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通过协同方法识别和量化全球耕地面积比较中的地方不确定性和差异


刘小杰 a,b,c a,b,c  ^("a,b,c "){ }^{\text {a,b,c }} , 金小彬 a,b,c,* a,b,c,*  ^("a,b,c,* "){ }^{\text {a,b,c,* }} , 罗秀丽 a,b,c a,b,c  ^("a,b,c "){ }^{\text {a,b,c }} , 周银康 a,b,c a,b,c  ^("a,b,c "){ }^{\text {a,b,c }}

南京大学地理与海洋科学学院,中国南京,210023

b b ^(b){ }^{\mathrm{b}} 自然资源部沿海区域开发与保护重点实验室,中国南京,210023

c ^("c "){ }^{\text {c }} 碳中和与区域优化重点实验室,自然资源部,南京,210023,中国


文章信息

 处理编辑:J Peng

 关键词:

 耕地制图
 协同图
 数据融合
SOSA
 空间一致性

 摘要


摘要 全球耕地范围的时空一致性信息对于资源管理和科学研究至关重要。目前已有多种基于遥感产品的耕地数据集。然而,它们之间存在显著的差异和不确定性,导致的耕地面积估算与官方统计数据相差甚远,从而限制了其适用性。为此,本文提出了一种新的分层优化协同方法(SOSA),旨在通过融合五个现有的土地覆盖图(即 CLCD、GLC_FCS30、Globeland30、GlobalCrop 和 ESA_CCI)以及次国家统计数据,创建 2000 年至 2020 年中国的混合耕地图。鉴于大规模耕地制图的成本效益相关挑战,该方法旨在平衡数据价值、真实性和可负担性。SOSA 简化了确定最佳一致性水平和最佳产品组合的常用协议程序。 初步验证了生成的耕地地图,评估表明,协同耕地地图的空间精度更高,与统计数据的吻合度也更接近于任何单一输入地图。这表明协同方法可以增强耕地制图的性能,并提高与统计数据的一致性。我们的结果预计将为数据用户提供有价值的参考,帮助未来改进耕地制图,以支持前瞻性应用,并增强我们对全球农业系统的理解和建模。

 1. 引言


全球土地覆盖为农业生产提供了关键的基准信息,应用于联合国千年生态系统评估(MA)、《生物多样性报告》和《全球环境展望》(GEO)(Meng et al., 2023; Zabel et al., 2019)。特别是,农业系统对气候变化的独特影响表明,耕地管理可以显著影响温室气体排放和生物地球化学循环(Akpoti, Kabo-bah, & Zwart, 2019; Hatfield et al., 2018; Wang et al., 2022)。耕地作为一种不可替代的农业资源和生产要素,对于社会经济的可持续性至关重要(Duan et al., 2021)。根据《2022 年世界人口展望》,预计到 2050 年全球人口将超过 90 亿,日益增长的食品需求意味着维持人均饮食所需的可耕地面积将继续增加(Fritz et al., 2013; Liang et al., 2023),这给已经超负荷的农田带来了巨大的压力(Duro et al., 2020)。


此外,农业活动显著改变了陆地生态系统的结构和功能(黄等,2015;庞等,2019),影响了它们与周围大气、水和土壤的相互作用(刘、巴克希等,2020)。联合国可持续发展目标(SDGs)呼吁在提高农业生产和维持生态系统服务之间取得平衡。一个紧迫而艰巨的挑战是确保耕地系统提供安全、充足和富含营养的食物,同时最小化不利的生态影响(帕兹等,2020;维亚纳等,2022)。因此,实现这些目标需要关于耕地模式及其时空动态的一致、独立和及时的信息,这对于跟踪可持续粮食生产的进展以及各种空间明确的作物预警和监测模型(贝克-雷谢夫等,2020;佩雷斯-霍约斯等,2017)、作物水利用(特卢贡特拉等,2018)、产量预测(克雷恩-德罗什,2018)和特定政策影响评估(洛尔丹等,2023;里多特和加西亚,2020)也是至关重要的。

遥感已成为收集全球或区域土地利用/覆盖变化(LUCC)信息的强大工具,其衍生的数据集广泛用于识别农田分布和确定适合种植谷物和非谷物作物的区域(Fritz et al., 2013; Weiss et al., 2020)。在过去几十年中,已经从卫星和航空影像生成了多个大陆农田产品,并免费与公众分享。然而,早期的土地覆盖制图通常依赖于低分辨率图像,限制了其在国家或区域层面的应用(Benhammou et al., 2022)。自 2000 年代初以来,更加精细的全球土地覆盖数据已成为国际议程的一部分。这些制图产品随着地球物理仪器和技术的进步而改善,尽管这增加了收集和更新高分辨率实时地面训练样本所需的时间。特别是,随着 Landsat 档案的开放,高分辨率农田制图受到了越来越多的关注(Gumma et al., 2020; Teluguntla et al., 2018),使得创建 30 m 30 m 30-m30-\mathrm{m} 分辨率的农田数据集成为可能(Hu et al., 2020)。 每个产品都表现出可接受的整体准确性,可以用于在不同尺度上检测和记录农田变化(Waldner et al., 2015)。然而,这些产品之间的显著差异已被广泛报道(Fritz et al., 2013;Lu et al., 2017;Nabil et al., 2020),尤其是在过渡区和地理景观碎片化复杂的地区(Lu et al., 2020)。这些差异主要归因于传感器、分类器、获取方法、更新频率、主观解释、验证技术和地面真实参考的变化(Hua et al., 2018),这妨碍了可靠的比较,并使其在特定地区的应用变得复杂。这使得产品间的兼容性变得至关重要(Zhang et al., 2022)。已经采取了一些有益的努力来标准化这些不一致性,促进了对不断增加的现有土地覆盖图的性能比较和验证(Fritz et al., 2011)。 尽管如此,用户在选择合适的数据集时仍然面临一些困惑,因为他们常常难以找到与所需的 LUCC 水平或感兴趣的地理区域相匹配的产品(Chen et al., 2019)。此外,这些产品通常提供有限的细节和有限的类别数量(Weiss et al., 2020)。

与此同时,这些产品估算的农田面积与官方统计数据不一致,因为遥感影像中不可避免的像素混合(Claverie et al., 2013)。这种差异阻碍了它们在食品政策和农业经济中的推广。学术研究揭示了卫星反演估算与特定行业统计之间的差异,原因在于农田定义的不同(Potapov et al., 2021)。大多数制图产品往往会高估或低估农田面积,这取决于如何计算包括马赛克在内的混合类型(Lu et al., 2017)。此外,现有数据集通常强调土地覆盖而非土地利用,因为航空和卫星观测具有直接性,导致基于卫星的地图可能无法充分捕捉与土地利用变化相关的农田特征(Liu et al., 2023)。作为一种共存的集合,农田不仅由覆盖地表的作物定义,还受到与人类活动相关的粮食生产的影响(Paz et al., 2020)。 农业统计数据通常通过抽样调查和访谈收集,然后通过将这些数据与行政记录结合进行计算(Fritz et al., 2013)。值得注意的是,这些部门统计数据提供了通过 Landsat 数据无法获得的有价值的实用信息。然而,由于它们是在行政区划层面收集的,因此通常缺乏空间细节(Nabil et al., 2020)。

协同方法通过结合现有的土地覆盖图和权威统计数据,为解决上述差距提供了一种替代方案。不同的实地制图产品可以用于创建农田混合百分比图,这通常更为准确(Chen et al., 2019; Zhang et al., 2022)。当前的地图协同算法一般可分为回归和协议评分方法(Lu et al., 2017)。其中,地理加权回归(GWR)是一种具有空间变化回归参数的解决方法,已广泛应用于利用众包数据库在全球范围内构建混合地图(See et al., 2015)。Schepaschenko 等(2015)整合了来自广泛来源的森林产品,使用 GWR 模型生成了分辨率为 1 公里的全球混合森林覆盖图。然而,该过程需要大量的训练样本,并且在低采样密度下可能容易出现不稳定(Lu et al., 2020)。协议评分方法根据输入数据中的一致性水平分配分数,由于其优越的可操作性和简单性,使用更为普遍(Chen et al., 2019)。 实施这种方法,Jung、Henkel、Herold 和 Churkina(2006)以及 Ramankutty、Evan、Monfreda 和 Foley(2008)分别创建了用于碳循环建模的全球土地覆盖图和全球牧场面积图。然而,这类研究往往忽视了输入数据集之间的质量差异。为了解决这个问题,Fritz 等人(2011)对输入产品进行了排序,并根据专家判断分配了权重。传统上,评分分配是该过程的一个关键方面(See 等人,2015),但在处理大量输入数据集时,创建评分表可能会很繁琐(Gumma 等人,2020)。

