Please wait a minute...
Journal of Integrative Agriculture  2019, Vol. 18 Issue (12): 2883-2897    DOI: 10.1016/S2095-3119(19)62599-2
Agro-ecosystem & Environment Advanced Online Publication | Current Issue | Archive | Adv Search |
High resolution crop intensity mapping using harmonized Landsat-8 and Sentinel-2 data
HAO Peng-yu1, 2, TANG Hua-jun1, CHEN Zhong-xin1, YU Le3, WU Ming-quan4 
1 Key Laboratory of Agricultural Remote Sensing, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
2 Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation/Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, P.R.China
3 Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, P.R.China
4 State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, P.R.China
Download:  PDF in ScienceDirect  
Export:  BibTeX | EndNote (RIS)      
Abstract  An increase in crop intensity could improve crop yield but may also lead to a series of environmental problems, such as depletion of ground water and increased soil salinity.  The generation of high resolution (30 m) crop intensity maps is an important method used to monitor these changes, but this is challenging because the temporal resolution of the 30-m image time series is low due to the long satellite revisit period and high cloud coverage.  The recently launched Sentinel-2 satellite could provide optical images at 10–60 m resolution and thus improve the temporal resolution of the 30-m image time series.  This study used harmonized Landsat Sentinel-2 (HLS) data to identify crop intensity.  The sixth polynomial function was used to fit the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) curves.  Then, 15-day NDVI and EVI time series were then generated from the fitted curves and used to generate the extent of croplands.  Lastly, the first derivative of the fitted VI curves were used to calculate the VI peaks; spurious peaks were removed using artificially defined thresholds and crop intensity was generated by counting the number of remaining VI peaks.  The proposed methods were tested in four study regions, with results showing that 15-day time series generated from the fitted curves could accurately identify cropland extent.  Overall accuracy of cropland identification was higher than 95%.  In addition, both the harmonized NDVI and EVI time series identified crop intensity accurately as the overall accuracies, producer’s accuracies and user’s accuracies of non-cropland, single crop cycle and double crop cycle were higher than 85%.  NDVI outperformed EVI as identifying double crop cycle fields more accurately.
Keywords:  crop intensity        time series        sixth polynomial function        harmonized Landsat-8 and Sentinel-2  
Received: 15 August 2018   Accepted:
Fund: This research was supported by the China Postdoctoral Science Foundation (2017M620075 and BX201700286) and the National Natural Science Foundation of China (NSFC-61661136006).
Corresponding Authors:  Correspondence TANG Hua-jun, E-mail: tanghuajun@caas.cn   
About author:  HAO Peng-yu, Mobile: +86-13718668296, E-mail: haopy8296 @163.com;

Cite this article: 

HAO Peng-yu, TANG Hua-jun, CHEN Zhong-xin, YU Le, WU Ming-quan. 2019. High resolution crop intensity mapping using harmonized Landsat-8 and Sentinel-2 data. Journal of Integrative Agriculture, 18(12): 2883-2897.

Anindita S, Sucharita S, Animesh K. 2009. Rice-wheat cropping cycle in Punjab: A comparative analysis of sustainability status in different irrigation systems. Environment, Development and Sustainability, 11, 751–763.
Bajzelj B, Richards K S, Allwood J M, Smith P, Dennis J S, Curmi E, Gilligan C A. 2014. Importance of food-demand management for climate mitigation. Nature Climate Change, 4, 924–929.
Boryan C G, Yang Z, Willis P, Di L. 2017. Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers. Journal of Integrative Agriculture, 16, 312–323.
Breiman L. 2001. Random forests. Machine Learning, 45, 5–32.
Breiman L, Cutler A, Liaw A, Wiener M. 2013. Breiman and cutler’s random forests for classification and regression. [2018-12-09]. http://math.furman.edu/~dcs/courses/math47/R/library/randomForest/html/00Index.html
Challinor A J, Parkes B, Ramirez-Villegas J. 2015. Crop yield response to climate change varies with cropping intensity. Global Change Biology, 21, 1679–1688.
Congalton R G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46.
 Conrad C, Schonbrodt-Stitt S, Low F, Sorokin D, Paeth H. 2016. Cropping intensity in the aral sea basin and its dependency from the runoff formation 2000–2012. Remote Sensing, 8, 630.
ESA (European Space Agency). 2016. Sentinel-2 for agriculture. [2018-10-28]. http://www.esa-sen2agri.org/SitePages/sentinel2.aspx
Estel S, Kuemmerle T, Levers C, Baumann M, Hostert P. 2016. Mapping cropland-use intensity across Europe using MODIS NDVI time series. Environmental Research Letters, 11, 024015.
