Correlation Between the Growth Index and Vegetation Indices for Irrigated Soybeans Using Free Orbital Images

Abstract

Soybeans are key in generating foreign currency for the world economy. Geotechnologies, through vegetation indices (VIs) generated by orbital images or remotely piloted aircraft, are essential tools for assessing the impact of climate on productivity and the ecoclimatic suitability of crops. This study aimed to correlate the growth indices from the CLIMEX model, previously validated, with VIs derived from orbital remote sensing and ecological niche modeling for soybean cultivation in six irrigated pivots located in the northwest of Minas Gerais, Brazil. The maximum normalized difference vegetation index (NDVImax) and the maximum soil-adjusted vegetation index (SAVImax) were extracted from Landsat-8 OLI/TIRS sensor images for the 2016 to 2019 harvests during the R1 to R3 phenological stages. The maximum NDVI values varied across the study regions and crops, ranging from 0.27 to 0.95. Similarly, SAVI values exhibited variability, with the maximum SAVI ranging from 0.13 to 0.85. The growth index (GI_w), derived from the CLIMEX model, ranged from 0.88 to 1. The statistical analysis confirmed a significant correlation (p < 0.05) between NDVImax and GI_w only for the 2018/19 harvest, with a Pearson correlation coefficient of r = 0.86, classified as very strong. Across all harvests, NDVI consistently outperformed SAVI in correlation strength with GI_w. Using geotechnologies through remote sensing shows promise for correlating spectral indices and climate suitability models. However, when using a valid model, all crops did not correlate. Still, our study has the potential to be improved by investigating new hypotheses, such as using drone images with better resolution (spatial, spectral, temporal, and radiometric) and adjusting the response of soybean vegetation indices and the phenological stage. Our results correlating the CLIMEX model of growth indices with vegetation indices have the potential for monitoring soybean cultivation and analyzing the performance of varieties but require a more in-depth view to adapt the methodology.

1. Introduction

Soybean is a crop of great economic importance worldwide, with the United States, Brazil, and Argentina being the primary producers since 1990 [1]. Widely used in the food industry for producing soy milk and meat, sweets, oils, and other products, soybean also plays a crucial role in chemical processes related to cosmetics, biofuels, biodiesel production, and animal feed formulation. For the 2024/25 harvest, soybean productivity was estimated at 166.211 million tons, a 2.9% increase compared to the previous season [2]. Paraná and Mato Grosso stand out as the largest producers, reinforcing soybean as a pillar of Brazilian agribusiness [3], a success attributed to the country’s climatic diversity, which allows cultivation under different humidity and temperature conditions [4].

However, soybean cultivation is highly sensitive to climate variations and pathogen attacks, which can compromise productivity in major producing countries [5]. According to the Intergovernmental Panel on Climate Change (IPCC) [6], the global average temperature is expected to rise by more than 1.5 °C in the next 20 years due to anthropogenic activities, intensifying extreme weather events, changes in precipitation patterns, and prolonged drought periods. In this context, tools such as ecological niche modeling and remote sensing become essential to predict the impacts of climate change, identify more resilient areas, implement mitigation and adaptation strategies, and promote more sustainable agricultural practices [7].

Ecological niche modeling analyzes the interaction between species and the environment, using spatial and climatic data to determine optimal conditions for their development [8,9]. Among the available models, CLIMEX has been widely used to project the potential distribution of various crops [10], such as strawberry [11], coffee [12], oil palm [13], tomato [14], and peanuts [15]. It is a semi-mechanistic model that integrates bioclimatic parameters and climatic variables to predict crop development and map its geographic distribution [7,16]. Its main advantage is the ability to calibrate input parameters based on the species’ geographic distribution and biological characteristics [17].

In addition to modeling, remote sensing complements predictions by monitoring vegetative vigor on a large scale. Remote sensing is essential for collecting and analyzing spatial data, using imagery to identify and extract relevant geographic information [18]. Techniques such as the normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI) identify areas with healthy vegetation, reflecting a higher infrared radiation reflection [19]. Higher values of these indices indicate greater vegetative vigor due to the increased infrared radiation reflection by healthy vegetation [20]. In the northwest of Minas Gerais (NMG), a semi-arid region with increasing expansion of irrigated soybeans, this combination is strategic for mitigating climate risks and ensuring production stability.