中国的耕地模式研究长期受到缺乏可靠的空间和时间可比数据的限制(刘等,2015)。中国以仅占全球耕地的 8 % 8 % 8%8 \% ,养活了 18 % 18 % 18%18 \% 的世界人口,使其耕地极为珍贵(邹等,2020)。然而,近年来中国的农业景观也面临着严重挑战,包括资源错配、土地转让、耕地流失和废弃(陈等,2023;胡等,2020)。随着快速城市化,耕地被牺牲用于基础设施占用和其他非农业用途,这种情况尤为明显(田等,2021;钟等,2022)。为了弥补因耕地征用补偿平衡政策而损失的耕地,已启动大规模的土地改善、开发、复垦和整理项目(刘等,2019)。尽管通过一系列严格的法规和措施对农田实施了严格保护,但部分地区的农业结构仍然不合理和失衡(刘、孙等,2020;张等)。2015 年,优质耕地明显减少(韩 & 宋,2020)。此外,由于生态复垦、灾害和农业结构调整,中国的耕地在数量和分布上发生了显著变化,耕地保护形势严峻(刘等,2015)。虽然基于卫星监测的研究可以揭示耕地分布的模式,但由于卫星图像解读的有限准确性以及缺乏足够的实地调查和验证,它们并未准确捕捉到耕地保护政策所导致的数量变化(韦斯等,2020)。更重要的是,基于土地调查统计的先前研究使用省级行政区域作为研究单位来分析中国耕地的变化,通常忽视了不同地区耕地的空间变异。因此,获得关于耕地动态的时空一致信息的正确知识和理解,对于农业可持续性和国家粮食安全至关重要。

为了克服这些限制,本研究引入了一种基于输入数据集一致性的新的分层优化协同方法(SOSA),以提高卫星地图与统计数据之间的一致性和准确性,从而减少耕地时空演变中的不确定性。SOSA 通过结合五个全球土地覆盖数据集(CLCD、GLC_FCS30、Globeland30、GlobalCrop 和 ESA_CCI)与国家统计数据进行了校准和验证,以开发 2000 年至 2020 年间中国的协同耕地地图。我们的研究结果深入探讨了全球耕地数据集的区域多样性和不确定性,为未来的遥感制图提供了新的视角,并为根据当地条件进行产品选择和耕地保护的空间决策提供了指导。

 2. 材料和方法

 2.1. 研究框架


本研究的逻辑框架如图 1 所示。首先,通过掩膜提取了五组全球土地覆盖栅格数据,以获取中国地区土地利用类别的信息,并结合其分类系统对耕地和非耕地进行重新分类。其次,对这些重新分类的土地覆盖数据集进行了叠加分析,以研究它们的地理空间一致性。同时,我们分析了耕地的定量偏差和每个数据集的区域可靠性。该分析是通过使用中国 558 个县级行政区的耕地和非耕地测试样本作为参考,借助官方统计数据和第二次全国土地调查(SNLS)数据库进行的。在第三步中,空间一致性和整体准确性的评估结果被用来确定理想的一致性水平,并识别最佳的制图产品组合,随后用于创建协同耕地地图。 最后,我们通过利用标准误差和混淆矩阵,将所提出的方法的协同耕地图与原始输入产品进行比较,从而评估了该方法的性能,并基于这些校准的协同耕地图进一步探讨了过去 20 年中国耕地的时空动态。


2.2. 数据采集与处理


表 1 和表 2 列出了五个公开可用的全球土地覆盖数据集,这些数据集具有相对较高的分辨率和连续性。这些数据集在全球范围内的长期一致性、周期性更新和丰富的主题细节,使其在森林砍伐、沙漠化和淡水退化等多变量应用中具有吸引力,此外还有基础研究(华等,2018)。其中,中国土地覆盖数据集(CLCD)是一个基于 Landsat 数据的年度土地覆盖产品,提供了过去三十年中国土地利用变化的全面记录(杨和黄,2021)。CLCD 将土地覆盖分类为 9 个类别。值得注意的是,虽然 CLCD 的分类特定于中国,但该数据集的优势在于逐年土地利用分类结果,其更高的时间分辨率可以细致描绘空间非平稳关系和时空异质性。

图 1. 研究框架示意图。
 表 1

五组土地覆盖数据集的参数描述。
 数据集  制作人  解析  纪元  方法  方案  传感器
CLCD
武汉大学 (https://zenodo.org/)
 30 米 1990-2020(1)  随机森林 FAO  MODIS/陆地卫星
GLC_FCS30

中国科学院 (CAS) (https://data.cas earth.cn/)
Chinese Academy of Sciences (CAS) (https://data.cas earth.cn/)| Chinese Academy of Sciences (CAS) (https://data.cas | | :--- | | earth.cn/) |
 30 米 1985-2020 (5)  随机森林 FAO

Landsat/TM, ETM + / + / +//+/ OLI
Landsat/TM, ETM +// OLI| Landsat/TM, ETM $+/$ | | :--- | | OLI |
 全球土地 30

中国国家地理信息中心 (http://globeland30.org/)
National Geomatics Center of China (http://globela nd30.org/)| National Geomatics Center of China (http://globela | | :--- | | nd30.org/) |
 30 米
2000 2020 2000 2020 2000-20202000-2020
( 10 ) ( 10 ) (10)(10)
2000-2020 (10)| $2000-2020$ | | :--- | | $(10)$ |
2000-2020,(10)| $2000-2020$ <br> $(10)$ | | :--- |

监督分类
 自我分类
Self- classification| Self- | | :--- | | classification |
 Landsat/TM, HJ_1/CCD
Landsat/TM, HJ_1/ CCD| Landsat/TM, HJ_1/ | | :--- | | CCD |
Landsat/TM, HJ_1/,CCD| Landsat/TM, HJ_1/ <br> CCD | | :--- |
 全球作物

马里兰大学 (https://glad.umd.edu /dataset/)
University of Maryland (https://glad.umd.edu /dataset/)| University of Maryland (https://glad.umd.edu | | :--- | | /dataset/) |
 30 米 2000-2019 (4)
监督分类
 自我分类
Self- classification| Self- | | :--- | | classification |
 陆地卫星
ESA_CCI
欧洲空间局 (https://www.esa-landcover-cci.org/)
 300 米 1992 2020 ( 1 ) 1992 2020 ( 1 ) 1992-2020(1)1992-2020(1)

无监督分类
Unsupervised Classification| Unsupervised | | :--- | | Classification |
FAO MERIS/AVHRR
Dataset Producer Resolution Epoch Method Scheme Sensor CLCD Wuhan University (https://zenodo.org/) 30 m 1990-2020(1) Random Forest FAO MODIS/Landsat GLC_FCS30 "Chinese Academy of Sciences (CAS) (https://data.cas earth.cn/)" 30 m 1985-2020 (5) Random Forest FAO "Landsat/TM, ETM +// OLI" Globeland30 "National Geomatics Center of China (http://globela nd30.org/)" 30 m "2000-2020,(10)" Supervised Classification "Self- classification" "Landsat/TM, HJ_1/,CCD" GlobalCrop "University of Maryland (https://glad.umd.edu /dataset/)" 30 m 2000-2019 (4) Supervised Classification "Self- classification" Landsat ESA_CCI ESA (https://www.esa-landcover-cci.org/) 300 m 1992-2020(1) "Unsupervised Classification" FAO MERIS/AVHRR| Dataset | Producer | Resolution | Epoch | Method | Scheme | Sensor | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | CLCD | Wuhan University (https://zenodo.org/) | 30 m | 1990-2020(1) | Random Forest | FAO | MODIS/Landsat | | GLC_FCS30 | Chinese Academy of Sciences (CAS) (https://data.cas <br> earth.cn/) | 30 m | 1985-2020 (5) | Random Forest | FAO | Landsat/TM, ETM $+/$ <br> OLI | | Globeland30 | National Geomatics Center of China (http://globela <br> nd30.org/) | 30 m | $2000-2020$ <br> $(10)$ | Supervised Classification | Self- <br> classification | Landsat/TM, HJ_1/ <br> CCD | | GlobalCrop | University of Maryland (https://glad.umd.edu <br> /dataset/) | 30 m | 2000-2019 (4) | Supervised Classification | Self- <br> classification | Landsat | | ESA_CCI | ESA (https://www.esa-landcover-cci.org/) | 300 m | $1992-2020(1)$ | Unsupervised <br> Classification | FAO | MERIS/AVHRR |
 表 2

耕地的定义和百分比确定。
 数据集  耕地的定义

农田准确性由生产者发布
Cropland accuracy released by producer| Cropland accuracy | | :--- | | released by | | producer |
 耕地百分比
Cropland percentage| Cropland | | :--- | | percentage |
CLCD  耕地 79.31% 100 % 100 % 100%100 \%
GLC_FCS30  雨养农田 85% 100%
 草本覆盖 - 80 % 80 % 80%80 \%

树木或灌木覆盖(果园)
Tree or shrub cover (Orchard)| Tree or shrub cover | | :--- | | (Orchard) |
- 80 % 80 % 80%80 \%
 灌溉农田 88 % 88 % 88%88 \% 100 % 100 % 100%100 \%
 全球土地 30  耕地 83.5% 100%
 全球作物