Gong P, Wang J, Yu L, Zhao Y C, Zhao Y Y, Liang L, Niu Z G, Huang X M, Fu H H, Liu S, Li C C, Li X Y, Fu W, Liu C X, Xu Y, Wang X Y, Cheng Q, Hu LY, Yao W B, Zhang H, et al. 2013. Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing 34, 2607–2654.
Gray J, Friedl M, Frolking S, Ramankutty N, Nelson A, Gumma M K. 2014. Mapping Asian cropping intensity with MODIS. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 3373–3379.
Gumma M K, Thenkabail P S, Teluguntla P, Oliphant A J, Xiong J, Congalton R G, Yadav K, Phalke A, Smith C. 2017. NASA making earth system data records for use in research environments (MEaSUREs) global food security-support analysis data (GFSAD) @ 30-m for South Asia, Afghanistan and Iran: cropland extent product (GFSAD30SAAFGIRCE). [2018-10-28]. https://lpdaac.usgs.gov/dataset_discovery/measures/measures_products_table/gfsad30saafgirce_v001
Gupta R K. 1993. Scatterograms behaviour for AVHRR vegetation images of the crop growth cycle. International Journal of Remote Sensing, 14, 75–93.
Hao P, Wang L, Niu Z, Aablikim A, Huang N, Xu S, Chen F. 2014. The potential of time series merged from Landsat-5 TM and HJ-1 CCD for crop classification: A case study for Bole and Manas counties in Xinjiang, China. Remote Sensing, 6, 7610–7631.
Hao P Y, Zhan Y L, Wang L, Niu Z, Shakir M. 2015. Feature selection of time series MODIS data for early crop classification using random forest: A case study in Kansas, USA. Remote Sensing, 7, 5347–5369.
Huang Y, Chen Z X, Yu T, Huang X Z, Gu X F. 2018. Agricultural remote sensing big data: Management and applications. Journal of Integrative Agriculture, 17, 1915–1931.
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 Sensing of Environment, 83, 195–213.
Immitzer M, Vuolo F, Atzberger C. 2016. First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sensing, 8, 166.
Jain M, Mondal P, DeFries R S, Small C, Galford G L. 2013. Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors. Remote Sensing of Environment, 134, 210–223.
Jeong S J, Ho C H, Gim H J, Brown M E. 2011. Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008. Global Change Biology, 17, 2385–2399.
Kovalskyy V, Roy D P. 2013. The global availability of Landsat 5
TM and Landsat 7 ETM+ land surface observations and implications for global 30 m Landsat data product generation. Remote Sensing of Environment, 130, 280–293.
Li J, Roy D. 2017. A global analysis of Sentinel-2A, Sentinel-2B and Landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sensing, 9, 902.
Li L, Friedl M A, Xin Q C, Gray J, Pan Y Z, Frolking S. 2014. Mapping crop cycles in China using MODIS-EVI time series. Remote Sensing, 6, 2473–2493.
Li L, Zhao Y L, Fu Y C, Pan Y Z, Yu L, Xin Q C. 2017. High resolution mapping of cropping cycles by fusion of landsat and MODIS data. Remote Sensing, 9, 1232.
Liu Z, Wu C, Liu Y, Wang X, Fang B, Yuan W, Ge Q. 2017. Spring green-up date derived from GIMMS3G and SPOT-VGT NDVI of winter wheat cropland in the North China Plain. ISPRS Journal of Photogrammetry & Remote Sensing, 130, 81–91.
Maselli F, Gilabert M A, Conese C. 1998. Integration of high and low resolution NDVI data for monitoring vegetation in Mediterranean environments. Remote Sensing of Environment, 63, 208–218.
Masialeti I, Egbert S, Wardlow B D. 2010. A comparative analysis of phenological curves for major crops in Kansas. Giscience & Remote Sensing, 47, 241–259.
Mueller N D, Rhines A, Butler E E, Ray D K, Siebert S, Holbrook N M, Huybers P. 2017. Global relationships between cropland intensification and summer temperature extremes over the last 50 years. Journal of Climate, 30, 7505–7528.
NASA (National Aeronautics and Space Administration). 2017. Harmonized Landsat-8 and Sentinel-2. [2018-11-12]. https://hls.gsfc.nasa.gov/
Piao S, Fang J, Zhou L, Philippe C, Zhu B. 2010. Variations in satellite-derived phenology in China’s temperate vegetation. Global Change Biology, 12, 672–685.
Qiu B W, Zou F L, Chen C C, Tang Z H, Zhong J P, Yan X F. 2018. Automatic mapping afforestation, cropland reclamation and variations in cropping intensity in central east China during 2001–2016. Ecological Indicators, 91, 490–502.
Rodell M, Velicogna I, Famiglietti J S. 2009. Satellite-based estimates of groundwater depletion in India. Nature, 460, 999.
