Next Article in Journal
Water Disinfection by Immobilized Photosensitizers
Previous Article in Journal
At-Site Assessment of a Regional Design Criterium for Water-Demand Peak Factor Evaluation

Water 2019, 11(1), 25; https://doi.org/10.3390/w11010025

Article
Insentek Sensor: An Alternative to Estimate Daily Crop Evapotranspiration for Maize Plants
1
Key Laboratory of Crop Water Use and Regulation, Ministry of Agriculture and Rural Affairs, Farmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
2
Xuchang Experiment and Extension Station of Farmland Water Conservancy, Xuchang 461000, China
*
Authors to whom correspondence should be addressed.
Received: 25 November 2018 / Accepted: 17 December 2018 / Published: 24 December 2018

Abstract

:
Estimation of ground-truth daily evapotranspiration (ETc) is very useful for developing sustainable water resource strategies, particularly in the North China Plain (NCP) with limited water supplies. Weighing lysimetry is a well-known approach for measuring actual ETc. Here, we introduced an alternative to lysimetry for ETc determination using Insentek sensors. A comparison experiment was conducted for maize plants at Xuchang Irrigation Experiment Station, in the NCP, in 2015 and 2016. Insentek ETc was evaluated using data on clear days and rainy days independently. We found that daily ETc increased gradually from VE (emergence) to VT (tasseling) stages, peaked at the R1 (silking) stage with the highest value of 7.8 mm·d−1, and then declined until maturity. On average, cumulative total of lysimetric ETc was 19% higher than that of Insentek ETc. The major depth of soil water extraction might be 60 cm for maize plants on lysimeters according to soil water depletion depth monitored by Insentek sensors. Daily ETc significantly related to soil water content (SWC) in topsoil (0–30 cm) in an exponential function (coefficients of determination (R2) = 0.32–0.53), and to precipitation (Pre) in a power function (R2 = 0.84–0.87). The combined SWC (0–30 cm)–Pre–ETc model may offer significant potential for accurate estimation of maize ETc in semi-humid environment of the NCP.
Keywords:
water balance; automatic soil moisture record; lysimetry; North China Plain; Zea mays L.

1. Introduction

The North China Plain (NCP) produces 58 and 33% of the domestic wheat and maize yields in China, ensuring China’s food security [1]. In 2016, maize (Zea mays L.), with a total yield of 220 million tons, was planted on a 36 million hectare across China, making a 49% contribution to domestic increase in grain production [2]. Maize is also known as one of the highest water-using crops in different regions [3,4]. Thanks to a continent monsoon climate, annual precipitation falls between 400 and 600 mm in the NCP, with 65% of precipitation occurring in maize seasons [5], while annual evapotranspiration ranges from 800 to 900 mm: 50–100% higher than precipitation [6]. For decades, crop water consumption has been being supplemented by extracting the declining groundwater in the NCP at a speed exceeding aquifer recharge [7]. In the future, immense volumes of groundwater will be required for the agricultural sector with an ever-growing population [8]. Nevertheless, agronomic practices, such as cultivar updating and irrigation optimizing, have been made to extend the life of the aquifer. Consequently, only a 10% increase in crop evapotranspiration (ETc) contributed to a 50% yield increase over the past two decades [9]. Of those practices, ETc-based irrigation scheduling is considered a powerful tool to decrease long-term aquifer extraction [10].
In general, ETc is the process where water is consumed through soil evaporation and plant transpiration during the water cycling [11]. It has always been considered as an equivalent to crop water use [12]. Water balance equation is the most widely used method to estimate ETc. In the equation, ETc stands for water loss from soil surface and plants whereas precipitation and irrigation represents water input. Water-saving strategies largely depend on the monitoring and controlling of ETc [5,6]. There are numerous methods available for estimating ETc [12,13,14,15], among which weighing lysimetry is normally used as a standard means [16]. However, its construction is expensive and its operation requires professional personnel [17]. This restricts the popularity of lysimetry. Alternatives should be developed to facilitate ETc monitoring. ETc estimation based on the oven-drying method is simple; however, it is laborious and time-consuming [4]. Another way is to adopt the neutron probe method [18]. This enables the estimation of ETc regardless of crop types and soil properties. However, this method usually needs a span of time to measure soil water content (SWC), and is difficult to estimate daily or sub-daily ETc [19]. Another approach for ETc estimation is remote sensing technology. It enables us to use satellite observations to estimate ETc at a global and regional scale [20]. However, studies have shown that different algorithms of remote sensing models for ETc estimation have strong divergence [21]. Usually, the algorithms to estimate ETc have been based on a surface energy budget using thermal infrared data, which required plenty of ground-based measurements and were affected by cloud contamination, providing uncertainties to the ETc estimates [21,22]. Therefore, a real-time ground-truth monitoring of ETc is needed.
The Insentek sensor (Beijing Oriental Ecological Technology Ltd., Co., Beijing, China) is an emerging technology that can automatically monitor soil moisture data hourly or sub-hourly (Figure 1). It is an apparatus for real-time soil moisture monitoring powered by solar energy. Insentek sensor allows the determination of daily changes in soil water storage (SWS), making the calculation of daily ETc possible. Our previous study showed that root mean square error (RMSE) of SWC between the Insentek sensor and the oven-dry method was 0.927 cm3·cm−3, and relative prediction deviation (RPD) was 7.99 for silt loamy soils, indicating Insentek sensor is a reliable tool to represent real SWC values (Table 1). The latest figure have shown that there have been more than 15,000 sensors already installed across China, including remote areas such as Tibet (personal communication). An ETc monitoring network that covers the whole country has been formed. Through checking the year-round data, Insentek sensors have showed better continuity and stability than other soil moisture techniques (e.g., time-domain reflectometry, neutron probe, and oven-drying etc.). In this study, weighing lysimeters were adopted to continuously monitor ETc of summer maize along with Insentek sensors.
Until now, the degree to which ETc from Insentek sensor method represents that of the lysimetry has not been well tested. In this study, we started the comparison work between the two. Moreover, precipitation was recorded by a nearby weather station to analyze the relationship between ETc and precipitation. We hypothesized that ETc from the Insentek sensor method was similar to that of lysimetry, and that ETc was significantly related to precipitation and soil moisture. The objectives of this study were to evaluate the efficacy of Insentek sensor method using lysimetric data, and to quantify the relationships among ETc, SWC and precipitation for maize plants in the NCP.