一年生和多年生草本作物
Annual and perennial herbaceous crops| Annual and perennial | | :--- | | herbaceous crops |
79.78% 100 % 100 % 100%100 \%
ESA_CCI  雨养农田 81 % 81 % 81%81 \% 100 % 100 % 100%100 \%

灌溉农田或洪水后农田
Cropland irrigated or post-flooding| Cropland irrigated or | | :--- | | post-flooding |
88 % 88 % 88%88 \% 100 % 100 % 100%100 \%

自然植被 ( <50% <50%< 50%<50 \% )/马赛克农田 ( >50% >50%> 50%>50 \%
Natural vegetation ( < 50% )/mosaic cropland ( > 50% )| Natural vegetation | | :--- | | ( $<50 \%$ )/mosaic cropland | | ( $>50 \%$ ) |
68 % 68 % 68%68 \% 60 % 60 % 60%60 \%
 
Cropland ( < 50 % < 50 % < 50%<50 \% )/mosaic
natural vegetation
( > 50 % ) ( > 50 % ) ( > 50%)(>50 \%)
Cropland ( < 50% )/mosaic natural vegetation ( > 50%)| Cropland ( $<50 \%$ )/mosaic | | :--- | | natural vegetation | | $(>50 \%)$ |
63 % 63 % 63%63 \% 40 % 40 % 40%40 \%
Dataset Definition of cropland "Cropland accuracy released by producer" "Cropland percentage" CLCD Cropland 79.31% 100% GLC_FCS30 Rainfed cropland 85% 100% Herbaceous cover - 80% "Tree or shrub cover (Orchard)" - 80% Irrigated cropland 88% 100% Globeland30 Cropland 83.5% 100% GlobalCrop "Annual and perennial herbaceous crops" 79.78% 100% ESA_CCI Cropland rainfed 81% 100% "Cropland irrigated or post-flooding" 88% 100% "Natural vegetation ( < 50% )/mosaic cropland ( > 50% )" 68% 60% "Cropland ( < 50% )/mosaic natural vegetation ( > 50%)" 63% 40%| Dataset | Definition of cropland | Cropland accuracy <br> released by <br> producer | Cropland <br> percentage | | :---: | :---: | :---: | :---: | | CLCD | Cropland | 79.31% | $100 \%$ | | GLC_FCS30 | Rainfed cropland | 85% | 100% | | | Herbaceous cover | - | $80 \%$ | | | Tree or shrub cover <br> (Orchard) | - | $80 \%$ | | | Irrigated cropland | $88 \%$ | $100 \%$ | | Globeland30 | Cropland | 83.5% | 100% | | GlobalCrop | Annual and perennial <br> herbaceous crops | 79.78% | $100 \%$ | | ESA_CCI | Cropland rainfed | $81 \%$ | $100 \%$ | | | Cropland irrigated or <br> post-flooding | $88 \%$ | $100 \%$ | | | Natural vegetation <br> ( $<50 \%$ )/mosaic cropland <br> ( $>50 \%$ ) | $68 \%$ | $60 \%$ | | | Cropland ( $<50 \%$ )/mosaic <br> natural vegetation <br> $(>50 \%)$ | $63 \%$ | $40 \%$ |
of cropland changes. The CLCD datasets have been rigorously compared to state-of-the-art 30 m 30 m 30-m30-\mathrm{m} resolution thematic products including forest, surface water, and impervious surface area (ISA) to comprehensively assess its property, but few have been compared to cropland (Zhang et al., 2022). Another noteworthy dataset is the Global Land Cover-FCS30 (GLC_FCS30), a global land cover dynamic monitoring product. It adopts an exemplary classification system derived from surface reflectance (SR) images and local adaptive modeling, integrating the Food and Agriculture Organization of the United Nations (FAO) classification system to categorize land cover into 29 classes (Zhang et al., 2021). Globeland30, the world’s first 30-m resolution global land cover dataset, amalgamates multispectral imagery from the US Landsat and the China Disaster Monitoring Constellation (DMC). It employs the innovative Pixel-Object-Knowledge (POK) hierarchical classification method to classify land cover into 10 categories (Brovelli et al., 2015). GlobalCrop, in contrast, leverages normalized SR data from Landsat Analysis Ready Data (ARD) as input for mapping, generating a probability layer for each pixel via a bagged decision tree ensemble to creat a global cropland map (Potapov et al., 2021). While GlobalCrop is one year apart from the other datasets, careful studies have demonstrated that the land use and land cover changes during this period, on a large scale, are almost negligible when compared to classification error. The European Space Agency (ESA) Climate Change Initiative (CCI) project has contributed a suite of satellite-based products that incorporate medium-resolution imaging spectrometer (MERIS) SR time-series configuration parameters as input to generate land cover maps, enabling lengthy observations and analysis of global land cover dynamics since the 1990s (Zhong et al., 2022). 

由于原始数据集之间坐标系统、空间分辨率和分类方案的差异,预处理和转换是必要的,以便进行有意义的比较分析。我们的第一步是使用领土边界矢量数据裁剪五组全球数据产品及其马赛克图像,以获得中国区域数据集。然后采用 GlobeLand30 的标准椭球体作为基准,将其余数据集统一为世界大地测量系统 1984,以最小化这些数据产品之间几何精度差异的影响。此外,我们选择了阿尔伯斯等面积投影作为投影转换的基础,确保参与图像产品的一致性和无面积失真。在进行逐像素一致性比较之前,对所有数据产品进行了叠加分析。任何显示像素间隙的产品都导致相应的像素从其他产品中移除。 此外,特殊像素,如填充值 0 和 255,从每个数据集中被消除,以防止像素状伪影干扰后续处理。最后,前述数据集使用最近邻插值法重新采样至 300 米的分辨率。
A unified classification system is a prerequisite for contrasting data products from various sources (Hua et al., 2018). Opting for a simplified classification helps mitigate uncertainty that can arise from the wide variety of detailed land cover types (Nabil et al., 2020). To achieve this, we reclassified the datasets into cropland and non-cropland while retaining the cropland category’s information. We excluded the effects of other classes and selected the relevant types in each dataset for merging, adhering to the FAO definition of cropland. Thereinto, cropland includes arable lands, which encompass areas under temporary agricultural crops, land used for market and kitchen gardens, temporary meadows for mowing or grazing, and temporarily fallow land. Permanent crops encompass long-term crops that are sown above ground and do not require replanting for many years, flower crops that can grow under trees and shrubs, and nurseries. Additionally, cropland-related classes in each dataset were extracted and assigned percentage weights in compliance with their definitions (i.e. mosaic cropland classes received lower weights, while pure cropland classes were given higher percentage weights). This process generated percentage maps of cropland at a 300 m 300 m 300-m300-\mathrm{m} resolution, all in the same coordinate system, for each satellite-based dataset. 
In Fig. 2, we present the geographical distribution of China’s cropland around 2020, which has been extracted from five existing datasets, along with the training samples obtained from the Second National Land Survey (SNLS) database. SNLS implemented a unified organizational model that combined government coordination, local field surveys, and national quality control. It was established on the foundation of a survey base map that encompassed the entire range of remote sensing images, ensuring consistency between maps, figures, and real-world observations (Zhong et al., 2022). To maintain the accuracy and relevance of geospatial information, the Ministry of Natural Resources has overseen an annual survey of territorial changes and updates to the SNLS results since 2010. These map delineation data were meticulously verified by local experts who relied on both remote sensing images and field surveys (Chen et al., 2023; Liu et al., 2015). The existence of independent operating systems and a substantial financial assistance budget have further bolstered the credibility of accurate, comparable, systematic, 

图 2. 2020 年中国耕地和训练样本的空间分布。


和连贯的 LUCC 数据。预计这种可信度将在未来十年内保持,直到 2019 年(Li et al., 2022)。此外,SNLS 汇编了关于地籍地块的类型、所有权、面积和分布的全面信息,最终形成了一个庞大的数据库,其中包括在全国 31 个省进行的 2800 多项县级土地利用调查。这个丰富的数据集以其权威性和广泛的细节特征,包括地图属性,使其成为我们研究的理想参考和测试样本。

为了确保创建一个足够、平衡和多样化的训练样本,我们采用了随机选择过程,从 SNLS 数据库中选取了 558 个县级土地利用调查数据。同时,我们旨在确保所选样本的普遍性和典型性,覆盖全国 31 个省和地区,特别强调主要粮食生产区(如辽宁、河北、山东、吉林、四川、河南、湖北、江苏、安徽、黑龙江)内的样本。所选县样本的数量从 5 到 30 不等,值得注意的是,这些选定的县单位平均包含超过 50,000 个土地利用多边形。这一因素使我们能够在细尺度上深入研究耕地的空间分布特性。这些统计数据的质量和准确性为我们提供了清晰的见解。
Additionally, the geographic distribution of cropland is regularly constrained by natural factors, such as topography, which poses certain intractability for remote sensing mapping (Zhang et al., 2022), hence further exploring their consistency in terms of altitude and slope within each landform interval. Of which, altitudes and slopes were calculated according to digital elevation models (DEM) that sprang from the integration of SRTM (Shuttle Radar Topography Mission) processing upgrades, vertical control, void filling, and merging with data unavailable at the time of the original SRTM formation (https://www.earthdata. nasa.gov/). Besides, the cropland statistics were obtained from the China Statistical Yearbook, published by the National Bureau of Statistics (NBS) (http://www.stats.gov.cn/), which counted the cropland acreage in each of provinces, municipalities, and autonomous regions and their proportion in the entire country. The cropland training samples were sourced from the foundational data of the SNLS, which was collected and compiled by the Ministry of Natural Resources of the People’s 

中华民国(前称国土资源部)(https://www.mnr.gov.cn/sj/).

