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Journal of Integrative Agriculture  2024, Vol. 23 Issue (1): 283-297    DOI: 10.1016/j.jia.2023.06.005
Agro-ecosystem & Environment Advanced Online Publication | Current Issue | Archive | Adv Search |

Quantifying the agreement and accuracy characteristics of four satellite-based LULC products for cropland classification in China

Jie Xue1, Xianglin Zhang2, Songchao Chen2, 3, Bifeng Hu4, 5, Nan Wang2, Zhou Shi2, 6#

1 Department of Land Management, Zhejiang University, Hangzhou 310058, China

2 Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China

3 ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China

4 Department of Land Resource Management, School of Public Finance and Public Administration, Jiangxi University of Finance and Economics, Nanchang 330013, China

5 Key Laboratory of Data Science in Finance and Economics, Jiangxi University of Finance and Economics, Nanchang 330013, China

6 Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China

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摘要  

随着遥感技术的发展,在过去的十几年中出现大量土地利用和土地覆盖(LULC)产品。然而,各种LULC产品的分类体系存在差异,尚缺乏在统一框架下评估各产品中耕地分类精度的研究。因此,本研究以FAO的分类体系为标准,统一四种常用的全球LULC产品(即MCD12Q1 V6GlobCover2009FROM-GLCGlobeLand30)的耕地分类体系,评估各产品中耕地分类的空间分布一致性与准确性和面积的准确性,并量化坡度、海拔、田块面积和耕作制度与耕地空间分布一致性的关系。结果表明,在空间分布的准确性方面,MCD12Q1GlobeLand30表现良好,总体准确率分别为94.9093.52%FROM-GLC表现最差,总体准确率为83.17%。在空间分布的一致性方面,四种LULC产品的耕地像元完全一致和不一致的比例分别为15.5144.72%。高度一致性区域主要出现在东北平原、黄淮海平原和中下游长江平原北部,低度一致性区域主要出现在华北半干旱区东部边缘、云贵高原和华南地区。田块面积是影响耕地制图的核心因素。在面积准确性方面,与《中国统计年鉴》数据对比,各产品在省级尺度的面积准确性从高到低依次为:GlobeLand30FROM-GLCMCD12Q1GlobCover2009。耕地分类体系的不是造成四种产品间面积偏差空间分布和面积准确性差异主要的原因。本研究为国家及省级尺度耕地产品选择提供数据支撑,为进一步提升耕地制图精度提供理论参考。本研究结果有助于粮食安全和作物管理相关研究的开展,对实现联合国提出的可持续发展目标意义重大。



Abstract  

Various land use and land cover (LULC) products have been produced over the past decade with the development of remote sensing technology.  Despite the differences in LULC classification schemes, there is a lack of research on assessing the accuracy of their application to croplands in a unified framework.  Thus, this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products (i.e., MCD12Q1 V6, GlobCover2009, FROM-GLC and GlobeLand30) based on the harmonised FAO criterion, and quantified the relationships between four factors (i.e., slope, elevation, field size and crop system) and cropland classification agreement.  The validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency, with overall accuracies of 94.90 and 93.52%, respectively.  The FROM-GLC showed the worst performance, with an overall accuracy of 83.17%.  Overlaying the cropland generated by the four global LULC products, we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification, respectively.  High consistency was mainly observed in the Northeast China Plain, the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain, China.  In contrast, low consistency was detected primarily on the eastern edge of the northern and semiarid region, the Yunnan-Guizhou Plateau and southern China.  Field size was the most important factor for mapping cropland.  For area accuracy, compared with China Statistical Yearbook data at the provincial scale, the accuracies of different products in descending order were: GlobeLand30, FROM-GLC, MCD12Q1, and GlobCover2009.  The cropland classification schemes mainly caused large area deviations among the four products, and they also resulted in the different ranks of spatial accuracy and area accuracy among the four products.  Our results can provide valuable suggestions for selecting cropland products at the national or provincial scale and help cropland mapping and reconstruction, which is essential for food security and crop management, so they can also contribute to achieving the Sustainable Development Goals issued by the United Nations.