Although CLIMEX has been applied to other crops, its integration with remote sensing for irrigated soybeans in semi-arid regions remains unexplored. This study uses geoprocessing and ecological niche modeling to correlate CLIMEX indices (GI_w and EI) with vegetation indices (NDVI and SAVI) in six irrigated soybean-producing regions in the NMG. The results will enable the zoning of climatically suitable areas, improve yield forecasts, guide irrigation management adjustments, and reduce losses due to environmental stress, contributing to more resilient and sustainable agricultural practices in the face of climate change.

2. Materials and Methods

2.1. Study Area

The study area is in the northwest region of Minas Gerais (Figure 1) and covers 18 municipalities. According to Dos Santos et al. [21], the predominant soils and their percentages of occurrences are as follows: lithologic neosols (31.38%); red–yellow latosols (26.64%); red latosols (17.25%); cambisols (14.42%); fluvic neosols (5.43%); quartzarenic neosols (3.53%); melanic gleissols (1.21%); and red–yellow argisols (0.14%).

The predominant climate is classified as tropical, with summer rains and a dry winter [22]. The region’s altitude ranges from 500 m to 1050 m (Figure 1). The northwest of Minas Gerais is characterized by average annual rainfall ranging from 1100 mm to 1400 mm. The average yearly evapotranspiration is 1300 mm, and the average temperature is 24.8 °C. The months from November to March are the wettest, contributing almost 80% of the annual rainfall, while the driest period is between April and October [23].

The methodology used in the study (Figure 2) integrates different data sources and tools to analyze the relationship between soybean growth and vegetation indices. Initially, agronomic data related to planting, harvesting, cultivars, productivity, geodesic coordinates and the irrigation radius of the center pivots is collected. This information is organized in Excel software for further processing. This data is then entered into the CLIMEX model, which generates the eco-climatic index (EI) and the weekly growth index (GWI), making it possible to assess the environmental conditions for crop development. At the same time, orbital images from the Landsat 8 satellite are processed in ArcMap software to obtain maps of the NDVI and SAVI vegetation indices, which indicate vegetation cover and crop health. Pearson's correlation is a statistical technique that measures the linear relationship between two variables, making it possible to assess the association between the eco-climate indices generated by the CLIMEX model and the vegetation indices extracted from the orbital images. It should be noted that we did not use an experimental design, since our study is based on correlation analysis between previously obtained variables. Finally, we carried out a statistical analysis using the R software to check the significance of the relationships found. This procedure makes it possible to identify patterns and contributes to a better understanding of the impact of climatic conditions on soybean growth.

2.2. CLIMEX

The CLIMEX model helps researchers estimate a species’ potential distribution under current climate conditions. It incorporates ecophysiological parameters and species’ responses to climate [16,24]. The model includes growth indices and stress factors [25]. Two essential components for assessing species growth and environmental adaptation are as follows: the weekly growth index (GI_w), which represents the weekly climatic suitability for species growth on a scale from 0 to 1, where values close to 0 indicate unsuitable periods for species growth and values closer to 1 indicate more favorable conditions [7]; and the Ecoclimatic Index (EI), which measures a location’s climatic suitability for a species, with values ranging from 0 to 100. A value close to 0 indicates unfavorable areas, while values above 30 indicate highly favorable areas for species development [7].

2.3. Parameters Used in CLIMEX

To determine the parameters, we first defined which regions have the most excellent climatic suitability (IE) for soybean cultivation in the current scenario. Then, we adjusted the data in the CLIMEX software 4.0.2 according to the ecophysiological parameters of the most productive regions so that the model was as close to reality as possible.

Table 1. CLIMEX parameter values used for soybean [26].

Index	Parameter	Values
Temperature	DV0 = lower threshold	10 °C
	DV1 = lower optimum temperature	15 °C
	DV2 = upper optimum temperature	32 °C
	DV3 = upper threshold	37 °C
Moisture	SM0 = lower soil moisture threshold	0.1 *
	SM1 = lower optimum soil moisture	0.5 *
	SM2 = upper optimum soil moisture	1.3 *
	SM3 = upper soil moisture threshold	1.5 *
Cold stress	TTCS = temperature threshold	10 °C
	TTHS = stress accumulation rate	−0.00001 week−1
Heat stress	TTHS = temperature threshold	37 °C
	THHS = stress accumulation rate	0.001 week−1
Dry stress	SMDS = soil moisture threshold	0.1 *
	HDS = stress accumulation rate	−0.006 week−1
Wet stress	SMWS = soil moisture threshold	1.7 *
	HWS = stress accumulation rate	0.001 week−1
Degree days	PDD = degree days per generation	-

* Values without units (“moisture”) are dimensionless indices related to the soil’s capacity to retain soil moisture.

illustrates the parameters of the ecological niche model published by Soares et al. [26].