 2.3. 研究方法


2.3.1. 空间一致性评估

Consistency assessment is a method used to evaluate the reliability of multi-source products in the absence of objective reference criteria. It is typically employed for mutual validation between datasets (Hua et al., 2018). This approach operates under the assumption that the data and methods employed in each land cover dataset are reasonable. Each data product is expected to approximate the true value of land cover to varying degrees. In other words, a significant portion of the data aligns with, or closely represents, the actual situation, while there is also a fraction of misjudged data (Lu et al., 2017). In this approach, each set of products is treated as an expert judgment of the actual status. The agreement level between the judgments of different datasets concerning the land cover type or quantitative characteristics of a given spatial unit or period is used as an indicator to assess the likelihood of the data’s reliability (Chen et al., 2019). The consistency ratio (CR) is defined as follows: 
C R j = M j 1 k j = 1 k N j × 100 % C R j = M j 1 k j = 1 k N j × 100 % CR_(j)=(M_(j))/((1)/(k)sum_(j=1)^(k)N_(j))xx100%C R_{j}=\frac{M_{j}}{\frac{1}{k} \sum_{j=1}^{k} N_{j}} \times 100 \%
where: M j M j M_(j)M_{j} denotes the pixel amount of product j j jj whose cover type is cropland at the same position; N j N j N_(j)N_{j} denotes the pixel amount of cropland in product j ; k j ; k j;kj ; k denotes the number of cropland products. The higher the consistency, the more likely the data product is to be robust (Fritz et al., 2011). 
To evaluate the agreement and discrepancies between the cropland products and the survey data, we computed the Root Mean Square Error (RMSE) for each dataset compared to the official statistics based on cropland proportion. Simultaneously, we conducted a correlation analysis between the datasets and the survey data. The RMSE and correlation coefficient ( R ) ( R ) (R)(R) were calculated as follows (Pérez-Hoyos et al., 2017): 
R M S E = i = 1 n ( x i y i ) 2 n R = i = 1 n ( x i x ¯ ) ( y i y ¯ ) i = 1 n ( x i x ¯ ) 2 i = 1 n ( y i y ¯ ) 2 R M S E = i = 1 n x i y i 2 n R = i = 1 n x i x ¯ y i y ¯ i = 1 n x i x ¯ 2 i = 1 n y i y ¯ 2 {:[RMSE=sqrt((sum_(i=1)^(n)(x_(i)-y_(i))^(2))/(n))],[R=(sum_(i=1)^(n)(x_(i)-( bar(x)))(y_(i)-( bar(y))))/(sqrt(sum_(i=1)^(n)(x_(i)-( bar(x)))^(2)*sum_(i=1)^(n)(y_(i)-( bar(y)))^(2)))]:}\begin{aligned} & R M S E=\sqrt{\frac{\sum_{i=1}^{n}\left(x_{i}-y_{i}\right)^{2}}{n}} \\ & R=\frac{\sum_{i=1}^{n}\left(x_{i}-\bar{x}\right)\left(y_{i}-\bar{y}\right)}{\sqrt{\sum_{i=1}^{n}\left(x_{i}-\bar{x}\right)^{2} \cdot \sum_{i=1}^{n}\left(y_{i}-\bar{y}\right)^{2}}} \end{aligned}
where: x i x i x_(i)x_{i} and x ¯ x ¯ bar(x)\bar{x} are the area ratio of unit i i ii and the average area ratio of all units computed in the cropland datasets; y i y i y_(i)y_{i} and y ¯ y ¯ bar(y)\bar{y} are the statistical area ratio of unit i i ii and the average of the acreage ratio of the statistics, respectively; n n nn denotes the number of units. The larger the RMSE, the higher the dispersion, while a larger R R RR refers to a higher goodness of fit (Claverie et al., 2013). 

 2.3.2. 混淆矩阵

The confusion matrix, one of the most commonly employed evaluation methods in satellite mapping, serves as a fundamental metric for assessing the accuracy of different products (Brovelli et al., 2015). The evaluation of classification accuracy in China is based on the confusion matrix of five sets of global land cover data and test samples, resulting in the computation of the Kappa coefficient, overall accuracy ( O A O A OAO A ), omission error, and commission error. 
O A = i = 1 n x i i N O A = i = 1 n x i i N OA=(sum_(i=1)^(n)x_(ii))/(N)O A=\frac{\sum_{i=1}^{n} x_{i i}}{N}
 卡拉 = N i = 1 n x i i i = 1 n x i x j N 2 i = 1 n x i x j = N i = 1 n x i i i = 1 n x i x j N 2 i = 1 n x i x j =(Nsum_(i=1)^(n)x_(ii)-sum_(i=1)^(n)x_(i)x_(j))/(N^(2)-sum_(i=1)^(n)x_(i)x_(j))=\frac{N \sum_{i=1}^{n} x_{i i}-\sum_{i=1}^{n} x_{i} x_{j}}{N^{2}-\sum_{i=1}^{n} x_{i} x_{j}}
where: N N NN denotes the total of participating samples; n n nn is the confusion matrix dimension; x i i x i i x_(ii)x_{i i} is the number of samples in the diagonal; x j x j x_(j)x_{j} and x i x i x_(i)x_{i} denote the total of samples in column j j jj and row i i ii. 


2.3.3. 分层优化协同方法 (SOSA)


SOSA 的一个基本原则是,现有卫星基础农田地图产品之间一致性较高的像素更可能是真正的农田像素(Fritz 等,2011;Lu 等,2020)。SOSA 将从统计数据中获得的农田面积分配给高概率为可耕地的像素,并自适应调整农田分布,直到累计农田面积与统计数据相匹配。该过程包括两个主要步骤:确定最佳协议水平和识别最佳产品组合。在本实验中,我们利用上述五个现有土地覆盖数据集来开发最终的农田地图,具体操作将在以下小节中阐明。
The protocol level plays a critical role in the integration of various remote sensing maps to develop an enriched dataset (See et al., 2015). Initially, subnational statistics were employed to assess accuracy and establish weights for the input cropland maps. It’s worth noting that the quality of the input products being evaluated can significantly impact the confidence of the synergy. Subsequently, agreement ranking scores were determined based on this accuracy and protocol. For each input product, the cropland acreage in each unit is calculated as follows: 
a i , j = m = 1 N ( S m × P m ) a i , j = m = 1 N S m × P m a_(i,j)=sum_(m=1)^(N)(S_(m)xxP_(m))a_{i, j}=\sum_{m=1}^{N}\left(S_{m} \times P_{m}\right)
where a i , j a i , j a_(i,j)a_{i, j} denotes the cropland acreage of unit j j jj estimated by input product i i ii; P m P m P_(m)P_{m} denotes the cropland percentage in pixel m m mm after data processing; S m S m S_(m)S_{m} denotes the pixel area computed by equal-area projection; m m mm denotes the pixel labeled as cropland. Besides, absolute difference A D i , j A D i , j AD_(i,j)A D_{i, j} between the statistics and cropland acreage estimated from input product i i ii is computed to evaluate the accuracy of input maps. 
A D i , j = abs ( a NBS j j a i , j a NBS , j ) A D i , j = abs a NBS j j a i , j a NBS , j AD_(i,j)=abs((a_(NBS_(j)j)-a_(i,j))/(a_(NBS_(,j))))A D_{i, j}=\operatorname{abs}\left(\frac{a_{\mathrm{NBS}_{j} j}-a_{i, j}}{a_{\mathrm{NBS}_{, j}}}\right)
where a NBS , j a NBS , j a_(NBS,j)a_{\mathrm{NBS}, j} is the cropland acreage statistics of unit j j jj derived from NBS. A lower value of A D i , j A D i , j AD_(i,j)A D_{i, j} signifies finer agreement with the official statistics and a superior ranking for the input map. 
Protocol ranking scores are generated using tables that depict the agreement and ranking of the input product. When working with five input products, they are typically labeled as A, B, C, D, and E, ranked from highest to lowest. Agreement levels, ranging from 0 to 5 , represent the number of input products that identify a pixel as cropland. Since there are 32 permutations ( 2 5 = 32 ) 2 5 = 32 (2^(5)=32)\left(2^{5}=32\right) for five input products, the scores range from 0 to 31 (Table 3). A higher score indicates a higher likelihood of a pixel being cropland. An agreement level of 5 signifies that all input products classify the pixel as cropland, giving the pixel the highest score of 31. Conversely, an agreement level of 0 indicates that all the products categorize the pixel as non-cropland, resulting in the lowest score of 0. For other agreement levels, there are various permutations for the products. For instance, when the agreement level is 3, there are ten possible combinations with score values ranging from 16 to 25 . In scenarios where A , B A , B A,B\mathrm{A}, \mathrm{B}, and C have higher rankings, the score value is set as 25 if all three indicate cropland, which is higher than other combinations. By following these guidelines, values for a complete scoring table with five input products were determined, and these values were subsequently used to transform the input cropland layers into an agreement-ranking map. 

