Developing a New Method to Identify Flowering Dynamics of Rapeseed Using Landsat 8 and Sentinel-1/2
Abstract
:1. Introduction
2. Materials and Methods
2.1. DWD Station and Study Area
2.2. Identifying the Rapeseed Parcels
2.3. Satellite Data
2.3.1. Optical Satellite
2.3.2. SAR Satellite
2.4. Developing a New Index—NRFI to Catch Flowering Dynamics
2.5. Detecting Peak Flowering Stages
2.6. Evaluating the Peak Flowering Stages Derived from the New Method
3. Results
3.1. Spectral Properties and NDVI Phenological Characteristics of Rapeseed
3.2. NRFI Better Characterize the Flowering Intensity
3.3. Radar Polarization Characteristics of Rapeseed
3.4. Comparing the Different Indexes for Monitoring Peak Flowering
4. Discussion
4.1. The Good Performance of NRFI for Identifying Flowering Stages of Rapeseed
4.2. The Ancillary Function of Morphological Indexes for Identifying Flowering Stages of Rapeseed
4.3. Limitations and Perspectives
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
References
- Gardy, J.; Hassanpour, A.; Lai, X.; Rehan, M. The influence of blending process on the quality of rapeseed oil-used cooking oil biodiesels. Int. Sci. J. (J. Environ. Sci.) 2014, 3, 233–240. [Google Scholar]
- Andrimont, R.; Taymans, M.; Lemoine, G.; Ceglar, A.; Yordanov, M.; van der Velde, M. Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and-2 time series. Remote Sens. Environ. 2020, 239, 111660. [Google Scholar] [CrossRef] [PubMed]
- Tao, J.; Wu, W.; Liu, W.; Xu, M. Exploring the Spatio-Temporal Dynamics of Winter Rape on the Middle Reaches of Yangtze River Valley Using Time-Series MODIS Data. Sustainability 2020, 12, 466. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Y.; Chipanshi, A.; Daneshfar, B.; Koiter, L.; Champagne, C.; Davidson, A.; Reichert, G.; Bédard, F. Effect of using crop specific masks on earth observation based crop yield forecasting across Canada. Remote Sens. Appl. Soc. Environ. 2019, 13, 121–137. [Google Scholar] [CrossRef]
- Domínguez, J.A.; Kumhálová, J.; Novák, P. Winter oilseed rape and winter wheat growth prediction using remote sensing methods. Plant Soil Environ. 2015, 61, 410–416. [Google Scholar]
- Rondanini, D.P.; Gomez, N.V.; Agosti, M.B.; Miralles, D.J. Global trends of rapeseed grain yield stability and rapeseed-to-wheat yield ratio in the last four decades. Eur. J. Agron. 2012, 37, 56–65. [Google Scholar] [CrossRef]
- Ahmadi, M.; Bahrani, M.J. Yield and yield components of rapeseed as influenced by water stress at different growth stages and nitrogen levels. Am. Eurasian J. Agric. Environ. Sci. 2009, 5, 755–761. [Google Scholar]
- Kirkegaard, J.A.; Lilley, J.M.; Brill, R.D.; Ware, A.H.; Walela, C.K. The critical period for yield and quality determination in canola (Brassica napus L.). Field Crop. Res. 2018, 222, 180–188. [Google Scholar] [CrossRef]
- Zhang, H.; Flottmann, S. Source-sink manipulations indicate seed yield in canola is limited by source availability. Eur. J. Agron. 2018, 96, 70–76. [Google Scholar] [CrossRef]
- Behrens, T.; Müller, J.; Diepenbrock, W. Utilization of canopy reflectance to predict properties of oilseed rape (Brassica napus L.) and barley (Hordeum vulgare L.) during ontogenesis. Eur. J. Agron. 2006, 25, 345–355. [Google Scholar] [CrossRef]
- Alqudah, A.M.; Samarah, N.H.; Mullen, R.E. Drought stress effect on crop pollination, seed set, yield and quality. In Alternative Farming Systems, Biotechnology, Drought Stress and Ecological Fertilization; Springer: Berlin/Heidelberg, Germany, 2011; pp. 193–213. [Google Scholar]
