Mapping paddy rice in northeast China with a knowledge-based algorithm and time series optical, microwave, and thermal imagery

Abstract

Accurate and timely large-scale paddy rice maps with remote sensing are essential for crop monitoring and management and are used for assessing its impacts on food security, water resource management, and transmission of zoonotic infectious diseases. Optical image-based paddy rice mapping studies employed the unique spectral feature during the flooding/transplanting period of paddy rice. However, the lack of high-quality observations during the flooding/transplanting stage caused by rain and clouds and spectral similarity between paddy rice and natural wetlands often introduce errors in paddy rice identification, especially in paddy rice and wetland coexistent areas. In this study, we used a knowledge-based algorithm and time series observation from optical images (Sentinel-2 and Landsat 7/8) and microwave images (Sentinel-1) to address these issues. The final 10-m paddy rice map had user’s accuracy, producer’s accuracy, F1-score, and overall accuracy of 0.91 ± 0.004, 0.74 ± 0.010, 0.82, and 0.98 ± 0.001 (± value is the standard error), respectively. Over half (62.0%) of the paddy rice pixels had a confidence level of 1 (detected by both optical images and microwave images), while 38.0% had a confidence level of 0.5 (detected by either optical images or microwave images). The estimated paddy rice area in northeast China for 2020 was 60.83 ± 0.86 × 10³ km². Provincial and municipal rice areas in our data set agreed well with other existing paddy rice data sets and the Agricultural Statistical Yearbooks. These findings indicate that knowledge-based paddy rice mapping algorithms and a combination of optical and microwave images hold great potential for timely and frequently accurate paddy rice mapping in large-scale complex landscapes.

Graphical abstract

Keywords

paddy rice / rice-wetland coexistence area / flooding signal / knowledge-based algorithm / confidence map

References

[1] Brisco B (2015). Mapping and monitoring surface water and wetlands with synthetic aperture radar. Remote Sensing of Wetlands: Applications and Advances, 119–136

[2] Brisco B, Short N, Sanden J, Landry R, Raymond D (2009). A semi-automated tool for surface water mapping with RADARSAT-1.Can J Rem Sens, 35(4): 336–344

[3] Chandrasekar K, Sesha Sai M, Roy P, Dwevedi R (2010). Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product.Int J Remote Sens, 31(15): 3987–4005

[4] Chen W, Li X, Wang L (2019). Fine land cover classification in an open pit mining area using optimized support vector machine and worldview-3 imagery.Remote Sens (Basel), 12(1): 82

[5] Dong J, Xiao X (2016). Evolution of regional to global paddy rice mapping methods: a review.ISPRS J Photogramm Remote Sens, 119: 214–227

[6] Dong J, Xiao X, Kou W, Qin Y, Zhang G, Li L, Jin C, Zhou Y, Wang J, Biradar C, Liu J, Moore B III (2015). Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms.Remote Sens Environ, 160: 99–113

[7] Dong J, Xiao X, Menarguez M A, Zhang G, Qin Y, Thau D, Biradar C, Moore B III (2016a). Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine.Remote Sens Environ, 185: 142–154

[8] Dong J, Xiao X, Zhang G, Menarguez M, Choi C, Qin Y, Luo P, Zhang Y, Moore B (2016b). Northward expansion of paddy rice in northeastern Asia during 2000–2014.Geophys Res Lett, 43(8): 3754–3761

[9] Elliott J, Deryng D, Müller C, Frieler K, Konzmann M, Gerten D, Glotter M, Flörke M, Wada Y, Best N, Eisner S, Fekete B M, Folberth C, Foster I, Gosling S N, Haddeland I, Khabarov N, Ludwig F, Masaki Y, Olin S, Rosenzweig C, Ruane A C, Satoh Y, Schmid E, Stacke T, Tang Q, Wisser D (2014). Constraints and potentials of future irrigation water availability on agricultural production under climate change.Proc Natl Acad Sci USA, 111(9): 3239–3244

