Deriving Wheat Crop Productivity Indicators Using Sentinel-1 Time Series
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Site Ground Measurements of Wheat Development and Yield
2.3. SAR Data to Obtain VH/VV Time Series in Each Wheat Field
2.3.1. SAR Preprocessing for Field Specific VH/VV Time Series
2.3.2. Temporal Analysis and Wheat Development Definition
2.3.3. Logistic Curve Fitting
2.3.4. Parameter Optimisation
2.3.5. Automatic Curve Extraction and Correlation Analysis
3. Results
3.1. Annual Analysis of VH/VV Ratio Curve Parameters, 2017 to 2019
3.2. Relationship Between SAR-Derived Parameters and Yield
4. Discussion
4.1. Technical and Statistical Process, the Goodness of Fit, Robustness, and Uncertainty
4.2. Environmental and Biophysical Understanding
4.3. Opportunities for Agronomic Management and Modelling
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Jin, X.; Kumar, L.; Li, Z.; Feng, H.; Xu, X.; Yang, G.; Wang, J. A review of data assimilation of remote sensing and crop models. Eur. J. Agron. 2018, 92, 141–152. [Google Scholar] [CrossRef]
- Veloso, A.; Mermoz, S.; Bouvet, A.; Le Toan, T.; Planells, M.; Dejoux, J.-F.; 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]
- De Sy, V.; Herold, M.; Achard, F.; Asner, G.P.; Held, A.; Kellndorfer, J.; Verbesselt, J. Synergies of multiple remote sensing data sources for REDD+ monitoring. Curr. Opin. Environ. Sustain. 2012, 4, 696–706. [Google Scholar] [CrossRef]
- Kansakar, P.; Hossain, F. A review of applications of satellite earth observation data for global societal benefit and stewardship of planet earth. Space Policy 2016, 36, 46–54. [Google Scholar] [CrossRef]
- Steele-Dunne, S.C.; McNairn, H.; Monsivais-Huertero, A.; Judge, J.; Liu, P.-W.; Papathanassiou, K. Radar Remote Sensing of Agricultural Canopies: A Review. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2017, 10, 2249–2273. [Google Scholar] [CrossRef] [Green Version]
- Kasampalis, D.; Alexandridis, T.; Deva, C.; Challinor, A.; Moshou, D.; Zalidis, G. Contribution of Remote Sensing on Crop Models: A Review. J. Imaging 2018, 4, 52. [Google Scholar] [CrossRef] [Green Version]
- Betbeder, J.; Fieuzal, R.; Baup, F. Assimilation of LAI and Dry Biomass Data From Optical and SAR Images Into an Agro-Meteorological Model to Estimate Soybean Yield. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 2540–2553. [Google Scholar] [CrossRef]
- Huang, J.; Gómez-Dans, J.L.; Huang, H.; Ma, H.; Wu, Q.; Lewis, P.E.; Liang, S.; Chen, Z.; Xue, J.H.; Wu, Y.; et al. Assimilation of remote sensing into crop growth models: Current status and perspectives. Agric. For. Meteorol. 2019, 276, 107609. [Google Scholar] [CrossRef]
- Blaes, X.; Defourny, P.; Wegmuller, U.; Della Vecchia, A.; Guerriero, L.; Ferrazzoli, P. C-band polarimetric indexes for maize monitoring based on a validated radiative transfer model. IEEE Trans. Geosci. Remote Sens. 2006, 44, 791–800. [Google Scholar] [CrossRef]
- Casanova, J.J.; Judge, J.; Jang, M. Modeling Transmission of Microwaves Through Dynamic Vegetation. IEEE Trans. Geosci. Remote Sens. 2007, 45, 3145–3149. [Google Scholar] [CrossRef]
- Van Emmerik, T.; Steele-Dunne, S.C.; Judge, J.; van de Giesen, N. Impact of Diurnal Variation in Vegetation Water Content on Radar Backscatter From Maize During Water Stress. IEEE Trans. Geosci. Remote Sens. 2015, 53, 3855–3869. [Google Scholar] [CrossRef]
- Vicente-Guijalba, F.; Martinez-Marin, T.; Lopez-Sanchez, J.M. Crop Phenology Estimation Using a Multitemporal Model and a Kalman Filtering Strategy. IEEE Geosci. Remote Sens. Lett. 2014, 11, 1081–1085. [Google Scholar] [CrossRef] [Green Version]