Keywords:  global LULC products        cropland mapping        accuracy evaluation        food security        China   
Received: 16 February 2023   Accepted: 08 May 2023
Fund: 

This study was supported by the National Key Research and Development Program of China (2022YFB3903503), the National Natural Science Foundation of China (U1901601), and the Science and Technology Project of the Department of Education of Jiangxi Province, China (GJJ210541)

About author:  Jie Xue, E-mail: xj2019@zju.edu.cn; #Correspondence Zhou Shi, E-mail: shizhou@zju.edu.cn

Cite this article: 

Jie Xue, Xianglin Zhang, Songchao Chen, Bifeng Hu, Nan Wang, Zhou Shi. 2024.

Quantifying the agreement and accuracy characteristics of four satellite-based LULC products for cropland classification in China . Journal of Integrative Agriculture, 23(1): 283-297.

Alexandratos N, Bruinsma J. 2012. World agriculture towards 2030/2050: The 2012 revision. Food and Agriculture Organization of the United Nations. [2022-5-9]. https://doi.org/10.22004/ag.econ.288998

Ban Y F, Gong P, Giri C P. 2015. Global land cover mapping using Earth observation satellite data: Recent progresses and challenges. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 1–6.

Bontemps S, Defourny P, Bogaert E, Arino O, Kalogirou V, Perez J. 2011. GLOBCOVER 2009. Products description and validation report. [2022-4-10]. https://core.ac.uk/download/pdf/11773712.pdf

Breiman L. 2001. Random forests. Machine Learning, 45, 5–32.

Chen J, Ban Y F, Li S N. 2014. Open access to Earth land-cover map. Nature, 514, 434.

Chen J, Chen J, Liao A P, Cao X, Chen L J, Chen X H, He C Y, Han G, Peng S, Lu M, Zhang W W, Tong X H, Mills J. 2015. Global land cover mapping at 30m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 7–27.

Chen S C, Arrouays D, Mulder V L, Poggio L, Minasny B, Roudier P, Libohova Z, Lagacherie P, Shi Z, Hannam J, Meersmans J, Richer-de-Forges A C, Walter C. 2022. Digital mapping of GlobalSoilMap soil properties at a broad scale: A review. Geoderma, 409, 115567.

Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, Wehberg J, Wichmann V, Böhner J. 2015. System for automated geoscientific analyses (SAGA) v. 2.1.4. Geoscientific Model Development, 8, 1991–2007.

Defourny P, Kirches G, Brockmann C, Boettcher M, Peters M, Bontemps S, Lamarche C, Schlerf M, Santoro M. 2016. Land cover CCI: Product user guide version 2. [2022-4-3]. http://maps.elie.ucl.ac.be/CCI/viewer/download.php

Duan J K, Ren C C, Wang S T, Zhang X M, Reis S, Xu J M, Gu B J. 2021. Consolidation of agricultural land can contribute to agricultural sustainability in China. Nature Food, 2, 1014–1022.

Foley J A, DeFries R, Asner G P, Barford C, Bonan G, Carpenter S R, Chapin F S, Coe M T, Daily G C, Gibbs H K, Helkowski J H, Holloway T, Howard E A, Kucharik C J, Monfreda C, Patz J A, Prentice I C, Ramankutty N, Snyder P K. 2005. Global consequences of land use. Science, 309, 570–574.

Friedl M, Sulla-Menashe D. 2019. MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. [2022-7-20]. https://doi.org/10.5067/MODIS/MCD12Q1.006

Friedl M A, McIver D K, Hodges J C F, Zhang X Y, Muchoney D, Strahler A H, Woodcock C E, Gopal S, Schneider A, Cooper A, Baccini A, Gao F, Schaaf C. 2002. Global land cover mapping from MODIS: Algorithms and early results. Remote Sensing of Environment, 83, 287–302.

Friedl M A, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A, Huang X M. 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114, 168–182.

Fritz S, McCallum I, Schill C, Perger C, See L, Schepaschenko D, van der Velde M, Kraxner F, Obersteiner M. 2012. Geo-Wiki: An online platform for improving global land cover. Environmental Modelling & Software, 31, 110–123.

Fritz S, See L, McCallum I, You L, Bun A, Moltchanova E, Duerauer M, Albrecht F, Schill C, Perger C, Havlik P, Mosnier A, Thornton P, Wood-Sichra U, Herrero M, Becker-Reshef I, Justice C, Hansen M, Gong P, Abdel Aziz S, et al. 2015. Mapping global cropland and field size. Global Change Biology, 21, 1980–1992.

Fu J Y, Bu Z Q, Jiang D, Lin G, Li X. 2022. Sustainable land use diagnosis based on the perspective of production–living–ecological spaces in China. Land Use Policy, 122, 106386.

