Field maturity detection via interferometric synthetic aperture radar images time-series: a case study for maize crop

References

• Abdikan, S., A. Sekertekin, M. Ustunern, F. Balik Sanli, and R. Nasirzadehdizaji. 2018. “Backscatter Analysis Using Multi-Temporal Sentinel-1 Sar Data for Crop Growth of Maize in Konya Basin, Turkey.” ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII–3: 9–13. doi:10.5194/isprs-archives-xlii-3-9-2018.
   Google Scholar
• Ahmed, R., P. Siqueira, S. Hensley, B. Chapman, and K. Bergen. 2011. “A Survey of Temporal Decorrelation from Spaceborne L-Band Repeat-Pass InSar.” Remote Sensing of Environment 115 (11): 2887–2896. doi:10.1016/j.rse.2010.03.017.
   Web of Science ®Google Scholar
• Amherdt, S., N. C. Di Leo, S. Balbarani, A. Pereira, C. Cornero, and M. C. Pacino. 2021. “Exploiting Sentinel-1 Data Time-Series for Crop Classification and Harvest Date Detection.” International Journal of Remote Sensing 42 (19): 7313–7331. doi:10.1080/01431161.2021.1957176.
   Web of Science ®Google Scholar
• Amherdt, S., N. C. Di Leo, A. Pereira, C. Cornero, and M. C. Pacino. 2022. “Assessment of Interferometric Coherence Contribution to Corn and Soybean Mapping with Sentinel-1 Data Time Series.” Geocarto international. doi:10.1080/10106049.2022.2144472.
   Web of Science ®Google Scholar
• Archontoulis, S. V., F. E. Miguez, and K. J. Moore. 2014. “Evaluating APSIM Maize, Soil Water, Soil Nitrogen, Manure, and Soil Temperature Modules in the Midwestern United States.” Agronomy Journal 106 (3): 1025–1040. doi:10.2134/agronj2013.0421.
   Web of Science ®Google Scholar
• Arslan, İ., M. Topakcı, and N. Demir.2022. Monitoring Maize Growth and Calculating Plant Heights with Synthetic Aperture Radar (SAR) and Optical Satellite Images. Agriculture 12 (6): 800. MDPI AG Retrieved fromhttp://dx.doi.org/10.3390/agriculture12060800
   Google Scholar
• Balboa, G. R., S. V. Archontoulis, F. Salvagiotti, F. O. Garcia, W. M. Stewart, E. Francisco, P. V. Vara Prasad, and I. A. Ciampitti. 2019. “A Systems-Level Yield Gap Assessment of Maize-Soybean Rotation Under High-And Low-Management Inputs in the Western US Corn Belt Using APSIM.” Agricultural Systems 174: 145–154. doi:10.1016/j.agsy.2019.04.008.
   Web of Science ®Google Scholar
• Bandaru, V., R. Yaramasu, K. Pnvr, J. He, S. Fernando, R. Sahajpal, B. D. Wardlow, A. Suyker, and C. Justice. 2020. “PhenoCrop: An Integrated Satellite-Based Framework to Estimate Physiological Growth Stages of Corn and Soybeans.” International Journal of Applied Earth Observation and Geoinformation: ITC Journal 92 (102188): 102188. doi:10.1016/j.jag.2020.102188.
   Google Scholar
• Blaes, X., and P. Defourny. 2003. “Retrieving Crop Parameters Based on Tandem ERS 1/2 Interferometric Coherence Images.” Remote Sensing of Environment 88 (4): 374–385. doi:10.1016/j.rse.2003.08.008.
   Web of Science ®Google Scholar
• Borrás, L., G. A. Maddonni, and M. E. Otegui. 2003. “Leaf Senescence in Maize Hybrids: Plant Population, Row Spacing and Kernel Set Effects.” Field Crops Research 82 (1): 13–26. doi:10.1016/s0378-4290(03)00002-9.
   Web of Science ®Google Scholar
• Busquier, M., J. M. Lopez-Sanchez, A. Mestre-Quereda, E. Navarro, M. P. González-Dugo, and L. Mateos. 2020. “Exploring TanDem-X Interferometric Products for Crop-Type Mapping.” Remote Sensing 12 (11): 1774. doi:10.3390/rs12111774.
   Web of Science ®Google Scholar
• Ciampitti, I. A., R. W. Elmore, and J. Lauer. 2016. “Corn Growth and Development. K-State University. MF3305.”. https://bookstore.ksre.ksu.edu/pubs/MF3305.pdf
   Google Scholar
• Corcione, V., F. Nunziata, L. Mascolo, and M. Migliaccio. 2016. “A Study of the Use of COSMO-SkyMed SAR PingPong Polarimetric Mode for Rice Growth Monitoring.” International Journal of Remote Sensing 37 (3): 633–647. doi:10.1080/01431161.2015.1131902.