2.4. Climatological Data

To develop the current climate scenarios, we used climatological data from CliMond 30’ (https://www.climond.org, accessed on 11 June 2024). Monthly average data on maximum and minimum temperature, relative humidity, and precipitation represent the historical climate (1981–2010, CM30_1995H_V2). To analyze soybean responses to climate variables over time, based on the weekly growth index (GIw) [7], we used data from the time series grid (TS) version 4.05 of the Climatic Research Unit (CRU) (https://catalogue.ceda.ac.uk/uuid/c26a65020a5e4b80b20018f148556681/) accessed on 2 June 2024, which provides monthly climate variations from 1901 to 2020, in high-resolution grids (0.5° × 0.5°).

2.5. Orbital Data and Processing to Determine the Maximum Vegetation Index

2.5.1. Obtaining Vegetation Indices

To develop the current climate scenarios, we used climatological data. We used the USGS Landsat-8 dataset (top of atmosphere (TOA), with 30 m spatial resolution), accessed through the Google Earth Engine (GEE) interface. We selected representative images from the period covering the end of the vegetative stage to the reproductive stage R3, approximately 50 days after planting (10/10/16, 27/12/17, 17/10/18, 25/10/19). These images correspond to the harvests (2016/17, 2017/18, 2018/19, and 2019/20) in the study area, totaling four images throughout the soybean cycle. Soybean planting in the study area occurs between October and December, according to the management practices adopted by the producers.

The images underwent preprocessing, including atmospheric and geometric corrections and cloud contamination removal, followed by the generation of vegetation indices (NDVI and SAVI). The images containing the indices were then exported to ArcMap 10.8 (student version), where a cross-tabulation was performed between the pivot shapes and the vegetation indices to extract pixel values within the pivots of interest throughout the four harvests. Subsequently, cartographic maps were created.

The NDVI was selected due to its widespread use in research and strong correlation with vegetation cover, leaf area index, biomass, and land [27]. Although NDVI is effective, it may experience saturation in areas with high vegetation density [28]. On the other hand, the SAVI was chosen for its ability to adjust for soil effects on reflectance, making it particularly useful in areas with sparse ground cover and during the early stages of plant growth [29].

The atmospheric corrections, calculating the reflectance at the top of the atmosphere without adjusting for the solar zenith angle, were applied using the following expression:

Pl′ = Mp × Qcal + Ap

where

Pl′—reflectance at the top of the atmosphere without sun angle correction;

Mp—band-specific multiplicative correction factor;

Qcal—quantized and calibrated digital pixel number;

Ap—band-specific additive correction factor.

Regarding the calculation of reflectance at the top of the atmosphere with the correction for the solar zenith angle, the following equation was used:

$$p\lambda ={\displaystyle \frac{p\lambda \prime }{\mathrm{S}\mathrm{i}\mathrm{n}\left({\mathsf{\theta }}_{\mathrm{s}\mathrm{e}}\right)}}$$

where

pλ—top-of-atmosphere reflectance corrected for the solar elevation angle;

pλ′—top-of-atmosphere reflectance without correction for the solar elevation angle;

θ_se—solar elevation angle.

The NDVI was calculated using the red (B4) and near-infrared (B5) bands. To do so, the following equation was applied:

$$NDVI={\displaystyle \frac{{p\lambda }_{NIR}-{p\lambda }_R}{{p\lambda }_{NIR}+{p\lambda }_R}}$$

where

NDVI—normalized difference vegetation index;

pλ_NIR—top-of-atmosphere infrared reflectance corrected for solar angle;

pλ_R—top-of-atmosphere red reflectance corrected for solar elevation angle.

