Busetto, L., Zwart, S. J., and Boschetti, M.: Analysing spatial–temporal changes in rice cultivation practices in the Senegal River Valley using MODIS time-series and the PhenoRice algorithm, Int. J. Appl. Earth Obs., 75, 15–28, 2019.
Bush, E. R., Abernethy, K. A., Jeffery, K., Tutin, C., White, L., Dimoto, E., Dikangadissi, J. T., Jump, A. S., and Bunnefeld, N.: Fourier analysis to detect phenological cycles using long-term tropical field data and simulations, Meth. Ecol. Evol., 8, 530–540, 2017.
Cao, J., Cai, X., Tan, J., Cui, Y., Xie, H., Liu, F., Yang, L., and 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., 42, 1556–1576, 2021.
Carleton, T. A.: Crop-damaging temperatures increase suicide rates in India, P. Natl. Acad. Sci. USA, 114, 8746–8751, 2017.
Clauss, K., Yan, H., and Kuenzer, C.: Mapping paddy rice in China in 2002, 2005, 2010 and 2014 with MODIS time series, Remote Sens., 8, 434, https://doi.org/10.3390/rs8050434, 2016.
Didan, K.: MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MOD13Q1.061, 2021.
Dong, J. and Xiao, X.: Evolution of regional to global paddy rice mapping methods: A review, ISPRS J. Photogramm. Remote, 119, 214–227, 2016.
Fahad, S., Adnan, M., Noor, M., Arif, M., Alam, M., Khan, I.A., Ullah, H., Wahid, F., Mian, I. A., and Jamal, Y.: Major constraints for global rice production, Advances in rice research for abiotic stress tolerance, Elsevier, 1–22, https://doi.org/10.1016/B978-0-12-814332-2.00001-0, 2019.
Franke, J. A., Müller, C., Elliott, J., Ruane, A. C., Jägermeyr, J., Snyder, A., Dury, M., Falloon, P. D., Folberth, C., François, L., Hank, T., Izaurralde, R. C., Jacquemin, I., Jones, C., Li, M., Liu, W., Olin, S., Phillips, M., Pugh, T. A. M., Reddy, A., Williams, K., Wang, Z., Zabel, F., and Moyer, E. J.: The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO
2, temperature, water, and nitrogen (version 1.0), Geosci. Model Dev., 13, 3995–4018, https://doi.org/10.5194/gmd-13-3995-2020, 2020.
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD12Q1.006, 2019.
Fritz, S., See, L., Bayas, J. C. L., Waldner, F., Jacques, D., Becker-Reshef, I., Whitcraft, A., Baruth, B., Bonifacio, R., and Crutchfield, J.: A comparison of global agricultural monitoring systems and current gaps, Agr. Syst., 168, 258–272, 2019.
Frolking, S., Qiu, J., Boles, S., Xiao, X., Liu, J., Zhuang, Y., Li, C., and Qin, X.: Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China, Global Biogeochem. Cy., 16, 38-1–38-10, 2002.
Gao, F. and Zhang, X.: Mapping crop phenology in near real-time using satellite remote sensing: Challenges and opportunities, J. Remote Sens., 2021, 8379391, https://doi.org/10.34133/2021/8379391, 2021.
Gocic, M. and Trajkovic, S.: Analysis of changes in meteorological variables using Mann-Kendall and Sen's slope estimator statistical tests in Serbia, Global Planet. Change, 100, 172–182, 2013.
Gumma, M. K., Nelson, A., Thenkabail, P. S., and Singh, A. N.: Mapping rice areas of South Asia using MODIS multitemporal data, J. Appl. Remote Sens., 5, 053547, https://doi.org/10.1117/1.3619838, 2011.
Guo, L., An, N., and Wang, K.: Reconciling the discrepancy in ground- and satellite-observed trends in the spring phenology of winter wheat in China from 1993 to 2008, J. Geophys. Res.-Atmos., 121, 1027–1042, 2016.
Han, J., Zhang, Z., Luo, Y., Cao, J., Zhang, L., Zhuang, H., Cheng, F., Zhang, J., and Tao, F.: Annual paddy rice planting area and cropping intensity datasets and their dynamics in the Asian monsoon region from 2000 to 2020, Agr. Syst., 200, 103437, https://doi.org/10.1016/j.agsy.2022.103437, 2022.
He, Y., Dong, J., Liao, X., Sun, L., Wang, Z., You, N., Li, Z., and Fu, P.: Examining rice distribution and cropping intensity in a mixed single-and double-cropping region in South China using all available Sentinel 1/2 images, Int. J. Appl. Earth Obs., 101, 102351, https://doi.org/10.1016/j.jag.2021.102351, 2021.
IPCC: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Pörtner, H.-O., Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., Okem, A., and Rama, B., Cambridge University Press. Cambridge University Press, Cambridge, UK and New York, NY, USA, 3056 pp., https://doi.org/10.1017/9781009325844, 2022.
