浏览全部资源
扫码关注微信
Published： 2019-11 ，
Accepted： 11 May 2018
扫 描 看 全 文
吴炳方, 张淼, 曾红伟, 闫娜娜, 张鑫, 邢强, 常胜. 2019. 全球农情遥感速报系统20年. 遥感学报, 23(6): 1053–1063
Wu B F, Zhang M, Zeng H W, Yan N N, Zhang X, Xing Q and Chang S. 2019. Twenty years of CropWatch: Progress and prospect. Journal of Remote Sensing, 23(6): 1053–1063
面向国家粮食安全的重大战略需求，1998年中国科学院建立了“中国农情遥感速报系统”(CropWatch)，持续运行20年后，现已发展成为“全球农情遥感速报系统”(CropWatch)。本文重点论述了2013年建立参与式全球农情遥感监测云平台(CropWatch-Cloud)以来，所采用的农情监测体系、可定制的农情监测云平台理念以及CropWatch-Cloud在国内外的应用推广情况，介绍了技术方法与农情信息服务方式的创新与进步带来的国际影响力的提升。系统总结了全球农情遥感速报系统发展的农情监测指标、农情预警能力、作物长势综合监测方法以及众源数据支持的作物面积监测方法，论文进一步阐述了CropWatch未来的发展方向，借助众源地理信息、大数据技术等的发展，打通从地块—村—镇—县—市—省—国家—全球的体系化全链条监测，满足从农户到政府决策部门对农情信息的差异化需求。
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.
遥感全球农情遥感速报农情监测云平台
remote sensingCropWatchglobal agriculture monitoringcloud platform
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
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
范锦龙, 吴炳方. 2004. 复种指数遥感监测方法. 遥感学报, 8(6): 628–636
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
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
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
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
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
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
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
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
Guo H D. 2018. Steps to the digital silk road. Nature, 554(7690): 25–27
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
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
李中元, 吴炳方, Gommes R, 张淼, 陈波. 2015. 农情遥感监测云服务平台建设框架. 遥感学报, 19(4): 578–585
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
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
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
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
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
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
Sui D, Elwood S and Goodchild M. 2013. Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice. Dordrecht: Springer
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
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
吴炳方. 2000. 全国农情监测与估产的运行化遥感方法. 地理学报, 55(1): 25–35
Wu B F. 2000. Operational remote sensing methods for agricultural statistics. Acta Geographica Sinica, 55(1): 25–35
吴炳方. 2004. 中国农情遥感速报系统. 遥感学报, 8(6): 481–497
Wu B F. 2004. China crop watch system with remote sensing. Journal of Remote Sensing, 8(6): 481–497
吴炳方, Ahamd S, 何昌垂. 2017. 构建" 一带一路”国家农情信息命运共同体. 中国科学院院刊, 32(S1): 34–41
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
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
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
吴炳方, 李强子, 迟耀斌, 黄进良, 周万村, 张维奇, 吴双. 2008. 2008年1—2月雪灾作物灾情遥感监测方法. 中国工程科学, 10(6): 63–69
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
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
吴炳方, 蒙继华, 李强子, 张飞飞, 杜鑫, 闫娜娜. 2010. " 全球农情遥感速报系统(CropWatch)”新进展. 地球科学进展, 25(10): 1013–1022
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
吴炳方, 邢强. 2015. 遥感的科学推动作用与重点应用领域. 地球科学进展, 30(7): 751–762
Wu B F and Xing Q. 2015. Remote sensing roles on driving science and major applications. Advances in Earth Science, 30(7): 751–762
吴炳方, 张淼. 2017. 从遥感观测数据到数据产品. 地理学报, 72(11): 2093–2111
Wu B F and Zhang M. 2017. Remote sensing: observations to data products. Acta Geographica Sinica, 72(11): 2093–2111
吴炳方, 张淼, 曾红伟, 张鑫, 闫娜娜, 蒙继华. 2016. 大数据时代的农情监测与预警. 遥感学报, 20(5): 1027–1037
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
闫娜娜, 吴炳方, 李强子, 常胜, 张飞飞, 张士昌. 2010. HJ-1A/B卫星在干旱应急监测中的应用. 遥感技术与应用, 25(5): 675–681
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
曾红伟, 吴炳方, 邹文涛, 闫娜娜, 张淼. 2015. 灌溉区与雨养区作物长势差异比较分析——以美国内布拉斯加为例. 遥感学报, 19(4): 560–567
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
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
张淼, 吴炳方, 于名召, 邹文涛, 郑阳. 2015. 未种植耕地动态变化遥感识别——以阿根廷为例. 遥感学报, 19(4): 550–559
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
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
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
赵旦, 张淼, 于名召, 曾源, 吴炳方. 2014. 汶川地震灾后农田和森林植被恢复遥感监测. 遥感学报, 18(4): 958–970
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
郑阳, 吴炳方, 张淼. 2017. Sentinel-2数据的冬小麦地上干生物量估算及评价. 遥感学报, 21(2): 318–328
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
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
邹文涛, 吴炳方, 张淼, 郑阳. 2015. 农作物长势综合监测——以印度为例. 遥感学报, 19(4): 539–549
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
相关文章
相关作者
相关机构
京公网安备11010802024621