物候窗口和多源中高分辨率影像的稻虾田提取

物候窗口和多源中高分辨率影像的稻虾田提取 Phenology windows and multi-source medium-/high-resolution image extraction for rice-crayfish paddy fields mapping 1 first-author 魏浩东，研究方向为农业遥感。E-mail： hzau-rsaa@webmail.hzau.edu.cn 魏浩东，研究方向为农业遥感。E-mail： hzau-rsaa@webmail.hzau.edu.cn hzau-rsaa@webmail.hzau.edu.cn 1 1 2 1 1 3 4 corresp 胡琼，研究方向为农业遥感。E-mail： huqiong@mail.ccnu.edu.cn 胡琼，研究方向为农业遥感。E-mail： huqiong@mail.ccnu.edu.cn huqiong@mail.ccnu.edu.cn 华中农业大学 资源与环境学院 宏观农业研究院, 武汉 430070 华中农业大学 资源与环境学院 宏观农业研究院, 武汉 430070 College of Resources and Environment, Macro Agriculture Research Institute, Huazhong Agricultural University, Wuhan 430070, China College of Resources and Environment, Macro Agriculture Research Institute, Huazhong Agricultural University, Wuhan 430070, China 华中农业大学 植物科学技术学院, 武汉 430070 华中农业大学 植物科学技术学院, 武汉 430070 College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China 中国科学院空天信息创新研究院 遥感科学国家重点实验室, 北京 100101 中国科学院空天信息创新研究院 遥感科学国家重点实验室, 北京 100101 State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China 华中师范大学 城市与环境科学学院, 武汉 430079 华中师范大学 城市与环境科学学院, 武汉 430079 College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China 由于显著的经济效益和生态效益，近年来稻虾共作模式分布面积迅猛扩张。准确获取稻虾田空间分布信息，对于水稻种植结构调整、产量估算和水资源管理具有重要意义。本文以“小龙虾之乡”——湖北省潜江市为研究区域，基于Google Earth Engine平台协同Landsat 7/8和Sentinel-2卫星数据，通过分析稻虾田的农业耕作管理和季相节律特征，提取了稻虾田区别其他农作物的关键“水淹”信号和“植被”信号。基于实地稻虾田样本统计分析关键特征的阈值，构建稻虾田规则集识别模型，提取了湖北省潜江市2019年稻虾田空间分布。最后，基于实地样本验证该物候窗口特征方法的精度，并评估和比较了该方法与随机森林和基于水体季相差异方法的表现。结果表明：物候窗口1月1日—4月30日内的水淹信号（LSWI>NDVI或EVI）、物候窗口7月15日—9月30日内的植被信号（NDVI或EVI>LSWI）和物候窗口11月10日—12月31日内的水淹信号是稻虾田遥感识别的典型特征。基于该方法提取的2019年潜江市稻虾田制图精度和用户精度分别为90.74%、94.69%，显著高于水体季相差异方法和随机森林方法的精度。基于关键物候窗口的稻虾田提取方法具有较高的泛化能力，能以较少的实地样本进行时空尺度的延展，从而为大尺度长时序稻虾田遥感制图提供重要的方法支撑。 Rice-crayfish co-culture is a kind of comprehensive ecological agriculture pattern. Rice-crayfish co-culture has expanded rapidly in China in the past decade due to its outstanding ecological and economic benefits. The accurate spatial distribution information of this newly emerging agricultural pattern is crucial for growth monitoring, yield estimation, and water resource management. However, most studies have focused on field-level research on the farmland ecosystem, and rice-crayfish mapping at regional or larger scales has received less attention. In this study, Qianjiang City in Hubei Province, known as the “hometown of crayfish” was selected as the test area. The cloud computing approach was used for all available Landsat 7/8 and Sentinel-2 imagery in 2019 with the Google Earth Engine (GEE) platform. By analyzing farming characteristics and the spectral curves of rice-crayfish fields, we identified the crucial phenology windows and classification features (i.e., flooding and vegetation signals) for rice-crayfish mapping. On the basis of the key phenological characteristics and associated frequency thresholds derived from field samples, we developed a rule-based algorithm for rice-crayfish mapping and generated the rice-crayfish map of Qianjiang City in 2019. To further evaluate the potential of our proposed method, we compared it with the random forest method and a method based on seasonal differences of water bodies. The spectral analysis of time series images showed that the unique phenological characteristics of rice-crayfish co-culture were flooding signal (LSWI > NDVI or LSWI > EVI) in phenology window 1 (from January 1 to April 30), vegetation signal (NDVI > LSWI or EVI > LSWI) in phenology window 2 (from July 15 to September 30), and flooding signal in phenology window 3 (from November 10 to December 31). With the mapping results, the total area for rice-crayfish planting in Qianjiang City in 2019 was estimated to 575.58 km 2 , and rice-crayfish plots were mainly distributed in the southwest. The producer’s accuracy of the classification result was 90.74%, the user’s accuracy was 94.69%, and the overall accuracy was 95.23%. The