñuelas, J., Janssens, I. A., & Balzarolo, M. (2022). Soil water depletion induces discrepancies between in situ measured vegetation indices and photosynthesis in a temperate heathland. Agricultural and Forest Meteorology, 324(August), 109110. https://doi.org/10.1016/j.agrformet.2022.109110
  • Mandal, D., Bhattacharya, A., & Rao, Y. S. (2021). Radar remote sensing for crop biophysical parameter estimation (Springer, Ed.). Springer Remote Sensing/Photogrammetry. https://doi.org/10.1007/978-981-16-4424-5
  • Mandal, D., Kumar, V., Ratha, D., Dey, S., Bhattacharya, A., Lopez-Sanchez, J. M., McNairn, H., & Rao, Y. S. (2020). Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data. Remote Sensing of Environment, 247, 247. https://doi.org/10.1016/j.rse.2020.111954
  • Martínez-Maldonado, F. E., Castaño-Marin, A. M., Góez-Vinasco, G. A., & Marin, F. R. (2021). Gross primary production of rainfed and irrigated potato (Solanum tuberosum L.) in the colombian andean region using eddy covariance technique. Water, 13(3223), 15. https://doi.org/10.3390/w13223223
  • McNairn, H., & Brisco, B. (2004). The application of C-band polarimetric SAR for agriculture: A review. Canadian Journal of Remote Sensing, 30(3), 525–542. https://doi.org/10.5589/m03-069
  • Nasirzadehdizaji, R., Sanli, F. B., & Cakir, Z. (2019). Application of Sentinel-1 multi-temporal data for crop monitoring and mapping. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(4/W18), 803–807. https://doi.org/10.5194/isprs-archives-XLII-4-W18-803-2019
  • Noumonvi, K. D., Ferlan, M., Eler, K., Alberti, G., Peressotti, A., & cerasoli, S. (2019). Estimation of carbon fluxes from eddy Covariance data and satellite-derived vegetation indices in a karst grassland (Podgorski Kras, Slovenia). Remote Sensing, 11(6), 649. https://doi.org/10.3390/rs11060649
  • Pahari, R., Leclerc, M. Y., Zhang, G., Nahrawi, H., & Raymer, P. (2018). Carbon dynamics of a warm season turfgrass using the eddy-covariance technique. Agriculture, Ecosystems and Environment, 251, 11–25. https://doi.org/10.1016/j.agee.2017.09.015
  • Periasamy, S. (2018). Significance of dual polarimetric synthetic aperture radar in biomass retrieval: An attempt on sentinel-1. Remote Sensing of Environment, 217(August), 537–549. https://doi.org/10.1016/j.rse.2018.09.003
  • Rambal, S., Lempereur, M., Limousin, J. M., Martin-Stpaul, N. K., Ourcival, J. M., & Rodríguez-Calcerrada, J. (2014). How drought severity constrains gross primary production(GPP) and its partitioning among carbon pools in a Quercus ilex coppice? Biogeosciences Discuss, 11(23), 6855–6869. https://doi.org/10.5194/bgd-11-8673-2014
  • Running, S. W., Thornton, P. E., Nemani, R., & Glassy, J. M. (2000). Global terrestrial gross and net primary productivity from the earth observing system. Methods in Ecosystem Science, 44–57. https://doi.org/10.1007/978-1-4612-1224-9_4
  • Scientific, C. (2022). Product Manual. Campbell Scientific. https://s.campbellsci.com/documents/us/manuals/easyflux-dl-cr6op.pdf
  • Scientific, C. (2023). DL is a,-covariance (EC) system. Easy Flux DL. https://www.campbellsci.com/easyflux-dl#:~:text=EasyFlux®
  • Sentinel Hub. (2022). Sentinel-1 GRD. https://docs.sentinel-hub.com/api/latest/data/sentinel-1-grd/
  • Shang, J., Liu, J., Chen, Z., Mcnairn, H., & Davidson, A. (2022). Recent advancement of Synthetic Aperture Radar (SAR) systems and their applications to crop growth monitoring. In Recent Remote Sensing Sensor Applications - Satellites and Unmanned Aerial Vehicles (UAVs) (pp. 202). IntechOpen. https://doi.org/10.5772/intechopen.95162
  • Sims, D. A., Rahman, A. F., Cordova, V. D., El-Masri, B. Z., Baldocchi, D. D., Bolstad, P. V., Flanagan, L. B., Goldstein, A. H., Hollinger, D. Y., Misson, L., Monson, R. K., Oechel, W. C., Schmid, H. P., Wofsy, S. C., & Xu, L. (2008). A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS. Remote Sensing of Environment, 112(4), 1633–1646. https://doi.org/10.1016/j.rse.2007.08.004
  • Sims, D. A., Rahman, A. F., Cordova, V. D., El-Masri, B. Z., Baldocchi, D. D., Flanagan, L. B., Goldstein, A. H., Hollinger, D. Y., Misson, L., Monson, R. K., Oechel, W. C., Schmid, H. P., Wofsy, S. C., & Xu, L. (2006). On the use of MODIS EVI to assess gross primary productivity of North American ecosystems. Journal of Geophysical Research: Biogeosciences, 111(4). https://doi.org/10.1029/2006JG000162
  • Sivasankar, T., Kumar, D., Srivastava, H. S., & Patel, P. (2018). Advances in radar remote sensing of agricultural crops: A review. International Journal on Advanced Science, Engineering and Information Technology, 8(4), 1126–1137. https://doi.org/10.18517/ijaseit.8.4.5797
  • Small, D., & Schubert, A. (2008). Guide to ASAR geocoding. ESA-ESRIN Technical Note RSL-ASAR-GC-AD, 1.01, 1–36.