Jönsson, P. and Eklundh, L.: TIMESAT—a program for analyzing time-series of satellite sensor data, Comput. Geosci., 30, 833–845, https://doi.org/10.1016/j.cageo.2004.05.006, 2004.
Kong, D., McVicar, T. R., Xiao, M., Zhang, Y., Peña-Arancibia, J. L., Filippa, G., Xie, Y., and Gu, X.: phenofit: An R package for extracting vegetation phenology from time series remote sensing, Meth. Ecol. Evol., 13, 1508–1527, https://doi.org/10.1111/2041-210X.13870, 2022.
Kotsuki, S. and Tanaka, K.: SACRA – a method for the estimation of global high-resolution crop calendars from a satellite-sensed NDVI, Hydrol. Earth Syst. Sci., 19, 4441–4461, https://doi.org/10.5194/hess-19-4441-2015, 2015.
Laborte, A. G., Gutierrez, M. A., Balanza, J. G., Saito, K., Zwart, S. J., Boschetti, M., Murty, M., Villano, L., Aunario, J. K., and Reinke, R.: RiceAtlas, a spatial database of global rice calendars and production, Sci. Data, 4, 1–10, 2017.
Liu, J., Li, H., Wang, X., Wang, S., Liu, Y., Liu, Z., Chen, S., Wang Q., Zhu, T., Wang, L., and Wang, L.: ChinaRiceCalendar, Harvard Dataverse [data set], https://doi.org/10.7910/DVN/EUP8EY, 2023.
Liu, L., Huang, J., Xiong, Q., Zhang, H., Song, P., Huang, Y., Dou, Y., and Wang, X.: Optimal MODIS data processing for accurate multi-year paddy rice area mapping in China, GISci. Remote Sens., 57, 687–703, 2020.
Liu, Y., Zhou, W., and Ge, Q.: Spatiotemporal changes of rice phenology in China under climate change from 1981 to 2010, Climatic Change, 157, 261–277, 2019.
Luo, W., Chen, M., Kang, Y., Li, W., Li, D., Cui, Y., Khan, S., and Luo, Y.: Analysis of crop water requirements and irrigation demands for rice: Implications for increasing effective rainfall, Agr. Water Manage., 260, 107285, https://doi.org/10.1016/j.agwat.2021.107285, 2022.
Luo, Y., Zhang, Z., Chen, Y., Li, Z., and 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, 12, 197–214, https://doi.org/10.5194/essd-12-197-2020, 2020.
Mishra, B., Busetto, L., Boschetti, M., Laborte, A., and Nelson, A.: RICA: A rice crop calendar for Asia based on MODIS multi year data, Int. J. Appl. Earth Obs., 103, 102471, https://doi.org/10.1016/j.jag.2021.102471, 2021.
More, R. S., Manjunath, K., Jain, N. K., Panigrahy, S., and Parihar, J. S.: Derivation of rice crop calendar and evaluation of crop phenometrics and latitudinal relationship for major south and south-east Asian countries: A remote sensing approach, Comput. Electron. Agr., 127, 336–350, 2016.
Pan, Z., Huang, J., Zhou, Q., Wang, L., Cheng, Y., Zhang, H., Blackburn, G. A., Yan, J., and Liu, J.: Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data, Int. J. Appl. Earth Obs., 34, 188–197, 2015.
Qiu, J., Tang, H., Frolking, S., Boles, S., Li, C., Xiao, X., Liu, J., Zhuang, Y. and Qin, X.: Mapping single-, double-, and triple-crop agriculture in China at 0.5°
× 0.5° by combining county-scale census data with a remote sensing-derived land cover map, Geocarto Int., 18, 3–13, 2003.
Reed, B. C., Brown, J. F., VanderZee, D., Loveland, T. R., Merchant, J. W., and Ohlen, D. O.: Measuring phenological variability from satellite imagery, J. Veg. Sci., 5, 703–714, 1994.
Sakamoto, T.: Refined shape model fitting methods for detecting various types of phenological information on major US crops, ISPRS J. Photogramm., 138, 176–192, 2018.
Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., and Ohno, H.: A crop phenology detection method using time-series MODIS data, Remote Sens. Environ., 96, 366–374, 2005.
Sakamoto, T., Wardlow, B. D., Gitelson, A. A., Verma, S. B., Suyker, A. E., and Arkebauer, T. J.: A two-step filtering approach for detecting maize and soybean phenology with time-series MODIS data, Remote Sens. Environ., 114, 2146–2159, 2010.
Shen, R., Pan, B., Peng, Q., Dong, J., Chen, X., Zhang, X., Ye, T., Huang, J., and Yuan, W.: High-resolution distribution maps of single-season rice in China from 2017 to 2022, Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, 2023.
Shen, Y., Zhang, X., Yang, Z., Ye, Y., Wang, J., Gao, S., Liu, Y., Wang, W., Tran, K. H., and Ju, J.: Developing an operational algorithm for near-real-time monitoring of crop progress at field scales by fusing harmonized Landsat and Sentinel-2 time series with geostationary satellite observations, Remote Sens. Environ., 296, 113729, https://doi.org/10.1016/j.rse.2023.113729, 2023.
