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Twenty years of CropWatch: Progress and prospect

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

  • Email：wubf@radi.ac.cn
  • Introduction：吴炳方，1962年生，男，研究员，研究方向为农业遥感、水资源遥感与管理、生态遥感。E-mail：wubf@radi.ac.cn
WU Bingfang,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

ZHANG Miao,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

ZENG Hongwei,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

YAN Nana,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

ZHANG Xin,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

XING Qiang,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

CHANG Sheng

Resümee

The Chinese Academy of Sciences has launched a Chinese remote sensing-based agriculture monitoring system (called CropWatch) in 1998 to provide agriculture information for supporting national food security strategies. CropWatch has provided operational bulletins and has been upgraded to a global agriculture monitoring system, which is unique and uses remote sensing data as the major data source. This study systematically summarizes the development of CropWatch in terms of agricultural monitoring approach/methodology and matrix, system platform, and agricultural information dissemination since 2013. This study emphasizes the innovative agricultural monitoring approach and matrix and pioneering customizable agricultural monitoring system since the establishment of the CropWatch participatory global agricultural monitoring cloud platform (CropWatch-Cloud). A hierarchical approach is adopted by CropWatch, in which a four-tier monitoring strategy, namely, global (sixty-five Crop Monitoring and Reporting Units, MRU), regional (seven Major Production Zones, MPZ), national (31 key countries), and subnational (subdivisions of nine large countries), is developed. CropWatch indicators/matrix can be divided into four categories, namely, CropWatch agroclimatic, arable land use intensity, crop condition, and crop production indicators. Four temporal and spatial resolutions of indicators adapted to each scale are used in CropWatch bulletin. Using the outputs on the four levels, a synthesis of crop condition and production estimates for four major crops is published in the CropWatch bulletin. In situ measurements, local experience, and knowledge are essential to upgrading and improving the robustness of the indicators, methodologies, and algorithms in the CropWatch system. Furthermore, acquiring valuable information outside China, in addition to the global information collection, is challenging. Recently, geotagged data from the crowd or from search engines, such as Google, have revealed interesting scientific trends and have been confirmed as an effective and costless means for data collection, that is, the ability to characterize crop planting dates, or training and validation samples for cropland mapping. External application promotion at home and abroad is emphasized. With the development of CropWatch Cloud, data analysis and reporting are feasible from any location in the world through the Internet. This technology provides unique opportunities to countries requiring work on crop monitoring but currently lack sufficient resources. The innovative approach of the participatory CropWatch-Cloud platform makes this technology an influential agricultural monitoring system. In terms of the originality and innovation of CropWatch, four items are systematically summarized from a methodological perspective. These items are (1) six unique remote sensing-based indicators for an agricultural assessment, (2) advances of early-warning capability, (3) integrated crop condition assessment methods that consider the advantages of six methods, and (4) the innovative crop area estimation method supported by crowd-sourcing data. This study expects the future development of CropWatch. The CropWatch team will leverage CropWatch based on the development of volunteered geographic information acquisition and big Earth data technology to provide agricultural information from the global scale for policy making to the field scale for farm management.