Gao Y, Liu L Y, Zhang X, Chen X D, Mi J, Xie S. 2020. Consistency analysis and accuracy assessment of three global 30-m land-cover products over the European Union using the LUCAS dataset. Remote Sensing, 12, 3479.

Godfray H C J, Beddington J R, Crute I R, Haddad L, Lawrence D, Muir J F, Pretty J, Robinson S, Thomas S M, Toulmin C. 2010. Food security: The challenge of feeding 9 billion people. Science, 327, 812–818.

Gong P, Liu H, Zhang M N, Li C C, Wang J, Huang H B, Clinton N, Ji L Y, Li W Y, Bai Y Q, Chen B, Xu B, Zhu Z L, Yuan C, Ping Suen H, Guo J, Xu N, Li W J, Zhao Y Y, Yang J, et al. 2019. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Science Bulletin, 64, 370–373.

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 L Y, 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.

Grekousis G, Mountrakis G, Kavouras M. 2015. An overview of 21 global and 43 regional land-cover mapping products. International Journal of Remote Sensing, 36, 5309–5335.

Hansen M C, Defries R S, Townshend J R G, Sohlberg R. 2000. Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing, 21, 1331–1364.

Herold M, Mayaux P, Woodcock C E, Baccini A, Schmullius C. 2008. Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets. Remote Sensing of Environment, 112, 2538–2556.

Hu B F, Xie M D, Li H Y, He R B, Zhou Y, Jiang Y F, Ji W J, Peng J, Xia F, Liang Z Z, Deng W M, Wang J J, Shi Z. 2023. Climate and soil management factors control spatio-temporal variation of soil nutrients and soil organic matter in the farmland of Jiangxi Province in South China. Journal of Soils and Sediments, doi: 10.1007/s11368-023-03471-5.

Hu B F, Xue J, Zhou Y, Shao S, Fu Z Y, Li Y, Chen S C, Qi L, Shi Z. 2020. Modelling bioaccumulation of heavy metals in soil-crop ecosystems and identifying its controlling factors using machine learning. Environmental Pollution, 262, 114308.

Hu B F, Zhou, Q, He C Y, Duan L X, Li W Y, Zhang G L, Ji W J, Peng J, Xie H X. 2021. Spatial variability and potential controls of soil organic matter in the Eastern Dongting Lake Plain in southern China. Journal of Soils and Sediments, 21, 2791–2804.

Lesiv M, Bayas J C L, See L, Duerauer M, Dahlia D, Durando N, Hazarika R, Sahariah P K, Vakolyuk M Y, Blyshchyk V, Bilous A, Perez-Hoyos A, Gengler S, Prestele R, Bilous S, Akhtar I U H, Singha K, Choudhury S B, Chetri T, Malek Z, et al. 2019. Estimating the global distribution of field size using crowdsourcing. Global Change Biology, 25, 174–186.

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. Journal of Geographical Sciences, 24, 195–210.

Loveland T R, Belward A S. 1997. The IGBP-DIS global 1km land cover data set, DISCover: First results. International Journal of Remote Sensing, 18, 3289–3295.

Lu M, Wu W B, Zhang L, Liao A P, Peng S, Tang H J. 2016. A comparative analysis of five global cropland datasets in China. Science China (Earth Sciences), 59, 2307–2317.

Mas J F, Kolb M, Paegelow M, Camacho Olmedo M T, Houet T. 2014. Inductive pattern-based land use/cover change models: A comparison of four software packages. Environmental Modelling & Software, 51, 94–111.

Pengra B W, Stehman S V, Horton J A, Dockter D J, Schroeder T A, Yang Z Q, Cohen W B, Healey S P, Loveland T R. 2020. Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program. Remote Sensing of Environment, 238, 111261.

Potapov P, Turubanova S, Hansen M C, Tyukavina A, Zalles V, Khan A, Song X P, Pickens A, Shen Q, Cortez J. 2022. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nature Food, 3, 19–28.

Ran Y H, Li X, Lu L. 2010. Evaluation of four remote sensing based land cover products over China. International Journal of Remote Sensing, 31, 391–401.

Stehman S V, Foody G M. 2019. Key issues in rigorous accuracy assessment of land cover products. Remote Sensing of Environment, 231, 111199.

Stehman S V, Pengra B W, Horton J A, Wellington D F. 2021. Validation of the U.S. geological survey’s land change monitoring, assessment and projection (LCMAP) collection 1.0 annual land cover products 1985–2017. Remote Sensing of Environment, 265, 112646.