图 3 显示了五个输入产品的统计分配流程图。最初,选择了得分最高的 31 个像素,并计算了它们的总面积。

A 31 = ( S 31 , m × P 31 , m ) A 31 = S 31 , m × P 31 , m A_(31)=sum(S_(31,m)xxP_(31,m))A_{31}=\sum\left(S_{31, m} \times P_{31, m}\right)
where P 31 , m P 31 , m P_(31,m)P_{31, m} and S 31 , m S 31 , m S_(31,m)S_{31, m} are the average percentage and pixel area of pixel m m mm labeled as the score 31 . If the acreage was far less than that the statistics, cropland pixels with the second-highest agreement rank, i.e., a score of 30 , were selected, and the total acreage was thenceforth computed. The cumulative cropland acres with scores of 30 and above were compared with the statistics. Pixels labeled with scores of 31 and 30 were designated as cropland pixels if the cumulative acreage was pretty close to the statistics, or else, pixels with lower scores were included until the accumulated acreage equaled the statistics. As sketched in Fig. 3, pixels with score values ranging from 29 to 31 were considered as cropland when the cumulative acreage with score of 29 was the closest to the statistics. By and large, the scoring values signaled the agreements among input products, reflecting the confidence level of cropland pixels. To standardize the scores to the same scale, a normalization process was adopted, resulting in confidence levels with values ranging from 0 % 0 % 0%0 \% to 100 % 100 % 100%100 \%. 

 3. 结果

 3.1. 协议分析


图 4 展示了五个农田制图数据集的一致性和不一致性区域。值 1-5 表示像素单元之间的一致性水平。值越高,表示一致性水平越大。例如,值为 1 表示只有一个数据集将像素单元分类为农田,而值为 5 则表示所有五个数据集之间完全一致。尽管区域间一致性的比例每年有所变化,但共识保持相对稳定,南部地区的空间一致性水平较低,而北部地区的水平较高(图 4a、b 和 c)。总体而言,这些数据集为东北平原、黄淮海平原和长江三角洲提供了一致的结果。平坦的地形和相对简单的自然环境。
 表 3

五个输入产品的排名评分表。

输入产品的协议级别
 得分 A B C D E
输入产品的协议级别
 得分 A B C D E
0 0 0 0 0 0 0 3 16 0 0 1 1 1
1 1 0 0 0 0 1 17 0 1 0 1 1
2 0 0 0 1 0 18 1 0 0 1 1
3 0 0 1 0 0 19 0 1 1 0 1
4 0 1 0 0 0 20 1 0 1 0 1
5 1 0 0 0 0 21 1 1 0 0 1
2 6 0 0 0 1 1 22 0 1 1 1 0
7 0 0 1 0 1 23 1 0 1 1 0
8 0 0 1 1 0 24 1 1 0 1 0
9 0 1 0 0 1 25 1 1 1 0 0
10 0 1 0 1 0 4 26 0 1 1 1 1
11 0 1 1 0 0 27 1 0 1 1 1
12 1 0 0 0 1 28 1 1 0 1 1
13 1 0 0 1 0 29 1 1 1 0 1
14 1 0 1 0 0 30 1 1 1 1 0
15 1 1 0 0 0 5 31 1 1 1 1 1
Agreement level of input products Score A B C D E Agreement level of input products Score A B C D E 0 0 0 0 0 0 0 3 16 0 0 1 1 1 1 1 0 0 0 0 1 17 0 1 0 1 1 2 0 0 0 1 0 18 1 0 0 1 1 3 0 0 1 0 0 19 0 1 1 0 1 4 0 1 0 0 0 20 1 0 1 0 1 5 1 0 0 0 0 21 1 1 0 0 1 2 6 0 0 0 1 1 22 0 1 1 1 0 7 0 0 1 0 1 23 1 0 1 1 0 8 0 0 1 1 0 24 1 1 0 1 0 9 0 1 0 0 1 25 1 1 1 0 0 10 0 1 0 1 0 4 26 0 1 1 1 1 11 0 1 1 0 0 27 1 0 1 1 1 12 1 0 0 0 1 28 1 1 0 1 1 13 1 0 0 1 0 29 1 1 1 0 1 14 1 0 1 0 0 30 1 1 1 1 0 15 1 1 0 0 0 5 31 1 1 1 1 1| Agreement level of input products | Score | A | B | C | D | E | Agreement level of input products | Score | A | B | C | D | E | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 16 | 0 | 0 | 1 | 1 | 1 | | 1 | 1 | 0 | 0 | 0 | 0 | 1 | | 17 | 0 | 1 | 0 | 1 | 1 | | | 2 | 0 | 0 | 0 | 1 | 0 | | 18 | 1 | 0 | 0 | 1 | 1 | | | 3 | 0 | 0 | 1 | 0 | 0 | | 19 | 0 | 1 | 1 | 0 | 1 | | | 4 | 0 | 1 | 0 | 0 | 0 | | 20 | 1 | 0 | 1 | 0 | 1 | | | 5 | 1 | 0 | 0 | 0 | 0 | | 21 | 1 | 1 | 0 | 0 | 1 | | 2 | 6 | 0 | 0 | 0 | 1 | 1 | | 22 | 0 | 1 | 1 | 1 | 0 | | | 7 | 0 | 0 | 1 | 0 | 1 | | 23 | 1 | 0 | 1 | 1 | 0 | | | 8 | 0 | 0 | 1 | 1 | 0 | | 24 | 1 | 1 | 0 | 1 | 0 | | | 9 | 0 | 1 | 0 | 0 | 1 | | 25 | 1 | 1 | 1 | 0 | 0 | | | 10 | 0 | 1 | 0 | 1 | 0 | 4 | 26 | 0 | 1 | 1 | 1 | 1 | | | 11 | 0 | 1 | 1 | 0 | 0 | | 27 | 1 | 0 | 1 | 1 | 1 | | | 12 | 1 | 0 | 0 | 0 | 1 | | 28 | 1 | 1 | 0 | 1 | 1 | | | 13 | 1 | 0 | 0 | 1 | 0 | | 29 | 1 | 1 | 1 | 0 | 1 | | | 14 | 1 | 0 | 1 | 0 | 0 | | 30 | 1 | 1 | 1 | 1 | 0 | | | 15 | 1 | 1 | 0 | 0 | 0 | 5 | 31 | 1 | 1 | 1 | 1 | 1 |

注意:0 表示没有耕地,1 表示有耕地。

图 3. 使用五个输入产品进行农田面积统计分配的方法概述。


景观和集中、均匀分布的农田使每个产品组能够准确提取这些地区的耕地。空间一致性从主要粮食种植区逐渐降低到农业与畜牧生态过渡区,然后到草原及其周边地区。在东北平原的情况下,松嫩、辽河和三江平原作为主要的粮食生产区,表现出最高的一致性水平,其次是北方农牧生态过渡区,而在蒙古高原的牧区带观察到的一致性最低。
Based on the physical geographic zoning of the CAS, the study area was divided into seven parts, as shown in Fig. 4, to investigate regional disparities in the consistency of cropland datasets. Of these, the Northeast exhibited the highest percentage of complete agreement (65%), followed by East and Central China, both with rates exceeding 50%, whereas South and Southwest China had a lower agreement percentage, at around 20 % 20 % 20%20 \% (Fig. 4 a 1 , b 1 4 a 1 , b 1 4a_(1),b_(1)4 a_{1}, b_{1} and c 1 c 1 c_(1)c_{1} ). Horizontally, the Northeast accounted for approximately 30 % 30 % 30%30 \% of the total, followed by East China, which represented more than 20 % 20 % 20%20 \%, and South China, which contributed less than 5 % 5 % 5%5 \%. Furthermore, the spatially consistent fractions at the provincial level posed variations across the years (Fig. 4 a 2 , b 2 4 a 2 , b 2 4a_(2),b_(2)4 a_{2}, b_{2}, and c 2 c 2 c_(2)c_{2} ). In 2000, Heilongjiang, Henan, and Shandong provinces exhibited the highest agreement, constituting about 70 % 70 % 70%70 \% of the total, while Tibet, Guizhou, and Fujian provinces had the worst agreement, comprising less than 10 % 10 % 10%10 \%. By 2020, Heilongjiang and Jilin had the highest agreement, though their share had decreased. The spectral and textural features of cropland in satellite images were challenging to differentiate in regions 

