基于去除土壤散射干扰SAR数据的玉米残茬生物量反演

doi:10.12082/dqxxkx.2023.220777

地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (10): 2070-2083.doi: 10.12082/dqxxkx.2023.220777

基于去除土壤散射干扰SAR数据的玉米残茬生物量反演

谢小曼¹^,²(), 洪梓翔¹^,², 李俐¹^,²^,^*(), 仇冰琦¹^,², 苏怡然¹^,²

1.中国农业大学土地科学与技术学院，北京 100083
2.农业农村部农业灾害遥感重点实验室，北京 100083

收稿日期:2022-10-11 修回日期:2023-01-09 出版日期:2023-10-25 发布日期:2023-09-22
通讯作者: * 李俐（1976—），女，河南南阳人，副教授，研究方向为微波遥感及其农业应用。E-mail: lilixch@cau.edu.cn
作者简介:谢小曼（1998—），女，海南儋州人，硕士生，研究方向为空间信息技术。E-mail: xxmh9657@163.com
基金资助:
国家自然科学基金项目(42171324);中国科学院地理所技术服务项目(202205511910797)

Retrieval of Corn Residue Biomass Based on SAR Data with Soil Scattering Interference Removed

XIE Xiaoman¹^,²(), HONG Zixiang¹^,², LI Li¹^,²^,^*(), QIU Bingqi¹^,², SU Yiran¹^,²

1. College of Land Science and Technology, China Agricultural University, Beijing 100083, China
2. Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China

Received:2022-10-11 Revised:2023-01-09 Online:2023-10-25 Published:2023-09-22
Contact: * LI Li, E-mail: lilixch@cau.edu.cn
Supported by:
National Natural Science Foundation of China(42171324);Technical Service Project from Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences(202205511910797)

摘要/Abstract

摘要：

作为保护性耕作的主要方式之一，作物留茬对农田生态系统的物质和能量循环有重要影响。及时、准确地获取作物残茬生物量分布状况对定量了解保护性耕作实施状况、评估残茬影响具有重要意义。本文利用AIEM-Oh模型和水云模型（Water Cloud Model，WCM）构建残茬后向散射模型，分离出去除土壤散射影响后玉米残茬后向散射系数；在此基础上选择适宜的雷达指数特征构建玉米残茬生物量反演模型，以Sentinel-1 SAR数据为主数据源，以梨树县为研究区实现其玉米残茬生物量估算。结果表明：基于残茬后向散射模型可以有效地消除土壤后向散射的干扰，选择合适特征参数构建的反演模型能够获得较好的残茬生物量反演效果。其中，基于双极化散射积（Product）的秋季生物量反演精度R²大于0.75，RMSE达85 g/m²以下。与去除土壤散射干扰前的生物量反演模型相比，去除土壤散射干扰后，采用常见指数特征的生物量反演精度R²增加至少0.15，RMSE减少至少17 g/m²。本研究验证了利用去除土壤散射干扰后残茬后向散射数据进行残茬生物量反演的可行性，为今后利用SAR遥感数据进行玉米残茬生物量的动态监测提供了有益的尝试。

关键词: Sentinel-1 SAR, 生物量反演, 玉米残茬, 残茬后向散射模型

Abstract:

As a main way of conservation tillage, crop residue has important influence on the cycle of material and energy in farmland ecosystem. Acquisition of the biomass information of corn residues timely and accurately is of great significance for understanding the implementation of conservation tillage and evaluating the impact of residues quantitatively. However，compared with crops, residues have lower coverage and contain less water,which makes it more difficult to acquire the biomass. To address this issue, we developed a residue backscattering model based on AIEM-Oh model and the Water Cloud Model (WCM) to remove the soil scattering interference. Using Sentinel-1 SAR images as the main data source, a regression model of corn residue biomass was constructed based on radar features selected to retrieve and map the corn residue biomass in Lishu County. Results show that the residue backscattering model can eliminate the interference of soil backscattering contribution effectively, and the inversion model based on the residue backscattering coefficient can improve the biomass inversion accuracy. The autumn biomass inversion model based on the dual-polarized scattering product (Product) has an R² greater than 0.75, and an RMSE less than 85 g/m², showing an increase of at least 0.12 in R² and a decrease of 17 g/m² in RMSE compared to the biomass inversion model before soil scattering contribution removed. This study verifies the feasibility of residue biomass inversion model based on backscattering data with soil scattering interference removed, and provides an attempt for the dynamic monitoring of corn residue biomass using SAR remote sensing data in the future.