2. Materials and Methods

2.1. Site Description

The experiment was carried out at the Xuchang Irrigation Experiment Station, North China Plain, in 2015 and 2016 (34°08′25″ N, 113°59′04″ E, a.s.l. 71 m) (Figure 2). Four sets of large-scale weighing lysimeters (2.0 m wide × 2.4 m long × 2.3 m in depth) along with four Insentek sensors were adopted to compare daily ETc. The place had a continent temperate monsoon climate. The soil was a fluvo-aquic soil. Soil characteristics of the lysimeters are presented in Table 2. The bottom 30 cm was filled with a very coarse sand and <3 cm gravels to permit drainage towards lysimeter outlet.
Mean annual precipitation is 640.9 mm, of which 65% falls during the maize growing season. Mean annual temperature is 14.7 °C, and annual sunshine hours are 2280 h [4]. Soil bulk density at the 0–60 cm soil layer was 1.45 g·cm−3 and soil organic matter at the same layer was 16.5 g·kg−1. Available N, P2O5, and K2O at the same layer were 36.5, 23.4, and 219.8 mg·kg−1, respectively [25]. The water table was detected more than 5 m below the soil surface.

2.2. Experimental Design

Crops were grown in a winter wheat (Triticum aestivum L.)-summer maize (Zea mays L.) relay cropping system. The lysimeters were made of steel metal sheets. Total lysimeter weight was approximately 24 t, including the container mass. The lysimeter was built in 2012, and ETc data have been monitored since March, 2014. Insentek sensors were installed at the center of each lysimeter in October, 2014. After three years of natural packing, the lysimeter monolith was regarded to duplicate the natural soil status [26].
The upper 2.0 m depth of soil monolith in each lysimeter was filled with undisturbed monolith. The large-scale weighing lysimeter system contains a main body, load cell, and data logger system (Figure 1). Weighing resolution was ±100 g, equal to ±0.1 mm of water column. The masses of the lysimeters were measured every 30 s, and the data were reported as 30 min means. The depth of lysimeter (2.3 m) permits development of normal rooting and water extraction for summer maize in the NCP [27]. Collecting buckets were suspended from the bottom of lysimeters to hold gravity drainage effluent. Load cells connected to the bucks were adopted to separately measure drainage mass without varying total weight of the lysimeters. Insentek sensors were installed between maize rows at the center of lysimeters. Insentek ETc was computed using water balance equation at daily interval. Lysimetric data are usually noisy due to wind and other external disturbance. The lysimeter noise was separated from signals using a filtering routine [28]. Additionally, biases on lysimeters were controlled by careful management of sowing, fertilization and irrigation.
A popular form of maize seeds (cultivar Pioneer 335) was sown on 5 June 2015 and 7 June 2016 (Figure 3). Maize was planted in a row with a spacing of 50 cm and plant–to–plant with a spacing of 30 cm. After maize plants were thinned, there were 32 plants left per lysimeter, equal to a density of 66,700 plant ha−1. Application rates of fertilizer for each lysimeter were 225 kg·ha−1 N, 180 kg·ha−1 P2O5, and 55 kg·ha−1 K2O, respectively. Diammonium phosphate and potassium sulfate were broadcast as base fertilizer prior to planting. One half amount of urea was applied as base fertilizer before sowing, whereas the rest amount of nitrogen was top-dressed at VT (tasseling) stage. Fertilizer was incorporated into soils to a depth of 20 cm using hand-cultural method. Besides a flood irrigation (55 mm) after maize sowing to guarantee seed germination, no supplemental irrigation was added as rainfall met the crop water requirement in both years. Weeds and pests control was applied according to the local governmental recommendations.

2.3. Data Collection and Measurements

2.3.1. Soil Water Content

Insentek sensors (Beijing Oriental Ecological Technology Ltd., Co., Beijing, China) were used to monitor soil water content (SWC, cm3·cm−3) at 10 cm increment to a depth of 100 cm. The radius of soil volume prospected by the Insentek sensors is 15 cm (Figure 1). The sensor is a wireless soil moisture sensor powered by a rechargeable battery, which was, in turn, charged by a solar panel. In order to evaluate the performance of Insentek sensors, relevant tests were conducted. Our experimental results showed that, compared to the oven-drying method, the Insentek sensor method is a promising tool for monitoring moisture across various soil textures.

2.3.2. Daily Crop Evapotranspiration

Daily lysimetric ETc was determined as the difference between the mass losses and gains on a whole day basis divided by the lysimeter area (4.8 m2), and the density of water (1.0 g·cm−3), converting lysimeter mass in kg to the equivalent depth of water in mm. Daily ETc was calculated using Equation (1):
ET c = Δ SWS + Pre + I R D
where ΔSWS is the daily changes in soil water storage (mm) of lysimeter; Pre is the precipitation (mm); I is the irrigation quota (mm); R is the surface runoff, assumed to be negligible due to flat surface and lysimeter freeboard; and D is the drainage flux, measured by vacuum drainage systems.
Daily ETc estimated using Insentek sensors was calculated on a daily basis using the same water balance as in Equation (1), except the calculation of ΔSWS, which was calculated based on the soil volumetric water content in 0–100 cm depth.

2.3.3. Grain Yield and Water Use Efficiency

At physiological maturity, all maize plants from each lysimeter were sampled. To determine the grain yield, the ears of all plants of maize in each lysimeter were air dried until constant mass, and then the grain was separated, cleaned, and weighed. Grain yield was calculated on a dry-matter basis. Water use efficiency (WUE, kg·ha−1·mm−1) was calculated as the grain yield (kg·ha−1) produced per unit of ETc (mm).

2.3.4. Relationship between ETc and Soil Water Content

To determine the response of ETc to SWC on clear days, an exponential function combined with a quadratic function was used as follows:
ET c = e a + b × SWC + c × SWC 2
where ETc is crop evapotranspiration (mm·d−1) and SWC is soil water content (cm3·cm−3) on clear days; a, b, and c are parameters to be fitted.