method based on phenology window features had fewer commission errors compared with the random forest method and fewer omission errors compared with the method based on water body seasonal differences. Among the three methods, the proposed method presented the highest overall classification accuracy. The rice-crayfish mapping method based on phenology windows, flooding signal, and vegetation signal showed high separability. The method based on phenology windows can be easily generalized to other regions and other images because of its strong physical interpretation for rice-crayfish. On the one hand, this method has relatively low dependence on training samples. On the other hand, as long as the key phenology window can be obtained, other medium-high resolution images, such as GF-1 and GF-6, can achieve high-accuracy mapping results for rice-crayfish. Therefore, the method based on phenology windows can be effectively extended to large areas and long time series. It can provide essential information for rice production management and decision-making in the crayfish industry. 遥感 稻虾田 作物提取 Google Earth Engine 物候窗口 Landsat Sentinel-2 remote sensing rice-crayfish crop extraction Google Earth Engine phenology window Landsat Sentinel-2 1　引言 1 稻虾共作是一种将水稻种植和小龙虾养殖结合在一起的高效生态农业模式（ 秦尊文，2016 秦尊文，2016 ）。一片稻虾田通常是由水稻田以及在其周围挖建的面积不超过田块总面积10%的环形小龙虾养殖围沟组成（ 陈松文 等，2020 陈松文 等，2020 ）。一方面，这种互利共生的种养模式可以获取良好的生态效益，例如：（1）提高土壤肥力；（2）减肥减药，提高水体养分含量；（3）减轻虫害（ 曹凑贵 等，2017 曹凑贵 等，2017 ）。另一方面，稻虾共作模式下可每年收获一季优质稻米和两季小龙虾，大幅提升了经营者的经济收益。正是这种突出的生态、经济双重效益，使得近十年间中国的稻虾田种植面积迅猛扩张。统计数据显示，2017年全国稻虾共作面积为5.67×10 3 km 2 （ 农业农村部渔业渔政管理局 等，2018 农业农村部渔业渔政管理局 等，2018 ），到2019年为1.1×10 4 km 2 （ 农业农村部渔业渔政管理局 等，2020 农业农村部渔业渔政管理局 等，2020 ），两年之间增幅接近95%，其中湖北省稻虾田分布面积最大，接近全国稻虾分布总面积的50%。对于稻虾共作这种新兴的农业模式，准确获取其空间分布信息对于水稻长势监测、种植结构调整、产量估算以及水资源管理等具有重要意义（ Löw等，2013 Löw等，2013 ； Wu等，2014 Wu等，2014 ）。但目前稻虾模式的相关研究多侧重于小尺度的田间生态系统研究（ Liu等，2019 Liu等，2019 ），区域尺度稻虾模式空间分布信息提取的研究不足。 遥感技术是大尺度农作物空间分布信息监测最有效的手段（ Liu等，2014 Liu等，2014 ； 胡琼 等，2015 胡琼 等，2015 ； Hu等，2016 Hu等，2016 ），基于不同卫星遥感数据进行水稻空间分布信息提取的研究已取得较大进展。大量研究表明，水稻水淹移栽期独特的物候特征（LSWI > NDVI或EVI）是水稻最敏感的识别特征，基于该特征和阈值法是识别水稻田的一种简便且可靠的手段（ Xiao等，2005 Xiao等，2005 ； Dong和Xiao，2016 Dong和Xiao，2016 ）。此外，为削弱不同区域、不同气候节律导致移栽期时间的不确定性，可以利用MODIS温度产品（MYD11A2）辅助确定移栽期的起始时间（SOT）和结束时间（EOT）（ Dong等，2016 Dong等，2016 ； Yin等，2019 Yin等，2019 ），再通过捕获移栽期水淹信号来进行水稻分类（ Dong等，2015 Dong等，2015 ； Zhang等，2017 Zhang等，2017 ）。为减少因云雨天气导致的水稻关键期影像缺失的问题，越来越多研究者通过协同“中和低”空间分辨率（ 谢登峰 等，2015 谢登峰 等，2015 ； Liu等，2018 Liu等，2018 ； Ding等，2020 Ding等，2020 ）或者“中和中”空间分辨率影像（ Mansaray等，2020 Mansaray等，2020 ）捕获水稻的关键物候期，从而高精度提取水稻空间分布。 稻虾共作是水稻的一种特殊种植模式，也拥有典型的水淹移栽物候特征。然而，仅仅依靠该物候期特征只能区分水稻和其他作物，无法区分普通水稻田和稻虾田。由于小龙虾幼苗的生长繁育离不开水体环境，稻虾田在一年中的1—5月以及11—12月休耕期需要进行灌水泡田（ 曹凑贵 等，2017 曹凑贵 等，2017 ），这是它与普通水稻田最主要的区别。这意味着相比普通水稻田，稻虾共作模式在一年内会表现出独特的“水淹—作物生长—水淹”的周期性物候特征。因此，如何基于遥感影像表达这种物候特征成为了高精度提取稻虾田空间分布信息的关键所在。 优质遥感影像的数量以及时间连续性是准确反映农作物季相节律特征的主要限制因素（ Wang等，2015 Wang等，2015 ）。尤其是在云雨频发的南方地区，优质中高分辨率影像的获取更加受限。越来越多研究者通过协同中空间分辨率影像识别水稻田。例如， Zhang等（2018a） Zhang等（2018a） 通过协同Landsat 8 OLI和Sentinel-2 MSI影像加密时间序列，充分捕捉水稻独特的物候特征。然而，已有的研究多针对传统水稻田，如何协同多源中高分辨率数据捕获稻虾田关键物候特征则鲜见报道。GEE（Google Earth Engine）是一个集数据获取、影像处理以及可视化于一体的云计算平台（ Gorelick等，2017 Gorelick等，2017 ），已被广泛应用于农作物分类研究（ Xiong等，2017 Xiong等，2017 ； You和Dong，2020 You和Dong，2020 ）。凭借强大的计算能力以及集成的海量开源遥感数据，GEE为协同多源数据的土地利用分类研究提供了便捷的数据处理平台。 本文以“小龙虾之乡”——湖北省潜江市为研究区，以捕获稻虾田一年内多次灌水泡田的物候特征为突破口，基于GEE云计算平台协同Landsat 7 ETM+、Landsat 8 OLI以及Sentinel-2 MSI影像构建特征指数，捕获稻虾田关键物候特征，从而准确提取稻虾田空间分布信息。具体的研究目标有：（1）识别稻虾田的关键物候窗口及分类特征；（2）提出一套基于物候窗口特征和GEE云平台提取稻虾共作区域的方法，并评估该方法的潜力。 2　研究区与数据 1 2.1　研究区概况 2 本文研究区为湖北省潜江市，全市国土总面积为2000 km 2 ，所处纬度范围是30°04′N—30°39′N，经度范围是112°29′E—113°01′E（ 图1 图1 ）。潜江市地处江汉平原腹地，属北亚热带季风性湿润气候，四季分明，雨热充沛。年平均气温为16.1 ℃，年平均日照时数为1949—1988 h，全年无霜期约为250 d，有着优良的农作物耕作气候。该区域的农作物种植结构复杂，主要农作物类型包括水稻、油菜（主要为油菜—水稻轮作）、小麦（主要为小麦—水稻轮作）以及稻虾共作田。潜江市小龙虾产业发展快速，已形成庞大且完善的产业链，因此潜江市也被誉为“小龙虾之乡”。作为长江中下游稻虾综合种养模式的发源地，近年来潜江市稻虾模式面积扩张迅猛。