Regional crop monitoring and assessment with quantitative remote sensing and data assimilation

利用定量遥感和数据同化进行区域作物监测和评估

• 批准号: ST/N006798/1
• 负责人: Philip Lewis
• 金额: $ 123.71万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ST%2FN006798%2F1
• 关键词: Regional; crop; monitoring; assessment; quantitative

China has only 10% of the world arable land and water resources, but has to feed 20% of the world population. Moreover, the population continues to increase, while the amount of arable land is shrinking due to pollution, urban sprawl, groundwater depletion, and other stresses. With future climate change expected to only worsen these pressures, the accurate monitoring of agricultural productivity is essential to China's future food security, in addition to the economic development of low-income rural regions. In no part of the country is this more essential than China's north plain. This has historically been the breadbasket of China. Today, however, it faces an exceptionally challenging combination of very high population densities and ecological stresses, and low levels of household income. Traditionally, researchers have used two general methods for monitoring agricultural productivity. The first, which has long been used by Chinese government agencies, is to combine field surveys of crop growth, with mathematical models of crop growth processes, to construct estimates of changing harvest yields over time. The second, which has risen to prominence more recently, is to use satellite imagery to continuously assess agricultural productivity. Each of these techniques has its own notable strengths and weaknesses. Survey-calibrated models of crop growth are able to produce highly accurate estimates of yields in the limited areas where survey data has been collected; however, their accuracy drops off significantly outside of these areas. On the other hand, satellite remote sensing data offers universal geographic coverage; however, the resolution of this data is extremely coarse over either time or space. MODIS data, for example, provides near-daily data that can be used to assess the productivity of every single farm in China. However, the spatial resolution of pixels is only 500-1000 meters, an area which will invariably be contaminated, in densely populated China, by a mixture of roads, villages, and other non-agricultural land uses in addition to the farmland actually being studied. Other satellites (e.g. LandSat TM and forthcoming Sentinel) provide finer scale pixel resolution than MODIS; however, they do not cover the same sites as often, making it harder to smoothly track agricultural production over time.Reflecting the wider explosion of the field of "big data" analysis, rapid strides have been made recent years in the development of so-called "data assimilation" techniques. These can be broadly described as statistical methodologies that allow for otherwise incompatible datasets to be combined together, in order to produce hybrid datasets that are superior to any of their predecessors. The basic objective of the proposed project is to apply advanced data assimilation techniques to multiple types of crop data-from both survey-calibrated crop growth models and satellite imagery-to produce superior estimates of Chinese agricultural productivity than would be possible using any of these data sources by itself. In addition to making use of more advanced statistical methods than previous studies, this analysis will be among the first to make use of data from the forthcoming Sentinel and the Chinese GF satellites. Taken together, we expect that the result will be the most accurate portrait created to date of changing agricultural production in the North China Plain. Moreover, having created this data, we will be able to apply it predictively in conjunction with modelled scenarios of future climate change, in order to map and assess the likely geographies of agricultural stress that this will create. Ultimately, the findings of this project will directly inform work by academic researchers, national and regional Chinese governmental authorities, agritech companies in both China and the UK, and extension workers directly advising farmers in China.

中国只有世界10%的耕地和水资源，却要养活世界20%的人口。此外，人口持续增长，而可耕地数量由于污染、城市扩张、地下水枯竭和其他压力而不断减少。由于未来的气候变化预计只会加剧这些压力，除了低收入农村地区的经济发展外，准确监测农业生产率对中国未来的粮食安全至关重要。在中国的任何一个地方，这都比中国的北方平原更重要。这里历来是中国的粮仓。然而，今天，它面临着极高的人口密度和生态压力，以及低水平的家庭收入的异常具有挑战性的组合。传统上，研究人员使用两种一般方法来监测农业生产力。第一种方法是将作物生长的实地调查与作物生长过程的数学模型结合起来，估算出作物产量随时间的变化。中国政府机构长期以来一直使用这种方法。第二种方法是利用卫星图像持续评估农业生产力，这一方法最近才引起人们的关注。每种技术都有其显著的优点和缺点。经调查校准的作物生长模型能够对收集调查数据的有限地区的产量作出高度准确的估计；然而，在这些区域之外，它们的准确性显著下降。另一方面，卫星遥感数据提供了普遍的地理覆盖；然而，这些数据的分辨率在时间或空间上都非常粗糙。例如，MODIS数据提供了几乎每天的数据，可以用来评估中国每个农场的生产力。然而，像素的空间分辨率只有500-1000米，在人口密集的中国，除了实际研究的农田外，道路、村庄和其他非农业用地的混合使用总是会污染这一区域。其他卫星（例如LandSat TM和即将推出的Sentinel）提供比MODIS更精细的尺度像素分辨率；然而，它们并不经常覆盖相同的地点，这使得随着时间的推移顺利跟踪农业生产变得更加困难。近年来，所谓的“数据同化”技术的发展取得了长足的进步，这反映了“大数据”分析领域的广泛爆发。这些可以被广泛地描述为统计方法，允许将其他不兼容的数据集组合在一起，以产生优于其任何前辈的混合数据集。该项目的基本目标是将先进的数据同化技术应用于多种类型的作物数据——来自调查校准的作物生长模型和卫星图像——以产生对中国农业生产力的更优估计，而不是单独使用任何这些数据源。除了利用比以前的研究更先进的统计方法外，这项分析将是第一批利用即将到来的哨兵卫星和中国GF卫星的数据的分析。综上所述，我们预计这一结果将是迄今为止对华北平原农业生产变化最准确的描述。此外，创建了这些数据后，我们将能够将其与未来气候变化的模拟情景结合起来进行预测，以便绘制和评估这将产生的农业压力的可能地理位置。最终，该项目的研究结果将直接为学术研究人员、中国国家和地区政府部门、中英两国农业科技公司以及直接为中国农民提供咨询的推广人员的工作提供信息。

项目成果

Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data

• DOI: 10.3390/rs9060557
• 发表时间: 2017-06
• 期刊: Remote. Sens.
• 作者: Hasituya;Zhongxin Chen

Quantitative Remote Sensing for Agricultural Monitoring in the Big Data Era

大数据时代农业监测定量遥感

• 发表时间: 2020
• 作者: Gomez-Dans, J.

High resolution crop intensity mapping using harmonized Landsat-8 and Sentinel-2 data

• DOI: 10.1016/s2095-3119(19)62599-2
• 发表时间: 2019-12-01
• 期刊: JOURNAL OF INTEGRATIVE AGRICULTURE
• 影响因子: 4.8
• 作者: Hao Peng-yu;Tang Hua-jun;Wu Ming-quan
• 通讯作者: Wu Ming-quan

Federal Data Science

联邦数据科学

• DOI: 10.1016/b978-0-12-812443-7.00007-7
• 发表时间: 2018
• 作者: Chen Z

A sampling workflow based on unsupervised clusters and multi-temporal sample interpretation (UCMT) for cropland mapping

• DOI: 10.1080/2150704x.2018.1500045
• 发表时间: 2018-08
• 期刊: Remote Sensing Letters
• 影响因子: 2.3
• 作者: Pengyu Hao;Huajun Tang;Zhongxin Chen;Le Yu;Mingquan Wu