[10] FAOSTAT (2020). Statistical database of the food and agricultural organization of the United Nations

[11] Fisher J R, Acosta E A, Dennedy-Frank P J, Kroeger T, Boucher T M (2018). Impact of satellite imagery spatial resolution on land use classification accuracy and modeled water quality.Remote Sens Ecol Conserv, 4(2): 137–149

[12] Geudtner D, Torres R, Snoeij P, Davidson M, Rommen B (2014). Sentinel-1 system capabilities and applications. In: 2014 IEEE Geoscience and Remote Sensing Symposium. IEEE, 1457–1460

[13] Ghorbanian A, Kakooei M, Amani M, Mahdavi S, Mohammadzadeh A, Hasanlou M (2020). Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples.ISPRS J Photogramm Remote Sens, 167: 276–288

[14] Gilbert M, Golding N, Zhou H, Wint G, Robinson T P, Tatem A J, Lai S, Zhou S, Jiang H, Guo D, Huang Z, Messina J P, Xiao X, Linard C, Van Boeckel T P, Martin V, Bhatt S, Gething P W, Farrar J J, Hay S I, Yu H (2014). Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia.Nat Commun, 5(1): 4116

[15] Gilić F, Gašparović M, Baučić M (2023). Current state and challenges in producing large-scale land cover maps: review based on recent land cover products.Geocarto Int, 38(1): 2242693

[16] Gómez C, White J C, Wulder M A (2016). Optical remotely sensed time series data for land cover classification: a review.ISPRS J Photogramm Remote Sens, 116: 55–72

[17] Guan X, Huang C, Liu G, Meng X, Liu Q (2016). Mapping rice cropping systems in Vietnam using an NDVI-based time-series similarity measurement based on DTW distance.Remote Sens (Basel), 8(1): 19

[18] Guan X, Liu G, Huang C, Meng X, Liu Q, Wu C, Ablat X, Chen Z, Wang Q (2018). An open-boundary locally weighted dynamic time warping method for cropland mapping.ISPRS Int J Geoinf, 7(2): 75

[19] Gumma M K, Nelson A, Thenkabail P S, Singh A N (2011). Mapping rice areas of South Asia using MODIS multitemporal data.J Appl Remote Sens, 5(1): 053547

[20] Hall D K, Riggs G A, Salomonson V V (1995). Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data.Remote Sens Environ, 54(2): 127–140

[21] Huang C, Zhang C (2023). Time-series remote sensing of rice paddy expansion in the Yellow River Delta: towards sustainable ecological conservation in the context of water scarcity.Remote Sens Ecol Conserv, 9(4): 454–468

[22] Huang C, Zhang C, He Y, Liu Q, Li H, Su F, Liu G, Bridhikitti A (2020). Land cover mapping in cloud-prone tropical areas using Sentinel-2 data: integrating spectral features with NDVI temporal dynamics.Remote Sens (Basel), 12(7): 1163

[23] Huete A, Didan K, Miura T, Rodriguez E P, Gao X, Ferreira L G (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices.Remote Sens Environ, 83(1−2): 195–213

[24] Huete A, Liu H, Batchily K, Van Leeuwen W (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS.Remote Sens Environ, 59(3): 440–451

[25] Inoue S, Ito A, Yonezawa C (2020). Mapping paddy fields in Japan by using a Sentinel-1 SAR time series supplemented by Sentinel-2 images on Google Earth Engine.Remote Sens (Basel), 12(10): 1622

[26] Jin C, Xiao X, Dong J, Qin Y, Wang Z (2016). Mapping paddy rice distribution using multi-temporal Landsat imagery in the Sanjiang Plain, northeast China.Front Earth Sci, 10(1): 49–62

[27] Jo H W, Lee S, Park E, Lim C H, Song C, Lee H, Ko Y, Cha S, Yoon H, Lee W K (2020). Deep learning applications on multitemporal SAR (Sentinel-1) image classification using confined labeled data: the case of detecting rice paddy in South Korea.IEEE Trans Geosci Remote Sens, 58(11): 7589–7601

