耕地复种指数遥感监测研究进展

doi:10.12082/dqxxkx.2021.200465

地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (7): 1169-1184.doi: 10.12082/dqxxkx.2021.200465

耕地复种指数遥感监测研究进展

葛中曦¹^,²^,³(), 黄静¹^,²^,³, 赖佩玉¹^,²^,³, 郝斌飞⁴, 赵银军⁵^,⁶, 马明国¹^,²^,³^,^*()

1.西南大学地理科学学院重庆金佛山喀斯特生态系统教育部野外科学观测研究站,重庆 400715
2.西南大学遥感大数据应用重庆市工程研究中心,重庆 400715
3.西南大学岩溶环境重庆市重点实验室,重庆 400715
4.广东海洋大学电子与信息工程学院,湛江 524088
5.南宁师范大学北部湾环境演变与资源利用教育部重点实验室,南宁 530001
6.南宁师范大学广西地表过程与智能模拟重点实验室,南宁 530001

收稿日期:2020-08-15 修回日期:2020-09-25 出版日期:2021-07-25 发布日期:2021-09-25
通讯作者: 马明国
作者简介:葛中曦（1989— ）,男,云南腾冲人,博士生,研究方向为环境遥感。E-mail: zhongxige0710@163.com
基金资助:
国家重点研发计划重点专项项目(2016YFC0500106);国家自然科学基金项目(41830648);国家自然科学基金项目(41771453);国家自然科学基金项目(41661085);广西科技基地和人才专项(AD19110140);重庆市研究生科研创新项目(CYS20107)

Research Progress on Remote Sensing Monitoring of Cultivated Land Cropping Intensity

GE Zhongxi¹^,²^,³(), HUANG Jing¹^,²^,³, LAI Peiyu¹^,²^,³, HAO Binfei⁴, ZHAO Yinjun⁵^,⁶, MA Mingguo¹^,²^,³^,^*()

1. Southwest University, School of Geographical Sciences, Chongqing Jinfo Mountain Field Scientific Observation and Research Station for Kaster Ecosystem, Ministry of Education, Chongqing 400715, China
2. Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China
3. Chongqing Key Laboratory of Karst Environment, School of Geographical Sciences, Southwest University, Chongqing 400715, China
4. College of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
5. Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Ministry of Education), Nanning Normal University, Nanning 530001, China
6. Key Laboratory of Earth Surface Process and Intelligent Simulation, Nanning Normal University, Nanning 530001, China

Received:2020-08-15 Revised:2020-09-25 Online:2021-07-25 Published:2021-09-25
Contact: MA Mingguo
Supported by:
National Key Research and Development Project of China(2016YFC0500106);National Natural Science Foundation of China(41830648);National Natural Science Foundation of China(41771453);National Natural Science Foundation of China(41661085);Guangxi Scientific Project(AD19110140);Chongqing Postgraduate Research and Innovation Project(CYS20107)

摘要/Abstract

摘要：

复种指数是进行粮食估产、耕地集约利用评价、农业生态系统模拟等的关键参数,及时、准确地提取复种指数对于粮食安全、土地管理和生态环境安全具有重要意义。在传统的研究中,复种指数主要来源于地面统计数据。使用统计数据来计算复种指数虽然过程简单,但是计算结果存在信息滞后、无法体现统计单元内部的空间异质性、精度低等不足。遥感技术因具有大范围、高时效、低成本等优点而被用于耕地复种指数监测,已有学者对耕地复种指数的遥感监测开展了大量工作。本文以复种指数遥感提取的关键环节为主线,对1997—2020年国内外相关研究进行综述：首先,梳理了已有研究中的监测方法、高质量时间序列遥感数据获取方法及提取结果精度验证方法,并对不同方法的优缺点进行了总结;其次,对已有研究中存在的不足进行了探讨,并提出未来研究的侧重点：① 开展已有监测方法的对比和分析;② 加强地形复杂地区、小农尺度的监测力度;③ 提高遥感数据时空分辨率及处理效率;④ 对提取结果进行多尺度验证。

关键词: 熟制, 时间序列, 植被指数, 去噪重建, 融合, 监测方法, 研究现状, 发展趋势

Abstract:

