CN110716198A - 一种vv极化单时相高频微波水稻估产的方法 - Google Patents

一种vv极化单时相高频微波水稻估产的方法 Download PDF

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CN110716198A
CN110716198A CN201910847388.1A CN201910847388A CN110716198A CN 110716198 A CN110716198 A CN 110716198A CN 201910847388 A CN201910847388 A CN 201910847388A CN 110716198 A CN110716198 A CN 110716198A
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吴学箫
邵芸
刘龙
李坤
刘致曲
叶舒
国贤玉
吕潇然
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Abstract

一种VV极化单时相高频微波水稻估产的方法，包括：a.随机选取十个水稻采样点，获取水稻采样数据、验证数据和GPS点数据，获取待测水稻灌浆期结束的遥感雷达影像，并获取附近平静水域遥感雷达影像，所述的水稻采样数据为水稻灌浆期结束时的水稻种植面积、行距、墩距、每墩水稻株数、水稻样本穗长、穗倾角、穗粒数、穗茎节点至水面高度、穗鲜重、穗干重、株鲜重、株干重，通过星载或机载雷达获取所述的获取待测水稻灌浆期结束的遥感雷达影像以及稻田周边平静水域的雷达影像；本发明的优点是：依靠高频微波遥感技术，误差率较低。

Description

一种VV极化单时相高频微波水稻估产的方法
技术领域
本发明涉及一种VV极化单时相高频微波水稻估产的方法，属于农业遥感技术领域，尤其涉及农业微波遥感领域。
背景技术
水稻是人类最主要的粮食来源,全球大约有25亿人以此为主食;我国以仅占世界水稻种植面积的21.4%,却获得了34.5%的产量，种植面积世界第二,产量位居世界第一。因此,对水稻播种面积的总体评估,长势监测和产量预估对保证我国乃至世界的粮食安全具有十分重要的意义。目前采用的大多是光学或者红外遥感评估方法，但是这2种方法无法穿透地表，且受到观测时间和天气情况的影响。为了解决上述困难，需要开发依靠高频微波遥感技术，误差率较低的一种VV极化单时相高频微波水稻估产的方法。
发明内容
本发明的目的是提供一种VV极化单时相高频微波水稻估产的方法。
为实现本发明的目的，本发明采用的技术方案是：
一种VV极化单时相高频微波水稻估产的方法，包括：
a.随机选取十个水稻采样点，获取水稻采样数据、验证数据和GPS点数据，获取待测水稻灌浆期结束的遥感雷达影像，并获取附近平静水域遥感雷达影像，所述的水稻采样数据为水稻灌浆期结束时的水稻种植面积、行距、墩距、每墩水稻株数、水稻样本穗长、穗倾角、穗粒数、穗茎节点至水面高度、穗鲜重、穗干重、株鲜重、株干重，通过星载或机载雷达获取所述的获取待测水稻灌浆期结束的遥感雷达影像以及稻田周边平静水域的雷达影像；
b.将获取的每个采样点数据和GPS点数据计算得到该样本点的经纬度、单位面积穗粒数ρ、平均穗层厚度h1、平均茎叶层厚度h2、穗层单位体积含水量W1、茎叶层单位体积含水量W2；所述十个或数十个样本点数据，构建穗层单位体积含水量与单位面积穗粒数的经验关系: =j*W1h1+k，并构建穗层单位体积含水量与茎叶层单位体积含水量的经验关系：W2h2=l*W1h1+m；样本点拟合训练求解常数j,k,l,m；
c.对获取的遥感雷达影像数据预处理，得到待测水稻雷达图像的经纬度和对应的后向散射系数及附近水域的平均后向散射系数；
d.提取所述的样本点及对应雷达图像的关键参数，包括雷达数据VV极化后向散射系数
Figure 100002_RE-DEST_PATH_IMAGE002
、样本点经纬度、单位面积穗粒数ρ，建立穗层、茎叶层及水面构建灌浆期结束时的双层水云模型；建立总后向向散射值与穗层后散射项、茎叶层后向散射项与与水面后向散射项的半经验关系：