2010年潜江市稻虾共作面积只有667 hm 2 ，2013年已增加10倍（约6670 hm 2 ），2016年底面积已增至2.11×10 4 hm 2 （ 曹凑贵 等，2017 曹凑贵 等，2017 ）。 2.2　数据及预处理 2 2.2.1　多源遥感数据 3 本研究协同使用2019年Landsat 7 ETM+，Landsat 8 OLI和Sentinel-2 MSI影像数据来提取潜江市稻虾共作区域。Landsat 7/8空间分辨率为30 m，重访周期为16 d。Sentinel-2（包含Sentinel-2A和Sentinel-2B）空间分辨率为10 m，重访周期为5 d。Landsat和Sentinel影像的预处理主要包含两个方面：（1）去除低质量观测；（2）反射率标准化。 本研究采用质量标识波段（ Roy等，2014 Roy等，2014 ）去除Landsat影像数据云、阴影以及SLC-off条带。云和云阴影标识有4种情况存在，包括“不确定”、“不是”（0—33%置信度）、“可能”（34%—66%置信度）以及“是”（67%—100%置信度），结合相关参考研究（ Scaramuzza等，2012 Scaramuzza等，2012 ； Dong等，2016 Dong等，2016 ）以及潜江市去云效果目视分析，本研究选择67%—100%置信度去除Landsat影像中云和云阴影。对于Sentinel-2影像，本文基于GEE平台，运用Simple Cloud Score算法（ Housman等，2018 Housman等，2018 ）去除云覆盖区。该算法使用蓝、绿、红、近红外、短波红外1、短波红外2以及归一化雪体指数NDSI，综合计算得到云分数。像元的云分数表示此像元为云污染像元的可能性，本文选择云分数阈值为30%进行Sentinel-2影像去云处理。 ETM+、OLI以及MSI传感器之间存在波段范围及光谱反射率的差异。为了协同使用它们并获得具有可比性的时间序列影像，需要对不同传感器的反射率做标准化处理。本文使用 Roy等（2016） Roy等（2016） 和 Zhang等（2018b） Zhang等（2018b） 基于普通最小二乘回归计算得到的转化系数，以OLI影像为参考，将ETM+和MSI影像光谱波段进行转化，转化公式见 表1 表1 。此外，最终的分类统一到30 m的空间分辨率进行。基于GEE平台对2019年优质Landsat、Sentinel-2观测数量进行像素尺度统计，结果如 图2 图2 所示。 波段 ETM+与OLI转化公式 MSI与OLI转化公式 蓝 <math id="M1"><mi 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25.90800095 2.03200006 短波红外 <math id="M13"><mi mathvariant="normal">O</mi><mi mathvariant="normal">L</mi><mi mathvariant="normal">I</mi></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33017990&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33017985&type= 4.40266657 2.03200006 <math id="M14"><mo>=</mo><mn>0.0260</mn><mo>+</mo><mn>0.9414</mn><mi mathvariant="normal">E</mi><mi mathvariant="normal">T</mi><mi mathvariant="normal">M</mi><mo>+</mo></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33017981&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33017977&type= 29.20999908 2.03200006 <math id="M15"><mi mathvariant="normal">O</mi><mi mathvariant="normal">L</mi><mi mathvariant="normal">I</mi></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33017990&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33017985&type= 4.40266657 2.03200006 <math id="M16"><mo>=</mo><mn>0.0019</mn><mo>+</mo><mn>0.9668</mn><mi mathvariant="normal">M</mi><mi mathvariant="normal">S</mi><mi mathvariant="normal">I</mi></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33017999&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33017995&type= 25.90800095 2.03200006 1,2;3,4;5; 2.2.2　农作物样本及辅助数据 3 本研究获取了2019年3月及5月采集的实地农作物样本共1626个，其中稻虾田样本541个，水稻样本221个，稻—麦轮作样本454个，稻—油轮作样本310个以及其他作物样本100个（包含莲藕16个，大棚66个，菜地18个），样本分布见 图1 图1 。提取总样本的60%（976个）作为训练样本，剩下40%（650个）作为验证样本。 此外，本研究使用了2020年GlobeLand30地表覆盖产品（ Chen等，2014 Chen等，2014 ）进行非耕地掩膜，去除了潜江区域的人造地表、水体、森林等非耕地类型，基于耕地图层进行稻虾田面积提取。同时，截取研究区域部分2019年Google Earth卫星影像用作评估分类效果的参考数据。 3　研究方法 1 稻虾田在一年内有2个灌溉泡田阶段和1次中稻种植阶段，基于该耕作特征，本研究提出了基于物候窗口特征的稻虾田提取方法。该方法具体包含以下3个步骤：首先，通过分析稻虾田的年内光谱动态变化曲线，构建时序NDVI、EVI和LSWI植被指数，获取稻虾田遥感识别的关键时相窗口及光谱特征。其次，基于关键物候特征构建规则集决策模型提取稻虾田。最后，验证基于物候窗口特征方法的精度以及评估它的潜力。完整的技术流程如 图3 图3 所示。PW 1 、PW 2 、PW 3 分别代表物候窗口1、物候窗口2、物候窗口3； <math id="M17"><mi mathvariant="normal">F</mi><mi mathvariant="normal">S</mi><mi mathvariant="normal">F</mi></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018114&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018109&type= 6.01133299 2.70933342 和 <math id="M18"><mi mathvariant="normal">V</mi><mi mathvariant="normal">S</mi><mi mathvariant="normal">F</mi></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018124&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018119&type= 6.43466663 2.70933342 分别代表水淹信号（蓝色方格）频率和植被信号（绿色方格）频率； <math id="M19"><mi>F</mi><mi>r</mi><mi>e</mi><msub><mrow><mi>q</mi></mrow><mrow><mn mathvariant="normal">1</mn></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018136&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018133&type= 7.78933382 4.57200003 、 <math id="M20"><mi>F</mi><mi>r</mi><mi>e</mi><msub><mrow><mi>q</mi></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018148&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018142&type= 7.78933382 4.57200003 、 <math id="M21"><mi>F</mi><mi>r</mi><mi>e</mi><msub><mrow><mi>q</mi></mrow><mrow><mn mathvariant="normal">3</mn></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018161&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018154&type= 7.78933382 4.57200003 分别代表稻虾田分类物候窗口1中水淹信号频率阈值、物候窗口2中植被信号频率阈值、物候窗口3中水淹信号频率阈值。 