图 4. 多区域中国输入映射数据集的一致性分布。


具有不规则地形、破碎的景观以及与其他陆地特征交错的农田,主要位于南部地区(Lu et al., 2017)。因此,这些地区的遥感影像分类被证明是复杂的,导致这些农田产品之间的符合度较低。
Fig. 4d illustrates the variation in spatial consistency concerning elevation and slope for the five sets of cropland data. The ratio of high and full agreement was more pronounced in plains with elevations below 20 m and hilly areas between 20 and 200 m , indicating strong consistency among the datasets in these region. Consistency decreased with increasing altitude, with elevations ranging from 500 to 1500 m primarily found on the Mongolian Plateau, Tarim Basin, Loess Plateau, and Yunnan-Guizhou Plateau. These areas were characterized by mountainous terrain and fragmented topography, making cropland extraction challenging and resulting in a high level of inconsistency (37.5%). Higher altitudes above 1500 m were mainly situated in the northwestern Tibetan Plateau, exhibiting an inconsistency rate of up to 50.6 % 50.6 % 50.6%50.6 \%. Similarly, slopes less than 2 2 2^(@)2^{\circ} were primarily scattered across flat plains and basins with relatively simple geographical landscapes, suitable for agricultural cultivation. This led to better agreement in cropland extraction, with 20.1 % 20.1 % 20.1%20.1 \% and 44.2 % 44.2 % 44.2%44.2 \% showing high and complete agreement. In the slope range of 2 6 2 6 2-6^(@)2-6^{\circ}, the proportion of inconsistency increased to 22.9 % 22.9 % 22.9%22.9 \%, while complete consistency decreased to 19.4 % 19.4 % 19.4%19.4 \%. Slopes of 15 25 15 25 15-25^(@)15-25^{\circ} and above were chiefly distributed in the Qinghai-Tibet Plateau and its periphery, with inconsistency accounting for 58.5 % 58.5 % 58.5%58.5 \% and 74.0 % 74.0 % 74.0%74.0 \%, respectively. Overall, the accuracy of land cover classification in 
the Southwest, Northwest, and South China, where terrain slope was significant, was heavily influenced by relief and roughness. However, when relief and roughness reached a certain level, the land cover type became relatively simple and mainly non-cropland due to its unsuitability for human exploitation (Zhang et al., 2015). This stability in land cover classification agreement resulted from the land’s limited suitability for agricultural use. 

 3.2. 准确性评估

Fig. 5a shows the deviation between the cropland acreage retrieved from the five datasets and the statistics. The estimated cropland ratio for each data product was distinct and overestimated or underestimated to varying degrees for the most part. Specifically, the CLCD underestimated cropland acreage in the Northeast, Northwest, and North China by 4.0 % 4.0 % 4.0%4.0 \%, 2.1 % 2.1 % 2.1%2.1 \%, and 2.0 % 2.0 % 2.0%2.0 \%, respectively, while the estimates for Central and South China were relatively consistent. GLC_FCS30 also underestimated cropland in Northeast and North China by 4.4 % 4.4 % 4.4%4.4 \% and 2.8 % 2.8 % 2.8%2.8 \% but overestimated it by 3.3 % , 2.1 % 3.3 % , 2.1 % 3.3%,2.1%3.3 \%, 2.1 \%, and 2.6 % 2.6 % 2.6%2.6 \% in East, Central, and South China, which broadly aligned with the statistics for the Northwest. Globeland30 exhibited a bias of approximately 5.9 % 5.9 % 5.9%5.9 \% and 3.1 % 3.1 % 3.1%3.1 \% in its estimates of cropland in Northeast and North China, in line with the statistics for Central China. GlobalCrop overestimated cropland in Northeast and East China by 2.1 % 2.1 % 2.1%2.1 \% and 2.6 % 2.6 % 2.6%2.6 \% while undervaluing it in Southwest and South China. In East, Central, and South China, ESA_CCI’s estimated cropland area was 3.8 % , 2.0 % 3.8 % , 2.0 % 3.8%,2.0%3.8 \%, 2.0 \%, and 2.6 % 2.6 % 2.6%2.6 \% higher than the statistical data, while there was a significant underestimation in Northeast and North China, with biases of 3.5 % 3.5 % 3.5%3.5 \% and 4.5 % 4.5 % 4.5%4.5 \%, respectively. Fig. 5b presents the overall accuracy estimates, using an error matrix based on training samples in each district. In North and Northeast China, GlobalCrop achieved the highest overall accuracy ( 84.0 % 84.0 % 84.0%84.0 \% and 85.1 % 85.1 % 85.1%85.1 \% ). In Central and South China, CLCD achieved the highest accuracy ( 89.5 % 89.5 % 89.5%89.5 \% and 87.7 % 87.7 % 87.7%87.7 \% ), and Globeland30 performed exceptionally well in the northwest with an overall accuracy of 86.1 % 86.1 % 86.1%86.1 \%. These input mapping datasets were ranked in each district to create the corresponding scoring sheet based on overall accuracy. 


3.3. SOSA 开发的耕地制图


图 6a 显示了输入制图产品中利用的平均耕地百分比。绿色区域代表较低的百分比,而蓝色区域则表示较高的百分比。平均百分比在同质区域往往较高,而在异质区域则较低,特别是在东南丘陵、南中国山脉和西南高原。每个省被视为协同处理的操作单元。对于每个省级单位,耕地面积的一致性水平从高到低进行汇总,并与统计数据进行比较,以确定最佳协议水平。图 6b 描绘了每个省区的最佳协议水平,从东到西逐渐降低。具体而言,河南和山东

图 5. 耕地产品与统计数据之间的整体准确性和偏差。主要粮食生产地区的省份,达到了最高的协议水平五,表明协议水平五中的总耕地面积可以进行统计分层。在江苏、安徽、江西等东部沿海省份,以及中下游长江平原,最佳协议水平为 4。在五个西北省份,协议水平降至 3,而在贵州、西藏和云南省,协议水平为 2。这些地区由于复杂的地形和破碎的景观,表现出混合像元单元,导致数据集之间的协议较低。相比之下,福建的协议水平最低,为 1。根据第三次土地调查的主要数据公报,福建的耕地面积报告为 9320 万公顷,输入制图数据中标记为耕地的总像元面积满足统计要求。

每个农田地图的整体准确性是针对每个空间单元计算的,它们的集体表现用于对各自的数据集进行排名。随后,为每个地理单元创建了映射产品组合的评分卡,并使用校准统计确定这些输入产品的最佳组合。如图 6c 所示,在丘陵和高原地区(例如,东南丘陵、西藏高原和云南-贵州高原)观察到低一致性区域,而高一致性百分比的区域主要位于平原和盆地(例如,东北平原、华北平原、汾河-渭河盆地),与实际情况相符。对于农田百分比图上的每个像素单元,根据这些输入产品的一致性值分配了一个置信水平。较高的一致性值通常表示更可靠的解释,置信水平的定义范围从 5(最高)到 1(最低)。因此,农田置信图详细展示了协同过程的结果(图 6d)。


3.4. 协同耕地的比较与验证

Fig. 7 presents the relationship between the percentage of cropland acreage derived from agricultural statistics and the estimates obtained from the province-by-province cropland datasets. A comparison of these individual cropland datasets reveals that the synergy maps exhibit the highest level of agreement with the statistics, with the lowest RMSE (Root Mean Square Error) of 0.21 % 0.21 % 0.21%0.21 \% and a high correlation coefficient ( R 2 ) R 2 (R^(2))\left(R^{2}\right) of 0.97 . It is worth noting that CLCD achieved a superior R 2 ( 0.93 ) R 2 ( 0.93 ) R^(2)(0.93)R^{2}(0.93) and a lower RMSE ( 0.68 % 0.68 % 0.68%0.68 \% ) compared to the other four datasets. This could be attributed to the fact that CLCD’s classification results are more closely related to China compared to other global land cover mappings. In contrast, the data points for GlobalCrop noticeably deviate from the 1:1 line, with a higher RMSE ( 0.81 % 0.81 % 0.81%0.81 \% ) and a lower R 2 R 2 R^(2)R^{2} ( 0.86 ). This divergence may be attributed to GlobalCrop’s definition of cropland, which includes various mosaic types. For instance, GlobalCrop encompasses land used for planting year-round and perennial herbaceous crops for feed, biofuels, and human consumption (Potapov et al., 2021). The presence of mixed pixels resulting from fragmented landscape patches leads to variations in classifications and discrepancies in cropland area estimates across different products and in comparison to the statistics. In summary, the synergy maps exhibit better agreement with the statistics, demonstrating the effectiveness of this approach in tackling inconsistencies between satellite-based cropland maps and departmental statistics. 
Table 4 lists the accuracy evaluation results for the five cropland datasets and their synergy maps within the training sample zone. GlobalCrop exhibited the highest spatial location reliability among these input datasets, achieving a Kappa coefficient of 0.64 and an overall accuracy of 87.7 % 87.7 % 87.7%87.7 \%. CLCD and GlobeLand30 followed closely with overall accuracies of 86.1 % 86.1 % 86.1%86.1 \% and 85.6 % 85.6 % 85.6%85.6 \%, and Kappa coefficients of 0.64 and 0.63 , respectively. In contrast, ESA_CCI and GLC_FCS30 demonstrated lower positional reliability, with overall accuracies of 84.1 % 84.1 % 84.1%84.1 \% and 84.0 % 84.0 % 84.0%84.0 \%, and Kappa coefficients of 0.59 and 0.60 , respectively. Specifically, CLCD exhibited a misclassification rate of 34.6 % 34.6 % 34.6%34.6 \% and an omission 
Fig. 6. Synergy outcomes based on five cropland mapping datasets: (a) average cropland percentage; (b) optimal agreement level; © cropland percentage map; (d) cropland confidence map. 
Fig. 7. Scatterplots of cropland percentage from statistics and those estimated by CLCD (a); GLC_FCS30 (b); Globeland30 ©; GlobalCrop (d); ESA_CCI (e) and synergy map ( f ). 