Shihua, L., Jingtao, X., Ping, N., Jing, Z., Hongshu, W., and Jingxian, W.: Monitoring paddy rice phenology using time series MODIS data over Jiangxi Province, China, Int. J. Agr. Biol. Eng., 7, 28–36, 2014.
Son, N.-T., Chen, C.-F., Chen, C.-R., Duc, H.-N., and Chang, L.-Y.: A phenology-based classification of time-series MODIS data for rice crop monitoring in Mekong Delta, Vietnam, Remote Sens., 6, 135–156, 2013.
Sun, C., Zhang, H., Xu, L., Ge, J., Jiang, J., Zuo, L., and Wang, C.: Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data, Earth Syst. Sci. Data, 15, 1501–1520, https://doi.org/10.5194/essd-15-1501-2023, 2023.
Waha, K., Müller, C., and Rolinski, S.: Separate and combined effects of temperature and precipitation change on maize yields in sub-Saharan Africa for mid-to late-21st century, Global Planet. Change, 106, 1–12, 2013.
Wan, Z., Hook, S., and Hulley, G.: MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V061, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MOD11A2.061, 2021.
Wang, J., Yu, K., Tian, M., and Wang, Z.: Estimation of rice key phenology date using Chinese HJ-1 vegetation index time-series images, 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), IEEE, 1–4, https://doi.org/10.1109/Agro-Geoinformatics.2019.8820262, 2019.
Wang, X., Ciais, P., Li, L., Ruget, F., Vuichard, N., Viovy, N., Zhou, F., Chang, J., Wu, X., and Zhao, H.: Management outweighs climate change on affecting length of rice growing period for early rice and single rice in China during 1991–2012, Agr. Forest Meteorol., 233, 1–11, 2017.
Wang, X., Folberth, C., Skalsky, R., Wang, S., Chen, B., Liu, Y., Chen, J., and Balkovic, J.: Crop calendar optimization for climate change adaptation in rice-based multiple cropping systems of India and Bangladesh, Agr. Forest Meteorol., 315, 108830, https://doi.org/10.1016/j.agrformet.2022.108830, 2022.
Wang, X., Wang, S., Folberth, C., Skalsky, R., Li, H., Liu, Y., and Balkovic, J.: Limiting global warming to 2° C benefits building climate resilience in rice-wheat systems in India through crop calendar management, Agr. Syst., 213, 103806, https://doi.org/10.1016/j.agsy.2023.103806, 2024.
Wu, Q.: geemap: A Python package for interactive mapping with Google Earth Engine, J. Open Source Softw., 5, 2305, https://doi.org/10.21105/joss.02305, 2020.
Xiao, X., Boles, S., Liu, J., Zhuang, D., Frolking, S., Li, C., Salas, W., and Moore III, B.: Mapping paddy rice agriculture in southern China using multi-temporal MODIS images, Remote Sens. Environ., 95, 480–492, 2005.
Xiao, X., Boles, S., Frolking, S., Li, C., Babu, J. Y., Salas, W., and Moore III, B.: Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images, Remote Sens. Environ., 100, 95–113, 2006.
Zeng, L., Wardlow, B. D., Wang, R., Shan, J., Tadesse, T., Hayes, M. J., and Li, D.: A hybrid approach for detecting corn and soybean phenology with time-series MODIS data, Remote Sens. Environ., 181, 237–250, 2016.
Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., Hodges, J. C., Gao, F., Reed, B. C., and Huete, A.: Monitoring vegetation phenology using MODIS, Remote Sens. Environ., 84, 471–475, 2003.
Zhao, C., Liu, B., Piao, S., Wang, X., Lobell, D. B., Huang, Y., Huang, M., Yao, Y., Bassu, S., and Ciais, P.: Temperature increase reduces global yields of major crops in four independent estimates, P. Natl. Acad. Sci. USA, 114, 9326–9331, 2017.
Zhao, H., Yang, Z., Di, L., and Pei, Z.: Evaluation of temporal resolution effect in remote sensing based crop phenology detection studies, International Conference on Computer and Computing Technologies in Agriculture, Springer, 135–150, https://doi.org/10.1007/978-3-642-27278-3_16, 2011.
Zheng, J., Song, X., Yang, G., Du, X., Mei, X., and Yang, X.: Remote sensing monitoring of rice and wheat canopy nitrogen: A review, Remote Sens., 14, 5712, https://doi.org/10.3390/rs14225712, 2022.
Zong, W., Ren, D., Huang, M., Sun, K., Feng, J., Zhao, J., Xiao, D., Xie, W., Liu, S., and Zhang, H.: Strong photoperiod sensitivity is controlled by cooperation and competition among Hd1, Ghd7 and DTH8 in rice heading, New Phytol., 229, 1635–1649, 2021.