2.3.5. Relationship between ETc and Precipitation

Daily ETc on rainy days was restrained by precipitation, especially for moderate to heavy rain. In this study, ETc on rainy days was related to precipitation in a negative power function as follows:
ET c = a × Pre b
where as ETc is crop evapotranspiration (mm·d−1) on rainy days; Pre is precipitation (mm); a and b are function parameters to be fitted.

2.3.6. Meteorological Data

A weather station was built adjacent to the lysimeter fields. The net radiation, air temperature, relative humidity, and wind speed and direction were recorded at 2.0 m height over mowed grass on an hourly basis.

2.3.7. Evaluation of Insentek Data

Performance of the Insentek sensor method to estimate daily ETc was evaluated using a combination of graphical and statistical methods. The evaluation factors include slope and intercept for linear regression between the lysimetric and Insentek data, coefficient of determination (R2), root mean square error (RMSE), and relative prediction deviation (RPD). The R2 describes the proportion of variance in lysimetric ETc explained by Insentek data. The RMSE can be used to investigate the differences between lysimetric and Insentek values. The RMSE is calculated using Equation (4):
RMSE = i = 1 n ( x I n s e n x l y s i ) 2 / n
where RMSE is the root mean square error; xInsen and xlysi are corresponding ETc values estimated based on Insentek and lysimetry, respectively; and n is the number of values evaluated. The smaller the RMSE values are, the more accurate the Insentek data turn out to be.
The RPD is used to indicate the reliability of Insentek data. RPD is calculated using Equation (5):
RPD = STDEV ( x l y s i ) / RMSE
where RPD is the relative prediction deviation and STDEV is the standard deviation of lysimetric ETc values. RPD ≥ 2.0 indicates Insentek data are reliable; 1.4 < RPD < 2.0 means the data are feasible but need to be improved; and RPD ≤ 1.4 indicates the data are unreliable [23].

2.4. Statistical Analysis

Data were analyzed using an analysis of variance with Statistical Analysis Software (version 19.0, SPSS Inc., Chicago, IL, USA). Significance was declared at the probability level of 0.05, unless otherwise stated. Relationships among ETc, SWC, and precipitation were analyzed by means of the Levenberg–Marquardt Algorithm. Figures were plotted using Original Pro 9.1 (Origin Lab Corporation, Northampton, MA, USA).

3. Results

3.1. Dynamics of Insentek Soil Moisture

Soil water content (SWC), monitored by Insentek sensors, was between 10.6 and 37.2 cm3·cm−3 in 0–30 cm depth, 23.4 and 38.4 cm3·cm−3 in 30–60 cm depth, and 27.3 and 38.8 cm3·cm−3 in 60–100 cm depth in both seasons (Figure 4). Precipitation mainly increased SWC for 0–60 cm depth. On average, SWC increased by 33%, 11%, 10%, 8%, 5%, and 4% for 0–10 cm, 10–20 cm, 20–30 cm, 30–40 cm, 40–50 cm, and 50–60 cm soil depths, respectively, by precipitation >15 mm. A limited effect on SWC was observed in 60–80 cm depth and minor effect in 80–100 cm depth. Except early vegetative phase before days after sowing (DAS) 40 (mid of July), a precipitation affect for SWC was discovered when soil depth was below 80 cm. In the rooted soil layers (0–60 cm), SWC gradually declined from DAS 40 to maturity, whereas in 60 cm below soil layers, it remained stable, indicating the major depth of soil water extraction for lysimeter maize might be 60 cm.

3.2. Daily Crop Evapotranspiration

Precipitation was 4% and 6% above normal in the maize growing seasons of 2015 and 2016. No supplemental irrigation was added after maize emergence (VE); thus, soil moisture was mainly affected by precipitation and ETc. Lysimeter and Insentek produced similar trends in daily ETc (Figure 5). Lower ETc rates were observed when precipitation >15 mm. However, ETc increased markedly after precipitation occurrence due to an increase in soil water evaporation. On average, peak ETc rates, usually occurring in intermittent periods of precipitation, exceeded 2.5 and 4.5 mm·d−1 over vegetative and reproductive phases of maize. However, mean peak lysimetric ETc rates were 29% higher than Insentek ETc (4.87 vs. 3.83 mm·d−1) in both growing seasons. The highest ETc rates occurred on days after sowing (DAS) 53 (VT) in 2015, and on DAS 43 (V12) in 2016, respectively. Daily ETc decreased appreciably and was kept to a relatively lower level until maturity. Throughout the seasons, variations in daily lysimetric and Insentek ETc basically changed with dynamics of daily mean temperature, except the period from DAS 55 (VT) to DAS 85 (R4) in 2016 with ongoing overcast and rain, giving rise to lower ETc than was expected.

3.3. Cumulative Crop Evapotranspiration

On average, cumulative Insentek ETc was 310 mm with an average of 2.63 mm·d−1 in both years (Figure 6). Lysimeter produced 19% higher cumulative ETc than did Insentek. Cumulative lysimetric ETc became constantly higher after DAS 40 (V10), and the discrepancies between them gradually grew from then on. Cumulative ETc increased with the accumulation of air temperature in a positive linear relationship. Lysimetric ETc showed a rapid response to precipitation with an average increase in ETc from 0.24 to 7.42 mm·d−1 during the following 5 days after heavy rainfall (>50 mm), whereas the response of Insentek ETc lagged behind lysimetric ETc, with a smaller increase from 0.89 to 5.87 mm·d−1.

3.4. Responses of ETc to Soil Water Content

As precipitation >15 mm noticeably reduced ETc, and increased SWC, only data collected on clear days were selected for correlation analysis between ETc and SWC. Our results showed that, over the two years, ETc was significantly correlated with SWC in 0–30 cm depth; however, it was not correlated with the SWC below 30 cm depth (Table 3). A detailed study showed that ETc increased with SWC in 0–30 cm depth in an exponential function combined with a quadratic function (Figure 7). Effects of SWC at 10 cm interval to 30 cm depth accounted for 32–59% (R2) of ETc variations, and the contributions of SWC to ETc declined from 10 to 30 cm soil layer.

3.5. Responses of ETc to Precipitation

Both Insentek and lysimetric ETc was significantly reduced on rainy days. Our results showed that precipitation explained 84–87% variations of daily ETc on rainy days (Figure 8a). Moreover, precipitation, as an independent factor controlling ETc when it rains, reduced daily ETc in a negative power function. Compared to ETc on clear days, precipitation averagely reduced lysimetric ETc by 72%, and Insentek ETc by 54%, indicating a faster response of lysimetric ETc to precipitation than Insentek ETc.

