Estimation of crop transpiration and its scale effect based on ground and UAV thermal infrared remote sensing images
Introduction
Evapotranspiration (ET), which links the water, energy, and carbon cycles, plays an important role in driving climate patterns (Fisher et al., 2017). In arid and semiarid areas where agriculture relies on irrigation, there exists a serious shortage in water resources, so many water-saving irrigation strategies, such as regulated deficit irrigation (RDI), are popular. Furthermore, irrigated water in arid areas is mainly consumed by ET. Over 90 % of the water absorbed by crops is consumed by transpiration (T), and ET is the foremost variable required for agricultural water management (Zang et al., 2012). Therefore, accurate calculation of ET and T is of great significance for calculating irrigation water demand and realizing precision irrigation to promote water saving in agriculture, especially from a spatial perspective.
Although many researchers have used different approaches to study ET (Anapalli et al., 2018; Delogu et al., 2018; Talsma et al., 2018; Tie et al., 2018; Wu et al., 2017; Yao et al., 2017), the ability to accurately capture its spatiotemporal dynamics from leaves to ecosystems and from seconds to years remains limited (Page et al., 2018). For example, more traditional methods, such as using eddy covariance (EC) and weighing lysimeters, can measure ET at a resolution of seconds to months in different regions (Goulden et al., 1996; Liu et al., 2002), but they cannot reflect the spatial variability of ET. Although satellite remote sensing (RS) technology overcomes the shortcomings of traditional methods because of its spatiotemporal continuity and large-span characteristics, the spatial resolution of satellite images is too coarse to provide the submeter-scale resolution data required for precision agriculture. It is worth mentioning that the pixel scales are larger than some small plots in most agricultural regions (Gowda et al., 2008), and its observation frequency is low, which may result in a particular period of crop growth being overlooked. Therefore, the low spatial and temporal resolution limits the use of satellite imagery in precision agriculture (Khanal et al., 2017).
Handheld thermal imaging systems can be used to estimate ET based on ground RS (Page et al., 2018). Improvements such as the reduction of the thermal infrared (TIR) imaging system’s weight and the increased availability of UAVs make it easier to apply the UAV-based thermal imaging system in agriculture (Gago et al., 2015; Sepúlveda-Reyes et al., 2016; Poblete et al., 2017; Santesteban et al., 2017). UAV RS can collect surface information with high spatiotemporal resolution (Dandois and Ellis, 2013; Faye et al., 2016) and fill the gap between ground measurement and satellite RS estimation. Ground and UAV-based thermal RS can quantitatively characterize differences at the centimeter level for pixels, and such RS meets the requirements of precision agriculture. These types to RS have also been used to estimate the leaf area index, crop water stress index, and ET under different conditions (Banerjee et al., 2018; Hoffmann et al., 2016; Ortega-Farías et al., 2016; Hou et al., 2018; Wang et al., 2017).
With the gradual improvement of ground-UAV-satellite RS systems, images with various spatial resolutions from different sources (TM/ETM+, ASTER, MODIS, AVHRR, Fluke, FLIR, and others) have been used for ET observation. However, when images with a range of resolutions are applied to the same model, the output is inconsistent, which limits the application of ET and its components, especially over heterogeneous landscapes (Wang et al., 2016). Surface heterogeneity is used to describe the complexity of the surface scene, and heterogeneity widely exists in various scales when it comes to land surface. The surface area represented by a pixel can be a mixture of many kinds of ground objects on a heterogeneous surface. In this sense, the pixel scale problem has become increasingly prominent when using images with different resolutions to estimate ET. Recently, many researchers have studied the scale effect on ET estimation that comes from using images with different spatial resolutions based on various RS models (Wu and Li, 2009). There are two main methods: one is to scale a high-resolution image up to a low-resolution image and then compare their estimated ETs, and the other is to invert ET independently using satellite images with different resolutions and then compare them directly, a method that is considered to have practical significance (Wang et al., 2016). For example, McCabe and Wood (2006) used three types of satellite images (ETM+, ASTER, and MODIS) independently to study the scale effect on ET estimation over a small watershed in central Iowa, and their results suggest that high-resolution datasets are better able to describe surface heterogeneity.