[28] Le Toan T, Ribbes F, Wang L F, Floury N, Ding K H, Kong J A, Fujita M, Kurosu T (1997). Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results.IEEE Trans Geosci Remote Sens, 35(1): 41–56

[29] Li H, Fu D, Huang C, Su F, Liu Q, Liu G, Wu S (2020). An approach to high-resolution rice paddy mapping using time-series Sentinel-1 SAR data in the Mun River Basin, Thailand.Remote Sens (Basel), 12(23): 3959

[30] Liu J, Kuang W, Zhang Z, Xu X, Qin Y, Ning J, Zhou W, Zhang S, Li R, Yan C, Wu S, Shi X, Jiang N, Yu D, Pan X, Chi W (2014). Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s.J Geogr Sci, 24(2): 195–210

[31] Liu L, Xiao X, Qin Y, Wang J, Xu X, Hu Y, Qiao Z (2020). Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine.Remote Sens Environ, 239: 111624

[32] Liu M, Li H, Li L, Man W, Jia M, Wang Z, Lu C (2017). Monitoring the invasion of Spartina alterniflora using multi-source high-resolution imagery in the Zhangjiang Estuary, China.Remote Sens (Basel), 9(6): 539

[33] McNairn H, Jiao X, Pacheco A, Sinha A, Tan W, Li Y (2018). Estimating canola phenology using synthetic aperture radar.Remote Sens Environ, 219: 196–205

[34] Nguyen T T H, De Bie C, Ali A, Smaling E, Chu T H (2012). Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis.Int J Remote Sens, 33(2): 415–434

[35] Ni R, Tian J, Li X, Yin D, Li J, Gong H, Zhang J, Zhu L, Wu D (2021). An enhanced pixel-based phenological feature for accurate paddy rice mapping with Sentinel-2 imagery in Google Earth Engine.ISPRS J Photogramm Remote Sens, 178: 282–296

[36] Oguro Y, Suga Y, Takeuchi S, Ogawa M, Konishi T, Tsuchiya K (2001). Comparison of SAR and optical sensor data for monitoring of rice plant around Hiroshima.Adv Space Res, 28(1): 195–200

[37] Olofsson P, Foody G M, Herold M, Stehman S V, Woodcock C E, Wulder M A (2014). Good practices for estimating area and assessing accuracy of land change.Remote Sens Environ, 148: 42–57

[38] Pan X Z, Uchida S, Liang Y, Hirano A, Sun B (2010). Discriminating different landuse types by using multitemporal NDXI in a rice planting area.Int J Remote Sens, 31(3): 585–596

[39] Panigrahy S, Parihar J (1992). Role of middle infrared bands of Landsat thematic mapper in determining the classification accuracy of rice.Int J Remote Sens, 13(15): 2943–2949

[40] Parmuchi M G, Karszenbaum H, Kandus P (2002). Mapping wetlands using multi-temporal RADARSAT-1 data and a decision-based classifier.Can J Rem Sens, 28(2): 175–186

[41] Qin Y, Xiao X, Dong J, Chen B, Liu F, Zhang G, Zhang Y, Wang J, Wu X (2017). Quantifying annual changes in built-up area in complex urban-rural landscapes from analyses of PALSAR and Landsat images.ISPRS J Photogramm Remote Sens, 124: 89–105

[42] Qin Y, Xiao X, Dong J, Zhang G, Roy P S, Joshi P K, Gilani H, Murthy M S R, Jin C, Wang J, Zhang Y, Chen B, Menarguez M A, Biradar C M, Bajgain R, Li X, Dai S, Hou Y, Xin F, Moore B III (2016). Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010.Sci Rep, 6(1): 20880

[43] Qin Y, Xiao X, Dong J, Zhang G, Shimada M, Liu J, Li C, Kou W, Moore B III (2015a). Forest cover maps of China in 2010 from multiple approaches and data sources: PALSAR, Landsat, MODIS, FRA, and NFI.ISPRS J Photogramm Remote Sens, 109: 1–16