- Aschbacher, J.; Milagro-Pérez, M.P. The European Earth monitoring (GMES) programme: Status and perspectives. Remote Sens. Environ. 2012, 120, 3–8. [Google Scholar] [CrossRef]
- Harfenmeister, K.; Spengler, D.; Weltzien, C. Analyzing Temporal and Spatial Characteristics of Crop Parameters Using Sentinel-1 Backscatter Data. Remote Sens. 2019, 11, 1569. [Google Scholar] [CrossRef] [Green Version]
- Friesen, J.; Steele-Dunne, S.C.; van de Giesen, N. Diurnal Differences in Global ERS Scatterometer Backscatter Observations of the Land Surface. IEEE Trans. Geosci. Remote Sens. 2012, 50, 2595–2602. [Google Scholar] [CrossRef]
- Paget, A.C.; Long, D.G.; Madsen, N.M. RapidScat Diurnal Cycles Over Land. IEEE Trans. Geosci. Remote Sens. 2016, 54, 3336–3344. [Google Scholar] [CrossRef]
- Ndikumana, E.; Ho Tong Minh, D.; Dang Nguyen, H.; Baghdadi, N.; Courault, D.; Hossard, L.; El Moussawi, I. Estimation of Rice Height and Biomass Using Multitemporal SAR Sentinel-1 for Camargue, Southern France. Remote Sens. 2018, 10, 1394. [Google Scholar] [CrossRef] [Green Version]
- Mattia, F.; Le Toan, T.; Picard, G.; Posa, F.I.; D’Alessio, A.; Notarnicola, C.; Gatti, A.M.; Rinaldi, M.; Satalino, G.; Pasquariello, G. Multitemporal c-band radar measurements on wheat fields. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1551–1560. [Google Scholar] [CrossRef]
- Alexakis, D.D.; Mexis, F.D.K.; Vozinaki, A.E.K.; Daliakopoulos, I.N.; Tsanis, I.K. Soil Moisture Content Estimation Based on Sentinel-1 and Auxiliary Earth Observation Products. A Hydrological Approach. Sensors 2017, 17, 1455. [Google Scholar] [CrossRef] [Green Version]
- 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]
- Dobson, M.; Ulaby, F.; Hallikainen, M.; El-Rayes, M. Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models. IEEE Trans. Geosci. Remote Sens. 1985, GE-23, 35–46. [Google Scholar] [CrossRef]
- Mironov, V.L.; Dobson, M.C.; Kaupp, V.H.; Komarov, S.A.; Kleshchenko, V.N. Generalized refractive mixing dielectric model for moist soils. IEEE Trans. Geosci. Remote Sens. 2004, 42, 773–785. [Google Scholar] [CrossRef]
- Snapir, B.; Waine, T.W.; Corstanje, R.; Redfern, S.; De Silva, J.; Kirui, C. Harvest Monitoring of Kenyan Tea Plantations With X-Band SAR. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2018, 11, 930–938. [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]
- McNairn, H.; Champagne, C.; Shang, J.; Holmstrom, D.; Reichert, G. Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories. ISPRS J. Photogramm. Remote Sens. 2009, 64, 434–449. [Google Scholar] [CrossRef]
- Van Tricht, K.; Gobin, A.; Gilliams, S.; Piccard, I. Synergistic use of radar Sentinel-1 and optical Sentinel-2 imagery for crop mapping: A case study for Belgium. Remote Sens. 2018, 10, 1642. [Google Scholar] [CrossRef] [Green Version]
- De Bernardis, C.; Vicente-Guijalba, F.; Martinez-Marin, T.; Lopez-Sanchez, J.M. Contribution to Real-Time Estimation of Crop Phenological States in a Dynamical Framework Based on NDVI Time Series: Data Fusion With SAR and Temperature. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 3512–3523. [Google Scholar] [CrossRef] [Green Version]
- Fieuzal, R.; Baup, F.; Marais-Sicre, C. Monitoring Wheat and Rapeseed by Using Synchronous Optical and Radar Satellite Data—From Temporal Signatures to Crop Parameters Estimation. Adv. Remote Sens. 2013, 2, 162–180. [Google Scholar] [CrossRef] [Green Version]
- Zhou, T.; Pan, J.; Zhang, P.; Wei, S.; Han, T. Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region. Sensors 2017, 17, 1210. [Google Scholar] [CrossRef]