SAVI is a correction factor for the influence of the soil in the infrared band [30]. It is an alternative for densely vegetated areas, showing fewer saturation problems, and represented by the following equation:

$$SAVI={\displaystyle \frac{{p\lambda }_{NIR}-{p\lambda }_R}{{p\lambda }_{NIR}+{p\lambda }_R+L}}$$

where

SAVI—vegetation index adjusted to the soil;

pλ_NIR—top-of-atmosphere infrared reflectance corrected for solar angle;

pλ_R—top-of-atmosphere red reflectance corrected for solar elevation angle;

L—correction factor, which can vary from 0 (areas with much vegetation) to 1 (areas with little vegetation) and is used as 0.25 in agricultural areas.

2.5.2. Image Processing and Statistical Analysis

The Pearson correlation coefficient (r) was used to assess the intensity and direction of the linear relationship between the variables. The necessary statistical assumptions were verified in the R program before conducting the Pearson correlation analysis between the vegetation indices (NDVI and SAVI) and the sum of the growth indices (GI_w) throughout the soybean crop cycle.

This method was chosen because it is suitable for evaluating linear relationships without assuming a direct causal link. Considering that various environmental and agronomic factors influence the vegetation indices, Pearson’s correlation provides a more flexible and direct approach to data analysis without requiring an explicit causal explanation [31].

Before applying the Pearson correlation, the normality of the data was verified and confirmed using the Shapiro–Wilk test, ensuring the data’s suitability for statistical analysis [32]. After confirming normality, Pearson’s correlation was performed to quantify the relationship between the vegetation and growth indices, allowing for a robust and efficient analysis of the studied variables.

The correlation coefficients were then classified according to [33], as shown in

Table 2. Classification of correlations according to the correlation coefficient.

Correlation Coefficient (R + or −)	Classification
0.0–0.1	Very low
0.1–0.3	Low
0.3–0.5	Moderate
0.5–0.7	High
0.7–0.9	Very high
0.9–1.0	Almost perfect

.

3. Results

3.1. Growth Indices

The weekly growth index (GI_w) values for the different harvests are shown in

Table 3. Weekly growth index for the six regions and four harvests.

Region	Safra	Minimum Value	Maximum Value	Average Value	Figure
1	2016/17	0	1	0.82	Figure 3A
1	2017/18	0	0.99	0.37	Figure 3B
1	2018/19	0	1	0.61	Figure 3C
1	2019/20	0.16	0.89	0.58	Figure 3D
2	2016/17	0.33	1	0.8	Figure 3E
2	2017/18	0	0.97	0.37	Figure 3F
2	2018/19	0	0.91	0.55	Figure 3G
2	2019/20	0	0.88	0.48	Figure 3H
3	2016/17	0.28	1	0.84	Figure 3I
3	2017/18	0	0.89	0.31	Figure 3J
3	2018/19	0	1	0.6	Figure 3K
3	2019/20	0	1	0.48	Figure 3L
4	2016/17	0.26	1	0.68	Figure 3M
4	2017/18	0	1	0.3	Figure 3N
4	2018/19	0	0.94	0.49	Figure 3O
4	2019/20	0	0.95	0.46	Figure 3P
5	2016/17	0.06	1	0.54	Figure 3Q
5	2017/18	0	0.98	0.24	Figure 3R
5	2018/19	0	1	0.37	Figure 3S
5	2019/20	0	0.98	0.57	Figure 3T
6	2016/17	0.04	0.97	0.54	Figure 3U
6	2017/18	0	0.94	0.25	Figure 3V
6	2018/19	0	0.95	0.95	Figure 3W
6	2019/20	0	0.99	0.47	Figure 3X

, highlighting the variations, averages, and the highest and lowest indices recorded in each period analyzed. Next, the growth rate and the soybean growing season are shown (Figure 3).

3.2. Vegetation Indices

The NDVI values for the different harvests are shown, highlighting the variations, averages, and the highest and lowest indices recorded in each period analyzed (Figure 4, Figure 5, Figure 6 and Figure 7).

The SAVI values for the different harvests are shown in Figure 7 highlighting the variations, averages, and the highest and lowest indices recorded in each period analyzed (Figure 8 and Figure 9).

3.3. Correlation Between Vegetation Indices and Growth Indices

The significant correlation in the 2018/19 harvest (0.86, p = 0.03) suggests a strong relationship between NDVI and GI_w, indicating that the vegetation index in this harvest could be a good indicator of productivity. The correlations were not significant in the other harvests, with the 2019/20 harvest showing a very low correlation (0.09, p = 0.87). This indicates that, in recent years, NDVI has not been a good predictor of GI_w. The moderate negative correlation in the 2018/19 crop year (−0.53) is interesting but insignificant (p = 0.29). It suggests a possible inverse relationship, where increases in SAVI could be associated with reductions in GI_w, but this is inconclusive. Further research is needed. In the other harvests, the correlations were very low and insignificant, indicating that SAVI was not a good indicator of GI_w.