   Web of Science ®Google Scholar
• Elmore, R. W., and F. W. Roeth. 1999. “Corn Kernel Weight and Grain Yield Stability During Post-Maturity Drydown.” Journal of Production Agriculture 12 (2): 300–305. doi:10.2134/jpa1999.0300.
   Google Scholar
• Fernandez, J. A., and I. A. Ciampitti. 2021. “Corn Grain Weight: Dependence Upon Nitrogen Supply and Source-Sink Relations.” Kansas Agricultural Experiment Station Research Reports 7: 5. doi:10.4148/2378-5977.8073.
   Google Scholar
• Foley, J. A., N. Ramankutty, K. A. Brauman, E. S. Cassidy, J. S. Gerber, M. Johnston, N. D. Mueller, et al. 2011. “Solutions for a Cultivated Planet.” Nature 478 (7369): 337–342. doi:10.1038/nature10452.
   PubMed Web of Science ®Google Scholar
• Frison, P. -L., B. Fruneau, S. Kmiha, K. Soudani, E. Dufrêne, T. Le Toan, T. Koleck, L. Villard, E. Mougin, and J. -P. Rudant. 2018. “Potential of Sentinel-1 Data for Monitoring Temperate Mixed Forest Phenology.” Remote Sensing 10 (12): 2049. doi:10.3390/rs10122049.
   Web of Science ®Google Scholar
• Geudtner, D., and R. Torres. 2012. “Sentinel-1 System Overview and Performance.” 2012 IEEE International Geoscience and Remote Sensing Symposium, July 22-27, 2012. IEEE, Munich, Germany.
   Google Scholar
• Gooding, D. G., J. E. Mitchell, M. C. Knapp, and R. E. Bivens. 1995. “Climate and Weather Atlas of Kansas.” Kansas Geological Survey Educational SER 12 (24): 7–13.
   Google Scholar
• Hanssen, R. F. 2001. “Radar Interferometry.” In Remote Sensing and Digital Image Processing. Springer. https://doi.org/10.1007/0-306-47633-9
   Google Scholar
• Holzworth, D. P., N. I. Huth, P. G. deVoil, E. J. Zurcher, N. I. Herrmann, G. McLean, K. Chenu, et al. 2014. APSIM – Evolution Towards a New Generation of Agricultural Systems Simulation. Environmental Modelling & Software: With Environment Data News 62: 327–350. doi:10.1016/j.envsoft.2014.07.009.
   Web of Science ®Google Scholar
• Hosseini, M., H. McNairn, A. Merzouki, and A. Pacheco. 2015. “Estimation of Leaf Area Index (LAI) in Corn and Soybeans Using Multi-Polarization C- and L-Band Radar Data.” Remote Sensing of Environment 170: 77–89. doi:10.1016/j.rse.2015.09.002.
   Web of Science ®Google Scholar
• Jacob, A. W., F. Vicente-Guijalba, C. Lopez-Martinez, J. M. Lopez-Sanchez, M. Litzinger, H. Kristen, A. Mestre-Quereda, et al. 2020. Sentinel-1 InSar Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 535–552. doi:10.1109/jstars.2019.2958847.
   Web of Science ®Google Scholar
• Jiao, X., H. McNairn McNairn, J. Shang, E. Pattey, J. Liu, and C. Champagne. 2011. “The Sensitivity of RADARSAT-2 Polarimetric SAR Data to Corn and Soybean Leaf Area Index.” Canadian Journal of Remote Sensing 37 (1): 69–81. doi:10.5589/m11-023.
   Web of Science ®Google Scholar
• Kavats, O., D. Khramov, K. Sergieieva, and V. Vasyliev. 2019. “Monitoring Harvesting by Time Series of Sentinel-1 SAR Data.” Remote Sensing 11 (21): 2496. doi:10.3390/rs11212496.
   Web of Science ®Google Scholar
• Keating, B. A., P. S. Carberry, G. L. Hammer, M. E. Probert, M. J. Robertson, D. Holzworth, N. I. Huth, et al. 2003. “An Overview of APSIM, a Model Designed for Farming Systems Simulation.” European Journal of Agronomy 18 (3–4): 267–288. doi:10.1016/s1161-0301(02)00108-9.
   Web of Science ®Google Scholar
• Kebebe, A. Z., L. M. Reid, X. Zhu, J. Wu, T. Woldemariam, C. Voloaca, and K. Xiang. 2015. “Relationship Between Kernel Drydown Rate and Resistance to Gibberella Ear Rot in Maize.” Euphytica; Netherlands Journal of Plant Breeding 201 (1): 79–88. doi:10.1007/s10681-014-1185-2.