- Gao, Q.; Zribi, M.; Escorihuela, M.; Baghdadi, N.; Segui, P. Irrigation Mapping Using Sentinel-1 Time Series at Field Scale. Remote Sens. 2018, 10, 1495. [Google Scholar] [CrossRef] [Green Version]
- Baghdadi, N.; El Hajj, M.; Zribi, M.; Bousbih, S. Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands. Remote Sens. 2017, 9, 969. [Google Scholar] [CrossRef] [Green Version]
- Bériaux, E.; Waldner, F.; Collienne, F.; Bogaert, P.; Defourny, P. Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model. Remote Sens. 2015, 7, 16204–16225. [Google Scholar] [CrossRef] [Green Version]
- Li, J.; Wang, S. Using SAR-derived vegetation descriptors in a water cloud model to improve soil moisture retrieval. Remote Sens. 2018, 10, 1370. [Google Scholar] [CrossRef] [Green Version]
- Clauss, K.; Ottinger, M.; Leinenkugel, P.; Kuenzer, C. Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data. Int. J. Appl. Earth Obs. Geoinf. 2018, 73, 574–585. [Google Scholar] [CrossRef]
- Mandal, D.; Kumar, V.; Ratha, D.; Dey, S.; Bhattacharya, A.; Lopez-Sanchez, J.M.; McNairn, H.; Rao, Y.S. Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data. Remote Sens. Environ. 2020, 247, 111954. [Google Scholar] [CrossRef]
- Chang, J.G.; Shoshany, M.; Oh, Y. Polarimetric Radar Vegetation Index for Biomass Estimation in Desert Fringe Ecosystems. IEEE Trans. Geosci. Remote Sens. 2018, 56, 7102–7108. [Google Scholar] [CrossRef]
- Attema, E.P.W.; Ulaby, F.T. Vegetation modeled as a water cloud. Radio Sci. 1978, 13, 357–364. [Google Scholar] [CrossRef]
- Brown, S.C.M.; Quegan, S.; Morrison, K.; Bennett, J.C.; Cookmartin, G. High-resolution measurements of scattering in wheat canopies—Implications for crop parameter retrieval. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1602–1610. [Google Scholar] [CrossRef] [Green Version]
- Huang, W.; Sun, G.; Ni, W.; Zhang, Z.; Dubayah, R. Sensitivity of multi-source SAR backscatter to changes in forest aboveground biomass. Remote Sens. 2015, 7, 9587–9609. [Google Scholar] [CrossRef] [Green Version]
- Lahoz, W.A.; Schneider, P. Data assimilation: Making sense of Earth Observation. Front. Environ. Sci. 2014, 2, 1–28. [Google Scholar] [CrossRef] [Green Version]
- Che, M.; Chen, B.; Zhang, H.; Fang, S.; Xu, G.; Lin, X.; Wang, Y. A New Equation for Deriving Vegetation Phenophase from Time Series of Leaf Area Index (LAI) Data. Remote Sens. 2014, 6, 5650–5670. [Google Scholar] [CrossRef] [Green Version]
- Klosterman, S.T.; Hufkens, K.; Gray, J.M.; Melaas, E.; Sonnentag, O.; Lavine, I.; Mitchell, L.; Norman, R.; Friedl, M.A.; Richardson, A.D. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences 2014, 11, 4305–4320. [Google Scholar] [CrossRef] [Green Version]
- Son, N.-T.; Chen, C.-F.; Chang, L.-Y.; Chen, C.-R.; Sobue, S.-I.; Minh, V.-Q.; Chiang, S.-H.; Nguyen, L.-D.; Lin, Y.-W. A logistic-based method for rice monitoring from multitemporal MODIS-Landsat fusion data. Eur. J. Remote Sens. 2016, 49, 39–56. [Google Scholar] [CrossRef] [Green Version]
- Østergaard, A.; Snoeij, P.; Navas Traver, I.; Ludwig, M.; Rostan, F.; Croci, R. C-band SAR for the GMES Sentinel-1 mission. In Proceedings of the European Microwave Week 2011 “Wave to Future” EuMW 2011,—8th European Radar Conference EuRAD 2011, Manchester, UK, 12–14 October 2011; pp. 234–240. [Google Scholar]
- 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. Crop monitoring using Sentinel-1 data: A case study from The Netherlands. Remote Sens. 2019, 11, 1887. [Google Scholar] [CrossRef] [Green Version]
- Cranfield University. The Soils Guide. 2019. Available online: www.landis.org.uk (accessed on 20 December 2019).