3.1　稻虾田物候特征 2 3.1.1　稻虾田耕作特征分析 3 一片稻虾田只种植一季中稻，养殖两季小龙虾（ 曹凑贵 等，2017 曹凑贵 等，2017 ）。稻虾田的构造通常是在稻田周围开挖2—4 m宽，0.8—1.5 m深的围沟（围沟面积不超过田块总面积的10%）。稻虾共作过程可以分为水稻种植和灌水泡田两个阶段（ 图4 图4 ）。水稻种植从每年的5月底6月初整田插秧开始，到10月份水稻收获完毕而结束。稻虾田与普通水稻田最主要的差异就在于稻田休耕期要灌水泡田，也即11月至次年5月稻田中要保证一定的水量，为小龙虾的生长繁育提供优良空间（ 魏妍冰 等，2019 魏妍冰 等，2019 ）。因此，从耕作周期来看，可以将稻虾田的物候状态变化总结为：水体覆盖（1—5月）—植被覆盖（7—9月）—水体覆盖（11—12月）。 3.1.2　时序光谱特征分析 3 本文基于Landsat 7/8和Sentinel-2数据计算归一化差值植被指数（NDVI）、增强型植被指数（EVI）和陆表水分指数（LSWI）分析稻虾田光谱年内动态变化特征。这3个植被指数在已有研究中被广泛用于识别传统水稻田，具体计算公式如下： <math display="block" id="M22"><mi mathvariant="normal">N</mi><mi mathvariant="normal">D</mi><mi mathvariant="normal">V</mi><mi mathvariant="normal">I</mi><mo>=</mo><mfrac><mrow><msub><mrow><mi>ρ</mi></mrow><mrow><mi mathvariant="normal">N</mi><mi mathvariant="normal">I</mi><mi mathvariant="normal">R</mi></mrow></msub><mo>-</mo><msub><mrow><mi>ρ</mi></mrow><mrow><mi mathvariant="normal">R</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">d</mi></mrow></msub></mrow><mrow><msub><mrow><mi>ρ</mi></mrow><mrow><mi mathvariant="normal">N</mi><mi mathvariant="normal">I</mi><mi mathvariant="normal">R</mi></mrow></msub><mo>+</mo><msub><mrow><mi>ρ</mi></mrow><mrow><mi mathvariant="normal">R</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">d</mi></mrow></msub></mrow></mfrac></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018206&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018201&type= 28.53266525 8.72066593 <math display="block" id="M23"><mi mathvariant="normal">E</mi><mi mathvariant="normal">V</mi><mi mathvariant="normal">I</mi><mo>=</mo><mn mathvariant="normal">2.5</mn><mo>×</mo><mfrac><mrow><msub><mrow><mi>ρ</mi></mrow><mrow><mi mathvariant="normal">N</mi><mi mathvariant="normal">I</mi><mi mathvariant="normal">R</mi></mrow></msub><mo>-</mo><msub><mrow><mi>ρ</mi></mrow><mrow><mi mathvariant="normal">R</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">d</mi></mrow></msub></mrow><mrow><msub><mrow><mi>ρ</mi></mrow><mrow><mi mathvariant="normal">N</mi><mi mathvariant="normal">I</mi><mi mathvariant="normal">R</mi></mrow></msub><mo>+</mo><mn mathvariant="normal">6</mn><mo>×</mo><msub><mrow><mi>ρ</mi></mrow><mrow><mi mathvariant="normal">R</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">d</mi></mrow></msub><mo>-</mo><mn mathvariant="normal">7.5</mn><mo>×</mo><msub><mrow><mi>ρ</mi></mrow><mrow><mi mathvariant="normal">B</mi><mi mathvariant="normal">l</mi><mi mathvariant="normal">u</mi><mi mathvariant="normal">e</mi></mrow></msub><mo>+</mo><mn mathvariant="normal">1</mn></mrow></mfrac></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018215&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018211&type= 66.80200195 8.72066593 <math display="block" id="M24"><mi mathvariant="normal">L</mi><mi mathvariant="normal">S</mi><mi mathvariant="normal">W</mi><mi mathvariant="normal">I</mi><mo>=</mo><mfrac><mrow><msub><mrow><mi>ρ</mi></mrow><mrow><mi mathvariant="normal">N</mi><mi mathvariant="normal">I</mi><mi mathvariant="normal">R</mi></mrow></msub><mo>-</mo><msub><mrow><mi>ρ</mi></mrow><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">W</mi><mi mathvariant="normal">I</mi><mi mathvariant="normal">R</mi></mrow></msub></mrow><mrow><msub><mrow><mi>ρ</mi></mrow><mrow><mi mathvariant="normal">N</mi><mi mathvariant="normal">I</mi><mi mathvariant="normal">R</mi></mrow></msub><mo>+</mo><msub><mrow><mi>ρ</mi></mrow><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">W</mi><mi mathvariant="normal">I</mi><mi