1. Introduction
Chinese Brassica (
Brassica chinensis L.
var. parachinensis (
Bailey)) has high nutritional value and is a critical vegetable for the Chinese population whose diet consists largely of vegetables in the Guangdong province of China, and Chinese Brassica is one of the highest produced vegetables in Guangdong province [
1]. During the growing process, Chinese Brassica is susceptible to soil water deficiency, which affects its ability to undergo photosynthesis and stomatal movements and eventually leads to a decrease in yield [
2]. The use of a suitable irrigation strategy concerning the time to trigger irrigation can enhance water use efficiency and crop productivity [
3]. It has been argued that the principal beneficiary of irrigation is the crop, rather than the soil [
4]; consequently, the irrigation strategy should be formulated according to the water status of the crops. Several crop-based indicators, including stomatal conductance, stem water potential, and canopy temperature, have been introduced to detect crop water status [
5]. As the channel of gas exchange, the stomata are sensitive and their conductance declines dramatically when the crop is suffering from water stress [
6,
7,
8]. Stem water potential has also been used to characterize crop water stress, showing a good correlation [
9,
10]. However, these measurements are time-consuming and leaf-destructive. With the rapid development of thermal infrared technology, the use of the canopy temperature as a metric has the merits of quick measurement [
11], explicit results [
12], and a lack of damage to the plants [
13]. It has also been shown to have a significantly negative relationship with the stomata; therefore, canopy temperature is a good indicator of water stress [
14,
15,
16]. However, methods which utilize canopy temperature to describe crop water status are highly variable and unstable [
12,
16]. Additionally, canopy temperature alone does not immediately change with water status, which needs to be normalized to the environmental conditions [
17].
The Crop Water Stress Index (CWSI), proposed in 1981, is derived from the canopy temperature. It has been widely applied to assess water status and schedule irrigation regimes [
18,
19,
20,
21]. Theoretically, the CWSI ranges from 0 to 1, where the maximum value of 1 indicates that the crop has sufficient water, whereas the minimum value of 0 indicates that the crop has an extreme water deficit. The CWSI is not only used to quantify the water stress gradient; the majority of studies have revealed that it has a strong association with a series of physical activities related to the crop, such as stomatal conductance, transpiration rate, and stem water potential [
16,
22,
23]. Generally, the CWSI is calculated via empirical and theoretical methods, with the empirical formula being easier and quicker to use. CWSI values obtained using the theoretical method based on energy balance are relatively precise. However, the data collection and calculation methods involved are arduous and complex [
24].
The main task of the empirical equation is to determine the lower and upper baseline canopy temperatures using the crop canopy temperature, air temperature, and relative humidity. Previous studies have developed different ways of obtaining the lower and upper baseline temperatures to refine the empirical formula. For example, multiple studies have clearly demonstrated the CWSI method, in which the lower limit is based on a linear equation between the vapor pressure difference (VPD) and the well-watered canopy temperature can effectively indicate crop water stress. This has been demonstrated in cotton [
24], pepper [
25], grape [
26], and olive [
14] crops. However, Idso et al. [
27] consider VPD to be influenced by the environment because it changes with the season, region, and crop growth process, making it challenging to create an equation. Using the canopy temperature of a well-watered plantation to determine the lower baseline temperature has been proven to be a viable method for sugar beet [
28], Indian mustard [
29], grape [
30], and olive [
31] crops.
With respect to the upper baseline, many researchers have applied a constant. Irmak et al. [
32] designed different water treatments for maize and found that the average canopy temperature was always 5.0 °C above the air temperature when maize was under severe water stress, and suggested that the air temperature plus a constant could be taken as the upper baseline for the canopy temperature to simplify the calculation. Considering this conclusion, previous studies have used the air temperature plus 5.0 °C to represent the upper baseline of corn [
33] and olive [
31] crops, and others have used the air temperature plus 2.0, 3.0, or 7.0 °C based on their experiments [
29,
34,
35]. It has been shown that different crops may require distinct methods. Rather than applying the average value, Cristina et al. [
21] and Gonzalez et al. [
36] took the maximum canopy temperature under conditions of severe stress as the upper baseline for citrus and demonstrated an excellent correlation between the CWSI and stem water potential. Certainly, the lower and upper baseline canopy temperatures must be measured.
