{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T22:23:04Z","timestamp":1768083784944,"version":"3.49.0"},"reference-count":52,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T00:00:00Z","timestamp":1728950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42101304"],"award-info":[{"award-number":["42101304"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2024NC-YBXM-213"],"award-info":[{"award-number":["2024NC-YBXM-213"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015401","name":"Key Research and Development Projects of Shaanxi Province","doi-asserted-by":"publisher","award":["42101304"],"award-info":[{"award-number":["42101304"]}],"id":[{"id":"10.13039\/501100015401","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015401","name":"Key Research and Development Projects of Shaanxi Province","doi-asserted-by":"publisher","award":["2024NC-YBXM-213"],"award-info":[{"award-number":["2024NC-YBXM-213"]}],"id":[{"id":"10.13039\/501100015401","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Reliable and continuous information on cropping intensity is crucial for assessing cropland utilization and formulating policies regarding cropland protection and management. However, there is still a lack of high-resolution cropping intensity maps for recent years, particularly in fragmented agricultural regions. In this study, we combined Landsat-8 and Sentinel-2 imagery to generate cropping intensity maps from 2019 to 2023 at a 10 m resolution for Shaanxi Province, China. First, the satellite imagery was harmonized to construct 10-day composite enhanced vegetation index (EVI) time series. Then, the cropping intensity was determined by counting the number of valid EVI peaks within a year. Assessment based on 578 sample points showed a high level of accuracy, with overall accuracy and Kappa coefficient values exceeding 0.96 and 0.93, respectively. We further analyzed the spatiotemporal patterns of cropping intensity and generated a map of abandoned cropland in Shaanxi. The results indicated that cropland in Shaanxi Province was mainly utilized for single-cropping (52.9% of area), followed by double-cropping (35.2%), with non-cropping accounting for 11.9%. Cropping intensity tended to be lower in the north and higher in the south. Temporally, the average cropping intensity of Shaanxi increased from 1.1 to over 1.3 from 2019 to 2023. Despite this upward trend, large areas of cropland were abandoned in northern Shaanxi. These results demonstrate the potential of utilizing Landsat-8 and Sentinel-2 imagery to identify cropping intensity dynamics in fragmented agricultural regions and to guide more efficient cropland management.<\/jats:p>","DOI":"10.3390\/rs16203832","type":"journal-article","created":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T09:12:50Z","timestamp":1728983570000},"page":"3832","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Revealing Cropping Intensity Dynamics Using High-Resolution Imagery: A Case Study in Shaanxi Province, China"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1283-3451","authenticated-orcid":false,"given":"Yadong","family":"Liu","sequence":"first","affiliation":[{"name":"Shaanxi Meteorological Service Center of Agricultural Remote Sensing and Economic Crops, Xi\u2019an 710016, China"},{"name":"State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China"}]},{"given":"Hongmei","family":"Li","sequence":"additional","affiliation":[{"name":"Shaanxi Meteorological Service Center of Agricultural Remote Sensing and Economic Crops, Xi\u2019an 710016, China"}]},{"given":"Lin","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China"}]},{"given":"Bin","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling 712100, China"}]},{"given":"Meirong","family":"Li","sequence":"additional","affiliation":[{"name":"Shaanxi Meteorological Service Center of Agricultural Remote Sensing and Economic Crops, Xi\u2019an 710016, China"}]},{"given":"Huijuan","family":"He","sequence":"additional","affiliation":[{"name":"Shaanxi Meteorological Service Center of Agricultural Remote Sensing and Economic Crops, Xi\u2019an 710016, China"}]},{"given":"Hui","family":"Zhou","sequence":"additional","affiliation":[{"name":"Shaanxi Meteorological Service Center of Agricultural Remote Sensing and Economic Crops, Xi\u2019an 710016, China"}]},{"given":"Zhao","family":"Wang","sequence":"additional","affiliation":[{"name":"Shaanxi Meteorological Service Center of Agricultural Remote Sensing and Economic Crops, Xi\u2019an 710016, China"}]},{"given":"Qiang","family":"Yu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100083","DOI":"10.1016\/j.farsys.2024.100083","article-title":"Developments and Prospects of Multiple Cropping in China","volume":"2","author":"Yin","year":"2024","journal-title":"Farming Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1177\/000944558301900402","article-title":"Multiple Cropping in China","volume":"19","author":"Liu","year":"1983","journal-title":"China Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"044041","DOI":"10.1088\/1748-9326\/8\/4\/044041","article-title":"Increasing Global Crop Harvest Frequency: Recent Trends and Future Directions","volume":"8","author":"Ray","year":"2013","journal-title":"Environ. Res. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"104182","DOI":"10.1016\/j.catena.2019.104182","article-title":"Detection and Attribution of Vegetation Greening Trend Across Distinct Local Landscapes Under China\u2019s Grain to Green Program: A Case Study in Shaanxi Province","volume":"183","author":"Qian","year":"2019","journal-title":"Catena"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhou, X., and Zhou, Y. (2021). Spatio-Temporal Variation and Driving Forces of Land-Use Change from 1980 to 2020 in Loess Plateau of Northern Shaanxi, China. Land, 10.","DOI":"10.3390\/land10090982"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ye, J., Hu, Y., Feng, Z., Zhen, L., Shi, Y., Tian, Q., and Zhang, Y. (2024). Monitoring of Cropland Abandonment and Land Reclamation in the Farming\u2013Pastoral Zone of Northern China. Remote Sens., 16.","DOI":"10.3390\/rs16061089"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107763","DOI":"10.1016\/j.compag.2023.107763","article-title":"Cropland Abandonment Mapping at Sub-Pixel Scales Using Crop Phenological Information and MODIS Time-Series Images","volume":"208","author":"Zhao","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2717","DOI":"10.1080\/01431161.2023.2205984","article-title":"Crop Mapping Using Supervised Machine Learning and Deep Learning: A Systematic Literature Review","volume":"44","author":"Mansouri","year":"2023","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","first-page":"476","article-title":"Crop Discrimination in Northern China with Double Cropping Systems Using Fourier Analysis of Time-Series MODIS Data","volume":"10","author":"Zhang","year":"2008","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"B\u00e9gu\u00e9, A., Arvor, D., Bellon, B., Betbeder, J., De Abelleyra, D., PD Ferraz, R., Lebourgeois, V., Lelong, C., Sim\u00f5es, M., and Ver\u00f3n, S.R. (2018). Remote Sensing and Cropping Practices: A Review. Remote Sens., 10.","DOI":"10.3390\/rs10010099"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.3390\/rs6032473","article-title":"Mapping Crop Cycles in China Using MODIS-EVI Time Series","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_12","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_13","first-page":"101093","article-title":"Crop Monitoring by Multimodal Remote Sensing: A Review","volume":"33","author":"Karmakar","year":"2024","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Belgiu, M., and Stein, A. (2019). Spatiotemporal Image Fusion in Remote Sensing. Remote Sens., 11.","DOI":"10.3390\/rs11070818"},{"key":"ref_16","first-page":"101005","article-title":"A Review of Remote Sensing Image Spatiotemporal Fusion: Challenges, Applications and Recent Trends","volume":"32","author":"Xiao","year":"2023","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, L., Zhao, Y., Fu, Y., Pan, Y., Yu, L., and Xin, Q. (2017). High Resolution Mapping of Cropping Cycles by Fusion of Landsat and MODIS Data. Remote Sens., 9.","DOI":"10.3390\/rs9121232"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, J., and Roy, D. (2017). A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring. Remote Sens., 9.","DOI":"10.3390\/rs9090902"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2017.01.019","article-title":"Automated Cropland Mapping of Continental Africa Using Google Earth Engine Cloud Computing","volume":"126","author":"Xiong","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1109\/JSTARS.2019.2963539","article-title":"Large-Scale Crop Mapping From Multisource Remote Sensing Images in Google Earth Engine","volume":"13","author":"Liu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Htitiou, A., Boudhar, A., Chehbouni, A., and Benabdelouahab, T. (2021). National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13214378"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Guo, Y., Xia, H., Pan, L., Zhao, X., Li, R., Bian, X., Wang, R., and Yu, C. (2021). Development of a New Phenology Algorithm for Fine Mapping of Cropping Intensity in Complex Planting Areas Using Sentinel-2 and Google Earth Engine. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10090587"},{"key":"ref_24","first-page":"103504","article-title":"Cropping Intensity Map of China with 10 m Spatial Resolution from Analyses of Time-Series Landsat-7\/8 and Sentinel-2 Images","volume":"124","author":"Liu","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Mandanici, E., and Bitelli, G. (2016). Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use. Remote Sens., 8.","DOI":"10.3390\/rs8121014"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.rse.2018.04.031","article-title":"Characterization of Sentinel-2A and Landsat-8 Top of Atmosphere, Surface, and Nadir BRDF Adjusted Reflectance and NDVI Differences","volume":"215","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_28","unstructured":"(2024, July 12). Spectral Response of the Operational Land Imager In-Band, Band-Average Relative Spectral Response|Landsat Science, Available online: https:\/\/landsat.gsfc.nasa.gov\/satellites\/landsat-8\/spacecraft-instruments\/operational-land-imager\/spectral-response-of-the-operational-land-imager-in-band-band-average-relative-spectral-response\/."},{"key":"ref_29","unstructured":"(2024, July 12). S2 Mission. Available online: https:\/\/sentiwiki.copernicus.eu\/web\/s2-mission."},{"key":"ref_30","unstructured":"Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., and Fritz, S. (2022). ESA WorldCover 10 m 2021 V200. Zenoto."