- Ashraf, M.; Foolad, M.R. Pre-sowing seed treatment—A shotgun approach to improve germination, plant growth, and crop yield under saline and non-saline conditions. Adv. Agron. 2005, 88, 223–271. [Google Scholar]
- Chen, Y.; Zhang, Z.; Tao, F. Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data. Eur. J. Agron. 2018, 101, 163–173. [Google Scholar] [CrossRef]
- Veloso, A.; Mermoz, S.; Bouvet, A.; Le Toan, T.; Planells, M.; Dejoux, J.; Ceschia, E. Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sens. Environ. 2017, 199, 415–426. [Google Scholar] [CrossRef]
- Tian, H.; Meng, M.; Wu, M.; Niu, Z. Mapping spring canola and spring wheat using Radarsat-2 and Landsat-8 images with Google Earth Engine. Curr. Sci. 2019, 116, 291–298. [Google Scholar] [CrossRef]
- Yang, H.; Li, Z.; Chen, E.; Zhao, C.; Yang, G.; Casa, R.; Pignatti, S.; Feng, Q. Temporal polarimetric behavior of oilseed rape (Brassica napus L.) at C-band for early season sowing date monitoring. Remote Sens. 2014, 6, 10375–10394. [Google Scholar] [CrossRef] [Green Version]
- Sulik, J.J.; Long, D.S. Spectral considerations for modeling yield of canola. Remote Sens. Environ. 2016, 184, 161–174. [Google Scholar] [CrossRef] [Green Version]
- Sulik, J.J.; Long, D.S. Spectral indices for yellow canola flowers. Int. J. Remote Sens. 2015, 36, 2751–2765. [Google Scholar] [CrossRef]
- Bargiel, D. A new method for crop classification combining time series of radar images and crop phenology information. Remote Sens. Environ. 2017, 198, 369–383. [Google Scholar] [CrossRef]
- Luo, Y.; Zhang, Z.; Chen, Y.; Li, Z.; Tao, F. ChinaCropPhen1km: A high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products. Earth Syst. Sci. Data 2020, 12, 197–214. [Google Scholar] [CrossRef] [Green Version]
- Rüetschi, M.; Schaepman, M.E.; Small, D. Using multitemporal sentinel-1 c-band backscatter to monitor phenology and classify deciduous and coniferous forests in northern switzerland. Remote Sens. 2018, 10, 55. [Google Scholar] [CrossRef] [Green Version]
- Van der Meer, F.D.; Van der Werff, H.; Van Ruitenbeek, F. Potential of ESA’s Sentinel-2 for geological applications. Remote Sens. Environ. 2014, 148, 124–133. [Google Scholar] [CrossRef]
- Vermote, E.; Justice, C.; Claverie, M.; Franch, B. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens. Environ. 2016, 185, 46–56. [Google Scholar] [CrossRef] [PubMed]
- Liu, S.; Liu, X.; Liu, M.; Wu, L.; Ding, C.; Huang, Z. Extraction of rice phenological differences under heavy metal stress using EVI time-series from HJ-1A/B Data. Sensors 2017, 17, 1243. [Google Scholar]
- Magney, T.S.; Eitel, J.U.; Huggins, D.R.; Vierling, L.A. Proximal NDVI derived phenology improves in-season predictions of wheat quantity and quality. Agr. For. Meteorol. 2016, 217, 46–60. [Google Scholar] [CrossRef]
- Griffiths, P.; Nendel, C.; Hostert, P. Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping. Remote Sens. Environ. 2019, 220, 135–151. [Google Scholar] [CrossRef]
- Preidl, S.; Lange, M.; Doktor, D. Introducing APiC for regionalised land cover mapping on the national scale using Sentinel-2A imagery. Remote Sens. Environ. 2020, 240, 111673. [Google Scholar] [CrossRef]
- Nguyen, D.B.; Gruber, A.; Wagner, W. Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel-1A data. Remote Sens. Lett. 2016, 7, 1209–1218. [Google Scholar] [CrossRef]