Sun W Y, Ding X T, Su J B, Mu X M, Zhang Y Q, Gao P, Zhao G J. 2022. Land use and cover changes on the Loess Plateau: A comparison of six global or national land use and cover datasets. Land Use Policy, 119, 106165.

Tesfaye B, Lengoiboni M, Zevenbergen J, Simane B. 2021. Mapping land use land cover changes and their determinants in the context of a massive free labour mobilisation campaign: Evidence from South Wollo, Ethiopia. Remote Sensing, 13, 5078.

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.

Tubiello F N, Conchedda G, Casse L, Peng Y H, Zhong X C, De Santis G, Fritz S, Muchoney D. 2023. Measuring the world’s cropland area. Nature Food, 4, 30–32.

Venter Z S, Barton D N, Chakraborty T, Simensen T, Singh G. 2022. Global 10 m land use land cover datasets: A comparison of dynamic world, world cover and Esri land cover. Remote Sensing, 14, 4101

Verburg P H, Neumann K, Nol L. 2011. Challenges in using land use and land cover data for global change studies. Global Change Biology, 17, 974–989.

Wei Y B, Lu M, Wu W B, Ru Y T. 2020. Multiple factors influence the consistency of cropland datasets in Africa. International Journal of Applied Earth Observation and Geoinformation, 89, 102087.

Wu W B, Shibasaki R, Yang P, Zhou Q B, Tang H J. 2008. Remotely sensed estimation of cropland in China: A comparison of the maps derived from four global land cover datasets. Canadian Journal of Remote Sensing, 34, 467–479.

Xiong J, Thenkabail P S, Tilton J C, Gumma M K, Teluguntla P, Oliphant A, Congalton R G, Yadav K, Gorelick N. 2017. 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.

Yang J, Huang X. 2021. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019. Earth System Science Data, 13, 3907–3925.

Yang Y K, Xiao P F, Feng X Z, Li H X. 2017. Accuracy assessment of seven global land cover datasets over China. ISPRS Journal of Photogrammetry and Remote Sensing, 125, 156–173.

Yu L, Wang J, Gong P. 2013. Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: A segmentation-based approach. International Journal of Remote Sensing, 34, 5851–5867.

Yu L, Wang J, Li X C, Li C C, Zhao Y Y, Gong P. 2014. A multi-resolution global land cover dataset through multisource data aggregation. Science China Earth Sciences, 57, 2317–2329.

Yu Q Y, Wu W B, Tang H J. 2023. Increased food-miles and transport emissions. Nature Food, 4, 207–208.

Yu Z N, She S Q, Xia C Y, Luo J J. 2023. How to solve the dilemma of China’s land fallow policy: Application of voluntary bidding mode in the Yangtze River Delta of China. Land Use Policy, 125, 106503.

Zhang C, Dong J W, Ge Q S. 2022. Quantifying the accuracies of six 30-m cropland datasets over China: A comparison and evaluation analysis. Computers and Electronics in Agriculture, 197, 106946.

Zhang X, Liu L Y, Wu C S, Chen X D, Gao Y, Xie S, Zhang B. 2020. Development of a global 30m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform. Earth System Science Data, 12, 1625–1648.

Zhang X K, Shi W Z, Lv Z Y. 2019. Uncertainty assessment in multitemporal land use/cover mapping with classification system semantic heterogeneity. Remote Sensing, 11, 2509.

Zhang X L, Xue J, Chen S C, Wang N, Shi Z, Huang Y F, Zhuo Z Q. 2022. Digital mapping of soil organic carbon with machine learning in dryland of Northeast and North Plain China. Remote Sensing, 14, 2504.

Zhou Y, Dong J W, Liu J Y, Metternicht G, Shen W, You N S, Zhao G S, Xiao X M. 2019. Are there sufficient landsat observations for retrospective and continuous monitoring of land cover changes in China? Remote Sensing, 11, 1808.

Zuo L J, Zhang Z X, Carlson K M , MacDonald G K, Brauman K A, Liu Y C, Zhang W, Zhang H Y, Wu W B, Zhao X L, Wang X, Liu B, Yi L, Wen Q K, Liu F, Xu J Y, Hu S G, Sun F F, Gerber J S, West P C. 2018. Progress towards sustainable intensification in China challenged by land-use change. Nature Sustainability, 1, 304–313.


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