   Web of Science ®Google Scholar
• Khabbazan, S., P. Vermunt, S. Steele-Dunne, L. Ratering Arntz, C. Marinetti, D. van der Valk, L. Iannini, R. Molijn, K. Westerdijk, and C. van der Sande. 2019. “Crop Monitoring Using Sentinel-1 Data: A Case Study from the Netherlands.” Remote Sensing 11 (16): 1887. doi:10.3390/rs11161887.
   Web of Science ®Google Scholar
• Kumar, D. A., P. Srikanth, T. L. Neelima, M. U. Devi, K. Suresh, and S. Murthy. 2021. “Monitoring of Spectral Signatures of Maize Crop Using Temporal SAR and Optical Remote Sensing Data.” International Journal of Bio-Resource and Stress Management 12 (6): 745–750. doi:10.23910/1.2021.2482.
   Google Scholar
• Liao, C., J. Wang, J. Shang, X. Huang, J. Liu, and T. Huffman. 2018. “Sensitivity Study of Radarsat-2 Polarimetric SAR to Crop Height and Fractional Vegetation Cover of Corn and Wheat.” International Journal of Remote Sensing 39 (5): 1475–1490. doi:10.1080/01431161.2017.1407046.
   Web of Science ®Google Scholar
• Li, L., Q. Kong, P. Wang, L. Xun, L. Wang, L. Xu, and Z. Zhao. 2019. “Precise Identification of Maize in the North China Plain Based on Sentinel-1A SAR Time Series Data.” International Journal of Remote Sensing 40 (5–6): 1996–2013. doi:10.1080/01431161.2018.1504345.
   Web of Science ®Google Scholar
• Martinez-Feria, R. A., M. A. Licht, R. A. Ordóñez, J. L. Hatfield, J. A. Coulter, and S. V. Archontoulis. 2019. “Evaluating Maize and Soybean Grain Dry-Down in the Field with Predictive Algorithms and Genotype-By-Environment Analysis.” Scientific Reports 9 (1): 7167. doi:10.1038/s41598-019-43653-1.
   PubMedGoogle Scholar
• Mascolo, L., G. Forino, F. Nunziata, G. Pugliano, and M. Migliaccio. 2019. “A New Methodology for Rice Area Monitoring with COSMO-SkyMed HH–VV PingPong Mode SAR Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (4): 1076–1084. doi:10.1109/jstars.2019.2898727.
   Web of Science ®Google Scholar
• Moeremans, B., and S. Dautrebande. 1998. “Use of ERS SAR Interferometric Coherence and PRI Images to Evaluate Crop Height and Soil Moisture and to Identify Crops”, Proc. SPIE 3499, Remote Sensing for Agriculture, Ecosystems, and Hydrology, (11 December 1998); 10.1117/12.332746
   Google Scholar
• Nasirzadehdizaji, R., Z. Cakir, F. B. Sanli, S. Abdikan, A. Pepe, and F. Calò. 2021. “Sentinel-1 Interferometric Coherence and Backscattering Analysis for Crop Monitoring.” Computers and Electronics in Agriculture 185 (106118): 106118. doi:10.1016/j.compag.2021.106118.
   Google Scholar
• Nasirzadehdizaji, R., F. B. Sanli, S. Abdikan, Z. Cakir, A. Sekertekin, and M. Ustuner. 2019. Sensitivity Analysis of Multi-Temporal Sentinel-1 SAR Parameters to Crop Height and Canopy Coverage. Applied Sciences Vol. 9 (4): 655. Basel, Switzerland10.3390/app9040655
   Google Scholar
• Pingali, P. L. 2012. “Green Revolution: Impacts, Limits, and the Path Ahead.” Proceedings of the National Academy of Sciences of the United States of America 109 (31): 12302–12308. doi:10.1073/pnas.0912953109.
   PubMed Web of Science ®Google Scholar
• Ray, D. K., N. Ramankutty, N. D. Mueller, P. C. West, and J. A. Foley. 2012. “Recent Patterns of Crop Yield Growth and Stagnation.” Nature Communications 3 (1): 1293. doi:10.1038/ncomms2296.
   PubMedGoogle Scholar
• Rosen, P. A., S. Hensley, I. R. Joughin, F. K. Li, S. N. Madsen, E. Rodriguez, and R. M. Goldstein. 2000. “Synthetic Aperture Radar Interferometry.” Proceedings of the IEEE Institute of Electrical and Electronics Engineers 88 (3): 333–382. doi:10.1109/5.838084.
   Web of Science ®Google Scholar
• Santoro, M., U. Wegmuller, and J. I. Askne. 2010. “Signatures of ERS–Envisat Interferometric SAR Coherence and Phase of Short Vegetation: An Analysis in the Case of Maize Fields.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 48 (4): 1702–1713. doi:10.1109/tgrs.2009.2034257.
   Web of Science ®Google Scholar
• Seymour, M. S., and I. G. Cumming. 1994. “Maximum Likelihood Estimation for SAR Interferometry”. in: International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 8–12 August 1994, pp. 2272–2275.