17.6 % 17.6 % 17.6%17.6 \% 的比率,主要在西南和东北地区(图 8a)。GLC_FCS30 的误分类率较高(39.13%),但遗漏率较低 ( 17.3 % ) ( 17.3 % ) (17.3%)(17.3 \%) ,大多数遗漏的耕地集中在西北。GLC_FCS30 中耕地的高误分类率可能归因于将东南部的大块花园和林地错误分类为耕地。GlobeLand30 显示出最低的耕地遗漏率,但误分类率较高,错误标记的耕地
 表 4

五个数据集与协同图之间评估参数的比较。
 评估参数 CLCD GLC_FCS30  全球土地 30  全球作物 ESA_CCI  协同农田
 整体准确率(%) 86.07 83.95 85.63 87.72 84.06
 卡帕系数 0.637 0.595 0.633 0.640 0.586
 佣金错误(%) 34.58 39.13 35.98 24.86 38.16
 遗漏错误 (%) 17.58 17.34 15.70 31.14 0.816
Evaluation parameters CLCD GLC_FCS30 GlobeLand30 GlobalCrop ESA_CCI Synergy Cropland Overall accuracy(%) 86.07 83.95 85.63 87.72 84.06 Kappa coefficient 0.637 0.595 0.633 0.640 0.586 Commission error(%) 34.58 39.13 35.98 24.86 38.16 Omission error (%) 17.58 17.34 15.70 31.14 0.816 | Evaluation parameters | CLCD | GLC_FCS30 | GlobeLand30 | GlobalCrop | ESA_CCI | Synergy Cropland | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | Overall accuracy(%) | 86.07 | 83.95 | 85.63 | 87.72 | 84.06 | | | Kappa coefficient | 0.637 | 0.595 | 0.633 | 0.640 | 0.586 | | | Commission error(%) | 34.58 | 39.13 | 35.98 | 24.86 | 38.16 | | | Omission error (%) | 17.58 | 17.34 | 15.70 | 31.14 | 0.816 | |

图 8. 样本区域内漏标耕地和错误标记耕地的空间分布。

predominantly in southwest China (Fig. 8c). GlobalCrop exhibited the lowest rate of cropland misclassification but a higher rate of omission, mostly in western China (Fig. 8d). Additionally, ESA_CCI had a cropland commission rate of 38.2 % 38.2 % 38.2%38.2 \% and a cropland omission rate of 21.6 % 21.6 % 21.6%21.6 \% 

(图 8e),均低于 GLC_FCS30 和 GlobalCrop。这表明,当采用合适的技术时,低空间分辨率数据也可以实现正确的分类。协同耕地地图的准确性是通过误差矩阵进行评估的。

图 9. 2000-2020 年中国耕地的时空动态分布。

validation samples (Fig. 8f), and the validation results are presented in Table 4. The overall accuracy of the synergistic maps reached 93.3 % 93.3 % 93.3%93.3 \%, with a Kappa coefficient of 0.82 . The commission rate and omission rate for cropland were 12.9 % 12.9 % 12.9%12.9 \% and 7.8 % 7.8 % 7.8%7.8 \%, both lower than those observed for the individual datasets mentioned earlier. This indicates that the synergistic approach can harness multiple datasets to create more accurate hybrid maps. The enhanced overall accuracy of SOSA reflects the feasibility and traceability of the fusion method employed in these experiments and signifies the successful integration of multi-source cropland data into a newly refined dataset with scientifically improved features. 


3.5. 中国耕地分布的时空动态

Spatial analysis of input products and their synergy maps was employed to discern the evolving features and patterns of China’s cropland at the raster level (Fig. 9). The overall cropland distribution has remained relatively stable over the past two decades, with dynamics characterized by regional variations and subtle shifts. On a macroscopic scale, the center of cropland gravity has shifted towards the northwest and northeast, accelerated by the decline of high-quality arable land in the south, where water and temperature conditions are better suited for other land uses. On a micro scale, this shift is reflected in the reallocation of cropland resources between urban and rural areas, as well as the reclamation of sloped wasteland to expand cropland area. The total cropland area continued to decline rapidly until 2005, with an annual decrease of 278,000 ha. Factors contributing to this decline included ecological reclamation, land occupation for construction, damage from natural disasters, and agricultural restructuring (Duan et al., 2021). Subsequently, the central government implemented a series of stringent measures and protection systems. High-quality cropland was established, and local governments were held responsible for maintaining the cropland area and the protected basic farmland within their administrative districts, as outlined in the current territorial spatial planning. Specific policies included the demarcation of redlines for cropland, the abolition of agricultural taxes, and the establishment of a permanent mechanism for the protection of basic farmland (Tian et al., 2021). These measures have to some extent effectively curbed the trend of cropland reduction resulting from land misuse or unlawful appropriation. However, the decrease in cropland area between 2010 and 2020, despite stringent countermeasures against non-agricultural land encroachment, was due to land greening efforts and agricultural restructuring (Hu et al., 2020). With the deepening of conservation policies, continuous improvements and enhancements in land management models, methods, and technical measures, and the rigorous implementation of protection systems such as cropland balance (Zhong et al., 2022), China’s total cropland area has generally maintained a dynamic equilibrium. 

在此期间,耕地约占总土地面积的 63%,这一点在协同耕地地图中得到了体现。耕地主要集中在东部季风区的平原、盆地和低丘陵地区。另一方面,不稳定耕地主要出现在中国东部和南部,这些地区是经济最发达、人口密度最高的地区。这些地区面临着农业土地资源流失的更大风险(张等,2015)。耕地的快速减少在长江经济带、珠江三角洲和北方生态带尤为明显,约 22%的耕地被转为非耕地使用。值得注意的是,东南沿海地区的耕地减少,受到持续城市化、社会经济增长和建设用地扩张的驱动,仍然是一个具有挑战性的问题(钟等,2022)。在干旱和半干旱地区,生态倡议,如植被恢复,特别是在林地和草地,显著促进了耕地的减少(段等,2021)。 相反,新耕地的增加主要发生在中国西北和东北地区,约有 17%的原用于其他目的的土地被转为耕地。新疆、内蒙古、甘肃和宁夏等地区通过开垦盐碱地、荒地、裸地和以前未使用的土地,显著提高了储备耕地资源的利用率。相比之下,东北地区耕地的增加主要涉及草地、林地、未使用土地的转化以及农村发展的土地整合。
Regarding the shift of cropland within each province (Fig. 10), it was observed that 70.9 % 70.9 % 70.9%70.9 \% of provinces experienced a decrease in cropland area during the study period. Notably, Shandong, Henan, Jiangsu, Sichuan, and Hebei provinces exhibited a significant decline. These provinces are characterized by extensive agriculture and high population density, but they face challenges such as low per capita cropland availability and limited reserve resources. Recent urban expansion has resulted in increased agricultural land expropriation, intensifying the risk of cropland loss. Furthermore, anthropogenic cropland abandonment, ecological reclamation efforts, and disaster-related damage have compounded the difficulties of preserving local cropland (Zeng et al., 2023). In contrast, Xinjiang, Heilongjiang, Inner Mongolia, Shanxi, and Ningxia provinces witnessed a substantial increase in cropland area. Xinjiang, in particular, added 3.19 million hectares of cropland, which accounted for approximately 40 % 40 % 40%40 \% of the country’s total increase. Inner Mongolia and Heilongjiang also contributed significantly, with more than 25 % 25 % 25%25 \% and 15 % 15 % 15%15 \% of the total increase, respectively. These provinces, with significant influence over China’s cropland changes, have gradually achieved a balance between reducing and expanding cropland. This has been made possible through improved irrigation and water conservation infrastructure, increased investment in agricultural technology, large-scale land revitalization efforts, and pilot programs for cropland rotation. These initiatives have played a pivotal role in maintaining overall cropland stability across the country. 

