Mapping Rice Phenology Using MODIS Products in An Giang Province, Mekong River Delta, Vietnam
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. MODIS Data
2.3. Methods
2.3.1. Computation of Normalized Difference Vegetation Index
2.3.2. Smoothing Normalized Difference Vegetation Index
2.3.3. Rice Phenology Extraction
2.3.4. Unsupervised Rice Phenology Classification
2.3.5. Assessment of Rice Phenology Extraction
3. Results
3.1. Comparison of NDVI Time Series Smoothing Methods
3.2. Mapping the Rice Phenology
3.2.1. Determination of Rice Phenology Class
3.2.2. Rice Phenology in An Giang Province
3.3. Rice Phenology Mapping in An Giang Province from 2018 to 2021
4. Discussion
4.1. Comparative Evaluation of MODIS Products for Rice Phenology Mapping
4.2. Limitations and Challenges of the Proposed Approach
4.3. Factors Affecting Rice Phenological Patterns
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Bin Rahman, A.N.M.R.; Zhang, J. Trends in rice research: 2030 and beyond. Food Energy Secur. 2022, 12, e390. [Google Scholar] [CrossRef]
- Organization for Economic Co-operation and Development (OECD)/Food and Agriculture Organization of the United Nations (FAO). OECD-FAO Agricultural Outlook 2020–2029; OECD: Paris, France, 2020. [Google Scholar]
- Oyoshi, K.; Takeuchi, W.; Yasuoka, Y. Noise reduction algorithm for time-series NDVI data in phenological monitoring. J. Jpn. Soc. Photogramm. Remote Sens. 2008, 47, 4–16. [Google Scholar] [CrossRef]
- Zhang, X.; Friedl, M.A.; Schaaf, C.B.; Strahler, A.H.; Hodges, J.C.; Gao, F.; Reed, B.C.; Huete, A. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 2003, 84, 471–475. [Google Scholar] [CrossRef]
- Sakamoto, T.; Van Phung, C.; Kotera, A.; Nguyen, K.D.; Yokozawa, M. Analysis of rapid expansion of inland aquaculture and triple rice-cropping areas in a coastal area of the Vietnamese Mekong Delta using MODIS time-series imagery. Landsc. Urban Plan. 2009, 92, 34–46. [Google Scholar] [CrossRef]
- Kontgis, C.; Schneider, A.; Ozdogan, M. Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data. Remote Sens. Environ. 2015, 169, 255–269. [Google Scholar] [CrossRef]
- Guan, X.; Huang, C.; Liu, G.; Meng, X.; Liu, Q. Mapping rice cropping systems in Vietnam using an NDVI-based time-series similarity measurement based on DTW distance. Remote Sens. 2016, 8, 19. [Google Scholar] [CrossRef]
- Xiao, X.; Boles, S.; Liu, J.; Zhuang, D.; Frolking, S.; Li, C.; Salas, W.; Moore, B. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sens. Environ. 2005, 95, 480–492. [Google Scholar] [CrossRef]
- Nguyen, T.T.H.; De Bie, C.; Ali, A.; Smaling, E.; Chu, T.H. Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis. Int. J. Remote Sens. 2012, 33, 415–434. [Google Scholar] [CrossRef]
- Jinwei, D.; Xiangming, X.; Weili, K.; Yuanwei, Q.; Geli, Z.; Li, L.; Cui, J.; Yuting, Z.; Jie, W.; Chandrashekhar, B.; et al. 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]
- Vermote, E.F.; Ray, J.P.; Roger, J.-C. MODIS Surface Reflectance User’s Guide, Version 1.4; MODIS Land Surface Reflectance Science Computing Facility: Greenbelt, MD, USA, 2015. [Google Scholar]
- Didan, K.; Munoz, A.B.; Solano, R.; Huete, A. MODIS Vegetation Index User’s Guide (MOD13Series), Version 3.10; University of Arizona, Vegetation Index and Phenology Lab: Tucson, AZ, USA, 2019; p. 35. [Google Scholar]
- Phan, T.N.; Kappas, M. Application of MODIS land surface temperature data: A systematic literature review and analysis. J. Appl. Remote Sens. 2018, 12, 041501. [Google Scholar] [CrossRef]