[44] Qin Y, Xiao X, Dong J, Zhou Y, Zhu Z, Zhang G, Du G, Jin C, Kou W, Wang J, Li X (2015b). Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery.ISPRS J Photogramm Remote Sens, 105: 220–233

[45] Qin Y, Xiao X, Wigneron J P, Ciais P, Brandt M, Fan L, Li X, Crowell S, Wu X, Doughty R, Zhang Y, Liu F, Sitch S, Moore B III (2021). Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon.Nat Clim Chang, 11(5): 442–448

[46] Rao P N, Rao V (1987). Rice crop identification and area estimation using remotely-sensed data from Indian cropping patterns.Int J Remote Sens, 8(4): 639–650

[47] Shao Y, Fan X, Liu H, Xiao J, Ross S, Brisco B, Brown R, Staples G (2001). Rice monitoring and production estimation using multitemporal RADARSAT.Remote Sens Environ, 76(3): 310–325

[48] Singha M, Dong J, Zhang G, Xiao X (2019). High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data.Sci Data, 6(1): 26

[49] Tollefson J (2022). Scientists raise alarm over’dangerously fast’growth in atmospheric methane. Nature,doi: 10.1038/d41586-022-00312-2

[50] Torres R, Snoeij P, Geudtner D, Bibby D, Davidson M, Attema E, Potin P, Rommen B, Floury N, Brown M, Traver I N, Deghaye P, Duesmann B, Rosich B, Miranda N, Bruno C, L’Abbate M, Croci R, Pietropaolo A, Huchler M, Rostan F (2012). GMES Sentinel-1 mission.Remote Sens Environ, 120: 9–24

[51] Tucker C J (1979). Red and photographic infrared linear combinations for monitoring vegetation.Remote Sens Environ, 8(2): 127–150

[52] Turker M, Ozdarici A (2011). Field-based crop classification using SPOT4, SPOT5, IKONOS and QuickBird imagery for agricultural areas: a comparison study.Int J Remote Sens, 32(24): 9735–9768

[53] Wan Z (2008). New refinements and validation of the MODIS land-surface temperature/emissivity products.Remote Sens Environ, 112(1): 59–74

[54] Wang J, Xiao X, Qin Y, Dong J, Zhang G, Kou W, Jin C, Zhou Y, Zhang Y (2015). Mapping paddy rice planting area in wheat-rice double-cropped areas through integration of Landsat-8 OLI, MODIS and PALSAR images.Sci Rep, 5(1): 10088

[55] Wang X, Xiao X, Zou Z, Dong J, Qin Y, Doughty R B, Menarguez M A, Chen B, Wang J, Ye H, Ma J, Zhong Q, Zhao B, Li B (2020a). Gainers and losers of surface and terrestrial water resources in China during 1989–2016.Nat Commun, 11(1): 3471

[56] Wang X, Xiao X, Zou Z, Hou L, Qin Y, Dong J, Doughty R B, Chen B, Zhang X, Chen Y, Ma J, Zhao B, Li B (2020b). Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine.ISPRS J Photogramm Remote Sens, 163: 312–326

[57] Xian G, Shi H, Dewitz J, Wu Z (2019). Performances of WorldView 3, Sentinel 2, and Landsat 8 data in mapping impervious surface.Remote Sens Appl Soc Environ, 15: 100246

[58] Xiao X, Boles S, Frolking S, Li C, Babu J Y, Salas W, Moore B III (2006). Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images.Remote Sens Environ, 100(1): 95–113

[59] Xiao X, Boles S, Frolking S, Salas W, Moore B III, Li C, He L, Zhao R (2002a). Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data.Int J Remote Sens, 23(15): 3009–3022

[60] Xiao X, Boles S, Liu J, Zhuang D, Frolking S, Li C, Salas W, Moore B III (2005a). Mapping paddy rice agriculture in southern China using multi-temporal MODIS images.Remote Sens Environ, 95(4): 480–492