Predictions using easily attainable data allow canopy temperatures and CWSI values to be obtained more quickly and conveniently. King et al. [
37] utilized a neural network to estimate the canopy temperature of well-watered crops based on air temperature, relative humidity, wind speed, and solar radiation. The results indicated that the neural network performed well regarding the estimation and viability of canopy temperature simulation using a considerable amount of previous environmental data. Kumar et al. [
29] constructed two neural network models and obtained the canopy temperature, air temperature, and relative humidity, which were then used to predict CWSI values in order to avoid calculating the lower and upper baseline temperatures. To our knowledge, no studies have predicted the lower and upper baseline values using environmental factors at the same time. The canopy temperature is affected by the air temperature, relative humidity, wind speed, and solar radiation [
38]. When solar radiation increases, the canopy temperature changes evidently, increasing at a rapid rate [
31,
39]. Photosynthetic active radiation (Par), a portion of solar radiation absorbed by the photosynthetic system of crops, plays a critical role in providing energy to support photosynthetic processes related to the conversion of radiation energy into chemical energy [
40]. However, Par has not been considered for canopy temperature simulation in similar studies. It is noteworthy that the environmental conditions mentioned above are easy to replicate and monitor.
The random forest (RF) model has been applied successfully to predict agricultural information, for example, soil temperature [
41], soil moisture content [
42,
43], and crop yield [
44,
45]. Usually, a RF model consists of numerous decision trees. The tree numbers are randomly distributed, and the final prediction is the weighted mean of all trees. During the decision tree building process, a dataset is continually split with definite regularity using the best feature and value. It is eventually divided into different subsets to represent categories. The advantages over using a single decision tree are that the RF model does not require pruning and is more efficient for testing the performance of each tree. In contrast, single decision tree models split the performances of descriptors at each node, causing an increased calculation time [
46]. Due to its random characteristics, the RF model performs well for data with low variance and generalization [
41].
Traditional CWSI calculations are based on baseline canopy temperatures, which need to be measured multiple times to account for the variation in experimental conditions. Hence, simulating a baseline canopy temperature to simplify the measurements and experimental design would enhance the practicality of the CWSI. Thus, the specific objective of our study was to use an RF model to simulate the lower and upper baseline canopy temperatures for Chinese Brassica and to evaluate the accuracy of the predicted canopy temperatures.
In
Section 2, the description of the field experiments and data used are given. Additionally, details of the RF model and evaluation criterion are given. This is followed by results and a discussion of the modeling of the lower and upper baseline canopy temperatures using the RF model in
Section 3 and
Section 4. Finally, the conclusion of the study is presented in
Section 5.
4. Discussion
Frequently measuring and
restricts the practical application of the CWSI. In this study, an RF model using easily obtainable environmental parameters (Ta, RH, WS, and Par) exhibited viability for simulating
and
for Chinese Brassica. King et al. [
28] were the first to predict
based on a neural network and demonstrated an excellent R
2 value of 0.88. This result is similar to that obtained for the model developed in our study. However, our study illustrated the potential to simulate
at the same time. Furthermore, our study used Par rather than net solar radiation [
28,
51], and Par made a greater contribution to model construction than WS and RH. Neukam et al. [
50] developed an empirical regression model to predict winter wheat canopy temperature for three irrigation levels and the results showed R
2 values of 0.9. However, the RMSE values of 1.5~2.0 °C were higher than the RMSE in our study. Wang et al. [
55] simulated the canopy temperature in a green house and evaluated the performance of multiple linear regression using air temperature, relative humidity, and solar radiation; they found an R
2 value of 0.87. However, the environment in their study was not sufficiently variable, as the temperature and radiation in the greenhouse were ≤37 °C and 500 W/m
2. Duan et al. [
56] used the surface ground temperature and air temperature to predict the wheat canopy temperature, and the neural network model showed that the R
2 and RMSE value were 0.92 and 1.64, respectively. However, the tested samples were ≤50, lower than in our study.
RF models are principally driven by data, indicating the importance of the original dataset having many precise samples. Site-specific data are easily obtained, but there are potential difficulties. For example, the instruments are stationary and may be damaged by pests and extreme weather. Moreover, its energy-consuming nature entails high equipment requirement [
57]. The climatic characteristics used in this experiment, especially Par, were highly variable. However, the majority of values were relatively low, and the RF model performed better with lower values than with higher values. Nevertheless, our model could be optimized by adding corresponding data to minimize the level of skewness.
Overall, the study provides a feasible and reliable approach that can be used to determine the canopy temperature to calculate the CWSI and then make irrigation decisions. O’Shaughnessy et al. [
47] provided the CWSI-TT method for scheduling irrigation, where the decision rule was such that the CWSI was greater than a threshold value of 0.45 in accumulated time; the result indicated the effective trigger of CWSI-TT for automatic irrigation. Osroosh et al. [
48] used dynamic time and the CWSI as the threshold for irrigation and founded that a CWSI of 0.46 ± 0.11. According to
Figure 13 and
Figure 14, the Sc and Tr values between Groups T3 and T4 were distinct, whereas most CWSI values for Group T3 were ≤ 0.4 and those of Group T4 were ≥ 0.4, denoting a possibility of irrigation with a CWSI value of over 0.4. In further research, the CWSI-TT, where the CWSI is greater than 0.4 in accumulated time, could be used for irrigation in real time, which avoids over irrigation and enhances water efficiency.
