{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T18:11:23Z","timestamp":1781806283398,"version":"3.54.5"},"reference-count":49,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T00:00:00Z","timestamp":1695686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41971371"],"award-info":[{"award-number":["41971371"]}]},{"name":"National Natural Science Foundation of China","award":["32001368"],"award-info":[{"award-number":["32001368"]}]},{"name":"National Natural Science Foundation of China","award":["2022YFB3903504"],"award-info":[{"award-number":["2022YFB3903504"]}]},{"name":"National Natural Science Foundation of China","award":["CCNU22JC022"],"award-info":[{"award-number":["CCNU22JC022"]}]},{"name":"National Key Technologies Research and Development Program","award":["41971371"],"award-info":[{"award-number":["41971371"]}]},{"name":"National Key Technologies Research and Development Program","award":["32001368"],"award-info":[{"award-number":["32001368"]}]},{"name":"National Key Technologies Research and Development Program","award":["2022YFB3903504"],"award-info":[{"award-number":["2022YFB3903504"]}]},{"name":"National Key Technologies Research and Development Program","award":["CCNU22JC022"],"award-info":[{"award-number":["CCNU22JC022"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["41971371"],"award-info":[{"award-number":["41971371"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["32001368"],"award-info":[{"award-number":["32001368"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2022YFB3903504"],"award-info":[{"award-number":["2022YFB3903504"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["CCNU22JC022"],"award-info":[{"award-number":["CCNU22JC022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Agricultural cropping intensity plays an important role in evaluating the food security and the sustainable development of agriculture. The existing indicators measuring cropping intensity include cropping frequency and multiple cropping index. As a nominal measurement, cropping frequency classifies crop patterns into single-cropping and\/or double-cropping and leads to information loss. Multiple cropping index is calculated on the basis of statistical data, ignoring the spatial heterogeneity within the administrative region. Neither of these indicators can meet the requirements of precision agriculture, and new methods for fine cropping intensity mapping are still lacking. Time series remote sensing data provide vegetation phenology information and reveal temporal development of vegetation, which can be used to facilitate the fine cropping intensity mapping. In this study, a new temporal mixture analysis method is introduced to estimate the abundance level cropping intensity from time series remote sensing data. By analyzing phenological characteristics of major land-cover types in time series vegetatiosacan indices, a novel feature space was constructed by using the selected PCA components, and three unique endmembers (double-cropping, natural vegetations and water bodies) were found. Then, a linear spectral mixture analysis model was applied to decompose mixed pixels by replacing spectral data with multi-temporal data. The spatio-temporal continuous, fine resolution, abundance level cropping intensity maps were produced for the North China Plain and the middle and lower reaches of the Yangtze River Valley. The experiments indicate a good result at both county and pixel level validation. The method of manually delineating endmembers can well balance the accuracy and efficiency. We also found the size of the study area has little effect on the unmixing accuracy. The results demonstrated that the proposed method can model cropping intensity finely at large scale and long temporal span, at the same time with high efficiency and ease of implementation.<\/jats:p>","DOI":"10.3390\/rs15194712","type":"journal-article","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T08:58:17Z","timestamp":1695718697000},"page":"4712","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Estimating Agricultural Cropping Intensity Using a New Temporal Mixture Analysis Method from Time Series MODIS"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4844-7736","authenticated-orcid":false,"given":"Jianbin","family":"Tao","sequence":"first","affiliation":[{"name":"Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province\/School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province\/School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiqing","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Disaster Risk Science, Faculty of Geographical Sciences, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiyue","family":"Jiang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province\/School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2360-9076","authenticated-orcid":false,"given":"Yang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province\/School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,26]]},"reference":[{"key":"ref_1","first-page":"1346","article-title":"Main progress in the research on land use intensification","volume":"69","author":"Zhu","year":"2014","journal-title":"Acta Geogr. Sin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1002\/jsfa.6745","article-title":"The response of grain production to changes in quantity and quality of cropland in yangtze river delta, China","volume":"95","author":"Liu","year":"2015","journal-title":"J. Sci. Food Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s11442-014-1082-6","article-title":"Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s","volume":"24","author":"Liu","year":"2014","journal-title":"J. Geogr. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"106667","DOI":"10.1016\/j.compag.2021.106667","article-title":"Exploring cropping intensity dynamics by integrating crop phenology information using bayesian networks","volume":"193","author":"Tao","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"104435","DOI":"10.1016\/j.landusepol.2019.104435","article-title":"Identifying the driving forces of non-grain production expansion in rural China and its implications for policies on cultivated land protection","volume":"92","author":"Su","year":"2020","journal-title":"Land Use Policy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0378-4290(02)00042-4","article-title":"Fifteen years of research examining cultivation of continuous soybean in northeast China: A review","volume":"79","author":"Liu","year":"2002","journal-title":"Field Crops Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5415","DOI":"10.1073\/pnas.1718153115","article-title":"Importing food damages domestic environment: Evidence from global soybean trade","volume":"115","author":"Sun","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1002\/ldr.2924","article-title":"An estimation of the extent of cropland abandonment in mountainous regions of China","volume":"29","author":"Li","year":"2018","journal-title":"Land Degrad. Dev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"110046","DOI":"10.1016\/j.envres.2020.110046","article-title":"A review of historical and recent locust outbreaks: Links to global warming, food security and mitigation strategies","volume":"191","author":"Peng","year":"2020","journal-title":"Environ. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100659","DOI":"10.1016\/j.gfs.2022.100659","article-title":"Potential impacts of ukraine-russia armed conflict on global wheat food security: A quantitative exploration","volume":"35","author":"Mottaleb","year":"2022","journal-title":"Glob. Food Secur."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.tifs.2021.12.003","article-title":"Impact of the COVID-19 pandemic on food production and animal health","volume":"121","author":"Rahimi","year":"2022","journal-title":"Trends Food Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1108\/CAER-04-2020-0054","article-title":"The impact of COVID-19 on food prices in China: Evidence of four major food products from beijing, shandong and hubei provinces","volume":"12","author":"Yu","year":"2020","journal-title":"China Agric. Econ. Rev."},{"key":"ref_13","unstructured":"Dongyu, Q. (2022). Role and Potential of Potato in Global Food Security, FAO."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1016\/j.scitotenv.2018.03.306","article-title":"Water resources conservation and nitrogen pollution reduction under global food trade and agricultural intensification","volume":"633","author":"Liu","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_15","unstructured":"Rachele, R. (2020). Briefing-Desertification and Agriculture, European Parliament."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"60278","DOI":"10.1007\/s11356-022-20128-x","article-title":"China\u2019s agricultural non-point source pollution and green growth: Interaction and spatial spillover","volume":"29","author":"Xu","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1002\/ghg.1848","article-title":"Emission mechanism and reduction countermeasures of agricultural greenhouse gases\u2014A review","volume":"9","author":"Liu","year":"2019","journal-title":"Greenh. Gases Sci. Technol."},{"key":"ref_18","first-page":"604","article-title":"Spatiotemporal difference and determinants of multiple cropping index in China during 1998\u20132012","volume":"70","author":"Xie","year":"2015","journal-title":"Acta Geogr. Sin."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guo, Y., Xia, H., Pan, L., Zhao, X., and Li, R. (2022). Mapping the northern limit of double cropping using a phenology-based algorithm and google earth engine. Remote Sens., 14.","DOI":"10.3390\/rs14041004"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jiang, M., Xin, L., Li, X., Tan, M., and Wang, R. (2019). Decreasing rice cropping intensity in southern China from 1990 to 2015. Remote Sens., 11.","DOI":"10.3390\/rs11010035"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1080\/15481603.2017.1414010","article-title":"Regional-scale monitoring of cropland intensity and productivity with multi-source satellite image time series","volume":"55","author":"Biradar","year":"2018","journal-title":"Gisci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.apgeog.2017.01.001","article-title":"Mapping cropping intensity trends in China during 1982\u20132013","volume":"79","author":"Qiu","year":"2017","journal-title":"Appl. Geogr."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"035008","DOI":"10.1088\/1748-9326\/aaf9c7","article-title":"Tracking the spatio-temporal change of cropping intensity in China during 2000\u20132015","volume":"14","author":"Yan","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_24","first-page":"102376","article-title":"Mapping cropping intensity in huaihe basin using phenology algorithm, all sentinel-2 and landsat images in google earth engine","volume":"102","author":"Pan","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111624","DOI":"10.1016\/j.rse.2019.111624","article-title":"Mapping cropping intensity in China using time series landsat and sentinel-2 images and google earth engine","volume":"239","author":"Liu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"112095","DOI":"10.1016\/j.rse.2020.112095","article-title":"A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication","volume":"251","author":"Liu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tao, J., Wu, W., and Xu, M. (2019). Using the bayesian network to map large-scale cropping intensity by fusing multi-source data. Remote Sens., 11.","DOI":"10.3390\/rs11020168"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tao, J., Wu, W., and Liu, W. (2017). Spatial-temporal dynamics of cropping