- Avery, B.W.; Catt, J.A. The Soil-Map Units. In The Soil at Rothamsted; Lawes Agricultural Trust: Harpenden, UK, 1995; pp. 14–18. [Google Scholar]
- Rothamsted Research. Rothamsted Long-Term Monthly Rainfall; Rothamsted Research: Harpenden, UK, 2018. [Google Scholar]
- Mladenova, I.E.; Jackson, T.J.; Bindlish, R.; Hensley, S. Incidence Angle Normalization of Radar Backscatter Data. IEEE Trans. Geosci. Remote Sens. 2013, 51, 1791–1804. [Google Scholar] [CrossRef]
- Vaudour, E.; Baghdadi, N.; Gilliot, J.M. Mapping tillage operations over a peri-urban region using combined SPOT4 and ASAR/ENVISAT images. Int. J. Appl. Earth Obs. Geoinf. 2014, 28, 43–59. [Google Scholar] [CrossRef]
- Canisius, F.; Shang, J.; Liu, J.; Huang, X.; Ma, B.; Jiao, X.; Geng, X.; Kovacs, J.M.; Walters, D. Tracking crop phenological development using multi-temporal polarimetric Radarsat-2 data. Remote Sens. Environ. 2018, 210, 508–518. [Google Scholar] [CrossRef]
- Molijn, R.A.; Iannini, L.; Mousivand, A.; Hanssen, R.F. Analyzing C-band SAR polarimetric information for LAI and crop yield estimations. In Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI, Amsterdam, The Netherlands, 22–25 September 2014; Neale, C.M.U., Maltese, A., Eds.; International Society for Optics and Photonics: Bellingham, WA, USA, 2014; Volume 9239, p. 92390V. [Google Scholar]
- Zimmermann, B.; Kohler, A. Optimizing Savitzky–Golay Parameters for Improving Spectral Resolution and Quantification in Infrared Spectroscopy. Appl. Spectrosc. 2013, 67, 892–902. [Google Scholar] [CrossRef] [Green Version]
- Savitzky, A.; Golay, M.J.E. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Anal. Chem. 1964, 36, 1627–1639. [Google Scholar] [CrossRef]
- Biscoe, P.V.; Willington, V.B.A. Environmental effects on dry matter production. In Nitrogen Requirement of Cereals: Proceedings of a Conference Organised by the Agricultural Development and Advisory Service, September 1982; HMSO: London, UK, 1984. [Google Scholar]
- Monteith, J.L. Climatic variation and the growth of crops. Q. J. R. Meteorol. Soc. 2007, 107, 749–774. [Google Scholar] [CrossRef]
Field Name | No. S1 Pixels in Field | Perimeter (m) | Area (ha) | Ground Data Collected |
---|---|---|---|---|
Great_Knott_1 | 333 | 805 | 3.37 | 2017 |
Great_Knott_2A | 146 | 555 | 1.46 | 2017 |
Great_Knott_3A | 112 | 457 | 1.12 | 2017 |
Little_Knott_1 | 122 | 482 | 1.22 | 2017 |
Osier_1_2_3 | 479 | 1046 | 4.75 | 2017 |
Sawyers_3 | 228 | 645 | 2.27 | 2017, 2019 |
Whitehorse_2B | 191 | 649 | 1.90 | 2017, 2019 |
Bones_Close | 437 | 810 | 4.39 | 2018 |
Sawyers_2 | 116 | 469 | 1.16 | 2018 |
Sawyers_4 | 131 | 465 | 1.30 | 2017, 2018 |
Stackyard | 276 | 739 | 2.77 | 2017, 2018 |
West_Barnfield_1_2 | 365 | 910 | 3.62 | 2018, 2019 |
Drapers | 399 | 839 | 3.93 | 2019 |
No. | Symbol | Parameter Name | Definition | Derived From | Associated Crop Development Stage |
---|---|---|---|---|---|
1 | G_base | Baseline value for the growth stage | VH/VV ratio at the beginning of the season | Logistic curve | Tillering (GS20) |