Schlüsselwort

remote sensing;CropWatch;global agriculture monitoring;cloud platform

References

  1. 1.
    Becker-Reshef I, Justice C, Sullivan M, Vermote E, Tucker C, Anyamba A, Small J, Pak E, Masuoka E, Schmaltz J, Hansen M, Pittman K, Birkett C, Williams D, Reynolds C and Doorn B. 2010. Monitoring global croplands with coarse resolution earth observations: the global agriculture monitoring (GLAM) project. Remote Sensing, 2(6): 1589–1609
  2. 2.
    Chang S, Wu B F, Yan N N, Davdai B and Nasanbat E. 2017. Suitability assessment of satellite-derived drought indices for Mongolian Grassland. Remote Sensing, 9(7): 650
  3. 3.
    Fan J L and Wu B F. 2004. A Methodology for retrieving cropping index from NDVI profile. Journal of Remote Sensing, 8(6): 628–636
  4. 4.
    Fritz S, McCallum I, Schill C, Perger C, Grillmayer R, Achard F, Kraxner F and Obersteiner M. 2009. Geo-Wiki. Org: the use of crowdsourcing to improve global land cover. Remote Sensing, 1(3): 345–354
  5. 5.
    Fritz S, McCallum I, Schill C, Perger C, See L, Schepaschenko D, Van Der Velde M, Kraxner F and Obersteiner M. 2012. Geo-wiki: an online platform for improving global land cover. Environmental Modelling and Software, 31: 110–123
  6. 6.
    Fritz S, See L, McCallum I, You L Z, 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, Cipriani A, Cumani R, Cecchi G, Conchedda G, Ferreira S, Gomez A, Haffani M, Kayitakire F, Malanding J, Mueller R, Newby T, Nonguierma A, Olusegun A, Ortner S, Rajak D R, Rocha J, Schepaschenko D, Schepaschenko M, Terekhov A, Tiangwa A, Vancutsem C, Vintrou E, Wu W B, Van Der Velde M, Dunwoody A, Kraxner F and Obersteiner M. 2015. Mapping global cropland and field size. Global Change Biology, 21(5): 1980–1992
  7. 7.
    Genovese G P. 2001. Introduction to the MARS crop yield forecasting system (MCYFS)//Meeting on 4 and 5 October 2001. Luxembourg: Space Applications Institute, Joint Research Centre of the European Commission, Ispra, Italy: 15
  8. 8.
    Gommes R, Wu B F, Li Z Y and Zeng H W. 2016. Design and characterization of spatial units for monitoring global impacts of environmental factors on major crops and food security. Food and Energy Security, 5(1): 40–55
  9. 9.
    Gommes R, Wu B F, Zhang N, Feng X L, Zeng H W, Li Z Y and Chen B. 2017. CropWatch agroclimatic indicators (CWAIs) for weather impact assessment on global agriculture. International Journal of Biometeorology, 61(2): 199–215
  10. 10.
    Goodchild M F and Glennon J A. 2010. Crowdsourcing geographic information for disaster response: a research frontier. International Journal of Digital Earth, 3(3): 231–241
  11. 11.
    Guo H D. 2018. Steps to the digital silk road. Nature, 554(7690): 25–27
  12. 12.
    Han W G, Yang Z W, Di L P and Mueller R. 2012. CropScape: a Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support. Computers and Electronics in Agriculture, 84: 111–123
  13. 13.
    Hansen M C, Potapov P V, Moore R, Hancher M, Turubanova S A, Tyukavina A, Thau D, Stehman S V, Goetz S J, Loveland T R, Kommareddy A, Egorov A, Chini L, Justice C O and Townshend J R G. 2013. High-resolution global maps of 21st-century forest cover change. Science, 342(6160): 850–853
  14. 14.
    Li Z Y, Wu B F, Gommes R, Zhang M and Chen B. 2015. Design framework for CropWatch Cloud. Journal of Remote Sensing, 19(4): 578–585
  15. 15.
    Pekel J F, Cottam A, Gorelick N and Belward A. 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633): 418–422
  16. 16.
    Rojas O, Vrieling A and Rembold F. 2011. Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery. Remote Sensing of Environment, 115(2): 343–352
  17. 17.
    See L, McCallum I, Fritz S, Perger C, Kraxner F, Obersteiner M, Baruah U D, Mili N and Kalita N R. 2013. Mapping cropland in Ethiopia using crowdsourcing. International Journal of Geosciences, 4(6A): 6–13
  18. 18.
    Singha M, Wu B F and Zhang M. 2016. An object-based paddy rice classification using multi-spectral data and crop phenology in Assam, northeast India. Remote Sensing, 8(6): 479
  19. 19.
    Singha M, Wu B F and Zhang M. 2017. Object-based paddy rice mapping using HJ-1A/B data and temporal features extracted from time series MODIS NDVI data. Sensors, 17(1): 10
  20. 20.
    Sui D, Elwood S and Goodchild M. 2013. Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice. Dordrecht: Springer
  21. 21.
