Vegetation optical depth (VOD): review of SMAP products and prospects for agricultural applications in the southeastern Argentine Pampas region
DOI:
https://doi.org/10.59069/83nqhd97Keywords:
vegetation water status, crops, water deficit, passive microwaves, remote sensingAbstract
Microwave observations at frequencies between 1-2 GHz (L-band) are sensitive to surface soil moisture (SM) and vegetation water content, which can be parameterized by vegetation optical depth (VOD). The aim of this work was to present the physical basis of passive microwave measurements and a preliminary analysis of vegetation water dynamics in the southeastern Pampean Region by observing VOD and SM time series from the Soil Moisture Active Passive mission (SMAP). To complement these analyses, observations of normalized difference vegetation index (NDVI) and land surface temperature (LST) obtained in the field and derived from measurements recorded by Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor were incorporated. The results showed that VOD has the capacity to determine sub-weekly variations related to water content in vegetation through coupled behaviors with SM and LST under homogeneous surface conditions. Particularly, localized increases in LST were observed to coincide with decreases in VOD during summer seasons. These observations demonstrated the potential of VOD both for monitoring vegetation water dynamics at a sub-weekly scale and detecting punctual water stress events.
References
Afshar, M. H., Al-Yaari, A., & Yilmaz, M. T. (2021). Comparative evaluation of microwave L-band VOD and optical NDVI for agriculture drought detection over central Europe. Remote Sensing, 13(7). https://doi.org/10.3390/rs13071251
Beninato, S., Holzman, M., Taveira, G., y Rivas, R. (2023). Crop monitoring with L-Band Vegetation Optical Depth (VOD): Investigation in southeastern of Argentine Pampas. In 2023 XX Workshop on Information Processing and Control (RPIC) (pp. 1-4). IEEE. https://doi.org/10.1109/RPIC59053.2023.10530700
Bousquet, E., Mialon, A., Rodriguez-Fernandez, N., Prigent, C., Wagner, F. H., Kerr, Y. H., y Wag-, F. H. (2021). Influence of surface water variations on VOD and biomass estimates from passive microwave sensors. 257, 34–4257. https://doi.org/10.1016/j.rse.2021.112345ï
Bueso, D., Piles, M., Ciais, P., Wigneron, J.-P., Moreno-Martínez, Á., y Camps-Valls, G. (2023). Soil and vegetation water content identify the main terrestrial ecosystem changes. National Science Review, 10(5), nwad026. https://doi.org/10.1093/nsr/nwad026
Chaparro, D., Feldman, A., Chaubell, J., Yueh, S., & Entekhabi, D. (2022). Robustness of vegetation optical depth retrievals based on L-band global radiometry. IEEE Transactions on Geoscience and Remote Sensing, PP(1), 1-1. https://doi.org/10.1109/TGRS.2022.3201581
Chaparro, D., Piles, M., Vall-llossera, M., Camps, A., Konings, A. G., & Entekhabi, D. (2018). L-band vegetation optical depth seasonal metrics for crop yield assessment. Remote Sensing of Environment, 212, 249–259. https://doi.org/10.1016/j.rse.2018.04.049
Chaubell, J., Yueh, S., Chan, S., Dunbar, S., Colliander, A., Entekhabi, D., Chen, F., Bindlish, R., y O’Neill, P. (2021). Implementation and analysis of the dual-channel algorithm for the retrieval of soil moisture and vegetation optical depth for SMAP. In 2021 International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 6104–6107). https://doi.org/10.1109/IGARSS47720.2021.9553883
Choudhury, B., Schmugge, T., y Mo, T. (1982). A parameterization of effective soil temperature for microwave emission. Journal of Geophysical Research, 87, 1301-1304.
Degano, M.F, Beninato, S., Holzman M.E., Bayala, M., Rivas R.E y Massari, C. (2024). Soil Moisture: analysis of SMAP satellite products in plain zones. En Libro de Resúmenes de ARGENCON 2024. Recuperado de https://attend.ieee.org/argencon-2024/wp-content/uploads/sites/612/Libro-de-Resumenes-ARGENCON-2024.pdf
Faramiñán, A., Carmona, F., Rivas, R., Silicani, M., Olivera Rodriguez, P., & Degano, M. F. (2020, noviembre). Estación móvil de balance de energía para el monitoreo integral de cultivos: Caso de estudio en cebada. XVIII Reunión Argentina y IX Latinoamericana de Agrometeorología, Paraná, Entre Ríos.