3.6. Goodness of Fit

There exists a significant positive linear correlation between Insentek and lysimetric ETc data, with a slope of 0.68–0.69, and an intercept of 0.48–0.51, taking Insentek ETc as y values (Figure 8b). A test for goodness of fit showed that the RMSE values were less than 0.87 mm, and RPD values were around 2.0, indicating the Insentek sensors method is, to some extent, reliable for predicting real ETc of summer maize in the NCP (Table 4). Slopes smaller than 1.0 indicated that Insentek ETc was generally smaller than lysimetric ETc, though they have similar ETc trends. Thus, improvements should be considered to enhance Insentek sensors accuracy. Those measures include, but are not limited to, using longer Insentek sensors (e.g., 200 cm long) to represent SWC dynamics in deeper layers.

3.7. Grain Yield and Water Use Efficiency

In the NCP, maize is usually planted after the harvest of wheat in a winter wheat-summer maize double-cropping system. Due to soil water extraction by wheat and scarcity of rainfall, initial soil water storage (SWS0) prior to maize sowing was extremely low (Table 5). This necessitated an irrigation immediately after sowing to guarantee seed germination. Grain yields were on average 15% lower than the yield (9165 kg·ha−1) from surrounding field experiment, probably due to soil compaction and saline stress in a lysimetric environment. Because the Insentek sensor stood for 100 cm soil depth, whereas lysimeter for 200 cm, as well as lower SWC in upper layers, the Insentek SWS0 was extremely lower in 2015 and 2016. At harvest, lysimetric SWSh was 19% and 17% higher than Insentek SWSh. Consequently, lysimetry reported a 20% and 17% higher ETc in 2015 and 2016, resulting in a 16% and 14% lower WUE than the Insentek method.

4. Discussion

4.1. Advantage and Disadvantage of Lysimetry and Insentek Method

The Insentek sensor method had several advantages over the lysimetry method, including ease of deployment, lower initial expense, and wireless transmission of real-time data [4,25]. In this study, Insentek sensors were installed inside lysimeter soils, allowing to record daily dynamics of SWC to a depth of 100 cm. Previous studies indicated SWC measurement depth for ETc estimation should extend to the major depth of soil water extraction, which was flexible with crop types and weather conditions [11,29]. In Bushland, Texas, in a semiarid climate, it was found that 100 cm long neutron probe access tubes were sufficient to represent lysimetric ETc on an irrigated cotton field [18,19]. In this study, maize plants were grown under a semi-humid climate with seasonal precipitation ≥450 mm. Initially, the SWC in the upper layers was extremely low at maize sowing, giving rise to discrepancy between Insentek and lysimetric ETc, which was calculated based on different depths. This was probably attributable to the water uptake depth for wheat exceeding 100 cm under traditional border irrigation, and low water availability at wheat harvest in the NCP [30,31]. Maize crops usually have a shallower rooting system due to the high frequency of precipitation [32,33]. Nevertheless, according to soil water depletion depth monitored by Insentek sensors, the major depth of water extraction of maize on lysimeters might be 60 cm. Therefore, ETc rates simulated by a 100 cm long Insentek sensor can be assumed as representative of actual rates. Additionally, compared with the eddy covariance and remote sensing methods, ETc estimates by a network of soil moisture sensors were considered as viable source of ground truth ETc data that were convincing both in theory and practice [34]. Previous studies have shown that ETc determined by the neutron probe method can represent lysimetric ETc, however, it wasn’t able to calculate ETc on a daily or sub-daily scale due to a lack of automatic measurement [35,36]. This shortcoming can be overcome by Insentek sensors.
However, there still exist limitations for Insentek sensors to simulate ETc. For example, the Insentek method led to weaker ETc responses to precipitation compared to the lysimetry method [25]. This led to a bias in ETc simulation on rainy days. Nevertheless, Insentek method had merits to reduce potential ETc errors induced by animal invasion and other disturbance factors. Although response of Insentek ETc to precipitation lagged behind lysimetry, it increased stability of ETc estimation due to a good performance in keeping out outside disturbance factors.

4.2. Cumulative Evapotranspiration Responses to Soil Water Content

Cumulative ETc varied from 309 to 372 mm for summer maize in 2015 and 2016. The values were consistent with previous ETc total for summer maize in the NCP [37], but were up to 50% lower than that of irrigated spring maize in semi-arid region of northern China [38]. Additionally, the values were up to 100% lower than reported for the southern High Plains in the USA [3]. The lower ETc rates could be attributed to short growth duration (<90 days) and the almost rain-fed condition for summer maize in the NCP. In this study, water extraction depth mainly concentrated on 0–60 cm soil layer. This was probably due to favorable climate conditions such as adequate precipitation. Our study showed that daily ETc had a positive exponential relationship with soil moisture in topsoil (0–30 cm). One of the reasons might be that the largest variations in soil moisture occurred in topsoil because the shallow zone had the largest root density and water extraction by maize plants [19]. Some used neutron probe method to estimate ETc variations on a weekly basis; however, the Insentek sensor method had advances in automatic records of SWC, and was safe from radiation. Significant correlation between ETc and near surface SWC can be verified by the finding that only SWC in the upper 30 cm of soil significantly varied in irrigated cotton fields at Bushland, Texas [39].

4.3. Comparison of Insentek Method to Other Methods

Although prior studies concluded that ETc based on a 100 cm deep access tube was sufficient to represent lysimeter ETc [40], there was still a 14% under-estimate of mean daily ETc by the Insentek sensor method compared to the lysimetry method in the present study. One probable approach to improve the accuracy of Insentek ETc was to adopt a 200 cm long probe equivalent to lysimeter depth, which will be conducted in further study. A comparison of ETc calculated using lysimetry and neutron probe method has been conducted [35]. Some concluded that there was no difference between them [18,41,42], while others found that ETc from lysimeters was greater than that of the oven-drying or neutron probe method [43,44]. Using remotely sensed ETc, it was found that the ETc produced differences of around 20–45% with the ground measurements using large aperture scintillometer systems during the growing season, and the model performance deteriorated for cloudy days [20]. Larger errors of daily ETc estimates were associated with clouds and rain events, which affected satellite normal mapping, adding additional data noise [45]. Compared with remote sensing methodology, the Insentek method produced smaller differences. Compared with soil coring and neutron probe methods, Insentek sensors recorded soil moisture hourly without labor cost, directly improving the convenience and efficiency of ETc estimation.