    Waldner F, De Abelleyra D, Verón S R, Zhang M, Wu B F, Plotnikov D, Bartalev S, Lavreniuk M, Skakun S, Kussul N, Le Maire G, Dupuy S, Jarvis I and Defourny P. 2016. Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity. International Journal of Remote Sensing, 37(14): 3196–3231
  22. 22.
    Wanders N, Bachas A, He X G, Huang H, Koppa A, Mekonnen Z T, Pagán B R, Peng L Q, Vergopolan N, Wang K J, Xiao M, Zhan S, Lettenmaier D P and Wood E F. 2017. Forecasting the hydroclimatic signature of the 2015/16 El Niño event on the western United States. Journal of Hydrometeorology, 18(1): 177–186
  23. 23.
    Wu B F. 2000. Operational remote sensing methods for agricultural statistics. Acta Geographica Sinica, 55(1): 25–35
  24. 24.
    Wu B F. 2004. China crop watch system with remote sensing. Journal of Remote Sensing, 8(6): 481–497
  25. 25.
    Wu B F, Ahamd S and He C C. 2017. Shared agronomic information community for the belt and road initiative. Bulletin of Chinese Academy of Sciences, 32(S1): 34–41
  26. 26.
    Wu B F, Gommes R, Zhang M, Zeng H W, Yan N N, Zou W T, Zheng Y, Zhang N, Chang S, Xing Q and Van Heijden A. 2015. Global crop monitoring: a satellite-based hierarchical approach. Remote Sensing, 7(4): 3907–3933
  27. 27.
    Wu B F and Li Q Z. 2012. Crop planting and type proportion method for crop acreage estimation of complex agricultural landscapes. International Journal of Applied Earth Observation and Geoinformation, 16: 101–112
  28. 28.
    Wu B F, Li Q Z, Chi Y B, Huang J L, Zhou W C, Zhang W Q and Wu S. 2008. Crop damage monitoring using remote sensing in January and February in South China in 2008. Engineering Science, 10(6): 63–69
  29. 29.
    Wu B F, Meng J H, Li Q Z, Yan N N, Du X and Zhang M. 2014. Remote sensing-based global crop monitoring: experiences with China’s CropWatch system. International Journal of Digital Earth, 7(2): 113–137
  30. 30.
    Wu B F, Meng J H, Li Q Z, Zhang F F, Du X and Yan N N. 2010. Latest development of " CropWatch” - An global crop monitoring system with remote sensing. Advances in Earth Science, 25(10): 1013–1022
  31. 31.
    Wu B F and Xing Q. 2015. Remote sensing roles on driving science and major applications. Advances in Earth Science, 30(7): 751–762
  32. 32.
    Wu B F and Zhang M. 2017. Remote sensing: observations to data products. Acta Geographica Sinica, 72(11): 2093–2111
  33. 33.
    Wu B F, Zhang M, Zeng H W, Zhang X, Yan N N and Meng J H. 2016. Agricultural monitoring and early warning in the era of big data. Journal of Remote Sensing, 20(5): 1027–1037
  34. 34.
    Yan N N, Wu B F, Li Q Z, Chang S, Zhang F F and Zhang S C. 2010. HJ-1A/B satellite application on drought emergency monitoring. Remote Sensing Technology and Application, 25(5): 675–681
  35. 35.
    Zeng H W, Wu B F, Zou W T, Yan N N and Zhang M. 2015. Performance comparison of crop condition assessments in irrigated and rain-fed areas: a case study in Nebraska. Journal of Remote Sensing, 19(4): 560–567
  36. 36.
    Zhang M, Wu B F, Yu M Z, Zou W T and Zheng Y. 2014. Crop condition assessment with adjusted NDVI using the uncropped arable land ratio. Remote Sensing, 6(6): 5774–5794
  37. 37.
    Zhang M, Wu B F, Yu M Z, Zou W T and Zheng Y. 2015. Concepts and implementation of monthly monitoring of uncropped arable land: a case study in Argentina. Journal of Remote Sensing, 19(4): 550–559
  38. 38.
    Zhang X, Wu B F, Zeng H W, Zhang M and Yan N N. 2015. WEBGIS based CropWatch online agriculture monitoring system//AGU Fall Meeting Abstracts. San Francisco: AGU
  39. 39.
    Zhang X, Zhang M, Zheng Y and Wu B F. 2016. Crop mapping using PROBA-V time series data at the Yucheng and Hongxing farm in China. Remote Sensing, 8(11): 915
  40. 40.
    Zhao D, Zhang M, Yu M Z, Zeng Y and Wu B F. 2014. Monitoring agriculture and forestry recovery after the Wenchuan Earthquake. Journal of Remote Sensing, 18(4): 958–970
  41. 41.
    Zheng Y, Wu B F and Zhang M. 2017. Estimating the above ground biomass of winter wheat using the Sentinel-2 data. Journal of Remote Sensing, 21(2): 318–328
  42. 42.
    Zheng Y, Zhang M, Zhang X, Zeng H W and Wu B F. 2016. Mapping winter wheat biomass and yield using time series data blended from PROBA-V 100- and 300-m S1 products. Remote Sensing, 8(10): 824
  43. 43.
    Zou W T, Wu B F, Zhang M and Zheng Y. 2015. Synthetic method for crop condition analysis: a case study in India. Journal of Remote Sensing, 19(4): 539–549

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