  • Sonobe, R. (2019). Parcel-based crop classification using multi-temporal TerraSAR-X dual polarimetric data. Remote Sensing, 11(10), 1148. https://doi.org/10.3390/rs11101148
  • Spinosa, A., Fuentes-Monjaraz, M. A., & El Serafy, G. (2023). Assessing the use of Sentinel-2 data for spatio-temporal upscaling of flux tower gross primary productivity measurements. Remote Sensing, 15(3), 562. https://doi.org/10.3390/rs15030562
  • Steele-Dunne, S. C., McNairn, H., Monsivais-Huertero, A., Judge, J., Liu, P. W., & Papathanassiou, K. (2017). Radar remote sensing of agricultural canopies: A Review. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 10(5), 2249–2273. https://doi.org/10.1109/JSTARS.2016.2639043
  • Tagesson, T., Fensholt, R., Cropley, F., Guiro, I., Horion, S., Ehammer, A., & Ardö, J. (2015). Dynamics in carbon exchange fluxes for a grazed semi-arid savanna ecosystem in West Africa. Agriculture, Ecosystems & Environment, 205, 15–24. https://doi.org/10.1016/j.agee.2015.02.017
  • Valcarce-Diñeiro, R., Arias-Pérez, B., Lopez-Sanchez, J. M., & Sánchez, N. (2019). Multi-temporal dual- and quad-polarimetric synthetic aperture radar data for crop-type mapping. Remote Sensing, 11(13), 1518. https://doi.org/10.3390/rs11131518
  • Veloso, A., Mermoz, S., Bouvet, A., Le Toan, T., Planells, M., Dejoux, J. F., & Ceschia, E. (2017). Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sensing of Environment, 199, 415–426. https://doi.org/10.1016/j.rse.2017.07.015
  • Wagner, W., Bauer-Marschallinger, B., Navacchi, C., Reuß, F., Cao, S., Reimer, C., Schramm, M., & Briese, C. (2021). A Sentinel-1 backscatter datacube for global land monitoring applications. Remote Sensing, 13(22), 1–18. https://doi.org/10.3390/rs13224622
  • Wegmuller, U., Werner, C., Wiesmann, A., Strozzi, T., Kourkouli, P., & Frey, O. (2016). Time-series analysis of Sentinel-1 interferometric wide swath data: Techniques and challenges. International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 3898–3901). https://doi.org/10.1109/IGARSS.2016.7730012
  • Woodwell, G. M., & Whittaker, R. H. (1968). Primary production in terrestrial ecosystems. American Zoologist, 8(1), 19–30. https://doi.org/10.1093/icb/8.1.19
  • Wu, G., Guan, K., Jiang, C., Peng, B., Kimm, H., Chen, M., Yang, X., Wang, S., Suyker, A. E., Bernacchi, C. J., Moore, C. E., Zeng, Y., Berry, J. A., & Cendrero-Mateo, M. P. (2020). Radiance-based NIRv as a proxy for GPP of corn and soybean. Environmental Research Letters, 15(3), 034009. https://doi.org/10.1088/1748-9326/ab65cc
  • Wu, C., Niu, Z., & Gao, S. (2010). Gross primary production estimation from MODIS data with vegetation index and photosynthetically active radiation in maize. Journal of Geophysical Research Atmospheres, 115(12), 1–11. https://doi.org/10.1029/2009JD013023
  • Wu, C., Niu, Z., Tang, Q., Huang, W., Rivard, B., & Feng, J. (2009). Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices. Agricultural and Forest Meteorology, 149(6–7), 1015–1021. https://doi.org/10.1016/j.agrformet.2008.12.007
  • Xie, Q., Wang, J., Liao, C., Shang, J., Lopez-Sanchez, J. M., Fu, H., & Liu, X. (2019). On the use of Neumann decomposition for crop classification using multi-temporal RADARSAt-2 polarimetric SAR data. Remote Sensing, 11(7), 776. https://doi.org/10.3390/rs11070776
  • Xin, F., Xiao, X., Zhao, B., Miyata, A., Baldocchi, D., Knox, S., Kang, M., Shim, K., Min, S., Chen, B., Li, X., Wang, J., Dong, J., & Biradar, C. (2017). Modeling gross primary production of paddy rice cropland through analyses of data from CO2 eddy flux tower sites and MODIS images. Remote Sensing of Environment, 190, 42–55. https://doi.org/10.1016/j.rse.2016.11.025
  • Yang, H., Yang, G., Gaulton, R., Zhao, C., Li, Z., Taylor, J., Wicks, D., Minchella, A., Chen, E., & Yang, X. (2019). In-season biomass estimation of oilseed rape (Brassica napus L.) using fully polarimetric SAR imagery. Precision Agriculture, 20(3), 630–648. https://doi.org/10.1007/s11119-018-9587-0
  • Zhang, Q., Cheng, Y. B., Lyapustin, A. I., Wang, Y., Zhang, X., Suyker, A., Verma, S., Shuai, Y., & Middleton, E. M. (2015). Estimation of crop gross primary production (GPP): II. Do scaled MODIS vegetation indices improve performance? Agricultural and Forest Meteorology, 200, 1–8. https://doi.org/10.1016/j.agrformet.2014.09.003
  • Zhang, H., & Wen, X. (2015). Flux footprint climatology estimated by three analytical models over a subtropical coniferous plantation in Southeast China. Journal of Meteorological Research, 29(4), 654–666. https://doi.org/10.1007/s13351-014-4090-7