4.4. Simulating Crop Evapotranspiration on Rainy Days

Using a remote sensing ETc model, it was found that ETc had a negative correlation with precipitation in areas where the growing-season precipitation was 250 mm [21]. However, in arid areas with precipitation less than 200 mm, the correlation becomes weaker due to insufficient precipitation and a more complex water-heat flux interaction [46]. It should be noticed that those correlations by remote sensing were calculated mostly on an annual basis [47]. It did not reflect the instantaneous response of ETc to daily precipitation as was done in this study. Through analysis, we found that precipitation with an amount ≥15 mm significantly inhibited daily ETc. Thus, ETc data on rainy days were independently used to analyze the correlation. Our results showed that simulated ETc were in good agreement with the actual ETc on rainy days. Both lysimetric and Insentek ETc was related to precipitation in a negative power function. The effect of precipitation on Insentek ETc was smaller compared to lysimetric ETc, indicating a weaker response to precipitation. A probable reason was that Insentek ETc was estimated using soil water storage changes after rain-water infiltration, which needed time to finish the process [4].

5. Conclusions

The North China Plain (NCP) produces one third of China’s maize production (≈220 million tons per year), and is one of the most productive granaries in China. In this work, an alternative based on Insentek soil moisture data to estimate ETc was evaluated in comparison with lysimetry in the NCP. Insentek ETc had a significant linear correlation (R2 = 0.83–0.86) to lysimetric ETc, with RMSE < 0.87 mm, and RPD < 2.1, indicating that Insentek sensors are efficient tools in estimating maize ETc in the NCP with acceptable accuracy. Since precipitation and SWC play an important role in water balance calculation for ETc, responses of ETc to them were analyzed on different weather days. The results indicated that ETc significantly relates to precipitation (Pre) on rainy days in a power function (R2 = 0.84–0.87). On clear days, ETc significantly relates to SWC in topsoil (0–30 cm) in an exponential function (R2 = 0.32–0.53). The combined SWC (0–30 cm)–Pre–ETc model may offer significant potential for predicting ETc. Our method provides a reference for reducing lysimetric data noise and may be useful to the study on responses of ETc to climatic change in the NCP.

Author Contributions

Conceptualization, A.Q. and J.X.; methodology, D.N.; software, Z.L.; validation, B.S., and B.Z.; formal analysis, A.Q.; investigation, D.N.; resources, A.D.; data curation, A.D.; writing—original draft preparation, A.Q.; writing—review and editing, D.N.

Funding

This research was funded by the National Key Research and Development Program (2017YFD0301102), the China Agriculture Research System (CARS–02), the Agricultural Science and Technology Innovation Program (ASTIP), and the Central Public–interest Scientific Institution Basal Research Fund (Farmland Irrigation Research Institute, CAAS, FIRI2017–04).

Acknowledgments

The authors sincerely thank the three anonymous reviewers who made valuable comments on this paper.

Conflicts of Interest

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study, in the collection, analyses, or interpretation of data.