mathvariant="normal">R</mi></mrow></msub></mrow></mfrac></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018226&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018219&type= 29.88733101 8.72066593 式中， <math id="M25"><msub><mrow><mi>ρ</mi></mrow><mrow><mi mathvariant="normal">B</mi><mi mathvariant="normal">l</mi><mi mathvariant="normal">u</mi><mi mathvariant="normal">e</mi></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018239&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018231&type= 5.50333309 4.48733330 、 <math id="M26"><msub><mrow><mi>ρ</mi></mrow><mrow><mi mathvariant="normal">R</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">d</mi></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018244&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018241&type= 5.07999992 4.48733330 、 <math id="M27"><msub><mrow><mi>ρ</mi></mrow><mrow><mi mathvariant="normal">N</mi><mi mathvariant="normal">I</mi><mi mathvariant="normal">R</mi></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018253&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018248&type= 5.16466665 4.48733330 、 <math id="M28"><msub><mrow><mi>ρ</mi></mrow><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">W</mi><mi mathvariant="normal">I</mi><mi mathvariant="normal">R</mi></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018260&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018257&type= 6.60400009 4.48733330 分别代表Landsat和Sentinel-2数据的蓝、红、近红外以及短波红外的光谱反射率。基于GEE平台和2019全年的Landsat 7/8和Sentinel-2影像，选取主要作物稻虾共作、水稻、稻—麦轮作和稻—油轮作代表性训练样本提取时间序列植被指数，绘制植被指数时序变化曲线，为了整体趋势分析以及图形表达效果进行了15 d影像中值合成，如 图5 图5 所示。从儒略日第30天到第150天，由于稻田需要保证一定的水量为小龙虾生长繁育提供空间，所以稻虾共作区域表现出较高的LSWI值，并且LSWI整体大于NDVI或者EVI，表现出明显的水淹信号（ Xiao等，2005 Xiao等，2005 ）。儒略日第150天—180天，是水稻种植的灌水移栽期，同样表现出水淹信号。移栽期过后直到水稻收获期，也即儒略日第195天—300天，由于水稻的生长，使得稻虾田呈现高NDVI和高EVI值，表现出植被信号（NDVI或EVI > LSWI），并且在儒略日第240天也即8月底出现生长峰值。在水稻收割过后，稻虾田仍需要进行一定的水量灌溉，保障越冬期间龙虾幼苗的生长发育条件，所以在第330天之后，也会出现明显的水淹信号。普通水稻田虽然在灌水移栽期也出现水淹信号（第165天），在生长季呈现高NDVI和EVI值，但是在休耕期（150天之前以及300天之后）普通水稻田不需要进行灌溉泡田，所以极少会出现水淹信号，绝大多数休耕时间里，NDVI和EVI远大于LSWI。对于稻—麦和稻—油轮作，二者植被指数时序变化较为接近，都呈现出明显的生长“双峰”特征。但这两种耕作方式均不需要在种植过程中保障水量，因此，在整个生长周期中，除去轮种水稻时的灌水移栽期，其他时间段都极少出现水淹信号。 1,2;3,4;5; 结合稻虾田耕作特点以及对应的光谱年内动态变化曲线特征，可以总结出稻虾田独特的物候特征为：1月—5月表现出水淹信号（LSWI>NDVI或LSWI>EVI），7月—9月表现出植被信号（NDVI>LSWI或EVI>LSWI），11月—12月表现出水淹信号。 3.2　基于规则集模型的稻虾田提取 2 确定了稻虾田独特的水淹和植被信号变化规律之后，需构建规则集模型提取稻虾田分布。本文首先采用2020年GlobeLand30地表覆盖产品进行非耕地区域的掩膜，生成耕地图层，在耕地图层上提取稻虾田的空间分布信息。 通过时序光谱特征分析，可以得知稻虾共作独特的“三段式”光谱变化。通过实地调研得知，不同区域稻虾田的耕作节点有所区别。例如有些区域在5月中下旬即进行整田移栽，而有些区域到6月上旬或中旬才进行水稻移栽。移栽期虽然也会出现一定的水淹信号，但不同区域及不同耕作方式都会导致水淹信号减弱或者消失。因此，本文将稻虾田上半年休耕期出现水淹信号用以提取稻虾田的物候窗口定为1月1日—4月30日PW 1 （Phenology Window 1）。稻虾田中水稻生长季，出现明显植被信号的物候窗口定为7月15日—9月30日（PW 2 ）。考虑到影像数量的充足性以及水稻收割时间的区域差异，将下半年休耕期出现水淹信号的物候窗口定为11月10日—12月31日（PW 3 ）。将所有训练样本在PW 1 和PW 3 时间段进行植被指数及植被指数差值的均值提取并绘制相应的二维散点分布图（ 图6 图6 ），可以看出绝大多数稻虾田样点都表现出明显的水淹信号（LSWI-EVI > 0或LSWI-NDVI > 0），且这一特征在其他作物上很少出现，具有非常强的特征分离性。 1,2;3; 一些特殊的农作物类型（例如菜地、莲藕等）也可能存在偶然的水淹信号以及植被信号。由于稻虾田的这些光谱特征具有一定时间段内的连续性，为了避免错分情况，本文引入频率阈值的概念。也即PW 1 中水淹信号出现频率 <math id="M29"><mi mathvariant="normal">F</mi><mi mathvariant="normal">S</mi><msub><mrow><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">P</mi><msub><mrow><mi mathvariant="normal">W</mi></mrow><mrow><mn mathvariant="normal">1</mn></mrow></msub></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018378&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018374&type= 9.73666668 4.06400013 （Flooding signal frequency in PW 1 ， 式（4） 式（4） ）要大于一个阈值 <math id="M30"><mi>F</mi><mi>r</mi><mi>e</mi><msub><mrow><mi>q</mi></mrow><mrow><mn mathvariant="normal">1</mn></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018383&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018380&type= 7.78933382 4.57200003 ，PW 2 中植被信号出现频率 <math id="M31"><mi mathvariant="normal">V</mi><mi mathvariant="normal">S</mi><msub><mrow><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">P</mi><msub><mrow><mi