 4. 讨论


4.1. 结果解释


在广阔区域内准确且经济高效地绘制农田是一项既引人入胜又充满挑战的工作。本研究介绍了一种新颖的协同方法,称为 SOSA,旨在实现绘图精度与经济性的最佳平衡。该方法充分利用统计数据与土地覆盖数据集之间的互补性,通过将所有可用的卫星地图和统计数据合并为一个统一的产品。它因其在通过对可用土地覆盖数据集应用明确协议进行绘图的可行性和可靠性而获得认可。SOSA 方法的基础是输入农田产品之间的协议,其中输入农田地图根据农田面积统计进行排名。构建了一个评分表以描绘输入产品的一致性。核心原则是将次国家级别的统计数据分配给具有更高农田评分的像素,然后整合相应的结果以得出最终的农田范围。 在实际操作中,我们应用该方法生成混合中国耕地地图,利用多个可用的遥感产品和次国家统计数据。主要结果强调,协同地图的表现优于单独的输入地图,并与统计数据更为一致。这证实了协同方法提高制图准确性和增强与统计来源一致性的能力。
Significantly, scoring assignment played a pivotal role in determining combinations of remotely sensed map products. SOSA streamlined the scoring method, eliminating the need for training samples, which sets it apart from other harmonized methods. Previous approaches involved the creation of extensive static score sheets to distinguish cropland from non-cropland, with 2 n 2 n 2^(n)2^{n} possible score combinations for n n nn input products, making the process time-consuming and 
Bcijing (BD); Tianjin (TJ); Hebci (HEB); Shanxi (SX); Inner Mongolia (IM); Liaoning (LN); Jilin (JL); Heilongiang (HLJ); Shanghai (SH); Jiangsu (JS); Zhejiang (ZJ); Anhui ( AH ) ; ( AH ) ; (AH);(\mathrm{AH}) ; Fujian (FJ); Jiangxi (JX); Shandong (SD); Henan (HEN); Hubei (HUB); Hunan (HUN); Guangdong (GD); Guangxi (GX); Hainan ( HN ) ( HN ) (HN)(\mathrm{HN}); Chongqing ( CQ ) ( CQ ) (CQ)(\mathrm{CQ}); Sichuan ( SC ) ( SC ) (SC)(\mathrm{SC}); Guizhou ( GZ ) ( GZ ) (GZ)(\mathrm{GZ}); Yunnan ( YN ) ( YN ) (YN)(\mathrm{YN}); Tibet ( TB ) ( TB ) (TB)(\mathrm{TB}); Shannxi ( SHX ) ( SHX ) (SHX)(\mathrm{SHX}); Gansu (GS); Qinghai ( QH ) ( QH ) (QH)(\mathrm{QH}); Ningxia (NX); Xinjiang ( XI ) ( XI ) (XI)(\mathrm{XI}) 

图 10. 2000 年至 2020 年中国各省耕地变化。


劳动密集型(Fritz et al., 2013)。相比之下,SOSA 确定了最佳协议级别,并为每个单位建立了动态评分表。评分表是离散的,其分配规则可以适应各种耕地制图应用(Lu et al., 2017)。大多数省级单位的最佳协议级别范围从 2 到 4,形成了构建评分表的基础,显著减少了其体积并提高了效率。虽然一些基于网络的解决方案和在线平台众包样本,如 LACO-Wiki (https://laco-wiki.net/), Collect Earth (https://www.collect.earth/), 和 Geo-Wiki (https://www.geo-wiki.org/), 积累了大量的地面真实样本,但评估其可靠性仍然是一项具有挑战性的任务(Schepaschenko et al., 2015; See et al., 2015)。更重要的是,样本质量和不确定性相关的问题不能被忽视,因为这些样本主要是由志愿者收集的(Chen et al., 2019),即使验证方法有良好的文档记录,其应用也需要相当的专业知识。

由于这些统计数据来源于实地调查和航空照片的视觉解读,SOSA 假设这些统计数据代表实际的耕地面积。这些统计数据随后与每个省级单位内现有的数据集整合,以分配等效的面积。SOSA 的优势在于其将卫星影像的空间信息与经过验证的地面观测统计数据相结合,考虑了耕地的土地利用变化特征。然而,值得注意的是,协同图中呈现的耕地面积与统计数据大致一致,但并不完全一致,这主要是由于卫星地图与行政统计之间固有的空间尺度差异。如图 9 所示,GLC_FCS30、Globeland30 和 ESA_CCI 估算的耕地面积往往高于统计数据,主要是由于像素混合和马赛克耕地类别。为了解决这个问题,根据类别方案定义,预先确定的百分比权重被分配给输入产品,并计算均匀网格内的平均耕地百分比以进行分辨率标准化。 与传统的二元耕地或非耕地地图不同,生成了一张耕地百分比地图,该地图在统计上与数据一致。确定耕地类别的适当权重分配超出了本研究的范围,但这是一个值得在未来研究的主题,因为它可能会影响最终地图的结果。


4.2. 限制与展望


尽管从多个来源提取农田在这项实证研究中已被证明是可行的,但我们也意识到这种方法存在某些不确定性。如前所述,这里描述的方法在很大程度上依赖于输入层的准确性和范围以及次国家统计数据的可靠性,类似于大多数模型。首先,农田定义中的差异和模糊性可能会影响这些制图产品之间的一致性。例如,某些包含灌木和丘陵地区农田的马赛克类别在 GLC_FCS30 和 ESA_CCI 中很常见,而 GlobalCrop 将一年生和多年生作物分类为农田,但 CLCD 将其分类为森林,从而导致农业-牧场生态带的不确定性。此外,农业景观在确定协同农田地图输入数据集的一致性方面也发挥着关键作用(Peng et al., 2017)。农田置信度地图可以视为制图质量的空间显性指标,在同质区域中输入数据集之间的一致性较高,而在选定的累积区域中排名得分也较高。 相反,在异质区域内低一致性使得协同结果更加不确定。这表明,异质区域内农田产品的有限精度和相关不确定性阻碍了它们的有效性,因此在这些区域需要更全面的准确性方法,以提高协同结果的质量。随着未来高质量数据的增加,预计协同地图将进一步改善。

对遥感产品类别准确性的探索性分析为多源辅助数据集的融合提供了重要的先验知识。通过结合基本准确性评估,可以更全面地评估制图产品的质量。


一致性评估,从而补偿偏差或不直观的个体评估结果。信息融合技术在土地覆盖分类制图中的应用是全球变化研究中的一个显著趋势,特别是考虑到多源数据的共存和整合,解决了单一土地覆盖数据集在研究应用中日益突出的不足问题。协同融合是一种多层次、多方面的数据处理过程,可以用来解决数据分类系统之间的不兼容性,并在一定程度上提高映射数据的可靠性和稳健性,从而扩展时空感知范围。假设改进的协同图也被整合到空间显式模型中,以减少与农田面积相关的不确定性,这将进一步增强在土地利用变化、气候变化、全球植被和地球系统建模等领域的预测应用。

 5. 结论

Our study combines existing global land cover products synergistically to enhance the accuracy of Chinese cropland estimates. This approach is well-suited to reduce local discrepancies between remote sensing data and national cropland statistics. We introduce a new calibration method based on remote sensing images and statistics and validate the resulting cropland map. This method has significant potential to streamline the process of achieving dataset synergies compared to conventional scoring assignment methods. It is also adaptable, allowing for the seamless integration of new products as they become available. Initial evidence and comparisons demonstrate that the synergistic maps exhibit higher accuracy and better statistical consistency than the original individual mapping products. Furthermore, this approach can be extended beyond its current focus on cropland mapping to cover entire continents and even global scales. It is applicable to a wide variety of land cover types, including forests, grasslands, and water bodies. However, our evaluation also suggests that there is still room for improvement, as the method described here heavily relies on the accuracy and coverage of the input layer, as well as the reliability of sub-national statistics. This mapping is expected to be further refined as more high-quality data becomes available in the foreseeable future. 

 数据可用性


这些生成的协同耕地地图是开放获取的,可以在 https://doi.org/10.7910/DVN/J8WB8T 上找到。所有应用于协同耕地制图的注释代码都在此网站共享:https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ SECLXQ。


CRediT 作者贡献声明


刘小杰:方法论、软件、可视化、数据管理、撰写初稿、撰写审阅与编辑。金小彬:概念化、验证、撰写审阅与编辑、项目管理。罗秀丽:正式分析、调查、资源、数据管理、可视化。周银康:资源、监督、资金获取。


竞争利益声明


作者声明不存在利益冲突。

 致谢


本研究得到了中国国家自然科学基金(编号:41971234 和编号:41971235)、中国国家社会科学基金重大项目(编号:19ZDA096)以及沿海区域开发重点实验室开放基金的资助。


保护,自然资源部,中国(编号:2021CZEPK07)。

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    • 通讯作者。南京大学地理与海洋科学学院,中国南京,210023。

    电子邮件地址:jinxb@nju.edu.cn (X.-b. Jin)。