[61] Xiao X, He L, Salas W, Li C, Moore B III, Zhao R, Frolking S, Boles S (2002b). Quantitative relationships between field-measured leaf area index and vegetation index derived from VEGETATION images for paddy rice fields.Int J Remote Sens, 23(18): 3595–3604

[62] Xiao X, Hollinger D, Aber J, Goltz M, Davidson E A, Zhang Q, Moore B III (2004). Satellite-based modeling of gross primary production in an evergreen needleleaf forest.Remote Sens Environ, 89(4): 519–534

[63] Xiao X, Zhang Q, Hollinger D, Aber J, Moore B III (2005b). Modeling gross primary production of an evergreen needleleaf forest using MODIS and climate data.Ecol Appl, 15(3): 954–969

[64] Yan C, Li Z, Zhang Z, Sun Y, Wang Y, Xin Q (2023). High-resolution mapping of paddy rice fields from unmanned airborne vehicle images using enhanced-TransUnet.Comput Electron Agric, 210: 107867

[65] You N, Dong J, Huang J, Du G, Zhang G, He Y, Yang T, Di Y, Xiao X (2021). The 10-m crop type maps in northeast China during 2017–2019.Sci Data, 8(1): 41

[66] Zhan P, Zhu W, Li N (2021). An automated rice mapping method based on flooding signals in synthetic aperture radar time series.Remote Sens Environ, 252: 112112

[67] Zhang C, Xiao X, Wang X, Qin Y, Doughty R, Yang X, Meng C, Yao Y, Dong J (2024). Mapping wetlands in northeast China by using knowledge-based algorithms and microwave (PALSAR-2, Sentinel-1), optical (Sentinel-2, Landsat), and thermal (MODIS) images.J Environ Manage, 349: 119618

[68] Zhang C, Xiao X, Zhao L, Qin Y, Doughty R, Wang X, Dong J, Yang X (2023a). Mapping Eucalyptus plantation in Guangxi, China by using knowledge-based algorithms and PALSAR-2, Sentinel-2, and Landsat images in 2020.Int J Appl Earth Obs Geoinf, 120: 103348

[69] Zhang C, Zhang H, Tian S (2023b). Phenology-assisted supervised paddy rice mapping with the Landsat imagery on Google Earth Engine: experiments in Heilongjiang Province of China from 1990 to 2020.Comput Electron Agric, 212: 108105

[70] Zhang G, Xiao X, Biradar C M, Dong J, Qin Y, Menarguez M A, Zhou Y, Zhang Y, Jin C, Wang J, Doughty R B, Ding M, Moore B III (2017). Spatiotemporal patterns of paddy rice croplands in China and India from 2000 to 2015.Sci Total Environ, 579: 82–92

[71] Zhang G, Xiao X, Dong J, Kou W, Jin C, Qin Y, Zhou Y, Wang J, Menarguez M A, Biradar C (2015). Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data.ISPRS J Photogramm Remote Sens, 106: 157–171

[72] Zhang G, Xiao X, Dong J, Xin F, Zhang Y, Qin Y, Doughty R B, Moore B III (2020). Fingerprint of rice paddies in spatial–temporal dynamics of atmospheric methane concentration in monsoon Asia.Nat Commun, 11(1): 554

[73] Zhou Y, Xiao X, Qin Y, Dong J, Zhang G, Kou W, Jin C, Wang J, Li X (2016). Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images.Int J Appl Earth Obs Geoinf, 46: 1–12

[74] Zhu Z, Woodcock C E (2012). Object-based cloud and cloud shadow detection in Landsat imagery.Remote Sens Environ, 118: 83–94

[75] Zou Z, Xiao X, Dong J, Qin Y, Doughty R B, Menarguez M A, Zhang G, Wang J (2018). Divergent trends of open-surface water body area in the contiguous United States from 1984 to 2016.Proc Natl Acad Sci USA, 115(15): 3810–3815

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