3.4. Soybean Cultivation Data

In Region 1 (Figure 10), the Challenge soybean variety was sown during the four harvests. In pivot 1, in the 2016/17 harvest, the average yield reached 2500 kg ha⁻¹, while the 2017/18, 2018/19, and 2019/20 harvests had an average yield of 4250 kg ha⁻¹, 4250 kg ha⁻¹, and 4850 kg ha⁻¹, respectively. During the four harvests, 86.50 mm, 18.46 mm, 48.61 mm, and 44.71 mm of irrigation were applied, respectively. The average rainfall for the harvest period was 548 mm, 775 mm, 742 mm, and 757 mm, respectively. The maximum temperatures for the harvest period were 28.5 °C, 30.6 °C, 31.3 °C and 29.6 °C, respectively. The minimum temperatures for the harvest period were 17.6 °C, 19.7 °C, 14.6 °C, and 19.6 °C, respectively. Average temperatures were 23.18 °C; 23.26 °C; 23.38 °C and 24.17 °C, respectively. The average radiation for the four harvests was 261.1 W.m⁻², 79.3 W.m⁻², 324.36 W.m⁻² and 147.37 W.m⁻².

In Region 2 (Figure 11), the Monsoy and Nidera varieties were sown during the four soybean harvests. In pivot 2, in the 2016/17 harvest, the average yield reached 3000 kg ha⁻¹, while the 2017/18, 2018/19, and 2019/20 harvests had an average yield of 3400 kg ha⁻¹, 2860 kg ha⁻¹, and 3198 kg ha⁻¹, respectively. During the four harvests, 181.9 mm, 175.31 mm, 306.63 mm, and 224.81 mm of irrigation were applied, respectively. The average rainfall for the harvest period was 516 mm, 474 mm, 572 mm, and 721 mm, respectively. The maximum temperatures for the harvest period were 31.9 °C, 35.4 °C, 29.9 °C, and 30.8 °C, respectively. The minimum temperatures for the harvest period were 21.5 °C, 23.0 °C, 22.1 °C and 22.0 °C, respectively. Average temperatures were 25.16 °C; 27.03 °C; 24.44 °C and 24.9 °C, respectively. The average radiation for the four harvests was 227.27 W.m⁻², 185.1 W.m⁻², 146.26 W.m⁻² and 165.11 W.m⁻².

In Region 3 (Figure 12), the Riber, FTR 4160, and Desafio varieties were sown during the four harvests. In pivot 3, in the 2016/17 harvest, the average yield reached 3832 kg ha⁻¹, while the 2017/18, 2018/19, and 2019/20 harvests had an average yield of 3900 kg ha⁻¹, 3950 kg ha⁻¹, and 4580 kg ha⁻¹, respectively. During the four harvests, 78.2 mm, 18.39 mm, 136.32 mm, and 47.61 mm of irrigation were applied, respectively. The average rainfall for the harvest period was 431 mm, 763 mm, 543 mm, and 876 mm, respectively. The maximum temperatures for the harvest period were 27.9 °C, 31.7 °C, 26.73 °C and 27.9 °C, respectively. The minimum temperatures for the harvest period were 19.6 °C, 20.13 °C, 18.9 °C, and 17.9 °C, respectively. Average temperatures were 25.39 °C, 24.27 °C, 18.9 °C and 17.9 °C, respectively. The average radiation for the four harvests was 208.26 W.m⁻², 194.76 W.m⁻², 158.17 W.m⁻² and 158.94 W.m⁻².

