Research Progresses of Crop Growth Monitoring Based on Synthetic Aperture Radar Data

doi:10.12133/j.smartag.SA202308019

Abstract

Abstract:

Significance Crop production is related to national food security, economic development and social stability, so timely information on the growth of major crops is of great significance for strengthening the crop production management and ensuring food security. The traditional crop growth monitoring mainly judges the growth of crops by manually observing the shape, color and other appearance characteristics of crops through the external industry, which has better reliability and authenticity, but it will consume a lot of manpower, is inefficient and difficult to carry out monitoring of a large area. With the development of space technology, satellite remote sensing technology provides an opportunity for large area crop growth monitoring. However, the acquisition of optical remote sensing data is often limited by the weather during the peak crop growth season when rain and heat coincide. Synthetic aperture radar (SAR) compensates well for the shortcomings of optical remote sensing, and has a wide demand and great potential for application in crop growth monitoring. However, the current research on crop growth monitoring using SAR data is still relatively small and lacks systematic sorting and summarization. In this paper, the research progress of SAR inversion of crop growth parameters were summarized through comprehensive analysis of existing literature, clarify the main technical methods and application of SAR monitoring of crop growth, and explore the existing problems and look forward to its future research direction. Progress] The current research status of SAR crop growth monitoring were reviewed, the application of SAR technology had gone through several development stages: from the early single-polarization, single-band stage, gradually evolving to the mid-term multi-polarization, multi-band stage, and then to the stage of joint application of tight polarization and optical remote sensing. Then, the research progress and milestone achievements of crop growth monitoring based on SAR data were summarized in three aspects, namely, crop growth SAR remote sensing monitoring indexes, crop growth SAR remote sensing monitoring data and crop growth SAR remote sensing monitoring methods. First, the key parameters of crop growth were summarized, and the crop growth monitoring indexes were divided into morphological indicators, physiological and biochemical indicators, yield indicators and stress indicators. Secondly, the core principle of SAR monitoring of crop growth parameters was introduced, which was based on the interaction between SAR signals and vegetation, and then the specific scattering model and inversion algorithm were used to estimate the crop growth parameters. Then, a detailed summary and analysis of the radar indicators mainly applied to crop growth monitoring were also presented. Finally, SAR remote sensing methods for crop growth monitoring, including mechanistic modeling, empirical modeling, semi-empirical modeling, direct monitoring, and assimilation monitoring of crop growth models, were described, and their applicability and applications in growth monitoring were analyzed. Conclusions and Prospects Four challenges exist in SAR crop growth monitoring are proposed: 1) Compared with the methods of crop growth monitoring using optical remote sensing data, the methods of crop growth monitoring using SAR data are obviously relatively small. The reason may be that SAR remote sensing itself has some inherent shortcomings; 2) Insufficient mining of microwave scattering characteristics, at present, a large number of studies have applied the backward scattering intensity and polarization characteristics to crop growth monitoring, but few have applied the phase information to crop growth monitoring, especially the application study of polarization decomposition parameters to growth monitoring. The research on the application of polarization decomposition parameter to crop growth monitoring is still to be deepened; 3) Compared with the optical vegetation index, the radar vegetation index applied to crop growth monitoring is relatively less; 4 ) Crop growth monitoring based on SAR scattered intensity is mainly based on an empirical model, which is difficult to be extended to different regions and types of crops, and the existence of this limitation prevents the SAR scattering intensity-based technology from effectively realizing its potential in crop growth monitoring. Finally, future research should focus on mining microwave scattering features, utilizing SAR polarization decomposition parameters, developing and optimizing radar vegetation indices, and deepening scattering models for crop growth monitoring.

Key words: growth monitoring, synthetic aperture radar (SAR), radar vegetation index, mechanistic modeling method

HONG Yujiao, ZHANG Shuo, LI Li. Research Progresses of Crop Growth Monitoring Based on Synthetic Aperture Radar Data[J]. Smart Agriculture, 2024, 6(1): 46-62.