References

  1. Liu, S.; Mo, X.; Lin, Z.; Xu, Y.; Ji, J.; Gang, W.; Richey, J. Crop yield responses to climate change in the Huang-Huai-Hai Plain of China. Agric. Water Manag. 2010, 97, 1195–1209. [Google Scholar] [CrossRef][Green Version]
  2. Wang, X.; Ren, Y.; Zhang, S.; Chen, Y.; Wang, N. Applications of organic manure increased maize (Zea mays L.) yield and water productivity in a semi-arid region. Agric. Water Manag. 2017, 187, 88–98. [Google Scholar] [CrossRef]
  3. Howell, T.; Evett, S.R.; Tolk, J.A.; Schneider, A.D.; Steiner, J.L. Evapotranspiration of corn, Southern High Plains. In Proceedings of the International Conference in Evapotranspiration and Irrigation Scheduling, San Antonio, TX, USA, 3–6 November 1996; pp. 158–165. [Google Scholar] [CrossRef]
  4. Qin, A.; Ning, D.; Liu, Z.D.; Sun, B.; Zhao, B.; Xiao, J.; Liu, Z.G. Evaluating responses of crop water use, soil water storage and infiltration to precipitation using Insentek probes. In Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms, Chengdu, China, 25–26 March 2018; pp. 353–356. [Google Scholar] [CrossRef]
  5. Liu, Z.D.; Qin, A.; Zhang, J.; Sun, J.; Ning, D.; Zhao, B.; Xiao, J.; Liu, Z.G.; Duan, A. Maize yield as a function of water availability across precipitation years in the North China Plain. Crop Sci. 2017, 57, 2226–2237. [Google Scholar] [CrossRef]
  6. Liu, Z.; Qin, A.; Zhao, B.; Ataulkarim, S.T.; Xiao, J.; Sun, J.; Ning, D.; Liu, Z.; Nan, J.; Duan, A. Yield response of spring maize to inter-row subsoiling and soil water deficit in northern China. PLoS ONE 2016, 11, e0153809. [Google Scholar] [CrossRef]
  7. Fang, Q.; Ma, L.; Green, T.; Yu, Q.; Wang, T.; Ahuja, L. Water resources and water use efficiency in the North China Plain: Current status and agronomic management options. Agric. Water Manag. 2010, 97, 1102–1116. [Google Scholar]
  8. Rockström, J.; Barron, J. Water productivity in rainfed systems: Overview of challenges and analysis of opportunities in water scarcity prone savannahs. Irrig. Sci. 2007, 25, 299–311. [Google Scholar] [CrossRef]
  9. Zhang, X.; Chen, S.; Pei, D.; Liu, M.; Sun, H. Improved water use efficiency associated with cultivars and agronomic management in the North China Plain. Agron. J. 2005, 97, 783–790. [Google Scholar] [CrossRef]
  10. Marek, G.; Gowda, P.; Evett, S.; Baumhardt, R.; Brauer, D.; Howell, T.; Marek, T.; Srinivasan, R. Estimating evapotranspiration for dryland cropping systems in the semiarid Texas High Plains using SWAT. J. Am. Water Resour. Assoc. 2016, 52, 298–314. [Google Scholar] [CrossRef]
  11. Evett, S.; Kustas, W.; Gowda, P.; Prueger, J.; Howell, T. Overview of the Bushland evapotranspiration and agricultural remote sensing experiment 2008 (BEAREX08): A field experiment evaluating methods quantifying ET at multiple scales. Adv. Water Resour. 2012, 50, 4–19. [Google Scholar] [CrossRef]
  12. Zhang, X.; Chen, S.; Sun, H.; Wang, Y.; Shao, L. Water use efficiency and associated traits in winter wheat cultivars in the North China Plain. Agric. Water Manag. 2010, 97, 1117–1125. [Google Scholar] [CrossRef]
  13. Ford, C.R.; Hubbard, R.M.; Kloeppel, B.D.; Vose, J.M. A comparison of sap flux-based evapotranspiration estimates with catchment-scale water balance. Agric. For. Meteorol. 2007, 145, 176–185. [Google Scholar] [CrossRef]
  14. Zhang, B.; Kang, S.; Li, F.; Zhang, L. Comparison of three evapotranspiration models to Bowen ratio-energy balance method for a vineyard in an arid desert region of northwest China. Agric. For. Meteorol. 2008, 148, 1629–1640. [Google Scholar] [CrossRef]
  15. Zhang, Z.; Tian, F.; Hu, H.; Yang, P. A comparison of methods for determining field evapotranspiration: Photosynthesis system, sap flow, and eddy covariance. Hydrol. Earth Syst. Sci. 2014, 18, 1053–1072. [Google Scholar] [CrossRef]
  16. Lu, Y.; Ma, D.; Chen, X.; Zhang, J. A simple method for estimating field crop evapotranspiration from pot experiments. Water 2018, 10, 1823. [Google Scholar] [CrossRef]
  17. Schneider, A.; Howell, T.; Moustafa, A.; Evett, S.; Abou-Zeid, W. A simplified weighing lysimeter for monolithic or reconstructed soils. Appl. Eng. Agric. 1998, 14, 267–273. [Google Scholar] [CrossRef]
  18. Evett, S.; Schwartz, R.; Tolk, J.; Howell, T. Soil profile water content determination: Spatiotemporal variability of electromagnetic and neutron probe sensors in access tubes. Vadose Zone J. 2009, 8, 926–941. [Google Scholar] [CrossRef]
  19. Evett, S.; Tolk, J.; Howell, T. A depth control stand for improved accuracy with the neutron probe. Vadose Zone J. 2003, 2, 642–649. [Google Scholar] [CrossRef]
  20. Song, L.; Liu, S.; Kustas, W.; Nieto, H.; Sun, L.; Xu, Z.; Skaggs, T.; Yang, Y.; Ma, M.; Xu, T.; et al. Monitoring and validating spatially and temporally continuous daily evaporation and transpiration at river basin scale. Remote Sens. Environ. 2018, 219, 72–88. [Google Scholar] [CrossRef]
  21. Gu, C.; Ma, J.; Zhu, G.; Yang, H.; Zhang, K.; Wang, Y.; Gu, C. Partitioning evapotranspiration using an optimized satellite-based ET model across biomes. Agric. For. Meteorol. 2018, 259, 355–363. [Google Scholar] [CrossRef]
  22. Elnmer, A.; Khadr, M.; Kanae, S.; Tawfik, A. Mapping daily and seasonally evapotranspiration using remote sensing techniques over the Nile delta. Agric. Water Manag. 2019, 213, 682–692. [Google Scholar] [CrossRef]
  23. Chen, H.; Zhao, G.; Zhang, X.; Wang, R.; Sun, L.; Chen, J. Improving estimation precision of soil organic matter content by removing effect of soil moisture from hyperspectra. TCSAE 2014, 30, 91–100. [Google Scholar] [CrossRef]
  24. National Standards of China (NSC). China Soil Classification and Code (GB/T 17296-2009); NSC: Beijing, China, 2009. [Google Scholar]
  25. Qin, A.; Ning, D.; Liu, Z.; Zhao, B.; Liu, Z.; Duan, A.; Xiao, J. Simulating crop evapotranspiration using Insentek soil moisture for maize plants on lysimeters. In Proceedings of the 2018 International Conference on Modeling, Simulation and Analysis, Wuhan, China, 7–8 February 2018; pp. 311–315. [Google Scholar]
  26. Hillel, D. Introduction to Environmental Soil Physics; Elsevier Academic Press: Amsterdam, The Netherlands, 2004. [Google Scholar]