- Wang, H.; Magagi, R.; Goïta, K.; Trudel, M.; McNairn, H.; Powers, J. Crop phenology retrieval via polarimetric sar decomposition and random forest algorithm. Remote Sens. Environ. 2019, 231, 111234. [Google Scholar] [CrossRef]
- Betbeder, J.; Fieuzal, R.; Philippets, Y.; Ferro-Famil, L.; Baup, F. Contribution of multitemporal polarimetric synthetic aperture radar data for monitoring winter wheat and rapeseed crops. J. Appl. Remote Sens. 2016, 10, 26020. [Google Scholar] [CrossRef]
- Cable, J.W.; Kovacs, J.M.; Jiao, X.; Shang, J. Agricultural monitoring in northeastern Ontario, Canada, using multi-temporal polarimetric RADARSAT-2 data. Remote Sens. 2014, 6, 2343–2371. [Google Scholar] [CrossRef] [Green Version]
- McNairn, H.; Jiao, X.; Pacheco, A.; Sinha, A.; Tan, W.; Li, Y. Estimating canola phenology using synthetic aperture radar. Remote Sens. Environ. 2018, 219, 196–205. [Google Scholar] [CrossRef]
- Steele-Dunne, S.C.; McNairn, H.; Monsivais-Huertero, A.; Judge, J.; Liu, P.; Papathanassiou, K. Radar remote sensing of agricultural canopies: A review. IEEE J. Stars 2017, 10, 2249–2273. [Google Scholar] [CrossRef] [Green Version]
- Mercier, A.; Betbeder, J.; Baudry, J.; Le Roux, V.; Spicher, F.; Lacoux, J.; Roger, D.; Hubert-Moy, L. Evaluation of Sentinel-1 & 2 time series for predicting wheat and rapeseed phenological stages. ISPRS J. Photogramm. 2020, 163, 231–256. [Google Scholar]
- Pan, Z.; Huang, J.; Wang, F. Multi range spectral feature fitting for hyperspectral imagery in extracting oilseed rape planting area. Int. J. Appl. Earth Obs. 2013, 25, 21–29. [Google Scholar] [CrossRef]
- Wan, L.; Li, Y.; Cen, H.; Zhu, J.; Yin, W.; Wu, W.; Zhu, H.; Sun, D.; Zhou, W.; He, Y. Combining UAV-based vegetation indices and image classification to estimate flower number in oilseed rape. Remote Sens. 2018, 10, 1484. [Google Scholar] [CrossRef] [Green Version]
- Gong, Y.; Duan, B.; Fang, S.; Zhu, R.; Wu, X.; Ma, Y.; Peng, Y. Remote estimation of rapeseed yield with unmanned aerial vehicle (UAV) imaging and spectral mixture analysis. Plant Methods 2018, 14, 70. [Google Scholar] [CrossRef]
- Ashourloo, D.; Shahrabi, H.S.; Azadbakht, M.; Aghighi, H.; Nematollahi, H.; Alimohammadi, A.; Matkan, A.A. Automatic canola mapping using time series of sentinel 2 images. ISPRS J. Photogramm. 2019, 156, 63–76. [Google Scholar] [CrossRef]
- Kaspar, F.; Zimmermann, K.; Polte-Rudolf, C. An overview of the phenological observation network and the phenological database of Germany’s national meteorological service (Deutscher Wetterdienst). Adv. Sci. Res. 2015, 11, 93–99. [Google Scholar] [CrossRef] [Green Version]
- Böttcher, U.; Rampin, E.; Hartmann, K.; Zanetti, F.; Flenet, F.; Morison, M.; Kage, H. A phenological model of winter oilseed rape according to the BBCH scale. Crop Pasture Sci. 2016, 67, 345–358. [Google Scholar] [CrossRef]
- Ma, Y.; Fang, S.; Peng, Y.; Gong, Y.; Wang, D. Remote estimation of biomass in winter oilseed rape (Brassica napus L.) using canopy hyperspectral data at different growth stages. Appl. Sci. 2019, 9, 545. [Google Scholar] [CrossRef] [Green Version]
- She, B.; Huang, J.; Guo, R.; Wang, H.; Wang, J. Assessing winter oilseed rape freeze injury based on Chinese HJ remote sensing data. J. Zhejiang Univ. Sic. B 2015, 16, 131–144. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dong, J.; Xiao, X.; Kou, W.; Qin, Y.; Zhang, G.; Li, L.; Jin, C.; Zhou, Y.; Wang, J.; Biradar, C. Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms. Remote Sens. Environ. 2015, 160, 99–113. [Google Scholar] [CrossRef]