Feldman, A., Chaparro, D., & Entekhabi, D. (2021). Error propagation in microwave soil moisture and vegetation optical depth retrievals. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 11311–11323. https://doi.org/10.1109/JSTARS.2021.3124857
Feldman, A. F., Chaparro, D., & Entekhabi, D. (2022). Quantifying and reducing uncertainty in microwave vegetation optical depth and soil moisture retrievals. In 2022 International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 5586–5589). https://doi.org/10.1109/IGARSS46834.2022.9883833
Feldman, A.F., Short Gianotti, D.J., Konings, A.G. et al. (2018). Moisture pulse-reserve in the soil-plant continuum observed across biomes. Nature Plants 4, 1026–1033. https://doi.org/10.1038/s41477-018-0304-9
Frappart, F., Wigneron, J. P., Li, X., Liu, X., Al-Yaari, A., Fan, L., Wang, M., Moisy, C., le Masson, E., Lafkih, Z. A., Vallé, C., Ygorra, B., y Baghdadi, N. (2020). Global monitoring of the vegetation dynamics from the vegetation optical depth (VOD): A review. In Remote Sensing (Vol. 12, Issue 18). MDPI AG. https://doi.org/10.3390/RS12182915
Friedl, M., y Sulla-Menashe, D. (2022). MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2024-09-16 from https://doi.org/10.5067/MODIS/MCD12C1.061
Funk, Chris, Pete Peterson, Martin Landsfeld, Diego Pedreros, James Verdin, Shraddhanand Shukla, Gregory Husak, James Rowland, Laura Harrison, Andrew Hoell & Joel Michaelsen (2015). The climate hazards infrared precipitation with stations-a new environmental record for monitoring extremes. Scientific Data 2, 150066. https://doi.org/10.1038/sdata.2015.66
Gao, Y., Bindlish, R., & Jackson, T. (2018). Evaluation of the Tau–Omega model for passive microwave soil moisture retrieval using SMAPEx datasets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(3), 888-895. https://doi.org/10.1109/JSTARS.2018.2796546
Gruber, A., De Lannoy, G., Albergel, C., et al. (2020). Validation practices for satellite soil moisture retrievals: What are (the) errors? Remote Sensing of Environment, 244, 111806. https://doi.org/10.1016/j.rse.2020.111806
Jackson, T. J., y Schmugge, T. J. (1991). Vegetation effects on the microwave emission of soils. Remote Sensing of Environment, 36(3), 203-212. https://doi.org/10.1016/0034-4257(91)90057-D
Konings, A. G., Piles, M., Rötzer, K., McColl, K. A., Chan, S. K., & Entekhabi, D. (2016). Vegetation optical depth and scattering albedo retrieval using time series of dual-polarized L-band radiometer observations. Remote Sensing of Environment, 172, 178–189. https://doi.org/10.1016/j.rse.2015.11.009
Konings, A. G., Piles, M., Das, N., & Entekhabi, D. (2017). L-band vegetation optical depth and effective scattering albedo estimation from SMAP. Remote Sensing of Environment, 198, 460–470. https://doi.org/10.1016/j.rse.2017.06.037
Konings, A. G., Holtzman, N. M., Rao, K., Xu, L., y Saatchi, S. S. (2021). Interannual Variations of Vegetation Optical Depth are Due to Both Water Stress and Biomass Changes. Geophysical Research Letters, 48(16). https://doi.org/10.1029/2021GL095267
Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15, 259–263. https://doi.org/10.1127/0941-2948/2006/0130
Liu, Y. Y., Evans, J. P., McCabe, M. F., de Jeu, R. A. M., van Dijk, A. I. J. M., y Su, H. (2010). Influence of cracking clays on satellite-estimated and model-simulated soil moisture. Hydrology and Earth System Sciences, 14, 979–990. https://doi.org/10.5194/hess-14-979-2010
López de Sabando, M. (2021). Suelos de Mar y Sierras: Partido de Tandil. Buenos Aires: Ediciones INTA, Agencia de Extensión Rural Tandil. ISBN 978-987-8333-89-2 (digital).
Mironov, V. L., Kosolapova, L. G., y Fomin, S. V. (2009). Physically and mineralogically based spectroscopic dielectric model for moist soils. IEEE Transactions on Geoscience and Remote Sensing, 47(7), 2059–2070.