- Qunming, W.; Kaidi, P.; Yijie, T.; Xiaohua, T.; Peter, M.A. Blocks-removed spatial unmixing for downscaling MODIS images. Remote Sens. Environ. 2021, 256, 112325. [Google Scholar] [CrossRef]
- Jing, Y.; Maogui, H. Filling the missing data gaps of daily MODIS AOD using spatiotemporal interpolation. Sci. Total Environ. 2018, 633, 677–683. [Google Scholar] [CrossRef]
- Zhao, H.; Yang, Z.; Di, L.; Li, L.; Zhu, H. Crop phenology date estimation based on NDVI derived from the reconstructed MODIS daily surface reflectance data. In Proceedings of the 2009 17th International Conference on Geoinformatics, Fairfax, VA, USA, 12–14 August 2009. [Google Scholar] [CrossRef]
- Son, N.-T.; Chen, C.-F.; Chen, C.-R.; Duc, H.-N.; Chang, L.-Y. A Phenology-Based Classification of Time-Series MODIS Data for Rice Crop Monitoring in Mekong Delta, Vietnam. Remote Sens. 2014, 6, 135–156. [Google Scholar] [CrossRef]
- Alex, O.O.; George, A.B.; Qunming, W.; Peter, M.A.; Daniel, K.; Miao, Y. Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series. GIScience Remote Sens. 2018, 55, 659–677. [Google Scholar] [CrossRef]
- Arturo, G.C.; Roshanak, D.; Michael, S.; Andrew, N.; Alice, L. Estimation of transplanting and harvest dates of rice crops in the Philippines using Sentinel-1 data. Remote Sens. Appl. Soc. Environ. 2025, 37, 101435. [Google Scholar] [CrossRef]
- Xin, Z.; Kazuya, N.; Tomoko Kawaguchi, A.; Liguang, J.; Yuji, M.; Kenlo Nishida, N. Feature-based algorithm for large-scale rice phenology detection based on satellite images. Agric. For. Meteorol. 2023, 329, 109283. [Google Scholar] [CrossRef]
- Cao, J.; Cai, X.; Tan, J.; Cui, Y.; Xie, H.; Liu, F.; Yang, L.; Luo, Y. Mapping paddy rice using Landsat time series data in the Ganfu Plain irrigation system, Southern China, from 1988−2017. Int. J. Remote Sens. 2021, 42, 1556–1576. [Google Scholar] [CrossRef]
- Wang, M.; Wang, J.; Chen, L.; Du, Z. Mapping paddy rice and rice phenology with Sentinel-1 SAR time series using a unified dynamic programming framework. Open Geosci. 2022, 14, 414–428. [Google Scholar] [CrossRef]
- Nuarsa, I.W.; Nishio, F.; Hongo, C. Relationship between Rice Spectral and Rice Yield Using Modis Data. J. Agric. Sci. 2011, 3, 80–88. [Google Scholar] [CrossRef]
- Guo, Y.; Wu, W.; Liu, Y.; Wu, Z.; Geng, X.; Zhang, Y.; Bryant, C.R.; Fu, Y. Impacts of Climate and Phenology on the Yields of Early Mature Rice in China. Sustainability 2020, 12, 10133. [Google Scholar] [CrossRef]
- Na, L.; Yating, Z.; Jinsheng, H.; Qiliang, Y.; Jiaping, L.; Xiaogang, L.; Yazhou, W.; Zhengzhong, H. Impacts of future climate change on rice yield based on crop model simulation—A meta-analysis. Sci. Total Environ. 2024, 949, 175038. [Google Scholar] [CrossRef]
- Duy, V.Q.; Neuberger, D.; Suwanaporn, C. Access to credit and rice production efficiency of rural households in the Mekong Delta. Sociol. Anthropol. 2015, 3, 425–433. [Google Scholar] [CrossRef]
- Parry, M.L.; Canziani, O.; Palutikof, J.; Van der Linden, P.; Hanson, C. Climate Change 2007-Impacts, Adaptation and Vulnerability: Working Group II Contribution to the Fourth Assessment Report of the IPCC; Cambridge University Press: Cambridge, UK, 2007; Volume 4. [Google Scholar]
- Kontgis, C.; Schneider, A.; Ozdogan, M.; Kucharik, C.; Tri, V.; Duc, N.; Schatz, J. Climate change impacts on rice productivity in the Mekong River Delta. Appl. Geogr. 2019, 102, 71–83. [Google Scholar] [CrossRef]
- GSO. Statistical Yearbook of Vietnam 2022. Available online: https://www.gso.gov.vn/wp-content/uploads/2023/06/Sach-Nien-giam-TK-2022-final.pdf (accessed on 31 March 2025).