Key words: Sentinel-1 SAR, biomass retrieval, corn residue, Residue Backscattering Model

谢小曼, 洪梓翔, 李俐, 仇冰琦, 苏怡然. 基于去除土壤散射干扰SAR数据的玉米残茬生物量反演[J]. 地球信息科学学报, 2023, 25(10): 2070-2083.DOI:10.12082/dqxxkx.2023.220777

XIE Xiaoman, HONG Zixiang, LI Li, QIU Bingqi, SU Yiran. Retrieval of Corn Residue Biomass Based on SAR Data with Soil Scattering Interference Removed[J]. Journal of Geo-information Science, 2023, 25(10): 2070-2083.DOI:10.12082/dqxxkx.2023.220777

图/表 13

图1

表1

表2

图2

表3

图3

图4

表4

图5

表5

不同特征残茬生物量反演性能（去除土壤散射干扰前）

反演特征	2020年秋季样本（n=81）		2021年春季样本（n=94）
反演特征	R²	RMSE/(g/m²)	R²	RMSE/(g/m²)
$σ v h$	0.503 05	119.179 1	0.489 03	90.756 5
$σ v v$	0.338 38	137.600 8	0.322 47	82.587 7
Product	0.575 46	107.416 1	0.505 21	70.088 1
Ratio	0.179 61	147.443 5	0.145 56	121.008 3
RVI	0.140 37	156.845 6	0.169 93	94.537 2
SPAN	0.415 23	129.363 2	0.338 81	134.683 7

表5

表6

去除土壤散射干扰后不同特征生物量反演精度变化

反演特征	2020年秋季样本（n=81）		2021年春季样本（n=94）
反演特征	ΔR²	ΔRMSE/(g/m²)	ΔR²	ΔRMSE/(g/m²)
$σ v h$	0.221 13	30.334 6	0.213 48	42.639 6
$σ v v$	0.220 52	25.247 9	0.191 23	21.067 8
Product	0.183 25	24.318 1	0.214 18	23.355 5
Ratio	0.203 01	14.522 2	0.239 60	30.622 3
RVI	0.214 10	20.927 9	0.155 41	22.075 8
SPAN	0.200 10	24.442 9	0.271 84	79.636 8

表6

图6

图7

参考文献 36

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	4月	5月	6月	7月	8月	9月	10月
	上旬中旬下旬	上旬中旬下旬	上旬中旬下旬	上旬中旬下旬	上旬中旬下旬	上旬中旬下旬	上旬中旬下旬
水稻	播种、出苗				抽穗	成熟期
玉米		播种、出苗		抽穗			成熟期
大豆		播种、出苗			抽穗	成熟期

时期	数据类型	影像时间
秋季	S-1 Level-1 GRD	2020年11月03日
	S-2 Level-1C	2020年11月09日
	SMAP/Sentinel-1 Leve2级	2020年11月03日
春季	S-1 Level-1 GRD	2021年04月08日
	S-2 Level-1C	2021年04月18日
	SMAP/Sentinel-1 Leve2级	2021年04月08日

极化方式	最大值	最小值	均值
VV	12.469	0.0474	6.089
VH	18.214	0.19	8.416

基于去除土壤散射干扰SAR数据的玉米残茬生物量反演

Retrieval of Corn Residue Biomass Based on SAR Data with Soil Scattering Interference Removed

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参数	最小值	最大值	步进
入射角/°	25	55	2
土壤体积含水量/%	2	50	2
均方根高度/cm	0.2	3	0.2
相关长度/cm	4	50	2