Cropping intensity refers to the frequency of crop planting in the same cultivated land in one year. It is a key parameter for grain yield estimation, land use intensity evaluation, and agroecosystem modeling. Understanding the spatiotemporal change of cropping intensity provides support for food security, land management, and eco-environment security. The demand for timely and accurate information of cropping intensity is expected to increase in the future. Traditionally, cropping intensity is calculated from the statistical data. However, there are several shortcomings in the output from statistics, such as time-lag effect, homogeneity in one administrative unit, and low accuracy. In the past two decades, remote sensing technology has been widely used to monitor cropping intensity at different scales due to its multiple advantages such as high-efficiency and low-cost. However, the performance of remote sensing in monitoring cropping intensity has not been well evaluated. In this paper, remote sensing data, extraction algorithms, and accuracy evaluation methods employed in cropping intensity researches are summarized elaborately: ① cropping intensity extraction algorithms can be grouped into the following types: feature discrimination algorithm, curve feature comparison algorithm, peak detection algorithm, temporal mixture analysis algorithm, hierarchical training algorithm, continuous wavelet transform algorithm, growth cycle judgment algorithm, and time-series banning algorithm; ② time-series data from a single sensor is still the main data source for cropping intensity monitoring and can no longer meet the requirement of higher precision. As a result, data fusion has gradually become an effective way to obtain high-quality time-series data from remote sensing; and ③ results from different extraction algorithms are evaluated by statistics, visual interpretation, field survey, and previous studies. Furthermore, we conclude the advantages and disadvantages of remote sensing data and compare different extraction algorithms and accuracy evaluation methods. Finally, we discuss the deficiencies of previous studies, and put forward several tips for future studies: ① a reasonable evaluation system is expected to be established for comparing different extraction algorithms; ② more attention should be paid to regions having complex terrain and smallholder farms; ③ to obtain higher quality time-series data from remote sensing and improve the efficiency of data processing, denoising algorithms, data fusion, big data, and cloud computing techniques should be considered; ④ multi-scale validation of results is needed if data is available.

Key words: cropping system, time-series, vegetation index, reconstruction, data fusion, extracting algorithm, research status, development trend

葛中曦, 黄静, 赖佩玉, 郝斌飞, 赵银军, 马明国. 耕地复种指数遥感监测研究进展[J]. 地球信息科学学报, 2021, 23(7): 1169-1184.DOI:10.12082/dqxxkx.2021.200465

GE Zhongxi, HUANG Jing, LAI Peiyu, HAO Binfei, ZHAO Yinjun, MA Mingguo. Research Progress on Remote Sensing Monitoring of Cultivated Land Cropping Intensity[J]. Journal of Geo-information Science, 2021, 23(7): 1169-1184.DOI:10.12082/dqxxkx.2021.200465