- Zhou, Q.; Rover, J.; Brown, J.; Worstell, B.; Howard, D.; Wu, Z.; Gallant, A.L.; Rundquist, B.; Burke, M. Monitoring landscape dynamics in central us grasslands with harmonized Landsat-8 and Sentinel-2 time series data. Remote Sens. 2019, 11, 328. [Google Scholar] [CrossRef]
- Yang, Y.; Luo, J.; Huang, Q.; Wu, W.; Sun, Y. Weighted Double-Logistic Function Fitting Method for Reconstructing the High-Quality Sentinel-2 NDVI Time Series Data Set. Remote Sens. 2019, 11, 2342. [Google Scholar] [CrossRef]
- Uno, K.; Ishido, K.; Nguyen Xuan, L.; Nguyen Huu, C.; Minamikawa, K. Multiple drainage can deliver higher rice yield and lower methane emission in paddy fields in An Giang Province, Vietnam. Paddy Water Environ. 2021, 19, 623–634. [Google Scholar] [CrossRef]
- Pham, V.H.T.; Ho, V.H.; Tran, D.D.; Pham, T.D.; Huynh, D.N.; Chau, N.X.Q. Impact of water resources variation on winter–spring rice yield in the upper Vietnamese Mekong Delta: A case study of An Giang Province. Irrig. Drain. 2024, 73, 574–587. [Google Scholar] [CrossRef]
- Tran, D.D.; Park, E.; Thu Van, C.; Nguyen, T.D.; Nguyen, A.H.; Linh, T.C.; Quyen, P.H.; Tran, D.A.; Nguyen, H.Q. Advancing sustainable rice production in the Vietnamese Mekong Delta insights from ecological farming systems in An Giang Province. Heliyon 2024, 10, e37142. [Google Scholar] [CrossRef]
- Yuen, K.W.; Hanh, T.T.; Quynh, V.D.; Switzer, A.D.; Teng, P.; Lee, J.S.H. Interacting effects of land-use change and natural hazards on rice agriculture in the Mekong and Red River deltas in Vietnam. Nat. Hazards Earth Syst. Sci. 2021, 21, 1473–1493. [Google Scholar] [CrossRef]
- Phuoc, L.H.; Suliansyah, I.; Arlius, F.; Chaniago, I.; Xuan, N.T.T.; Tanh, N.T.N.; Quang, P.V. Rice Growth and Yield Responses to Climate Variabilities and Scenarios. Trends Sci. 2022, 20, 6390. [Google Scholar] [CrossRef]
- Nguyen, V.H.; Yen, H.P.H. Seasonal variation and its impacts in rice-growing regions of the Mekong Delta. Int. J. Clim. Change Strateg. Manag. 2021, 13, 483–491. [Google Scholar] [CrossRef]
- Vermote, E.F.; Ray, J.P.; Roger, J.-C. MODIS Surface Reflectance User’s Guide, Version 1.5; MODIS Land Surface Reflectance Science Computing Facility: Greenbelt, MD, USA, 2020. [Google Scholar]
- Vermote, E.; Vermeulen, A. Atmospheric Correction Algorithm: Spectral Reflectances (MOD09); ATBD Version; NASA: Washington, DC, USA, 1999; Volume 4, pp. 1–107. [Google Scholar]
- Fagua, J.C.; Ramsey, R.D. Comparing the accuracy of MODIS data products for vegetation detection between two environmentally dissimilar ecoregions: The Chocó-Darien of South America and the Great Basin of North America. GIScience Remote Sens. 2019, 56, 1046–1064. [Google Scholar] [CrossRef]
