1. Introduction
Optical remote sensing images can obtain rich spectral features of vegetation canopy, especially the red-edge bands sensitive to green vegetation, which have absolute advantages for monitoring crop growth. However, optical remote sensing images are susceptible to weather conditions. It is often difficult to obtain satisfactory optical images during the cloudy and rainy season of crop growth. In view of this, some scholars have studied methods for monitoring crop growth based on time-series Synthetic Aperture Radar (SAR) images [
1,
2]. However, these methods require SAR images throughout the entire crop growth season, with a large data volume and complex data processing. Moreover, using SAR data alone cannot effectively utilize the rich spectral features of crop canopies reflected by optical data. Therefore, removing the need for time-series SAR data, this study investigated a method for estimating corn growth parameters by combining multispectral and backscatter features based on partially obtainable optical and SAR data during the crop growth season.
Existing research has shown that there is a good correlation between the optical vegetation indexes and crop growth parameters [
3,
4]. However, when vegetation coverage or aboveground biomass is high, especially for corn crops with a large plant size, the optical vegetation indexes are more prone to saturation in the middle or later growth stages [
5,
6]. At this time, the optical vegetation indexes are less sensitive to the crop growth parameters. Some studies have found that there is a better correlation between the red-edge bands and the growth parameters of green vegetation. The red-edge vegetation indexes can weaken the saturation phenomenon to a certain extent when the vegetation coverage is high, and they can more effectively monitor the crop growth status and the growth parameters [
7,
8]. However, even the red-edge vegetation indexes, which are sensitive to crop growth changes, only reflect the spectral reflectance characteristics of the vegetation canopy and cannot reflect the internal structural characteristics of the crop canopy. In comparison, SAR signals are more sensitive to the internal structure and geometric characteristics of the crop due to their penetration ability [
9,
10], which to some extent compensates for the shortcomings of optical image data [
11]. At present, there is still relatively little research on using complementary information from optical and SAR data to estimate crop growth parameters, but some progress has been made [
12,
13]. Luo et al. proposed a method that combined optical and SAR features to estimate the corn LAI (Leaf Area Index) and biomass parameters, and the results showed that combining spectral and texture features can significantly improve the estimation accuracy of the crop LAI and biomass [
14]. Abdikan et al. verified the correlation between variables determined from Synthetic Aperture Radar (SAR) and optical images and the plant height of the sunflower [
15]. Yeasin et al., based on machine learning models, examined whether combined Sentinel-1 and Sentinel-2 data are more efficient in predicting the sugarcane phenology than Sentinel-1 and Sentinel-2 data alone [
16]. However, the current research based on the combination of these two different types of features is at the data level. There is little analysis of the different responses of optical and SAR features to crop growth parameters from at mechanistic level.
Radar vegetation scattering models describe the scattering mechanism of radar signals on vegetation-covered surfaces, and they accurately characterize the scattering and absorption characteristics of radar waves on these surfaces [
17]. Among them, the Michigan Microwave Canopy Scattering (MIMICS) model is currently the most comprehensive vegetation scattering model, which has been used in the study of scattering characteristics of various vegetation types since its establishment [
18,
19]. However, the MIMICS model has a large number of input parameters, which to some extent affects the accuracy of vegetation scattering model construction and crop growth parameter estimation [
20]. The Water Cloud Model (WCM), as one of the typical semi-empirical scattering models, does not require complex geometric mathematical models to describe the interaction between microwaves and vegetation. It is more general than empirical models and easier to use than physical models [
21,
22]. It has been widely used in the evaluation of vegetation growth parameters in recent years. Yang et al. used three scattering components obtained from polarization decomposition, coupling those with the modified WCM, and they estimated the rice LAI, plant height, and panicle biomass based on time-series RADARSAT-2 fully polarized SAR data throughout the entire growth cycle [
23]. Kweon et al. improved the WCM based on corn and soybean crops by introducing the mean and standard deviation of crop canopy leaf inclination distribution, further enhancing the estimation accuracy of crop growth parameters based on the WCM [
24]. In addition, Bai et al. proposed a soil moisture estimation method based on the WCM and the Advanced Integral Equation Model (AIEM), and they found that the method has the potential to estimate crop growth parameters [
25]. These studies have verified that the scattering mechanism of radar waves over vegetation-covered surfaces described by the WCM can effectively explain the scattering contribution of the vegetation layer and thus achieve growth parameter estimation for different crops.