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106428","DOI":"10.1016\/j.landusepol.2022.106428","article-title":"Toward Sustainable Land Use in China: A Perspective on China\u2019s National Land Surveys","volume":"123","author":"Chen","year":"2022","journal-title":"Land Use Policy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3495","DOI":"10.3390\/rs5073495","article-title":"Exploring Patterns and Effects of Aerosol Quantity Flag Anomalies in MODIS Surface Reflectance Products in the Tropics","volume":"5","author":"Grogan","year":"2013","journal-title":"Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Abraham","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A Simple Method for Reconstructing a High-Quality NDVI Time-Series Data Set Based on the Savitzky\u2013Golay Filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.rse.2016.10.025","article-title":"On the Performance of Remote Sensing Time Series Reconstruction Methods\u2014A Spatial Comparison","volume":"187","author":"Zhou","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bey, A., S\u00e1nchez-Paus D\u00edaz, A., Maniatis, D., Marchi, G., Mollicone, D., Ricci, S., Bastin, J.-F., Moore, R., Federici, S., and Rezende, M. (2016). Collect Earth: Land Use and Land Cover Assessment Through Augmented Visual Interpretation. Remote Sens., 8.","DOI":"10.3390\/rs8100807"},{"key":"ref_38","unstructured":"(2024, July 12). Copernicus Browser. Available online: https:\/\/browser.dataspace.copernicus.eu\/."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.rse.2012.05.019","article-title":"Mapping Abandoned Agriculture with Multi-Temporal MODIS Satellite Data","volume":"124","author":"Alcantara","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"142651","DOI":"10.1016\/j.scitotenv.2020.142651","article-title":"Mapping Abandoned Farmland in China Using Time Series MODIS NDVI","volume":"755","author":"Zhu","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"161928","DOI":"10.1016\/j.scitotenv.2023.161928","article-title":"The Pattern of Abandoned Cropland and Its Productivity Potential in China: A Four-Years Continuous Study","volume":"870","author":"Jiang","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6083","DOI":"10.1038\/s41467-023-41837-y","article-title":"The Neglected Role of Abandoned Cropland in Supporting Both Food Security and Climate Change Mitigation","volume":"14","author":"Zheng","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_43","unstructured":"Liu, X., and Han, X. (1987). China\u2019s Multiple Cropping System, Beijing Agricultural University Press."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.agrformet.2019.01.009","article-title":"Effects of Climate Change on the Extension of the Potential Double Cropping Region and Crop Water Requirements in Northern China","volume":"268","author":"Gao","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1002\/agj2.20497","article-title":"Impacts of Global Warming on the Cropping Systems of China Under Technical Improvements from 1961 to 2016","volume":"113","author":"Jiang","year":"2021","journal-title":"Agron. J."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"102245","DOI":"10.1016\/j.apgeog.2020.102245","article-title":"Abandoned Cropland: Patterns and Determinants Within the Guangxi Karst Mountainous Area, China","volume":"122","author":"Han","year":"2020","journal-title":"Appl. Geogr."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Eyre, R., Lindsay, J., Laamrani, A., and Berg, A. (2021). Within-Field Yield Prediction in Cereal Crops Using LiDAR-Derived Topographic Attributes with Geographically Weighted Regression Models. Remote Sens., 13.","DOI":"10.3390\/rs13204152"},{"key":"ref_48","first-page":"1073","article-title":"The Relief Degree of Land Surface in China and Its Correlation with Population Distribution","volume":"62","author":"Feng","year":"2007","journal-title":"Acta Geogr. Sin."},{"key":"ref_49","unstructured":"You, Z., Feng, Z., Feng, Z., Yang, Y., and Yang, Y. (2018). Relief Degree of Land Surface Dataset of China (1km). Dightal J. Glob. Change Data Repos."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"pgad057","DOI":"10.1093\/pnasnexus\/pgad057","article-title":"Increase in Grain Production Potential of China Under Climate Change","volume":"2","author":"Liang","year":"2023","journal-title":"PNAS Nexus"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"107070","DOI":"10.1016\/j.landusepol.2024.107070","article-title":"Spatial Pattern of Cultivated Land Fragmentation in Mainland China: Characteristics, Dominant Factors, and Countermeasures","volume":"139","author":"Ye","year":"2024","journal-title":"Land Use Policy"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"103815","DOI":"10.1016\/j.agsy.2023.103815","article-title":"Can the Transition of Multiple Cropping Systems Affect the Cropland Change?","volume":"214","author":"Yibin","year":"2024","journal-title":"Agric. Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/20\/3832\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:13:45Z","timestamp":1760112825000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/20\/3832"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,15]]},"references-count":52,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16203832"],"URL":"https:\/\/doi.org\/10.3390\/rs16203832","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,15]]}}}