- Didan, K.; Munoz, A.B.; Solano, R.; Huete, A. MODIS Vegetation Index User’s Guide (MOD13 Series), Version 3.0; University of Arizona: Vegetation Index and Phenology Lab: Tucson, AZ, USA, 2015. [Google Scholar]
- Zhao, Q.; Qu, Y. The Retrieval of Ground NDVI (Normalized Difference Vegetation Index) Data Consistent with Remote-Sensing Observations. Remote Sens. 2024, 16, 1212. [Google Scholar] [CrossRef]
- Huang, S.; Tang, L.; Hupy, J.P.; Wang, Y.; Shao, G.F. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J. For. Res. 2021, 32, 1–6. [Google Scholar] [CrossRef]
- Huete, A.; Justice, C.; Van Leeuwen, W. MODIS vegetation index (MOD13). In Algorithm Theoretical Basis Document; NASA: Washington, DC, USA, 1999; Volume 3, pp. 295–309. [Google Scholar]
- Agrawal, R.; Mohite, J.D.; Sawant, S.A.; Pandit, A.; Pappula, S. Estimation of NDVI for cloudy pixels using machine learning. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2022, 43, 813–818. [Google Scholar] [CrossRef]
- Eklundha, L.; Jönsson, P. TIMESAT 3.3 with Seasonal Trend Decomposition and Parallel Processing Software Manual; Lund University: Lund, Sweden, 2017. [Google Scholar]
- 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]
- Alcantara, C.; Kuemmerle, T.; Prishchepov, A.V.; Radeloff, V.C. Mapping abandoned agriculture with multi-temporal MODIS satellite data. Remote Sens. Environ. 2012, 124, 334–347. [Google Scholar] [CrossRef]
- Lobell, D.B.; Ortiz-Monasterio, J.I.; Sibley, A.M.; Sohu, V. Satellite detection of earlier wheat sowing in India and implications for yield trends. Agric. Syst. 2013, 115, 137–143. [Google Scholar] [CrossRef]
- Wang, Y.; Zang, S.; Tian, Y. Mapping paddy rice with the random forest algorithm using MODIS and SMAP time series. Chaos Solitons Fractals 2020, 140, 110116. [Google Scholar] [CrossRef]
- Zhang, G.; Hao, H.; Wang, Y.; Jiang, Y.; Shi, J.; Yu, J.; Cui, X.; Li, J.; Zhou, S.; Yu, B. Optimized adaptive Savitzky-Golay filtering algorithm based on deep learning network for absorption spectroscopy. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 2021, 263, 120187. [Google Scholar] [CrossRef]
- Bangura, R.; Johnson, S.; Mbulayi, O. Application of K-Means and Fuzzy K-Means to Rice Dataset in Sierra Leone. Sri Lankan J. Appl. Stat. 2020, 21, 69. [Google Scholar] [CrossRef]
- MathWorks. Matlab k-Means Clustering. Available online: https://www.mathworks.com/help/stats/kmeans.html#buefthh-2 (accessed on 15 January 2022).
- Kaufman, L.; Rousseeuw, P.J. Wiley Series in Probability and Mathematical Statistics. In Applied Probability and Statistics; Wiley: New York, NY, USA, 1990. [Google Scholar]
- Sakamoto, T. Spatio–Temporal Analysis of Agriculture in the Vietnamese Mekong Delta Using MODIS Imagery; Bulletin; National Institute for Agro-Environmental Sciences: Tokyo, Japan, 2009.