- Dong, J.; Xiao, X.; Menarguez, M.A.; Zhang, G.; Qin, Y.; Thau, D.; Biradar, C.; Moore, B., III. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sens. Environ. 2016, 185, 142–154. [Google Scholar] [CrossRef] [Green Version]
- You, N.; Dong, J. Examining earliest identifiable timing of crops using all available Sentinel 1/2 imagery and Google Earth Engine. ISPRS J. Photogramm. 2020, 161, 109–123. [Google Scholar] [CrossRef]
- Foga, S.; Scaramuzza, P.L.; Guo, S.; Zhu, Z.; Dilley Jr, R.D.; Beckmann, T.; Schmidt, G.L.; Dwyer, J.L.; Hughes, M.J.; Laue, B. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sens. Environ. 2017, 194, 379–390. [Google Scholar] [CrossRef] [Green Version]
- Torres, R.; Snoeij, P.; Geudtner, D.; Bibby, D.; Davidson, M.; Attema, E.; Potin, P.; Rommen, B.; Floury, N.; Brown, M. GMES Sentinel-1 mission. Remote Sens. Environ. 2012, 120, 9–24. [Google Scholar] [CrossRef]
- Mandal, D.; Kumar, V.; Bhattacharya, A.; Rao, Y.S.; Siqueira, P.; Bera, S. Sen4Rice: A Processing Chain for Differentiating Early and Late Transplanted Rice Using Time-Series Sentinel-1 SAR Data with Google Earth Engine. IEEE Geosci. Remote Sens. 2018, 15, 1947–1951. [Google Scholar] [CrossRef]
- Wang, D.; Fang, S.; Yang, Z.; Wang, L.; Tang, W.; Li, Y.; Tong, C. A regional mapping method for oilseed rape based on HSV transformation and spectral features. ISPRS Int. J. Geo Inf. 2018, 7, 224. [Google Scholar] [CrossRef] [Green Version]
- Mallinis, G.; Mitsopoulos, I.; Chrysafi, I. Evaluating and comparing Sentinel 2A and Landsat-8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece. Gisci. Remote Sens. 2018, 55, 1–18. [Google Scholar] [CrossRef]
- Helman, D. Land surface phenology: What do we really ‘see’from space? Sci. Total Environ. 2018, 618, 665–673. [Google Scholar] [CrossRef]
- Geng, L.; Ma, M.; Wang, X.; Yu, W.; Jia, S.; Wang, H. Comparison of eight techniques for reconstructing multi-satellite sensor time-series NDVI data sets in the Heihe river basin, China. Remote Sens. 2014, 6, 2024–2049. [Google Scholar] [CrossRef] [Green Version]
- Wang, C.; Zhang, Z.; Chen, Y.; Tao, F.; Zhang, J.; Zhang, W. Comparing different smoothing methods to detect double-cropping rice phenology based on LAI products–a case study in the Hunan province of China. Int. J. Remote Sens. 2018, 39, 6405–6428. [Google Scholar] [CrossRef]
- Eklundh, L.; Jönsson, P. TIMESAT 3.1 Software Manual; Lund University: Lund, Sweden, 2012; pp. 1–82. [Google Scholar]
- Benesty, J.; Chen, J.; Huang, Y.; Cohen, I. Pearson correlation coefficient. In Noise Reduction in Speech Processing; Springer: Berlin/Heidelberg, Germany, 2009; pp. 1–4. [Google Scholar]
- Duveiller, G.; Fasbender, D.; Meroni, M. Revisiting the concept of a symmetric index of agreement for continuous datasets. Sci. Rep. UK 2016, 6, 1–14. [Google Scholar] [CrossRef] [PubMed]
- Shen, M.; Chen, J.; Zhu, X.; Tang, Y. Yellow flowers can decrease NDVI and EVI values: Evidence from a field experiment in an alpine meadow. Can. J. Remote Sens. 2009, 35, 99–106. [Google Scholar] [CrossRef]
- Drusch, M.; Del Bello, U.; Carlier, S.; Colin, O.; Fernandez, V.; Gascon, F.; Hoersch, B.; Isola, C.; Laberinti, P.; Martimort, P. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens. Environ. 2012, 120, 25–36. [Google Scholar] [CrossRef]