In addition, the study illustrates a good correlation of Par with
and
for Chinese Brassica, and this is relevant for modeling. Previous literature has indicated that the canopy temperature of stressed crops shows a greater response to high radiation than well-watered crops [
31,
39]. When the average daily Ta was ≧35 °C and the daily Par was ≧1000 μmol·m
−2·s
−1, the difference between
and
abruptly increased, as shown in
Figure 5, indicating a relationship between the canopy temperature and Par. In this study, the RF model developed using ambient environmental parameters (Ta, RH, WS, and Par) demonstrated the viability of estimating
and
with good results. Considering the slightly greater relationship between climatic parameters and
compared with
, the prediction of
was better in terms of both model development and validation. Previous research has demonstrated similar performances using Ta plus a constant such as
[
27,
29,
32]. However, the maximum differences between
and Ta for Chinese Brassica can be up to 10.0 °C, whereas the minimum is close to 1.0 °C. Therefore, utilizing air temperature plus a specific constant to replace
could increase or decrease the value of the CWSI, whereas forecasting
could relatively diminish the difference.
Generally, the CWSI values were negatively correlated with soil moisture values, implying that water paucity leads to high CWSI values. The daily CWSI values decreased to near or exactly zero, mainly due to the slight difference between
and
in response to lower Ta and Par values. Thus, the discrepancies in the CWSI values between the four water treatment groups were not significant. The multiple prediction errors for
were lower than for
, as shown in
Figure 9 and
Figure 10, indicating higher values of
minus
based on predicted values. Thus, the average daily CWSI values determined using predicted
and
values slightly exceeded the measured values. The daily CWSI values based on the predicted values from the four water treatment groups had the characteristics of a narrow range and a slightly higher mean value compared with the measured values. In
Table 5, the daily mean CWSI values for the four groups are compared, and the results show that the CWSI increases as the soil moisture decreases, similar to the present research of Irmak al. [
32], Khorsandi et al. [
34], and Jamshidi et al. [
57]. The mean CWSI values within Groups T2–T5 were distinct with significance values < 0.05 either in E1 or E2.
According to
Table 3 and
Table 4, the significance of the F value for predicting
and
indicated a meaningful regression based on the RF model, whereas the high R
2 values in E1 and E2 were significant, implying an insignificant difference between measured values and predicted values. In addition, two plants in each group were lower in two experiments, but the measurement of multiple leaves from one plant was introduced to minimize the influence as much as possible, and this possibly affected the correlation between the CWSI and Sc and Tr.
Under soil water stress conditions, the stomatal closure further increases the canopy temperature and decrease the transpiration [
58]. The relationship between the daily CWSI and Sc was stronger than Tr. This is because the fluctuated range of Sc in Chinese Brassica was 0~0.015 μmol·m
−2·s
−1, lower than Tr (0~0.20 μmol·m
−2·s
−1), and the Sc was more sensitive to the soil moisture [
14]. The transpiration in Chinese Brassica, compared with the CWSI, does not change abruptly with the stomatal closure [
5], implying the retardation of variation in Tr that eventually influenced the correlation between Tr and the CWSI. Additional details have been presented by Ben et al. [
59] and Agam et al. [
31]. They mentioned the potential effect of clouds and low radiation on transpiration. The daily Sc variance between T3 and T4 was distinct, whereas there was no significant difference between T4 and T5. Many CWSI values in T4 and T5 were over 0.4. From the producer’s perspective, Chinese Brassica might suffer from water stress when the CWSI value exceeds 0.4, and the level of scheduled irrigation is also considerable. The correlation between the CWSI and irrigation volume could be analyzed in a future study to achieve precise water control.
Our study focuses on simulating the canopy temperature of Chinese Brassica using a machine learning algorithm (i.e., random forest), which has been used to predict biological parameters in agriculture well, such as the crop yield of cotton [
44], the leaf chlorophyll content of wheat [
60], and the leaf nitrogen content of wheat [
61], and the R
2 values were over 0.9. This research, along with our study, presented the generated predictions for different crops and biological parameters based on a random forest. When simulating, input data are easier to obtain than the targeted data. However, suitable parameters need to be found to obtain better performance, which is usually time-consuming.