Rouse J W, Haas R H, Schell J A, Deering D W, Harlan J C. 1974. Monitoring the Vernal Advancements and Retrogradation of Natural Vegetation. NASA’s Goddard Space Flight Center, USA. pp. 1–137.
Siebert S, Portmann F T, Döll P. 2010. Global patterns of cropland use intensity. Remote Sensing, 2, 1625.
Song Q, Zhou Q B, Wu W B, Hu Q, Lu M, Liu  B. 2017. Mapping regional cropping patterns by using GF-1 WFV sensor data. Journal of Integrative Agriculture, 16, 337–347.
Stumpf A, Michea D, Malet J P. 2018. Improved co-registration of Sentinel-2 and Landsat-8 imagery for earth surface motion measurements. Remote Sensing, 10, 160.
Tatsumi K. 2016. Cropping intensity and seasonality parameters across Asia extracted by multi-temporal SPOT vegetation data. Journal of Agricultural Meteorology, 72, 142–150.
Teluguntla P, Thenkabail P S, Oliphant A, Xiong J, Gumma M K, Congalton R G, Yadav K, Huete A. 2018. A 30-m Landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 144, 325–340.
Teluguntla P, Thenkabail P S, Xiong J, Gumma M K, Congalton R G, Oliphant A J, Sankey T, Poehnelt J, Yadav K, Massey R, Phalke A, Smith C. 2017. NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Australia, New Zealand, China, Mongolia (GFSAD30AUNZCNMOCE). [2018-10-28]. https://e4ftl01.cr.usgs.gov/MEASURES/GFSAD30AUNZCNMOCE.001/
Thenkabail K, Ozdogan M, Congalton M K, Wu Z, You S, Milesi C, Giri C. 2012. Assessing future risks to agricultural productivity, water resources and food security: How can remote sensing help? Photogrammetric Engineering & Remote Sensing, 78, 773–782.
Tilman D, Balzer C, Hill J, Befort B L. 2011. Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences of the United States of America, 108, 20260–20264.
USGS (U.S. Geological Survey). 2018. Global food security-support analysis data at 30 m. [2018-10-28]. https://geography.wr.usgs.gov/science/croplands/index.html
Van der Meer F D, van der Werff H M A, van Ruitenbeek F J A. 2014. Potential of ESA’s Sentinel-2 for geological applications. Remote Sensing of Environment, 148, 124–133.
Wang Q, Blackburn G A, Onojeghuo A O, Dash J, Zhou L, Zhang Y, Atkinson P M. 2017. Fusion of landsat 8 OLI and Sentinel-2 MSI data. IEEE Transactions on Geoscience and Remote Sensing, 55, 3885–3899.
Wang Z H, Liao R K, Lin H, Jiang G J, He X L, Wu W Y, Zhang L L Z. 2018. Effects of drip irrigation levels on soil water, salinity and wheat growth in North China. International Journal of Agricultural & Biological Engineering, 11, 146–156.
Wardlow B D, Egbert S L. 2008. Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains. Remote Sensing of Environment, 112, 1096–1116.
Wu M, Li H, Huang W, Niu Z, Wang C. 2015. Generating daily high spatial land surface temperatures by combining ASTER and MODIS land surface temperature products for environmental process monitoring. Environmental Science Processes & Impacts, 17, 1396–1404.
Xiong J, Thenkabail P, Tilton J, Gumma M, Teluguntla P, Oliphant A, Congalton R, Yadav K, Gorelick N. 2017a. Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on google earth engine. Remote Sensing, 9, 1065.
Xiong J, Thenkabail P S, Tilton J C, Gumma M K, Teluguntla P, Congalton R G, Yadav K, Dungan J, Oliphant A J, Poehnelt J, Smith C, Massey R. 2017b. NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Africa (GFSAD30AFCE). [2018-10-28]. https://e4ftl01.cr.usgs.gov/MEASURES/GFSAD30AFCE.001/
Yu L, Shi Y, Gong P. 2015. Land cover mapping and data availability in critical terrestrial ecoregions: A global perspective with Landsat thematic mapper and enhanced thematic mapper plus data. Biological Conservation, 190, 34–42.
Zhang G L, Dong J W, Zhou C P, Xu X L, Wang M, Ouyang H, Xiao X M. 2013. Increasing cropping intensity in response to climate warming in Tibetan Plateau, China. Field Crops Research, 142, 36–46.
Zhao Y, Bai L Y, Feng J Z, Lin X S, Wang L, Xu L J, Ran Q Y, Wang K. 2016. Spatial and temporal distribution of multiple cropping indices in the north china plain using a long remote sensing data time series. Sensors, 16, 557.
No related articles found!
No Suggested Reading articles found!