2 | G_steep | Steepness of logistic curve for growth period | Rate of coefficient in Equation (1) (b G) | Logistic curve | Stem elongation |
3 | G_midP | Time of midpoint of growth period (t0, G) | DOY when the midpoint of the logistic curve occurs in the growth period | Logistic curve | Stem elongation |
4 | G_max | Max value for growth stage | Maximum VH/VV ratio value for the full season | Logistic curve | End of stem elongation (GS39) and booting (GS49) |
5 | TZmax | Time of maximum point | DOY of maximum smoothed value of VH/VV | Smoothed curve | Time of booting, flag leaf unrolled |
6 | S_max | Value at the start of grain filling | Period of backscatter stabilisation | Logistic curve | Post anthesis: start of grain filling (GS71) |
7 | S_steep | Steepness of logistic curve for maturation period | Rate of coefficient in Equation (1) (b S) | Logistic curve | Maturation rate |
8 | S_midP | Time of midpoint of maturation (t0, S) | DOY when the midpoint of the logistic curve occurs in the maturation period | Logistic curve | Ripening (GS 85–89) |
9 | S_base | Baseline value at the end of the season | Background value of the VH/VV ratio | Logistic curve | After harvestPeriod with soil exposed |
10 | Duration | Duration of “full” vegetation to maturation | Time difference between midpoints (3, 8) | Combination | Period of most of the photosynthate accumulation and translocation |
11 | D_max | Structure change (Inflorescence) | VH/VV ratio value differences between booting and grain filling periods (4–6) | Combination | Backscatter change during the period when the ear emerges |
12 | D_base | Tillering backscatter | VH/VV ratio value differences between tillering and bare soil (1–9) | Combination | Tillering with reduced impact of soil |
Field | Season | RMSEG | RMSES | G_base | G_steep | G_max | S_max | S_steep | S_base | G_midP | S_midP | TZmax | Duration | D_max | D_base | Yield |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
DOY | DOY | DOY | days | t/ha | ||||||||||||
Great_Knott_1 | 2017 | 0.051 | 0.004 | 0.16 | 0.077 | 0.36 | 0.25 | −0.05 | 0.14 | 85 | 194 | 114 | 109 | 0.11 | 0.020 | 8.67 |
Great_Knott_2A | 2017 | 0.092 | 0.022 | 0.16 | 0.115 | 0.28 | 0.17 | −0.29 | 0.15 | 93 | 196 | 129 | 103 | 0.12 | 0.010 | 6.43 |
Great_Knott_3A | 2017 | 0.020 | 0.047 | 0.13 | 0.11 | 0.24 | 0.37 | −0.13 | 0.13 | 99 | 190 | 135 | 91 | −0.13 | 0.000 | 9.26 |
Little_Knott_1 | 2017 | 0.090 | 0.033 | 0.14 | 0.108 | 0.31 | 0.28 | −0.07 | 0.10 | 106 | 184 | 127 | 78 | 0.02 | 0.040 | 6.17 |
Osier_1_2_3 | 2017 | 0.054 | 0.004 | 0.10 | 0.072 | 0.34 | 0.20 | −0.08 | 0.10 | 93 | 190 | 130 | 97 | 0.14 | 0.000 | 6.63 |
Sawyers_3 | 2017 | 0.070 | 0.012 | 0.14 | 0.066 | 0.45 | 0.07 | −0.11 | 0.12 | 108 | 228 | 128 | 120 | 0.38 | 0.020 | 6.36 |
Sawyers_4 | 2017 | 0.047 | 0.004 | 0.18 | 0.115 | 0.33 | 0.26 | −0.12 | 0.14 | 108 | 199 | 136 | 90 | 0.07 | 0.040 | 6.85 |
Stackyard | 2017 | 0.037 | 0.002 | 0.16 | 0.159 | 0.22 | 0.30 | −0.08 | 0.13 | 103 | 191 | 139 | 88 | −0.08 | 0.030 | 5.04 |