mathvariant="normal">W</mi></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msub></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018388&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018386&type= 10.07533360 4.06400013 （Vegetation signal frequency in PW 2 ， 式（5） 式（5） ）要大于阈值 <math id="M32"><mi>F</mi><mi>r</mi><mi>e</mi><msub><mrow><mi>q</mi></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018393&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018390&type= 7.78933382 4.57200003 ，PW 3 中水淹信号出现频率 <math id="M33"><mi mathvariant="normal">F</mi><mi mathvariant="normal">S</mi><msub><mrow><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">P</mi><msub><mrow><mi mathvariant="normal">W</mi></mrow><mrow><mn mathvariant="normal">3</mn></mrow></msub></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018397&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018395&type= 9.73666668 4.06400013 （Flooding signal frequency in PW 3 ， 式（6） 式（6） ）要大于阈值 <math id="M34"><mi>F</mi><mi>r</mi><mi>e</mi><msub><mrow><mi>q</mi></mrow><mrow><mn mathvariant="normal">3</mn></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018402&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018400&type= 7.78933382 4.57200003 ，满足以上3个条件的像素才被分类为稻虾田，具体阈值规则集见 式（7） 式（7） 。 <math display="block" id="M35"><mi mathvariant="normal">F</mi><mi mathvariant="normal">S</mi><msub><mrow><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">P</mi><msub><mrow><mi mathvariant="normal">W</mi></mrow><mrow><mn mathvariant="normal">1</mn></mrow></msub></mrow></msub><mo>=</mo><mfrac><mrow><msub><mrow><mi>N</mi></mrow><mrow><mi mathvariant="normal">f</mi><mi 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mathvariant="normal">P</mi><msub><mrow><mi mathvariant="normal">W</mi></mrow><mrow><mn mathvariant="normal">3</mn></mrow></msub></mrow></msub><mo>=</mo><mfrac><mrow><msub><mrow><mi>N</mi></mrow><mrow><mi mathvariant="normal">f</mi><mi mathvariant="normal">l</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">d</mi><mi mathvariant="normal">i</mi><mi mathvariant="normal">n</mi><mi mathvariant="normal">g</mi><mo> </mo><mo> </mo><mi mathvariant="normal">i</mi><mi mathvariant="normal">n</mi><mo> </mo><mo> </mo><mi mathvariant="normal">P</mi><msub><mrow><mi mathvariant="normal">W</mi></mrow><mrow><mn mathvariant="normal">3</mn></mrow></msub></mrow></msub></mrow><mrow><msub><mrow><mi>N</mi></mrow><mrow><mi mathvariant="normal">t</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">t</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">l</mi><mo> </mo><mo> </mo><mi mathvariant="normal">i</mi><mi mathvariant="normal">n</mi><mo> </mo><mo> </mo><mi mathvariant="normal">P</mi><msub><mrow><mi mathvariant="normal">W</mi></mrow><mrow><mn mathvariant="normal">3</mn></mrow></msub></mrow></msub><mo> </mo><mo> </mo></mrow></mfrac></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018415&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018412&type= 29.46400070 9.31333351 <math display="block" id="M38"><mo/><mi>R</mi><mi>i</mi><mi>c</mi><mi>e</mi><mo>-</mo><mi>c</mi><mi>r</mi><mi>a</mi><mi>y</mi><mi>f</mi><mi>i</mi><mi>s</mi><mi>h</mi><mo> </mo><mo> </mo><mo> </mo><mo> </mo><mo> </mo><mfenced open="{" close="" separators="|"><mrow><mtable><mtr><mtd><mi mathvariant="normal">F</mi><mi mathvariant="normal">S</mi><msub><mrow><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">P</mi><msub><mrow><mi mathvariant="normal">W</mi></mrow><mrow><mn 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和 <math id="M41"><msub><mrow><mi>N</mi></mrow><mrow><mi mathvariant="normal">f</mi><mi mathvariant="normal">l</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">d</mi><mi mathvariant="normal">i</mi><mi mathvariant="normal">n</mi><mi mathvariant="normal">g</mi><mo> </mo><mo> </mo><mi mathvariant="normal">i</mi><mi mathvariant="normal">n</mi><mo> </mo><mo> </mo><mi mathvariant="normal">P</mi><msub><mrow><mi