In Region 4 (Figure 13), the varieties Coodetec, Agroeste 4160, and Nidera, and ns 7901 were sown during the four harvests. In pivot 4, in the 2016/17 harvest, the average yield reached 4010.5 kg ha⁻¹, while the 2017/18, 2018/19, and 2019/20 harvests had an average yield of 3850 kg ha⁻¹, 3607.5 kg ha⁻¹, and 4174.5 kg ha⁻¹, respectively. During the four harvests, 144.11 mm, 221.59 mm, 303.16 mm, and 246.68 mm of irrigation were applied, respectively. The average rainfall for the harvest period was 546 mm, 683 mm, 839 mm, and 751 mm, respectively. The maximum temperatures for the harvest period were 33.5 °C, 34.5 °C, 31.3 °C and 32.45 °C, respectively. The minimum temperatures for the harvest period were 18.0 °C, 20.9 °C, 22.9 °C and 17.4 °C, respectively. Average temperatures were 25.86 °C, 27.2 °C, 26.02 °C and 24.52 °C, respectively. The average radiation for the four harvests was 240.93 W.m⁻², 199.13 W.m⁻², 280.52 W.m⁻² and 254.16 W.m⁻².

In Region 5 (Figure 14), the soybean seeds used were of agarose variety during the four harvests. In pivot 5, in the 2016/17 harvest, the average yield reached 4445 kg ha⁻¹, while the 2017/18, 2018/19, and 2019/20 harvests had an average yield of 4500 kg ha⁻¹, 4155 kg ha⁻¹ and 4215 kg ha⁻¹, respectively. During the four harvests, irrigation was applied of 122 mm, 122 mm, 125 mm, and 110 mm, respectively. The average rainfall for the harvest period was 1018 mm, 1018 mm, 1047 mm, and 997 mm, respectively. The maximum temperatures for the harvest period were 26.6 °C, 30.3 °C, 29.6 °C and 26.1 °C, respectively. The minimum temperatures for the harvest period were 19.0 °C, 18.37 °C, 18.7 °C and 20.53 °C, respectively. Average temperatures were 21.6 °C to 23.5 °C, 22.83 °C and 21.95 °C, respectively. The average radiation for the four harvests was 92.79 W.m⁻². 190.05 W.m⁻², 192.17 W.m⁻² and 85.19 W.m⁻².

In Region 6 (Figure 15), the pioneer and Desafio varieties were sown during the four soybean harvests. In pivot 6, average productivity in the 2016/17 harvest reached 3969 kg ha⁻¹, while the 2017/18, 2018/19, and 2019/20 harvests had average productivity of 3969 kg ha⁻¹, 4046 kg ha⁻¹ and 4465 kg ha⁻¹, respectively. During the four harvests, 201.86 mm, 111.95 mm, 197.52 mm, and 235.67 mm of irrigation were applied, respectively. The average rainfall for the harvest period was 642 mm, 737 mm, 1251 mm, and 808.1 mm, respectively. The maximum temperatures for the harvest period were 29.0 °C, 30.9 °C, 30.8 °C and 30.3 °C, respectively. The minimum temperatures for the harvest period were 17.1 °C, 19.1 °C, 21.1 °C and 19.7 °C, respectively. Average temperatures were 22.6 °C to 23.82 °C, 24.67 °C and 22.84 °C, respectively. The average radiation for the four harvests was 272 W.m⁻², 122.66 W.m⁻², 200.36 W.m⁻² and 153.5 W.m⁻².

4. Discussion

This study sought to correlate the growth index (GI_w) from a model already validated for soybeans by Soares et al. [26] with the NDVI and SAVI. The 2018/2019 harvest showed a significant correlation between GI_w and NDVI, which was very strong. In addition, NDVI outperformed SAVI in the correlation with GI_w for the crops analyzed. Row closure explains this factor, taking into account that SAVI includes an adjustment factor to minimize the influence of the soil [34]. Studies have found that NDVI is a reliable index for estimating soybean biomass and nutrient uptake in a way that provides valuable information about the health and performance of the crop. Thus, the authors conclude that there is a strong correlation between NDVI and green canopy cover that allows effective monitoring of canopy closure in legumes, including soybeans, with correlation coefficients ranging from 0.95 to 0.98.

For the significant harvest, in Region 4, located in the municipality of Unaí/MG, one of the main hubs of irrigated agriculture, where large numbers of pivots in northwest Minas Gerais are concentrated, the GI_w was favorable for soybean cultivation (Figure 3K), and the maximum NDVI threshold reached 0.95 (Figure 5), showing a good vegetative stage. This region had an average yield of 4010.5 kg ha⁻¹ (Figure 11), higher than the average for Minas Gerais. According to the Brazilian grain crop monitoring report [35], the estimated average soybean yield for the 2018/19 crop in Minas Gerais was 3527 kg ha⁻¹, and Region 2 (Figure 9) had a lower average yield than the state of Minas Gerais. According to Dalla Rosa [36], historical averages for soybean yields in Minas Gerais increase in years when La Niña occurs. Although 2018 had an El Niño event, it did not impact grain yields in the regions because the global climate was weak or moderate [37].