  27. Wu, Y.; Du, T.; Li, F.; Li, S.; Ding, R.; Tong, L. Quantification of maize water uptake from different layers and root zones under alternate furrow irrigation using stable oxygen isotope. Agric. Water Manag. 2016, 168, 35–44. [Google Scholar] [CrossRef]
  28. Peters, A.; Nehls, T.; Schonsky, H.; Wessolek, G. Separating precipitation and evapotranspiration from noise—A new filter routine for high-resolution lysimeter data. Hydrol. Earth Syst. Sci. 2014, 18, 14645–14674. [Google Scholar] [CrossRef]
  29. Maayar, M.; Price, D.; Chen, J. Simulating daily, monthly and annual water balances in a land surface model using alternative root water uptake schemes. Adv. Water Resour. 2009, 32, 1444–1459. [Google Scholar] [CrossRef]
  30. Lv, G.; Kang, Y.; Lan, L.; Wan, S. Effect of irrigation method on root development and profile soil water uptake in winter wheat. Irrig. Sci. 2010, 28, 387–398. [Google Scholar] [CrossRef]
  31. Liu, Z.D.; Qin, A.; Duan, A.; Zhang, J.; Sun, J.; Ning, D.; Zhao, B.; Mi, Z.; Liu, Z.G. Soil moisture and crop evapotranspiration forecast for winter wheat based on weather information in North China Plain. In Proceedings of the 2nd International Conference on Modelling, Simulation and Applied Mathematics, Bangkok, Thailand, 26–27 March 2017; pp. 295–299. [Google Scholar] [CrossRef]
  32. Gaur, N.; Mohanty, B.P.; Kefauver, S.C. Effect of observation scale on remote sensing based estimates of evapotranspiration in a semi-arid row cropped orchard environment. Precis. Agric. 2017, 18, 762–778. [Google Scholar] [CrossRef]
  33. Soylu, M.E.; Loheide, S.P.I.; Kucharik, C.J. Effects of root distribution and root water compensation on simulated water use in maize influenced by shallow groundwater. Vadose Zone J. 2017, 16, 1–15. [Google Scholar] [CrossRef]
  34. Evett, S.; Schwartz, R.; Lascano, R.; Pelletier, M. In-soil and down-hole soil water sensors: Characteristics for irrigation management. In Proceedings of the 5th Decennial National Irrigation Symposium, Phoenix, AZ, USA, 5–8 December 2010. [Google Scholar]
  35. Tolk, J.; Evett, S. Lysimetry versus neutron moisture meter for evapotranspiration determination in four soils. Soil Sci. Soc. Am. J. 2009, 73, 1693–1698. [Google Scholar] [CrossRef]
  36. Oberholzer, S.; Prasuhn, V.; Hund, A. Crop water use under Swiss pedoclimatic conditions—Evaluation of lysimeter data covering a seven-year period. Field Crops Res. 2017, 211, 48–65. [Google Scholar] [CrossRef]
  37. Zhang, X.; Chen, S.; Sun, H.; Shao, L.; Wang, Y. Changes in evapotranspiration over irrigated winter wheat and maize in North China Plain over three decades. Agric. Water Manag. 2011, 98, 1097–1104. [Google Scholar] [CrossRef]
  38. Li, Y.; Cui, J.; Zhang, T.; Zhao, H. Measurement of evapotranspiration of irrigated spring wheat and maize in a semi-arid region of north China. Agric. Water Manag. 2003, 61, 1–12. [Google Scholar] [CrossRef]
  39. Alfieri, J.; Kustas, W.; Prueger, J.; Hipps, L.; Evett, S.; Basara, J.; Neale, C.; French, A.; Colaizzi, P.; Agam, N.; et al. On the discrepancy between eddy covariance and lysimetry-based surface flux measurements under strongly advective conditions. Adv. Water Resour. 2012, 50, 62–78. [Google Scholar] [CrossRef]
  40. Evett, S.; Schwartz, R.; Howell, T.; Baumhardt, R.; Copeland, K. Can weighing lysimeter ET represent surrounding field ET well enough to test flux station measurements of daily and sub-daily ET? Adv. Water Resour. 2012, 50, 79–90. [Google Scholar] [CrossRef]
  41. Kustas, W.; Alfieri, J.; Evett, S.; Agam, N. Quantifying variability in field-scale evapotranspiration measurements in an irrigated agricultural region under advection. Irrig. Sci. 2015, 33, 325–338. [Google Scholar] [CrossRef]
  42. Heinlein, F.; Biernath, C.; Klein, C.; Thieme, C.; Priesack, E. Evaluation of simulated transpiration from maize plants on lysimeters. Vadose Zone J. 2017, 16, 1–16. [Google Scholar] [CrossRef]
  43. Yang, F.; Zhang, Q.; Wang, R.; Zhou, J. Evapotranspiration measurement and crop coefficient estimation over a spring wheat farmland ecosystem in the Loess Plateau. PLoS ONE 2014, 9, e100031. [Google Scholar] [CrossRef]
  44. Jiménez-Carvajal, C.; Ruiz-Peñalver, L.; Vera-Repullo, J.; Jiménez-Buendía, M.; Antolino-Merino, A.; Molina-Martínez, J. Weighing lysimetric system for the determination of the water balance during irrigation in potted plants. Agric. Water Manag. 2016, 183, 78–85. [Google Scholar] [CrossRef]
  45. Duan, S.; Li, Z.; Leng, P. A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data. Remote Sens. Environ. 2017, 195, 107–117. [Google Scholar] [CrossRef]
  46. Li, X.; Yang, K.; Zhou, Y. Progress in the study of oasis-desert interactions. Agric. For. Meteorol. 2016, 230, 1–7. [Google Scholar] [CrossRef]
  47. Holzman, M.E.; Carmona, F.; Rivas, R.; Niclos, R. Early assessment of crop yield from remotely sensed water stress and solar radiation data. ISPRS J. Photogramm. 2018, 145, 297–308. [Google Scholar] [CrossRef]
Figure 1. Insentek sensor and design of lysimeter experiment. (a) The structure and principle of an Insentek sensor; (b) lysimeter setup details and installation of an Insentek sensor.
Figure 1. Insentek sensor and design of lysimeter experiment. (a) The structure and principle of an Insentek sensor; (b) lysimeter setup details and installation of an Insentek sensor.
Water 11 00025 g001
Figure 2. Schematic location of the Xuchang Irrigation Experiment Station and the lysimeter and Insentek sensor at the Station.
Figure 2. Schematic location of the Xuchang Irrigation Experiment Station and the lysimeter and Insentek sensor at the Station.
Water 11 00025 g002
Figure 3. Seasonal schedule of crop growth stages of summer maize (VE, emergence; V(n), (n)-leaf stage; VT, tasseling stage; R3, milk stage) at the Xuchang Irrigation Experiment Station. The date in the figure was recorded when more than three quarters (>75%) of the crops developed into the particular growth stage. The number in parentheses is days after sowing.
Figure 3. Seasonal schedule of crop growth stages of summer maize (VE, emergence; V(n), (n)-leaf stage; VT, tasseling stage; R3, milk stage) at the Xuchang Irrigation Experiment Station. The date in the figure was recorded when more than three quarters (>75%) of the crops developed into the particular growth stage. The number in parentheses is days after sowing.