mathvariant="normal">W</mi></mrow><mrow><mn mathvariant="normal">3</mn></mrow></msub></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018425&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018424&type= 14.56266594 4.06400013 分别指的是某一像素在物候窗口1内出现水淹信号的次数、物候窗口2内出现植被信号的次数和物候窗口3内出现水淹信号的次数； <math id="M42"><msub><mrow><mi>N</mi></mrow><mrow><mi mathvariant="normal">t</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">t</mi><mi mathvariant="normal">a</mi><mi 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为了准确提取出稻虾田分布区域，还需确定这3个信号出现频率的阈值（最小值），从而避免季节性短暂出现的水淹信号或者植被信号导致错分情况。本研究基于稻虾田样本进行水淹信号及植被信号频率分布直方图统计，对累计百分比进行分析，寻找超过95%的样点所处的频率区间的临界值，以此值作为该物候窗口内分类信号频率的阈值。 3.3　稻虾田提取方法表现评估 2 本研究采用混淆矩阵以及计算制图精度、用户精度、总体精度和Kappa系数对稻虾田提取进行精度评定。根据按分层面积比例的样本设置原则，参与验证的稻虾田样本共216个，非稻虾田样本共434个。 另外，为了进一步分析基于物候窗口特征提取稻虾田方法的潜力，评估和比较其与另外两种稻虾田提取方法：（1）随机森林分类；（2）基于水体季相差异（ 魏妍冰 等，2019 魏妍冰 等，2019 ）。随机森林分类同样在GEE环境下进行，输入影像的时相以及输入分类的植被指数特征与本文提出的方法保持一致。输入随机森林的影像是月度中值合成的NDVI、EVI和LSWI，包含1月、3月、4月、7—9月、11月以及12月（2月份云量过多，影像缺失），共计24景植被指数特征影像。基于水体季相差异的稻虾田提取方法，选取水稻生长季和休耕期影像各一景，用自动水域提取指数AWEI分别提取水体（ 魏妍冰 等，2019 魏妍冰 等，2019 ）。两类方法均采用与本研究相同的训练样本和验证样本进行训练和验证，最后比较3类方法产生的稻虾田的用户精度和制图精度。 4　结果与分析 1 4.1　稻虾田识别结果 2 图7 图7 展示的是基于325个稻虾田样本计算的水淹信号及植被信号频率统计直方图和累计百分比。该图表明超过95%样本点所处的 <math id="M45"><mi mathvariant="normal">F</mi><mi mathvariant="normal">S</mi><msub><mrow><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">P</mi><msub><mrow><mi mathvariant="normal">W</mi></mrow><mrow><mn 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mathvariant="normal">3</mn></mrow></msub></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018444&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018443&type= 9.73666668 4.06400013 阈值分别为0.6，0.1和0.1。也即是说，同时满足 <math id="M48"><mi mathvariant="normal">F</mi><mi mathvariant="normal">S</mi><msub><mrow><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">P</mi><msub><mrow><mi mathvariant="normal">W</mi></mrow><mrow><mn mathvariant="normal">1</mn></mrow></msub></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018446&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018445&type= 9.73666668 4.06400013 > 0.6， <math id="M49"><mi mathvariant="normal">V</mi><mi mathvariant="normal">S</mi><msub><mrow><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">P</mi><msub><mrow><mi mathvariant="normal">W</mi></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msub></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018507&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018506&type= 10.07533360 4.06400013 > 0.1和 <math id="M50"><mi mathvariant="normal">F</mi><mi mathvariant="normal">S</mi><msub><mrow><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">P</mi><msub><mrow><mi mathvariant="normal">W</mi></mrow><mrow><mn mathvariant="normal">3</mn></mrow></msub></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018509&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018508&type= 9.73666668 4.06400013 > 0.1这3个条件的像素将被分类为稻虾田。 1,2;3;4; 基于构建的规则集模型提取的2019年潜江市稻虾田空间分布信息结果如 图8 图8 所示。从空间分布趋势上看，潜江市稻虾田主要集中在西南部，东南部也有少许分布。基于该分布图计算得到2019年潜江稻虾共作区域总面积为575.58 km 2 。其中，浩口镇、龙湾镇和老新镇是稻虾田分布面积前三位的乡镇，分别为80.47 km 2 、79.39 km 2 和77.36 km 2 ，这些区域的稻虾田分布密集并且地块较大。然而，杨市街道、高石碑镇以及王场镇这些位于东部或北部的乡镇，稻虾共作面积都较小（均小于10 km 2 ）且分布较零散。 4.2　稻虾田提取精度评价 2 本研究提取的稻虾田制图精度为90.74%，用户精度为94.69%，总体分类精度为95.23%，Kappa系数为0.89（ 表2 表2 ）。整体上，本研究提取的稻虾田只存在5.31%的错分情况。该精度表明物候窗口PW 1 （1月1日—4月30日）的水淹信号、物候窗口PW 2 （7月15日—9月30日）的植被信号以及物候窗口PW 3 （11月10日—12月31日）的水淹信号是稻虾田区别于其他农作物的独特物候特征。 类别 稻虾田 其他 总计 制图精度/% 稻虾田 196 20 216 90.74 其他 11 423 434 97.47 总计 207 443 650 用户精度/% 94.69 95.49 总体精度/% 4 95.23 Kappa系数 4 0.89 稻虾田的制图精度相比于用户精度稍低，表明提取结果存在一定的漏分情况。已有相关研究及报道表明：随着稻虾模式的迅猛扩张，“重虾轻稻”以及种养技术不合规范的问题近些年越来越突出（ 陈松文 等，2020 陈松文 等，2020 ）。也即是说，很多经营者更看重小龙虾的经济效益而选择不种植水稻或者种植管理较粗糙。这样就会导致一些稻虾田只表现出泡田时的水淹信号，而不会表现出生长季的植被信号。另外，由于种养技术不合规范，也有可能导致上半年或者下半年灌水泡田时期的水淹信号较为微弱。由于这些因素的影响，使得基于该方法提取的稻虾田出现了一定的漏分现象。 