- Roy, D.P.; Wulder, M.A.; Loveland, T.R.; Woodcock, C.E.; Allen, R.G.; Anderson, M.C.; Helder, D.; Irons, J.R.; Johnson, D.M.; Kennedy, R. Landsat-8: Science and product vision for terrestrial global change research. Remote Sens. Environ. 2014, 145, 154–172. [Google Scholar] [CrossRef] [Green Version]
- Wiseman, G.; McNairn, H.; Homayouni, S.; Shang, J. RADARSAT-2 polarimetric SAR response to crop biomass for agricultural production monitoring. IEEE J. Stars 2014, 7, 4461–4471. [Google Scholar] [CrossRef]
- Vreugdenhil, M.; Wagner, W.; Bauer-Marschallinger, B.; Pfeil, I.; Teubner, I.; Rüdiger, C.; Strauss, P. Sensitivity of Sentinel-1 backscatter to vegetation dynamics: An Austrian case study. Remote Sens. 2018, 10, 1396. [Google Scholar] [CrossRef] [Green Version]
- van Vliet, A.J.H.; de Groot, R.S.; Bellens, Y.; Braun, P.; Bruegger, R.; Bruns, E.; Clevers, J.; Estreguil, C.; Flechsig, M.; Jeanneret, F.; et al. The European Phenology Network. Int. J. Biometeorol. 2003, 47, 202–212. [Google Scholar] [CrossRef]
- Chen, D.; Huang, J.; Jackson, T.J. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near-and short-wave infrared bands. Remote Sens. Environ. 2005, 98, 225–236. [Google Scholar] [CrossRef]
- Seelig, H.D.; Hoehn, A.; Stodieck, L.S.; Klaus, D.M.; Adams Iii, W.W.; Emery, W.J. The assessment of leaf water content using leaf reflectance ratios in the visible, near-, and short-wave-infrared. Int. J. Remote Sens. 2008, 29, 3701–3713. [Google Scholar] [CrossRef]
- Wilson, R.H.; Nadeau, K.P.; Jaworski, F.B.; Tromberg, B.J.; Durkin, A.J. Review of short-wave infrared spectroscopy and imaging methods for biological tissue characterization. J. Biomed. Opt. 2015, 20, 30901. [Google Scholar] [CrossRef] [PubMed]
- Luo, Y.; Zhang, Z.; Li, Z.; Chen, Y.; Zhang, L.; Cao, J.; Tao, F. Identifying the spatiotemporal changes of annual harvesting areas for three staple crops in China by integrating multi-data sources. Environ. Res. Lett. 2020, 15, 74003. [Google Scholar] [CrossRef]
- Wesołowski, M.; Suchacz, B. Classification of rapeseed and soybean oils by use of unsupervised pattern-recognition methods and neural networks. Fresenius J. Anal. Chem. 2001, 371, 323–330. [Google Scholar] [CrossRef]
- Singha, M.; Dong, J.; Zhang, G.; Xiao, X. High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data. Sci. Data 2019, 6, 1–10. [Google Scholar] [CrossRef]
- Cookmartin, G.; Saich, P.; Quegan, S.; Cordey, R.; Burgess-Allen, P.; Sowter, A. Modeling microwave interactions with crops and comparison with ERS-2 SAR observations. IEEE Trans. Geosci. Remote 2000, 38, 658–670. [Google Scholar] [CrossRef]
- Zhou, Y.; Dong, J.; Liu, J.; Metternicht, G.; Shen, W.; You, N.; Zhao, G.; Xiao, X. Are There Sufficient Landsat Observations for Retrospective and Continuous Monitoring of Land Cover Changes in China? Remote Sens. 2019, 11, 1808. [Google Scholar] [CrossRef] [Green Version]
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Han, J.; Zhang, Z.; Cao, J. Developing a New Method to Identify Flowering Dynamics of Rapeseed Using Landsat 8 and Sentinel-1/2. Remote Sens. 2021, 13, 105. https://doi.org/10.3390/rs13010105
Han J, Zhang Z, Cao J. Developing a New Method to Identify Flowering Dynamics of Rapeseed Using Landsat 8 and Sentinel-1/2. Remote Sensing. 2021; 13(1):105. https://doi.org/10.3390/rs13010105
Chicago/Turabian StyleHan, Jichong, Zhao Zhang, and Juan Cao. 2021. "Developing a New Method to Identify Flowering Dynamics of Rapeseed Using Landsat 8 and Sentinel-1/2" Remote Sensing 13, no. 1: 105. https://doi.org/10.3390/rs13010105