   Google Scholar
• Shang, J., J. Liu, V. Poncos, X. Geng, B. Qian, Q. Chen, T. Dong, et al. 2020. “Detection of Crop Seeding and Harvest Through Analysis of Time-Series Sentinel-1 Interferometric SAR Data.” Remote Sensing 12 (10): 1551. doi:10.3390/rs12101551.
   Web of Science ®Google Scholar
• Simard, M., S. Hensley, M. Lavalle, R. Dubayah, N. Pinto, and M. Hofton. 2012. “An Empirical Assessment of Temporal Decorrelation Using the Uninhabited Aerial Vehicle Synthetic Aperture Radar Over Forested Landscapes.” Remote Sensing 4 (4): 975–986. doi:10.3390/rs4040975.
   Web of Science ®Google Scholar
• Tilman, D., C. Balzer, J. Hill, and B. L. Befort. 2011. “Global Food Demand and the Sustainable Intensification of Agriculture.” Proceedings of the National Academy of Sciences of the United States of America 108 (50): 20260–20264. doi:10.1073/pnas.1116437108.
   PubMed Web of Science ®Google Scholar
• Tollenaar, M., and T. B. Daynard. 1978. “Leaf Senescence in Short-Season Maize Hybrids.” Canadian Journal of Plant Science 58 (3): 869–874. doi:10.4141/cjps78-126.
   Web of Science ®Google Scholar
• Ulaby, F. T., C. T. Allen, G. Eger III, and E. Kanemasu. 1984. “Relating the Microwave Backscattering Coefficient to Leaf Area Index.” Remote Sensing of Environment 14 (1–3): 113–133. doi:10.1016/0034-4257(84)90010-5.
   Web of Science ®Google Scholar
• USDA-NASS. 2022. National Agricultural Statistics Service (NASS). https://www.nass.usda.gov/. Last accessed June 2022.
   Google Scholar
• Veloso, A., S. Mermoz, A. Bouvet, T. Le Toan, M. Planells, J. -F. Dejoux, and R. Ceschia. 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. doi:10.1016/j.rse.2017.07.015.
   Web of Science ®Google Scholar
• Venkatesan, M., S. Pazhanivelan, and N. S. Sudarmanian. 2019. “Multi-Temporal Feature Extraction for Precise Maize Area Mapping Using Time-Series Sentinel 1A SAR Data.” ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (6): 169–173. doi:10.5194/isprs-archives-xlii-3-w6-169-2019.
   Google Scholar
• Verma, A., A. Kumar, and K. Lal. 2019. “Kharif Crop Characterization Using Combination of SAR and MSI Optical Sentinel Satellite Data Sets.” Journal of Earth System Science 128 (8). doi:10.1007/s12040-019-1260-0.
   Web of Science ®Google Scholar
• Villarroya-Carpio, A., and J. M. Lopez-Sanchez. 2023. “Multi-Annual Evaluation of Time Series of Sentinel-1 Interferometric Coherence as a Tool for Crop Monitoring.” Sensors 23 (4): 1833. doi:10.3390/s23041833.
   PubMed Web of Science ®Google Scholar
• Vreugdenhil, M., W. Wagner, B. Bauer-Marschallinger, I. Pfeil, I. Teubner, C. Rüdiger, and P. Strauss. 2018. “Sensitivity of Sentinel-1 Backscatter to Vegetation Dynamics: An Austrian Case Study.” Remote Sensing 10 (9): 1396. doi:10.3390/rs10091396.
   Web of Science ®Google Scholar
• Wang, Y., S. Fang, L. Zhao, X. Huang, and X. Jiang. 2022. “Parcel-Based Summer Maize Mapping and Phenology Estimation Combined Using Sentinel-2 and Time Series Sentinel-1 Data.” International Journal of Applied Earth Observation and Geoinformation: ITC Journal 108 (102720): 102720. doi:10.1016/j.jag.2022.102720.
   Google Scholar
• Xu, J., J. Meng, and L. J. Quackenbush. 2019. “Use of Remote Sensing to Predict the Optimal Harvest Date of Corn.” Field Crops Research 236: 1–13. doi:10.1016/j.fcr.2019.03.003.
   Web of Science ®Google Scholar
• Zebker, H. A., and J. Villasenor. 1992. “Decorrelation in Interferometric Radar Echoes.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 30 (5): 950–959. doi:10.1109/36.175330.
   Web of Science ®Google Scholar
• Zeng, L., and D. Xiang. 2018. “A Study on Phenology Detection of Corn in Northeastern China with Fused Remote Sensing Data.” In Recent Advances and Applications in Remote Sensing, edited by M.-C. Hung and Y.-H. Wu. London: IntechOpen. doi:10.5772/intechopen.73096.
   Google Scholar