Therefore, the aim of this study was to explore the combined method of using complementary optical and SAR information at the mechanistic level, and to construct a growth parameter estimation model suitable for the corn crop. Since the WCM is most suitable for describing the microwave scattering mechanism in areas with a relatively uniform vegetation cover [
26,
27], the optical and SAR satellite images selected in this paper were all in the late jointing or tasseling stages of the corn crop. At this time, the corn already has a large plant body size, and the ground is completely covered by the vegetation, meeting the condition that volume scattering dominates, which is in accordance with the principles of the WCM. In order to obtain a more accurate growth parameter estimation model, this study comprehensively utilized the advantages of optical and SAR data, combined with the principle of the WCM, without the need for other external data or a large volume of data. This approach led us to develop a new method that integrates multispectral vegetation indexes and differential radar information (DRI) features, achieving the high-precision estimation of crop growth parameters. In this paper, estimation models of field corn plant height and phenological stage parameters, respectively, are presented.
4. Discussion
Optical satellite sensors have developed rapidly in recent years, with the advantages of low cost and high accuracy. Multispectral remote sensing data have a unique advantage in crop growth monitoring due to their rich spectral reflectance characteristics. The existing research has shown that the optical vegetation indexes are highly related to the aboveground biomass, LAI, vegetation coverage, etc., of crops [
37]. However, the optical vegetation indexes are always sensitive to low vegetation coverage, and there is a serious saturation phenomenon when the vegetation coverage is medium to high [
5,
6]. In recent years, scholars have discovered important spectral bands, which are highly related to crop growth in the red-edge spectral regions [
7]. It has been found that the multispectral vegetation indexes based on the red-edge bands are closely related to the physical and chemical parameters of the vegetation [
8]. Based on Sentinel-2 multispectral data, this study compared and analyzed the correlations between the multispectral vegetation indexes NDVI, NDVIre1, NDVIre2, and S2REP and the corn growth parameters (plant height and BBCH). It was found that the NDVI had the lowest correlation with the measured plant height and BBCH when compared with the multispectral vegetation indexes based on the red-edge bands, confirming that the multispectral vegetation indexes based on the red-edge bands are more sensitive to the growth parameters of the corn crop. In addition, the NDVIre2 and S2REP indexes calculated by introducing the red-edge 2 band showed better correlations with plant height and BBCH than the NDVIre1 index calculated by introducing the red-edge 1 band, which verified that the Sentinel-2 red-edge 2 band has great advantages in monitoring vegetation growth parameters. This result is consistent with the research of Dong et al., who found that introducing the red-edge band to multispectral vegetation indices can effectively improve the estimation accuracy of crop biomass [
38]. However, even the red-edge band indexes, which are sensitive to crop growth, only display the spectral reflectance characteristics at the top of the vegetation canopy. Especially for a corn crop with a large plant size, the optical-based vegetation indexes cannot reflect the internal structural characteristics of the vegetation layer. Therefore, this study investigated a method of estimating corn growth parameters by combining multispectral and backscatter features based on optical and SAR data during the crop growth season.