Therefore, the irregularity of rainfall distribution is a major challenge in achieving satisfactory soybean yields, especially during periods that require more water, such as flowering and grain filling. Although there is a high demand for water during some stages, it is essential to have an adequate volume, with rainfall distributed proportionally throughout the crop, especially in the most critical phases. To obtain the maximum grain yield, the water requirement varies from 450 to 800 mm/cycle [38]. Although considerable rainfall occurred during 2016/17, 2017/18, and 2019/20 harvests, the indices did not correlate.

However, with the tool used, there was no correlation between the GI_w and the vegetation indices for 2017, 2019, and 2020, although we used a model already validated and published by Soares et al. [39]. Some factors may have influenced the lack of correlation, including the quality of the images. The manuscript by [40] found that image quality can be influenced by the satellite’s pointing accuracy and the stability of its altitude. Other factors are micro-vibrations and atmospheric conditions, such as fog and cloud cover, which can damage the quality of the image, as well as blurring and reducing the quality of the photos that may have significantly influenced the non-correlation. In addition, the resolution of the Landsat-8 satellite used for the research is 30 m, which is why the area of the pivots is smaller and may also have influenced the correlation between the vegetation indices and the GI_w. To this end, images from new methods such as the remotely piloted aircraft (RPA) system can be used to solve the problems of spatial, spectral, temporal, and radiometric resolution, significantly influencing image quality. Regarding temporal resolution, Crusiol et al. [41] evaluated soybean cultivars from 9:00 a.m. to 4:00 p.m., showing that at 9:00 a.m., the NDVI showed higher values, while there were decreasing values between 1:00 p.m. and 2:00 p.m. Shane [42] researched the application of vegetation indices generated by different spatial and temporal resolutions of orbital images in soybeans, showing that indices generated with high-resolution images correlate better with soybean yields.

RPA’s multispectral sensors provide a flexible, efficient, high-resolution method for capturing vegetation indices. In our work, unmanned aerial vehicles could capture higher-resolution images quickly and efficiently, allowing the generation of other vegetation indices and a more detailed analysis of vegetation health and biomass and enabling the correlation between vegetation indices and GI_w for soybean cultivation.

Although the cultivars have different NDVI and SAVI values, our work showed that it was not the characteristic that influenced correlation and non-correlation since, in 2018 and other years, we had the same cultivars under the same management conditions.

When searching for the available data of Landsat-8 images to generate vegetation indices, it is not always possible to synchronize the date of the image with the phenological stage, a fact that directly influences the values of the VIs and, subsequently, the correlation. Crusiol et al. [34], comparing vegetation indices at different soybean phenological stages, found that cultivars more tolerant to water deficit showed higher NDVI when compared to less tolerant cultivars at the reproductive phenological stage (R1 to R5), making it possible to distinguish between cultivars during the dry season. However, biomass productivity does not follow the NDVI at later stages, as it presents saturation problems. Gao et al. [43] found that the NDVI is more sensitive to chlorophyll and pigments resulting from the absorption of solar radiation in the red band. Thus, the NDVI may have a saturation that slightly alters the detection of variations with the increase in plant biomass as the phenological stages of the plants pass [44].

We emphasize that other factors can influence the results obtained, such as crop management, phytosanitary history (attack by pests, diseases, and nematodes), and soil fertility data. These variables could be considered in future studies, and this work is a starting point for new studies that include and analyze other variables capable of affecting vegetation indices with the improvement of different types of images (drones) and/or other sensors that make it possible to explore other more robust vegetation indices.

5. Conclusions

The CLIMEX growth index model correlated with vegetation indices has shown promising potential for monitoring soybeans in the regions studied. The results obtained in this study highlight the importance of correlating these parameters to better understand soybean performance in different environmental conditions. However, further research is needed to improve the methodology for selecting the best vegetation indices and sensors, both orbital and aerial. In addition, the challenge of synchronizing the phenological stage with the date of the satellite images or drone flights must be addressed to guarantee the accuracy of the analyses. It is suggested that future research consider strategies to overcome these challenges, explore new approaches to improve soybean monitoring, and provide vital information for farmers and managers in northwest Minas Gerais, Brazil.