Figure 100002_DEST_PATH_IMAGE002
，
基于所述的穗层单位体积含水量与茎叶层单位体积含水量的经验关系，建立穗层单位体积含水量与总后向散射值的等式模型，并基于穗层单位体积含水量与单位面积穗粒数的经验关系，建立单位面积穗粒数与总后向散射值的等式模型：
Figure DEST_PATH_IMAGE003
，
A,C,D为常数，θ为雷达微波入射角,是直接来自穗层后向散射系数，
Figure 100002_RE-DEST_PATH_IMAGE006
是直接来自茎叶层的后向散射系数，
Figure RE-DEST_PATH_IMAGE007
是平静水面的后向散射系数，
Figure 100002_RE-DEST_PATH_IMAGE008
雷达微波穿透穗层的双向衰减因子，
Figure RE-DEST_PATH_IMAGE009
是微波穿透茎叶层的双向衰减因子；
e.基于所述十个或数十个样本点数据，训练单位面积穗粒数与总后向散射值的等式模型，求解A,C,D得到单位面积穗粒数与总后向散射值的定量经验关系；
f.基于所述的单位面积穗粒数与总后向散射值的定量经验关系，反演水稻雷达图像的单位面积的水稻穗粒数；
g.将所述的雷达图像的单位面积的水稻穗粒数累加得到稻田总的穗粒数，并查询相同品种的稻穗千粒重，计算等到总产量。
所述机载或星载雷达其图像分辨率小于1米。
所述GPS定位误差小于1米。
具体实施方式
下面结合实施例对本发明作进一步的说明。
本发明一种VV极化单时相高频微波水稻估产的方法，包括a.随机选取十个水稻采样点，获取水稻采样数据、验证数据和GPS点数据，获取待测水稻灌浆期结束的遥感雷达影像，并获取附近平静水域遥感雷达影像，所述的水稻采样数据为水稻灌浆期结束时的水稻种植面积、行距、墩距、每墩水稻株数、水稻样本穗长、穗倾角、穗粒数、穗茎节点至水面高度、穗鲜重、穗干重、株鲜重、株干重，通过星载或机载雷达获取所述的获取待测水稻灌浆期结束的遥感雷达影像以及稻田周边平静水域的雷达影像；
b.将获取的每个采样点数据和GPS点数据计算得到该样本点的经纬度、单位面积穗粒数、平均穗层厚度、平均茎叶层厚度、穗层单位体积含水量、茎叶层单位体积含水量；所述十个或数十个样本点数据，构建穗层单位体积含水量与单位面积穗粒数的经验关系，并构建穗层单位体积含水量与茎叶层单位体积含水量的经验关系；
c.对获取的遥感雷达影像数据预处理，得到待测水稻雷达图像的经纬度和对应的后向散射系数及附近水域的平均后向散射系数；
d.提取所述的样本点及对应雷达图像的关键参数，包括雷达数据VV极化后向散射系数样、样本点经纬度、单位面积穗粒数，建立穗层、茎叶层及水面构建灌浆期结束时的双层水云模型；建立总后向向散射值与穗层后散射项、茎叶层后向散射项与与水面后向散射项的关系，基于所述的穗层单位体积含水量与茎叶层单位体积含水量的经验关系：W2h2=4.68*W1h1-240，建立穗层单位体积含水量与总后向散射值的等式模型，并基于穗层单位体积含水量与单位面积穗粒数的经验关系：ρ=90.38*W1h1-2229，建立单位面积穗粒数与总后向散射值的等式模型；
e.基于所述十个或数十个样本点数据，训练单位面积穗粒数与总后向散射值的等式模型，得到单位面积穗粒数与总后向散射值的定量经验关系；
f.基于所述的单位面积穗粒数与总后向散射值的定量经验关系，反演水稻雷达图像的单位面积的水稻穗粒数；
g.将所述的雷达图像的单位面积的水稻穗粒数累加得到稻田总的穗粒数，并查询相同品种的稻穗千粒重，计算等到总产量。
所述机载或星载雷达其图像分辨率小于1米。
所述GPS定位误差小于1米。
作为优选实施例，本发明在的随机采样点对应的雷达图像上是3*3的像元窗口，每个像元对应面积范围内的采样水稻5-10株，求得像元内水稻样本穗长、穗倾角、穗粒数、穗茎节点至水面高度、穗鲜重、穗干重、株鲜重、株干重采样株的平均值，所述的采样点水稻样本穗长、穗倾角、穗粒数、穗茎节点至水面高度、穗鲜重、穗干重、株鲜重、株干重为3*3像元的采样株均值的均值，对于所述的像元的后向散射值出现的异常值予以剔除，求得采样点3*3窗口后向散射均值。
所述的雷达微波频段为X波段。
所述的雷达微波频段为Ku波段。