Whitehorse_2B | 2017 | 0.061 | 0.035 | 0.14 | 0.143 | 0.30 | 0.18 | −0.11 | 0.11 | 93 | 215 | 127 | 122 | 0.12 | 0.030 | 9.59 |
Bones_Close | 2018 | 0.021 | 0.017 | 0.08 | 0.098 | 0.28 | 0.33 | −0.11 | 0.07 | 117 | 194 | 150 | 78 | −0.05 | 0.010 | 4.09 |
Sawyers_2 | 2018 | 0.118 | 0.035 | 0.13 | 0.102 | 0.31 | 0.23 | −0.45 | 0.10 | 104 | 196 | 141 | 91 | 0.08 | 0.030 | 6.58 |
Sawyers_4 | 2018 | 0.051 | 0.069 | 0.18 | 0.12 | 0.40 | 0.29 | −0.18 | 0.10 | 111 | 195 | 132 | 84 | 0.11 | 0.080 | 5.49 |
Stackyard | 2018 | 0.044 | 0.009 | 0.13 | 0.071 | 0.49 | 0.39 | −0.12 | 0.11 | 121 | 187 | 143 | 66 | 0.10 | 0.020 | 4.81 |
W_Barnfield_1_2 | 2018 | 0.057 | 0.012 | 0.17 | 0.094 | 0.43 | 0.28 | −0.20 | 0.11 | 101 | 189 | 132 | 88 | 0.15 | 0.060 | 7.90 |
Drapers | 2019 | 0.018 | 0.047 | 0.15 | 0.079 | 0.32 | 0.26 | −0.08 | 0.13 | 97 | 207 | 129 | 110 | 0.06 | 0.018 | 9.65 |
Sawyers_3 | 2019 | 0.007 | 0.026 | 0.14 | 0.065 | 0.41 | 0.24 | −0.26 | 0.12 | 108 | 205 | 128 | 97 | 0.17 | 0.012 | 7.99 |
W_Barnfield_1_2 | 2019 | 0.030 | 0.036 | 0.14 | 0.114 | 0.40 | 0.23 | −0.34 | 0.15 | 113 | 209 | 133 | 96 | 0.17 | −0.008 | 8.28 |
Whitehorse_2B | 2019 | 0.021 | 0.029 | 0.11 | 0.053 | 0.47 | 0.23 | −0.12 | 0.11 | 112 | 211 | 132 | 99 | 0.23 | −0.002 | 10.17 |
Season | G_base | G_steep | G_max | S_max | S_steep | S_base | G_midP | S_midP | TZmax | Duration | D_max | D_base | Yield | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
DOY | DOY | DOY | days | t/ha | ||||||||||
2017 | Mean | 0.15 | 0.11 | 0.31 | 0.23 | −0.12 | 0.12 | 99 | 199 | 129 | 100 | 0.08 | 0.02 | 7.22 |
Std dev | 0.02 | 0.03 | 0.07 | 0.09 | 0.07 | 0.02 | 8 | 14 | 7 | 15 | 0.14 | 0.02 | 1.56 | |
2018 | Mean | 0.14 | 0.10 | 0.38 | 0.30 | −0.21 | 0.10 | 111 | 192 | 140 | 81 | 0.08 | 0.04 | 5.77 |
Std dev | 0.04 | 0.02 | 0.09 | 0.06 | 0.14 | 0.02 | 8 | 4 | 8 | 10 | 0.08 | 0.03 | 1.50 | |
2019 | Mean | 0.13 | 0.08 | 0.40 | 0.24 | −0.20 | 0.13 | 107 | 208 | 131 | 101 | 0.16 | 0.01 | 9.02 |
Std dev | 0.02 | 0.03 | 0.06 | 0.01 | 0.12 | 0.01 | 7 | 3 | 2 | 7 | 0.07 | 0.01 | 1.00 |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Vavlas, N.-C.; Waine, T.W.; Meersmans, J.; Burgess, P.J.; Fontanelli, G.; Richter, G.M. Deriving Wheat Crop Productivity Indicators Using Sentinel-1 Time Series. Remote Sens. 2020, 12, 2385. https://doi.org/10.3390/rs12152385
Vavlas N-C, Waine TW, Meersmans J, Burgess PJ, Fontanelli G, Richter GM. Deriving Wheat Crop Productivity Indicators Using Sentinel-1 Time Series. Remote Sensing. 2020; 12(15):2385. https://doi.org/10.3390/rs12152385
Chicago/Turabian StyleVavlas, Nikolaos-Christos, Toby W. Waine, Jeroen Meersmans, Paul J. Burgess, Giacomo Fontanelli, and Goetz M. Richter. 2020. "Deriving Wheat Crop Productivity Indicators Using Sentinel-1 Time Series" Remote Sensing 12, no. 15: 2385. https://doi.org/10.3390/rs12152385
APA StyleVavlas, N.-C., Waine, T. W., Meersmans, J., Burgess, P. J., Fontanelli, G., & Richter, G. M. (2020). Deriving Wheat Crop Productivity Indicators Using Sentinel-1 Time Series. Remote Sensing, 12(15), 2385. https://doi.org/10.3390/rs12152385