Water 11 00025 g003
Figure 4. Seasonal variations in soil water content (cm3·cm−3) at 10 cm interval to a depth of 100 cm measured by Insentek sensors during maize growing seasons of (a) 2015 and (b) 2016. Vertical drop lines represent daily precipitation.
Figure 4. Seasonal variations in soil water content (cm3·cm−3) at 10 cm interval to a depth of 100 cm measured by Insentek sensors during maize growing seasons of (a) 2015 and (b) 2016. Vertical drop lines represent daily precipitation.
Water 11 00025 g004
Figure 5. Daily evapotranspiration, air temperature, and precipitation during maize growing seasons of (a) 2015 and (b) 2016, at the Xuchang Irrigation Experiment Station, in the North China Plain. Vertical drop lines represent precipitation and solid green lines stand for temperature.
Figure 5. Daily evapotranspiration, air temperature, and precipitation during maize growing seasons of (a) 2015 and (b) 2016, at the Xuchang Irrigation Experiment Station, in the North China Plain. Vertical drop lines represent precipitation and solid green lines stand for temperature.
Water 11 00025 g005
Figure 6. Cumulative evapotranspiration, cumulative air temperature, and precipitation during maize growing seasons of (a) 2015 and (b) 2016, at the Xuchang Irrigation Experiment Station, in the North China Plain.
Figure 6. Cumulative evapotranspiration, cumulative air temperature, and precipitation during maize growing seasons of (a) 2015 and (b) 2016, at the Xuchang Irrigation Experiment Station, in the North China Plain.
Water 11 00025 g006
Figure 7. Relationships between crop evapotranspiration (ETc, mm) and soil water content (SWC, cm3·cm−3) on clear days at 10 cm interval to a depth of 30 cm. (a) ETc related to 10 cm SWC; (b) ETc related to 20 cm SWC; (c) ETc related to 30 cm SWC; (d) ETc related to 0–30 average SWC.
Figure 7. Relationships between crop evapotranspiration (ETc, mm) and soil water content (SWC, cm3·cm−3) on clear days at 10 cm interval to a depth of 30 cm. (a) ETc related to 10 cm SWC; (b) ETc related to 20 cm SWC; (c) ETc related to 30 cm SWC; (d) ETc related to 0–30 average SWC.
Water 11 00025 g007
Figure 8. (a) Relationships between crop evapotranspiration (ETc, mm) and precipitation (Pre, mm) on rainy days fitted to a negative power function. (b) Linear regression between Insentek ETc and lysimetric ETc.
Figure 8. (a) Relationships between crop evapotranspiration (ETc, mm) and precipitation (Pre, mm) on rainy days fitted to a negative power function. (b) Linear regression between Insentek ETc and lysimetric ETc.
Water 11 00025 g008
Table 1. Test for goodness of fit between soil water content (soil water content (SWC), cm3·cm−3) measured using an Insentek sensor and oven-dry method across different soil textures.
Table 1. Test for goodness of fit between soil water content (soil water content (SWC), cm3·cm−3) measured using an Insentek sensor and oven-dry method across different soil textures.
Soil TextureSWC Measured by Insentek SensorGoodness of FitCorrelationSignificance
MaximumMinimumMeanRMSERPDR2p
Sand3.4628.6418.091.0446.780.9970.0001
Silt loam7.2635.1122.710.9277.990.9950.0001
Clay17.7433.7125.350.8975.610.9970.0001
RMSE is root mean square error used to evaluate the differences between estimated and observed SWC; RPD is relative prediction deviation, values measured by Insentek sensor are reliable with RPD ≥ 2.0 [23]; R2 is coefficient of determination; p is probability.
Table 2. Soil physical properties prior to the start of the experiment at the Xuchang experiment station, North China Plain, in 2015.
Table 2. Soil physical properties prior to the start of the experiment at the Xuchang experiment station, North China Plain, in 2015.
Soil Layers
(cm)
Clay
(<0.002 mm)
Silt
(0.002–0.05 mm)
Sand
(>0.05 mm)
Soil Texture 1Wilting PointField CapacitySoil Bulk Density
0–3022%36%42%Silt loam13.427.31.43
30–6024%39%37%Silt loam12.525.81.46
60–10020%41%39%Silt loam12.226.91.43
100–15015%32%53%Sandy loam10.424.11.54
150–20010%22%68%Sandy loam9.422.91.51
1 Soil texture was determined according to the Chinese Soil Classification System [24].
Table 3. Correlation coefficient (R) of evapotranspiration (ETc) related to soil water content at each soil depth.
Table 3. Correlation coefficient (R) of evapotranspiration (ETc) related to soil water content at each soil depth.
Soil Depth (cm)102030405060708090100
Lysimetry0.67 **0.57 **0.53 **0.210.190.170.120.09−0.04−0.12
Insentek0.68 **0.58 **0.52 **0.220.210.050.01−0.11−0.17−0.05
** refers to significant correlation at p < 0.01.
Table 4. Test for linear regression and goodness of fit between Insentek ETc (y) and lysimetric ETc (x) data of summer maize in 2015 and 2016.
Table 4. Test for linear regression and goodness of fit between Insentek ETc (y) and lysimetric ETc (x) data of summer maize in 2015 and 2016.
YearLinear CorrelationGoodness of FitSignificance
SlopeInterceptR2RMSERPDp
20150.68390.48050.82610.8721.9210.001
20160.68840.50850.85710.7762.0610.001
R2 is coefficient of determination; RMSE is root mean square error used to evaluate the differences between estimated and observed ETc; RPD is relative prediction deviation, values measured by Insentek sensor are reliable with RPD ≥ 2.0 [23]; p is probability.
Table 5. Grain yield and estimated soil water storage, crop evapotranspiration, and water use efficiency using the Insentek and lysimetric methods, at the Xuchang Irrigation Experiment Station in 2015 and 2016.
Table 5. Grain yield and estimated soil water storage, crop evapotranspiration, and water use efficiency using the Insentek and lysimetric methods, at the Xuchang Irrigation Experiment Station in 2015 and 2016.
YearTreatmentIrrigation
(mm)
Precipitation
(mm)
Grain Yield
(kg·ha−1)
SWS0 1
(mm)
SWSh 1
(mm)
ETc
(mm)
WUE 3
(kg·ha−1·mm−1)
2015Insentek55434791986 b 2264 b311 b25.4 a
Lysimeter197 a314 a372 a21.3 b
2016Insentek554427670101 b289 b309 b24.8 a
Lysimeter203 a338 a362 a21.2 b
1 SWS0 and SWSh is soil water storage prior to sowing and after harvest of maize, respectively, as estimated by the Insentek and lysimetric method; 2 Different letters stand for significant differences at p < 0.05; 3 WUE is water use efficiency.

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Back to TopTop