- Julien, Y.; Sobrino, J.A. Comparison of cloud-reconstruction methods for time series of composite NDVI data. Remote Sens. Environ. 2010, 114, 618–625. [Google Scholar] [CrossRef]
- Zhao, W.; Duan, S.-B. Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS/Terra land products and MSG geostationary satellite data. Remote Sens. Environ. 2020, 247, 111931. [Google Scholar] [CrossRef]
- Meraner, A.; Ebel, P.; Zhu, X.X.; Schmitt, M. Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion. ISPRS J. Photogramm. Remote Sens. 2020, 166, 333–346. [Google Scholar] [CrossRef] [PubMed]
- Gao, F.; Hilker, T.; Zhu, X.; Anderson, M.; Masek, J.; Wang, P.; Yang, Y. Fusing Landsat and MODIS Data for Vegetation Monitoring. IEEE Geosci. Remote Sens. Mag. 2015, 3, 47–60. [Google Scholar] [CrossRef]
- Onoda, S.; Ikkai, Y.; Kobayashi, T.; Komoda, N. Definition of deadlock patterns for business processes workflow models. In Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences, Honolulu, HI, USA, 5–8 January 1999. [Google Scholar]
- Filgueiras, R.; Mantovani, E.C.; Fernandes-Filho, E.I.; Cunha, F.F.d.; Althoff, D.; Dias, S.H.B. Fusion of MODIS and Landsat-like Images for Daily High Spatial Resolution NDVI. Remote Sens. 2020, 12, 1297. [Google Scholar] [CrossRef]
- Livsey, J.; Da, C.T.; Scaini, A.; Lan, T.H.P.; Long, T.X.; Berg, H.; Manzoni, S. Floods, soil and food–Interactions between water management and rice production within An Giang province, Vietnam. Agric. Ecosyst. Environ. 2021, 320, 107589. [Google Scholar] [CrossRef]
- Vergara, B.; Tanaka, A.; Lilis, R.; Puranabhavung, S. Relationship between growth duration and grain yield of rice plants. Soil. Sci. Plant Nutr. 1966, 12, 31–39. [Google Scholar] [CrossRef]
- Zhang, X.; Friedl, M.A.; Schaaf, C.B. Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements. J. Geophys. Res. Biogeosciences 2006, 111, G04017. [Google Scholar] [CrossRef]
- Tien, D.N.; Hoang, H.G.; Sen, L.T.H. Understanding farmers’ behavior regarding organic rice production in Vietnam. Org. Agric. 2022, 12, 63–73. [Google Scholar] [CrossRef]
- Chu, L.; Nguyen, H.T.; Kompas, T.; Dang, K.; Bui, T. Rice land protection in a transitional economy: The case of Vietnam. Heliyon 2021, 7, e06754. [Google Scholar] [CrossRef]
Product | Version | Grid Level | Bands | Temporal Resolution | Spatial Resolution |
---|---|---|---|---|---|
MOD09GQ | 6.1 | L2G | B1 (620–670 nm) B2 (841–876 nm) | Daily | 250 m |
MOD09Q1 | 6.1 | L3G | B1 (620–670 nm) B2 (841–876 nm) | 8-day | 250 m |
MOD13Q1 | 6.1 | L3G | NDVI EVI B1 (645 nm) B2 (858 nm) B3 (469 nm) B7 (2130–2155 nm) | 16-day | 250 m |
Product | Temporal Range | Annual Temporal Sequences | Number of Images |
---|---|---|---|
MOD-1D | 1 January 2018–31 December 2022 | 365 (daily) | 1826 |
MOD-8D | 1 January 2018–31 December 2022 | 46 (8 days) | 230 |
MOD-16D | 1 January 2018–31 December 2022 | 23 (16 days) | 115 |
Variable | Definition |
---|---|
Start-of-Season (SOS) | The date of the start of rice season, approximating the sowing date, measured in Day of Year (DOY) |
End-of-Season (EOS) | The date of the end of rice season, approximating the harvesting date, measured in Day of Year (DOY) |
Middle-of-Season (MOS) | The date of the middle of rice season, approximating the heading date, measured in Day of Year (DOY) |
Length-of-Season (LOS) | The time between SOS and EOS, measured in Day of Year (DOY) |