4.3　不同方法提取稻虾田表现对比 2 对比3种方法的精度结果（ 图9 图9 ）可发现：本文基于物候窗口特征方法与随机森林相比稻虾田的制图精度差异较小，但用户精度显著高出5%，即基于物候特征提取稻虾田的错分误差更少。基于水体季相差异的方法提取稻虾共作的制图精度只有81.48%，用户精度为91.67%，均低于本文提出的方法，其中制图精度与基于物候窗口特征方法相差10%，即基于水体季相差异方法存在较严重的漏分误差。稻虾田进行水淹泡田时，本身与开放水体的特征存在一定的差别，依靠单一水体指数提取水域的方法具有一定的不确定性。另外，基于水体季相方法只采取了两景典型时相（一景水稻生长季，一景休耕泡田期）的优质影像进行分类，而稻虾田在休耕期中要进行多次的泡田、换水、再泡田的耕作管理。因此，休耕泡田期的影像很有可能出现水体覆盖信息不明显导致稻虾田水域提取不完整的情况，从而导致最终的漏分误差。相比之下，本文提出的方法基于一年完整时序影像分析，充分利用了3个关键物候窗口，既考虑了两段休耕期的水淹信号，同时考虑了水稻生长季的植被信号，大大减少了错分漏分情况。 图10 图10 为本研究提出的基于物候窗口特征方法与随机森林、水体季相差异方法的比对结果。从空间分布趋势来看，随机森林和水体季相差异方法提取的稻虾田区域都集中在西南部地区。相比本研究的方法，随机森林提取的稻虾空间分布面积更大，基于水体季相差异方法提取的稻虾分布面积略小。 图11 图11 为选取的4个典型的小区域结果对比。以2019年3月的Google Earth卫星数据为参考，对比l号和2号区域的结果可发现随机森林将部分水稻田错分为稻虾田；3号和4号区域的结果对比显示基于水体季相差异的方法漏分了部分稻虾田。 1,2,3;4; 1,2,3,4;5; 5　结论 1 本研究以小龙虾之乡—湖北省潜江市为测试区域，协同Landsat 7/8和Sentinel-2卫星影像数据，基于Google Earth Engine平台，提出了基于物候窗口特征的稻虾田提取方法。采用实地农作物样本对稻虾田提取结果进行精度评定，同时比较和评估它与另外两种方法的表现差异。主要结论如下： （1）稻虾田提取的关键物候窗口及特征为：PW 1 （1月1日—4月30日）的水淹信号（LSWI > EVI或NDVI）、PW 2 （7月15日—9月30日）的植被信号（NDVI或EVI > LSWI）、PW 3 （11月10日—12月31日）的水淹信号。 （2）构建了基于关键物候窗口特征和频率阈值的稻虾田提取方法。当像素满足：PW 1 内水淹信号频率 <math id="M51"><mi mathvariant="normal">F</mi><mi mathvariant="normal">S</mi><msub><mrow><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">P</mi><msub><mrow><mi mathvariant="normal">W</mi></mrow><mrow><mn mathvariant="normal">1</mn></mrow></msub></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018505&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018504&type= 9.73666668 4.06400013 > 0.6，PW 2 内植被信号频率 <math id="M52"><mi mathvariant="normal">V</mi><mi mathvariant="normal">S</mi><msub><mrow><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">P</mi><msub><mrow><mi mathvariant="normal">W</mi></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msub></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018507&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018506&type= 10.07533360 4.06400013 > 0.1，PW 3 内水淹信号频率 <math id="M53"><mi mathvariant="normal">F</mi><mi mathvariant="normal">S</mi><msub><mrow><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">P</mi><msub><mrow><mi mathvariant="normal">W</mi></mrow><mrow><mn mathvariant="normal">3</mn></mrow></msub></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018509&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=33018508&type= 9.73666668 4.06400013 > 0.1即被分类为稻虾田。该方法获取的稻虾田制图精度为90.74%，用户精度为94.69%，总体精度达到95.23%。 （3）与随机森林分类结果相比，本文构建的基于关键物候窗口特征的方法具有更小的错分误差。与基于水体季相差异的方法相比，该方法表现更小的漏分误差。另外，相比这两类方法，本研究的方法具有最高的稻虾田提取总体精度。 基于关键物候窗口特征的稻虾田提取方法具有较强的泛化能力，选取的关键特征具有很强的物理解释性。相比传统机器学习算法（例如：随机森林），该方法对样本的依赖性较低，易于在更大的空间尺度和时间尺度进行推广应用。此外，该方法也能泛化应用到其他多源中高分辨率影像（例如：国产高分一号和六号卫星影像），即只要获得覆盖稻虾田关键物候窗口和对应光谱波段（蓝、红、近红和短波红外）的影像数据，则能提取较优精度的稻虾田空间分布。因此，本研究提出的基于物候窗口的方法能够实现中国南方地区稻虾田空间分布快速、大面积提取。 此外，本研究也存在一些不足有待进一步的完善。首先，基于像素分类的方法不可避免存在一定的“椒盐噪声”现象，在田块分类细节上可能表现得比较零碎。在后续的研究中，可考虑在对象或地块尺度上提取稻虾田，提高分类结果的精度以及结果的实际应用价值。其次，本研究使用了2020年GlobeLand30地表覆盖分类产品进行非耕地类别的掩膜去除。地表覆盖产品本身不可避免的存在一定的分类误差，使稻虾田提取结果具有较大不确定性。不同地区具有不同的气候以及种植管理方式，因此稻虾共作模式也可能存在种植节点上的细微差异。例如：稻田水淹频率以及中稻移栽期的差异也会给稻虾田精确提取带来不确定性，因此，需在更多典型种植区域对稻虾模式物候特征进行提取分析。未来研究中将进一步优化算法，研制大区域长时间序列稻虾田空间分布图，揭示中国稻虾田时空变化规律，为水稻生产和小龙虾产业布局提供重要的信息支撑。 参考文献（References） Cao C G , Jiang Y , Wang J P , Yuan P L and Chen S W . 2017 . “ Dual character” of rice-crayfish culture and strategies for its sustainable development . 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National Remote Sensing Bulletin， 26（7）：1423-1436 国家自然科学基金（编号：41901380，42001303）；中央高校基本科研业务费专项基金（编号：2662018QD066，CCNU20QN032）；湖北省自然科学基金（编号：2019CFC848）；遥感科学国家重点实验室开放基金（编号：OFSLRSS202022，OFSLRSS201914） Supported by National Natural Science Foundation of China (No. 41901380, 42001303); Fundamental Research Funds for the Central Universities (No. 2662018QD066, CCNU20QN032); Natural Science Foundation of Hubei Province (No. 2019CFC848); Open Fund of State Key Laboratory of Remote Sensing Science (No. OFSLRSS202022, OFSLRSS201914) 2021-02-08 2022-07-07 2022-08-08 2021-05-16 遥感学报 7 26