Compared with optical satellite sensors, Synthetic Aperture Radar (SAR) can obtain internal information on the crop vegetation layer and even soil layer due to its certain penetration ability. Directly using the original backscatter coefficient to estimate crop growth parameters can cause significant errors. This is similar to the research conducted by Baghdadi et al., where SAR signals could penetrate the crops to obtain vegetation and soil information under the crop cover, while directly using the backscatter coefficient for retrieving the soil moisture was not advantageous in presence of a well-developed vegetation cover [
39]. However, a radar vegetation scattering model can accurately describe these scattering mechanisms between the vegetation and soil. Among them, the WCM is the most concise and widely used, and is applicable to explain the microwave scattering mechanism in areas with a relatively uniform vegetation coverage such as crop fields [
40]. According to the principle of the WCM, the total backscatter received by the radar is related to the vegetation growth parameters (LAI, biomass, etc.) and the surface soil parameters (including surface roughness and soil moisture) [
23,
24,
25]. Therefore, in this study, based on the WCM theory, we developed a corn crop growth parameter estimation model that combines optical and SAR data without any other external data input. By calculating the difference value ∆σ
0 between the backscatter coefficients of two SAR images synchronized with optical images, it was found that ∆σ
0 weakened the influence of the surface layer on SAR data, revealing the internal structural characteristics of the crop vegetation layer. It also described the differences in radar backscatter coefficient characteristics of a corn crop under the same plant height increment, reflecting the complexity of the vegetation layer structure in a cross-section for different fields. The larger the ∆σ
0 value, the more complex the structural characteristics of the vegetation layer in a unit cross-section, and the larger the LAI or biomass per unit volume of the crop. As Wang et al. proposed, the scattering intensity of vegetation layers increases with the increase in crop LAI [
41]. Therefore, in this study, we introduced the DRI into the attenuation coefficient τ
2 of the WCM to calibrate the optical vegetation indexes VI
opt, and we constructed corn growth parameter estimation models VI
DRI. Essentially, the introduction of radar DRI when constructing the VI
DRI indexes proved to have a better correlation with the corn plant height and BBCH than the use of multispectral indexes VI
opt. Among them, the S2REP
DRI index calculated based on the red-edge 2 band showed the highest estimation accuracy of corn plant height and BBCH phenology, indicating that the VI
DRI indexes constructed based on the WCM principle, combined with the red-edge multispectral vegetation indexes and radar DRI, had certain advantages in improving the estimation accuracy of corn growth parameters. Similarly, based on various regression learning algorithms, David et al. proposed that the combination of optical SAR and optical data can improve the accuracy of vegetation biomass estimation. At the same time, the study found that the Sentinel-2 red-edge 2 band has greater advantages in this regard [
42]. However, the existing models were established based on different phenological periods of crops, and the accuracy of the models is difficult to guarantee when the vegetation coverage is high [
43,
44]. Therefore, this study directly constructed a growth parameter inversion model based on the corn heading phenology, which has important research value for effectively improving the inversion accuracy of a model under a high vegetation coverage.
Additionally, when comparing the estimation results using data from experimental area 1 and experimental area 2, the results showed that the VI
DRI indexes had slightly better accuracy in improving the VI
opt indexes for estimating corn plant height and BBCH in experimental area 1 than in experimental area 2. This is because the SAR images of experimental area 1 were largely synchronized with the optical images, and there was a small interval between the two SAR images. Meanwhile, the interval between the two SAR images in experimental area 2 was relatively large, and the synchronization between the SAR and optical images was also relatively poor. This indicates that a larger interval between SAR image data will affect the sensitivity of the DRI feature to estimate the crop growth parameters, thereby affecting the accuracy of the VI
DRI model in estimating crop growth parameters. Therefore, in practical applications, the interval between the two SAR images used for calculating DRI should not be too large, and the optical and SAR satellite images should be synchronized as much as possible to ensure that the ground object states observed by the two types of satellites are consistent. In addition, this study found that the VI
DRI indexes have a slightly better accuracy in improving the VI
opt indexes for estimating corn plant height than estimating the BBCH parameter. This is because the plant height reflects the structural characteristics of crops in the vertical direction. SAR data, due to their certain penetrability, can offer insights into the interior of the corn canopy and vertical structural information on the crop. Meanwhile, the phenotypic differences of the corn plant during the tasseling stage are mainly reflected in the extraction status of male spikes at the top of the vegetation canopy, demonstrating the characteristics of two-dimensional plane changes at the top of the corn canopy. This viewpoint is consistent with Caicoya et al., who proposed that SAR features are beneficial for detecting vertical structural information on the vegetation [
45]. As such, it seems that the VI
DRI index proposed in this article is more conducive to improving the estimation accuracy of corn plant height compared to the BBCH parameter.
However, the conclusion of this study is mainly based on dual-polarization (σ
0VH, σ
0VV) SAR satellite data in the C-band. Beyond these, there are many full-polarization (σ
0VV, σ
0VH, σ
0HH, σ
0HV) SAR data, and different polarization modes have different abilities in estimating growth parameters of crops. Furthermore, the radar electromagnetic wave scattering characteristics of crop fields vary in different wave bands and polarization modes [
2]. Therefore, it is necessary to further verify the applicability of the estimation model in other radar bands and polarization modes. In addition, we mainly studied the corn crops normally planted on the North China Plain. Although we have achieved satisfactory estimation results, due to the limitations of the crop planting region and varieties, additional layout experiments are needed to further verify the universality of the research methods proposed in this paper, by testing them in other corn planting areas such as China’s southwest mountainous areas, southern hilly areas, and the Qinghai Tibet Plateau.