Claims (1)

1.一种VV极化单时相高频微波水稻估产的方法，其特征是，包括：
a.随机选取十个水稻采样点，获取水稻采样数据、验证数据和GPS点数据，获取待测水稻灌浆期结束的遥感雷达影像，并获取附近平静水域遥感雷达影像，所述的水稻采样数据为水稻灌浆期结束时的水稻种植面积、行距、墩距、每墩水稻株数、水稻样本穗长、穗倾角、穗粒数、穗茎节点至水面高度、穗鲜重、穗干重、株鲜重、株干重，通过星载或机载雷达获取所述的获取待测水稻灌浆期结束的遥感雷达影像以及稻田周边平静水域的雷达影像；
b.将获取的每个采样点数据和GPS点数据计算得到该样本点的经纬度、单位面积穗粒数ρ、平均穗层厚度h1、平均茎叶层厚度h2、穗层单位体积含水量W1、茎叶层单位体积含水量W2；所述十个或数十个样本点数据，构建穗层单位体积含水量与单位面积穗粒数的经验关系:
Figure RE-DEST_PATH_IMAGE001
=j*W1h1+k，并构建穗层单位体积含水量与茎叶层单位体积含水量的经验关系：W2h2=l*W1h1+m；样本点拟合训练求解常数j,k,l,m；
c.对获取的遥感雷达影像数据预处理，得到待测水稻雷达图像的经纬度和对应的后向散射系数及附近水域的平均后向散射系数；
d.提取所述的样本点及对应雷达图像的关键参数，包括雷达数据VV极化后向散射系数、样本点经纬度、单位面积穗粒数ρ，建立穗层、茎叶层及水面构建灌浆期结束时的双层水云模型；建立总后向向散射值与穗层后散射项、茎叶层后向散射项与与水面后向散射项的半经验关系：
Figure DEST_PATH_IMAGE001
，
基于所述的穗层单位体积含水量与茎叶层单位体积含水量的经验关系，建立穗层单位体积含水量与总后向散射值的等式模型，并基于穗层单位体积含水量与单位面积穗粒数的经验关系，建立单位面积穗粒数与总后向散射值的等式模型：
，
A,C,D为常数，θ为雷达微波入射角,
Figure RE-DEST_PATH_IMAGE006
是直接来自穗层后向散射系数，
Figure RE-DEST_PATH_IMAGE008
是直接来自茎叶层的后向散射系数，是平静水面的后向散射系数，
Figure RE-DEST_PATH_IMAGE012
雷达微波穿透穗层的双向衰减因子，
Figure RE-DEST_PATH_IMAGE014
是微波穿透茎叶层的双向衰减因子；
e.基于所述十个或数十个样本点数据，训练单位面积穗粒数与总后向散射值的等式模型，求解A,C,D得到单位面积穗粒数与总后向散射值的定量经验关系；
f.基于所述的单位面积穗粒数与总后向散射值的定量经验关系，反演水稻雷达图像的单位面积的水稻穗粒数；
g.将所述的雷达图像的单位面积的水稻穗粒数累加得到稻田总的穗粒数，并查询相同品种的稻穗千粒重，计算等到总产量。
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