frequency in hubei province over 2001\u20132015. Sensors, 17.","DOI":"10.3390\/s17112622"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.ecolind.2015.03.039","article-title":"Mapping paddy rice areas based on vegetation phenology and surface moisture conditions","volume":"56","author":"Qiu","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tian, H.F., Huang, N., Niu, Z., Qin, Y.C., Pei, J., and Wang, J. (2019). Mapping winter crops in China with multi-source satellite imagery and phenology-based algorithm. Remote Sens., 11.","DOI":"10.3390\/rs11070820"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111660","DOI":"10.1016\/j.rse.2020.111660","article-title":"Detecting flowering phenology in oil seed rape parcels with sentinel-1 and-2 time series","volume":"239","author":"Taymans","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.isprsjprs.2017.10.005","article-title":"An easily implemented method to estimate impervious surface area on a large scale from modis time-series and improved dmsp-ols nighttime light data","volume":"133","author":"Pok","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","first-page":"136","article-title":"Classification and extraction of land use information in hilly area based on mesma and rf classifier","volume":"48","author":"Chen","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Mach"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2165","DOI":"10.1080\/01431169508954549","article-title":"Exploring a vis (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: Comparative anatomy for cities","volume":"16","author":"Ridd","year":"1995","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ji, C., Li, X., Wei, H., and Li, S. (2020). Comparison of different multispectral sensors for photosynthetic and non-photosynthetic vegetation-fraction retrieval. Remote Sens., 12.","DOI":"10.3390\/rs12010115"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.11834\/jrs.20219178","article-title":"High-resolution urban vegetation coverage estimation based on multi-source remote sensing data fusion","volume":"25","author":"Pi","year":"2021","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhuo, R., Xu, L., and Fang, Y. (2023). A spatial-temporal bayesian deep image prior model for moderate resolution imaging spectroradiometer temporal mixture analysis. Remote Sens., 15.","DOI":"10.3390\/rs15153782"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Caballero, I., and Stumpf, R.P. (2020). Towards routine mapping of shallow bathymetry in environments with variable turbidity: Contribution of sentinel-2a\/b satellites mission. Remote Sens., 12.","DOI":"10.3390\/rs12030451"},{"key":"ref_39","first-page":"1","article-title":"User Guide to Collection 6 Modis Land Cover Dynamics (mcd12q2) Product","volume":"Volume 6","author":"Gray","year":"2019","journal-title":"NASA EOSDIS Land Process"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1038\/s41597-021-01065-9","article-title":"Annual dynamic dataset of global cropping intensity from 2001 to 2019","volume":"8","author":"Liu","year":"2021","journal-title":"Sci. Data"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4799","DOI":"10.5194\/essd-13-4799-2021","article-title":"Gci30: A global dataset of 30 m cropping intensity using multisource remote sensing imagery","volume":"13","author":"Zhang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_42","unstructured":"Boardman, J.W. (1993, January 25\u201329). Automated Spectral Unmixing of Aviris Data Using Convex Geometry Concepts. Proceedings of the Jplairborne Geoscience Workshop, Washington, DC, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.rse.2013.01.011","article-title":"Detecting interannual variation in deciduous broadleaf forest phenology using landsat tm\/etm+ data","volume":"132","author":"Melaas","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Bell\u00f3n, B., B\u00e9gu\u00e9, A., Seen, D.L., Almeida, C.A.d., and Sim\u00f5es, M. (2017). A remote sensing approach for regional-scale mapping of agricultural land-use systems based on ndvi time series. Remote Sens., 9.","DOI":"10.3390\/rs9060600"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"717","DOI":"10.2307\/2260569","article-title":"Spatial and temporal patterns in the seed bank and vegetation of a desert grassland community","volume":"76","author":"Henderson","year":"1988","journal-title":"J. Ecol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.1080\/01431160902929263","article-title":"Temporal and spatial patterns of ndvi and their relationship to precipitation in the loess plateau of China","volume":"31","author":"Wang","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Yang, C., Wu, G., Ding, K., Shi, T., Li, Q., and Wang, J. (2017). Improving land use\/land cover classification by integrating pixel unmixing and decision tree methods. Remote Sens., 9.","DOI":"10.3390\/rs9121222"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1007\/s10661-022-10576-w","article-title":"Evaluating automated endmember extraction for classifying hyperspectral data and deriving spectral parameters for monitoring forest vegetation health","volume":"195","author":"Singh","year":"2022","journal-title":"Environ. Monit. Assess."},{"key":"ref_49","first-page":"388","article-title":"Extraction of impervious surface in hai basin using remote sensing","volume":"15","author":"Wang","year":"2011","journal-title":"J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4712\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:58:49Z","timestamp":1760129929000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4712"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,26]]},"references-count":49,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15194712"],"URL":"https:\/\/doi.org\/10.3390\/rs15194712","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,26]]}}}