图/表 2

表1

复种指数监测方法对比

监测方法		是否需要去噪重建	优点	缺点	辅助数据	复种指数提取限制条件	代表文献	应用范围
特征时相分离法	-	否	① 只需部分时相遥感数据 ② 对遥感数据的时间分辨率要求低 ③ 信息提取速度快	① 需要对研究区的作物类型及种植模式比较熟悉 ② 适用范围有限	① 土地利用数据 ② 物候观测数据	无	Panigraphy 等^[13]、左丽君等^[14]、Jiang等,2019^[15]	印度孟加拉邦、河西走廊、我国南方稻区
曲线特征对比法	交叉拟合度检验法	是	① 充分利用曲线信息 ② 样本曲线和标准曲线之间的比较定量且客观	① 对典型样点的依赖性较大,典型样点存在错选、漏选的可能 ② 由于交叉拟合度对较短时间的波动敏感性低,影响高频复种模式的精度	① 种植制度区划图 ② 植被矢量图 ③ 物候观测数据	生长季长度最小值	辜智慧^[16]	我国大陆地区
曲线特征对比法	形状匹配法	否	① 不需对时间序列遥感数据去噪重建,直接从时间序列曲线中提取熟制信息 ② 模型仅需两个输入参数,且参数直接来源于时间序列曲线	① 关键参数之一的作物最小生长振幅的阈值需人为确定 ② 存在迭代过程,信息提取耗时长	① 土地利用数据 ② 物候观测数据	作物生长幅度最小值	Liu等^[17]	江苏省
峰值点探测法	二次差分法	是	原理简单、算法容易实现	对曲线的峰较敏感,需要多个限制条件去除“伪波峰”	① 土地利用数据 ② 物候观测数据 ③ 植被矢量图 ④ 种植制度区划图 ⑤ 气象数据	① 峰值点植被指数的最小值 ② 可能的峰值点振幅占曲线最大振幅的比例 ③ 峰值点出现的最早、最晚时间 ④ 最短生长季间隔 ⑤ 不同熟制年积温最小值	范锦龙等^[19]、唐鹏钦等^[40]、徐昔保等^[38]、丁明军等^[41]、Chen等^[18]、Zhao等^[42]、Xiang等^[20]	全球主要粮食产区、我国大陆地区、华北平原、太湖流域、江西进贤县等
峰值点探测法	特征点检测法	是	原理简单、算法容易实现	① 要求去噪重建后的时间序列曲线平滑 ② 需要限制条件去除“伪波峰”	① 土地利用数据 ② 物候观测数据 ③ 气象数据	① 不同熟制年积温最小值 ② 峰值点植被指数的最小值 ③ 峰值点出现前后曲线的单调性	闫慧敏等^[43]、Sakamoto等^[21]、Hao等^[22]	全球典型农作区、我国大陆地区、湄公河三角洲
峰值点探测法	邻域比较法	是	原理简单、算法容易实现	需要限制条件去除“伪波峰”	土地利用数据	① 峰值点植被指数的最小值 ② 可能的峰值点振幅占曲线最大振幅的比例	Galford等^[24]、 Wu等^[23]	全球、巴西马托格罗索州
峰值点探测法	滑动分割法	是	① 原理简单、算法容易实现 ② 不需熟制分区、物候等辅助数据	已有研究中部分地区提取结果精度与统计数据、前人结果存在较大差异	土地利用数据	峰值点植被指数的最小值	刘爽等^[25]	我国大陆地区
监测方法		是否需要去噪重建	优点	缺点	辅助数据	复种指数提取限制条件	代表文献	应用范围
线性混合模型法	-	是	以少量的端元作为辅助信息即可获得较大区域的复种指数	① 端元选取严格,异质性较高区域精度低 ② 应用于小范围时精度低	土地利用数据	无	Chen等^[26]、 Jain等^[27]	湄公河三角洲上游、印度古吉拉特邦和中央邦
层次训练法	-	是	仅需样区关键生长期的高分辨率影像即可获得较大范围的复种指数	受关键生长季高分辨率影像可得性限制较大	土地利用数据	无	Jain等^[27]	印度古吉拉特邦和中央邦
连续小波变换法	-	否	① 不需要对时间序列遥感数据去噪重建,直接从时间序列曲线中提取熟制信息 ② 将时间序列数据转换为时间-频率信号,有效地避免了曲线波动的影响	需要人为确定特征峰阈值,区分一熟和两熟的阈值较难确定	土地利用数据	特征峰阈值	Qiu等^[28]、 Qiu等^[29]	河南省、全国
生长周期判断法	关键物候参数比较法	是	① 原理简单、算法容易实现 ② 可以区分农作物和自然植被,提高了研究对象的准确性 ③ 能够识别跨年际复种模式	① 为了设置合理的物候参数阈值,需要对研究区的作物种植信息比较了解 ② 采用同一阈值确定不同作物物候期,可能影响提取结果的精度	① 土地利用数据 ② 物候观测数据	① 生长季长度最小值、最大值 ② 生长曲线变化幅度最小值	Liu等^[30]	河南省
生长周期判断法	全生长过程模拟法	是	① 使用地表水指数进行作物播种前和收割后的耕地状态判定,有效地避免了“伪波峰”的干扰 ② 基于作物生长全过程进行建模,更加符合作物生理特点 ③ 该方法考虑了多种复种模式,适用场景较广	受影像质量和植被类型差异影响,可能存在预先设置生长季最大NDVI阈值无法区分作物和自然植被的情况	① 土地利用数据 ② 物候观测数据	① 区分作物和自然植被的NDVI阈值 ② 识别裸土的LSWI阈值	Liu等^[31]	我国7个代表性粮食产区
时间序列分箱法	-	是	① 充分利用遥感数据多波段信息,弥补了单一植被指数提取熟制信息的不足 ② 可以同时兼顾大范围和高精度	① 遥感数据收集和处理的工作量较大 ② 低纬度、多云多雨地区受天气影响较大,该方法可能不适用	数字高程模型	无	Rufin等^[32]	土耳其

表1

表2

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卫星	传感器	数据类型	空间分辨率	时间分辨率/d	数据时间	代表文献
NOAA	AVHRR	NDVI3g	8 km	15	1981—2015	Wu等^[23]
NOAA	AVHRR	NDVI	8 km	10	1982—现在	闫慧敏等^[43]、Canicius等^[65]
SPOT	VGT	NDVI	1 km	10	1998—2014	朱孝林等^[66]、Panigrahy等^[67]、丁明军等^[41]
Terra/Aqua	MODIS	NDVI/EVI	250 m、500 m、 1000 m	8、16	2000—现在	李卓等^[68]、Gray等^[35]、彭代亮等^[69]、杨婷等^[70]
NOAA&Terra&Aqua	AVHRR&MODIS	GLASS LAI	1 km、5 km	8	1981—2018	Zhao等^[42]、Joeng等^[71]
TM	Landsat5	NDVI	30 m	16	1982—2011	Li等^[54]、Jain等^[27]
ETM+	Landsat7	NDVI	30 m	16	1999—现在
OLI	Landsat8	NDVI	30 m	16	2013—现在
OLI&Sentinel-2	Landsat8&MSI	NDVI/EVI	30 m	5	2017—现在	Hao等^[22]
Gaofen-1	WFV	Image	16 m	4	2013—现在	Xiang等^[20]
Sentinel-2A&2B	MSI	Image	10 m、20 m、60 m	5	2017—现在	Liu等^[31]

耕地复种指数遥感监测研究进展

Research Progress on Remote Sensing Monitoring of Cultivated Land Cropping Intensity

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