Base Value (BVL) | The average minimum NDVI values before SOS and after EOS, measured in unitless NDVI |
Maximum Value of Fitted Data (MAX) | The largest NDVI value of the rice season, measured in unitless NDVI |
Amplitude (AMP) | The difference between MAX and BVL, measured in unitless NDVI |
Left Derivative (LED) | The ratio of the difference between the left 20% and 80% levels |
Right Derivative (RID) | The ratio of the difference between the right 20% and 80% levels |
Large Integral (LIN) | The integral of the function from the SOS to EOS |
Small Integral (SIN) | The integral of the function from the SOS to EOS, above the BLV |
Start-of-Season Value (SVA) | The NDVI value at the SOS |
End-of-Season Value (EVA) | The NDVI value at the EOS |
Season, Year | Season | Min | 25th Percentile | Median | 75th Percentile | Max |
---|---|---|---|---|---|---|
WS 2020–2021 | Sowing | 332 | 345 | 351 | 355 | 362 |
Heading | 42 | 49 | 58 | 60 | 74 | |
Harvesting | 67 | 72 | 82 | 84 | 100 | |
SA 2021 | Sowing | 86 | 95 | 104 | 108 | 125 |
Heading | 165 | 168 | 175 | 180 | 195 | |
Harvesting | 109 | 194 | 199 | 205 | 219 |
Rice Season | Product | MOD-1D | MOD-8D | MOD-16D | |||
---|---|---|---|---|---|---|---|
Filter | DLF | SGF | DLF | SGF | DLF | SGF | |
WS 2020–2021 | SOS | 7 | 36 | 36 | 36 | 73 | 73 |
MOS | 4 | 30 | 22 | 30 | 56 | 78 | |
EOS | 7 | 37 | 7 | 37 | 60 | 31 | |
Average | 6 | 34.3 | 21.7 | 34.3 | 63.0 | 60.7 | |
SA 2021 | SOS | 9 | 10 | 12 | 15 | 19 | 30 |
MOS | 8 | 10 | 6 | 18 | 21 | 30 | |
EOS | 6 | 32 | 17 | 39 | 50 | 68 | |
Average | 7.7 | 17.3 | 11.7 | 24.0 | 30.0 | 42.7 |
Class | SOS | MOS | EOS | LOS | BVL | MAX | AMP | LED | RID | LIN | SIN | SVA | EVA |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Winter–Spring | |||||||||||||
PN01 | 29 November | 17 January | 11 March | 101 | 0.14 | 0.75 | 0.59 | 0.015 | 0.009 | 60 | 46 | 0.24 | 0.4 |
PN02 | 3 December | 13 February | 8 May | 168 | 0.4 | 0.66 | 0.26 | 0.004 | 0.003 | 97 | 31 | 0.48 | 0.48 |
PN03 | 6 November | 1 January | 5 March | 122 | 0.32 | 0.66 | 0.31 | 0.006 | 0.004 | 66 | 28 | 0.41 | 0.43 |
PN04 | 30 November | 26 January | 7 May | 173 | 0.25 | 0.72 | 0.48 | 0.013 | 0.006 | 106 | 60 | 0.36 | 0.43 |
Summer–Autumn | |||||||||||||
PN01 | 15 April | 2 August | 5 August | 106 | 0.16 | 0.65 | 0.48 | 0.012 | 0.006 | 54 | 35 | 0.36 | 0.26 |
PN02 | 21 May | 24 June | 31 August | 97 | 0.25 | 0.53 | 0.26 | 0.005 | 0.005 | 42 | 17 | 0.38 | 0.28 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 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 (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Chiang, S.-H.; Ton, M.-B. Mapping Rice Phenology Using MODIS Products in An Giang Province, Mekong River Delta, Vietnam. Remote Sens. 2025, 17, 1583. https://doi.org/10.3390/rs17091583
Chiang S-H, Ton M-B. Mapping Rice Phenology Using MODIS Products in An Giang Province, Mekong River Delta, Vietnam. Remote Sensing. 2025; 17(9):1583. https://doi.org/10.3390/rs17091583
Chicago/Turabian StyleChiang, Shou-Hao, and Minh-Binh Ton. 2025. "Mapping Rice Phenology Using MODIS Products in An Giang Province, Mekong River Delta, Vietnam" Remote Sensing 17, no. 9: 1583. https://doi.org/10.3390/rs17091583
APA StyleChiang, S.-H., & Ton, M.-B. (2025). Mapping Rice Phenology Using MODIS Products in An Giang Province, Mekong River Delta, Vietnam. Remote Sensing, 17(9), 1583. https://doi.org/10.3390/rs17091583