Figures/Tables 6

Table 1

Table 2

Main radar indicators and application characteristics

类型	SAR参数	应用特性
H/A/ $α ¯$ 分解	散射熵Entropy（H）^{［73， 74］}	H的取值范围为［0，1］，代表散射随机性的大小，H越小表示散射随机程度越低；H对植物的茎高有较高的敏感性
	反熵Anisotropy（A）^［75］	A反映了Cloude分解中 $λ$ ₂和λ ₃对应的两种散射机制的关系；当A较小时，λ ₃所对应的散射机制会影响散射结果；而当A较大时， $λ$ ₂所对应的散射机制对散射结果产生影响；A对作物的物候期变化最为敏感
	散射角Alpha（ $α$ ）^［76］	$α$ 取值范围为［0°， 90°］，其和目标的散射机理相关。当 $α$ =0°时，目标为平面散射；当 $α$ =45°时，目标为体散射；当 $α$ =90°时，目标为偶次散射，与H相似， $α$ 也对作物的茎高敏感
极化特征参数	特征值λ^［77］	λ是通过对相干矩阵进行分解获得的，其大小和分布可以提供有关信号的极化特性和目标散射特性的信息，适用于对作物的健康监测
	香农熵Shannon Entropy （SE）^{［78， 79］}	将SE引入极化雷达应用中，有助于测量地物目标散射的信息量，同时反映了地物目标散射的随机性，可以准确反映作物的散射特征
	相干矩阵主对角线元素（S _HH、S _VV）^［77］	S _HH、S _VV通常用来描述SAR接收到的极化信号的功率，可以用来追踪作物的健康状况变化；健康的作物有较高的极化信号率，而有疾病或营养不良的作物则有较低的极化信号功率
	基高Base Height^［80］	基高参数在极化SAR中是一个关键参数，可以用于监测不同作物的生长情况；不同类型的作物在生长期间会有不同的高度变化，通过基高参数的变化，可以追踪作物的生长进程，识别作物类型并评估其生长状况
散射强度特征指数	VV-HV、HH-HV、HH-VV、HH/VV、VH/VV、HH+HV、HH+VV、VV+VH^{［20， 81-83］}	VV和HH为同极化，VV表示SAR接收和发射的信号都为垂直分量，HH表示SAR接收和发射的信号都为水平分量；HV和VH都为交叉极化，HV表示SAR发射的是水平信号，接收的反射信号为垂直信号，VH则与HV相反；通过加减乘除构建散射强度特征指数，可以突出作物的特征。例如，VV-VH对玉米冠层很敏感，HH/VV对小麦LAI敏感
植被指数	Radar Vegetation Index （ $R V I K i m$ ）^［84］	$R V I K i m = 8 σ H V 0 σ H H 0 + σ V V 0 + 2 σ H V 0 (1), 0 ≤ R V I K i m ≤ 1, R V I K i m$ 用于全极化SAR数据，是散射随机性的度量，对植被生物量与介电特性敏感
	Radar Vegetation Index （ $R V I V V$ ）^［85］	$R V I V V = 4 σ V H 0 σ V V 0 + σ V H 0 (2), 0 ≤ R V I V V ≤ 1$ ， $R V I V V$ 是对 $R V I K i m$ 的改进，用于双极化数据（VV与VH），是体散射的度量，当体散射为主要散射机制时其与 $R V I K i m$ 差异很小，适用于农作物监测
	Dual Polarimetric Radar Vegetation Index （ $D p R V I$ ）^［86］	$D p R V I = 1 - m β (3), 0 ≤ D p R V I ≤ 1$ ， $m = 1 - 4 C 2 (T r (C 2)) 2$ $(4)$ ； $D p R V I$ 对植被形态结构和介电特性敏感，与植被含水量、种植面积和干生物量相关性高，适用于大区域农作物长势监测以及作物生物物理参数的反演
	Generalized Radar Vegetion Index（GRVI）^［87］	$G R V I = β f V (5), 0 ≤ G R V I ≤ 1$ ， $f V = 1 - G D V (6), β = (p q) 2 G D V (7)$ ， $p = m i n G D K, K t G D K, K c G D K, K d G D K, K n d 8, q = m a x G D K, K t G D K, K c G D K, K d G D K, K n d (9)$ ； GRVI对植被形态结构和介电特性敏感，与植被含水量和植物面积指数相关性好，适用于农作物长势监测以及作物生物物理参数的反演
	Compact-pol Radar Vegetation Index（CpRVI）^［88］	$C p R V I = β f I D (10), 0 ≤ C p R V I ≤ 1$ ， $β = (p q) 2 (3 / 2 G D I D) (11)$ ， $p = m i n S C, O C 12, q = m a x S C, O C (13)$ ，CpRVI是对GRVI的改进，用于紧致极化合成孔径雷达（CP-SAR），其对植被介电特性和冠层结构，适用于农作物长势监测，但性能弱于GRVI

Table 2

Fig. 1

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Fig. 4

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分类	参数名称	定义
形态指标	株高（Plant Height）^［61］	植物的根茎部到顶部的距离，其中顶部是指植物的主干顶
	冠层覆盖度（Canopy Coverage）^［62］	植物冠层覆盖地表的面积与该区域表面积之比，通常用百分比表示
	倒伏率（Fall Rate）^［63］	分为根倒和茎折。根倒是指倒伏倾斜度大于45°者作为倒伏指标，以百分比表示；茎折是指抽雄后，果穗以下部位折断的植株占全区株树的百分比
生理生化指标	叶面积指数（Leaf Area Index）^［64］	单位土地面积上植物叶片总面积与该土地面积之比
生理生化指标	叶片叶绿素含量（Leaf Chlorophyll Content）^{［65， 66］}	叶片中所含叶绿素的量。测定叶片叶绿素含量可以反映叶片光合能力和生长状态
产量指标	生物量（Biomass）^{［67， 68］}	某一时刻单位面积内实存生活的有机物质（干重）总量，通常用kg/m²或t/ha表示。
胁迫指标	植株含水量（Plant Water Content）^［69］	植被冠层体积内的含水量，其动态可以捕捉植物的水分胁迫
胁迫指标	干旱胁迫（Drought Stress）因子^［70］	由于干旱条件下植物获取的水分有限，因此植物生长明显受到抑制的现象