Mo, T., Choudhury, B. J., Schmugge, T. J., Wang, J. R., y Jackson, T. J. (1982). A model for microwave emission from vegetation-covered fields. Journal of Geophysical Research, 87, 11229–11237.
Olivera Rodríguez P., Holzman M.E., Aldaya M.M., Rivas R.E. (2024). Water footprint in rainfed sum-mer and winter crops: The role of soil moisture. Agricultural Water Management 296:108787, https://doi.org/10.1016/j.agwat.2024.108787
O'Neill, P. E., Chan, S., Njoku, E. G., Jackson, T., Bindlish, R., Chaubell, J., y Colliander, A. (2023). SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture. Boulder, Colorado, USA: NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/M20OXIZHY3RJ
O'Neill, P., Bindlish, R., Chan, S., et al. (2020). Soil moisture active passive (SMAP) algorithm theoretical basis document level 2 & 3 soil moisture (passive) data products (ATBD). [Online]. Available: https://api.semanticscholar.org/CorpusID:16348925
O'Neill, P. E., Chan, S., Njoku, E. G., Jackson, T., Bindlish, R., Chaubell, J. & Colliander, A. (2023). SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture. (SPL3SMP_E, Version 6). [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/M20OXIZHY3RJ. Date Accessed 04-29-2024.
Piles, M., Camps-Valls, G., Chaparro, D., Entekhabi, D., Konings, A. G., y Jagdhuber, T. (2017). Remote sensing of vegetation dynamics in agro-ecosystems using SMAP vegetation optical depth and optical vegetation indices. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 4346–4349). https://doi.org/10.1109/IGARSS.2017.8127964
Secretaría de Agricultura, Ganadería y Pesca - SISA - Argentina. (2021). Informe final sobre la producción de cebada en la campaña 2020. Recuperado de https://www.argentina.gob.ar/sites/default/files/informe_cebada2020.pdf
Secretaría de Agricultura, Ganadería y Pesca - SISA - Argentina. (2020). Informe final sobre la producción de soja en la campaña 2020-2021. Recuperado de https://www.argentina.gob.ar/sites/default/files/if_sisa_final_soja_2020_2021.pdf
Schmidt, L., Forkel, M., Zotta, R. M., Scherrer, S., Dorigo, W. A., Kuhn-Régnier, A., van der Schalie, R., & Yebra, M. (2023). Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties. Biogeosciences, 20(5), 1027–1046. https://doi.org/10.5194/bg-20-1027-2023
Seo, D., Lakhankar, T., & Khanbilvardi, R. (2010). Sensitivity analysis of b-factor in microwave emission model for soil moisture retrieval: A case study for SMAP mission. Remote Sensing, 2, 1273-1286. https://doi.org/10.3390/rs2051273
Schaaf, C., & Wang, Z. (2021). MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF Adjusted Ref Daily L3 Global - 500m V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2024-09-16 from https://doi.org/10.5067/MODIS/MCD43A4.061
Servicio Meteorológico Nacional - SMN. (2017). Informe especial por ola de calor (16 de febrero al 2 de marzo de 2017). Recuperado de http://hdl.handle.net/20.500.12160/893
Servicio Meteorologico Nacional - SMN. (2021). Servicio Meteorológico Nacional de Argentina. URL: https://www.smn.gob.ar/descarga-de-datos, accessed June 2, 2021.
Ulaby, F. T., Aslam, A., y Dobson, M. C. (1982). Effects of vegetation cover on the radar sensitivity to soil moisture. IEEE Transactions on Geoscience and Remote Sensing, GE-20, 476–481.
Ulaby, F. T., Long, D. G., Blackwell, W. J., Elachi, C., Fung, A. K., Ruf, C., Sarabandi, K., Van Zyl, J., y Zebker, H. (2015). Microwave radar and radiometric remote sensing. University of Michigan Press.
Van De Griend, A. A., & Wigneron, J. P. (2004). The b-factor as a function of frequency and canopy type at H-polarization. IEEE Transactions on Geoscience and Remote Sensing, 42(4), 786–794.
Wan, Z., Hook, S., & Hulley, G. (2021). MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2024-09-16 from https://doi.org/10.5067/MODIS/MOD11A1.061
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Sabrina Beninato, Mauro Ezequiel Holzman, Raúl Eduardo Rivas, Jorge Francisco Diez
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Attribution - Non-Commercial - Share Alike (by-nc-sa): No commercial use of the original work or any derivative works is permitted, distribution of which must be under a license equal to that governing the original work.