According to Ershadi et al. (2013), the accuracy of ET estimation decreases with the increase of pixel scale, and it is greatly influenced by surface properties when models contain aerodynamic resistance. In this regard, the three-temperature (3T) model without aerodynamic resistance has been widely used to estimate ET with promising results (Qiu and Ben-Asher, 2010; Luo et al., 2012; Xiong et al., 2012; Tian et al., 2014; Zhang et al., 2019). For example, Hou et al. (2019) and Tian et al. (2020) used the T measured by the LI-6400 portable photosynthesis measurement system and the stable isotope method to verify the soybean and corn T estimated by the 3T model and ground thermal images with a mean absolute percentage error (MAPE) and a coefficient of determination (R2) of 9.53 % and 0.86–0.88, respectively. In addition, Wang et al. (2016) studied the scale effect on ET estimation that uses aerial photos (3 m) and images from ETM+ (60 m), ASTER (90 m), and MODIS (1000 m) combined with the 3T model, and they verified the estimated ET against EC tower measurements with a MAPE of 12.0 %. However, the scale effect on ET estimation that comes from ground and UAV TIR RS images has not been addressed, especially in heterogeneous farmland.
Different RDIs have various effects on crop growth, such as plant height and leaf area, among other characteristics, thus resulting in different canopy structures. And the transpiration rate of crops varies greatly under different types of deficit irrigation. Since T is the central part of ET during the crop reproductive period, this study mainly estimated crop T using the 3T model and analyzed the scale effect on T estimation under RDI. The objectives are to (1) validate the reliability of T estimated using the 3T model combined with ground and UAV TIR images, (2) use the 3T model to estimate the diurnal T of corn and soybean under different levels of water stress, and (3) compare the temporal and spatial characteristics of T estimated by the 3T model based on ground and UAV TIR images to analyze the scale effect on estimating T that comes from using this method.
Section snippets
Experimental site and design
The experiment was conducted at China Agricultural University’s Shiyanghe Experimental Station during the crop reproductive stage. The experimental station is located in the Liangzhou District, Wuwei City, Gansu province, of Northwest China (37°52′20′′N, 102°50′50′′E). The site’s climate is a typical inland desert climate, with an average annual rainfall, pan evaporation, and air temperature of about 164.4 mm, 2000 mm, and 7.8 °C, respectively.
The measurements focused on two typical crops, corn
Verification with T-ES
The findings indicate that the changes in T-3T and T-ES were consistent, T-3T-UAV was closer to T-ES, and T-3T-Ground was higher than them (Fig. 4 (a)). This may be related to the fact that the EC system underestimates ET due to its unclosed energy (Li et al., 2018). The scatter plot shows that the slope and R2 of the best-fit lines between T-3T-UAV and T-ES were 1.023 and 0.838, and those of the T-3T-Ground and T-ES were 1.114 and 0.883, respectively (Fig. 4 (b)). The error analysis of the
Discussion
TIR RS based on ground and UAV approaches not only overcomes the problem of spatial changes being difficult to spot in traditional ground point observations but also makes up for the shortage of satellite RS with low spatial and temporal resolution. It can better meet the needs of precision agriculture and detect water consumption in farmland. In this study, the daytime variations of T of RDI corn and soybean were estimated using the 3T model combined with ground and UAV TIR images. The 3T
Conclusions
In this study, the transpiration of corn and soybean under RDI was estimated using a method that combines the 3T model with ground and UAV TIR images. The main advantage of this method is that it requires fewer input parameters and simplifies the application of RS. The estimated transpiration was evaluated against the measurements based on the EC system and the stable isotope technique, and the scale effect of estimating T based on ground and UAV TIR images was analyzed. The conclusions are as
Author contributions
Conceptualization, Fei Tian; Methodology, Mengjie Hou, Tong Zhang and Fei Tian; Software, Mengjie Hou; Writing-original draft preparation, Mengjie Hou; Writing-review & editing, Fei Tian, S. Ortega-Farias, C. Riveros-Burgos and Aiwen Lin; Project administration, Fei Tian; Funding acquisition, Fei Tian.
Funding
This work was supported by the International and regional cooperation and exchange projects of the National Natural Science Foundation of China (51961125205) and National Agency for Research and Development (ANID)/PCI (NSFC190013), the financial support provided by the Major Program of the National Natural Science Foundation of China (51790534).
Declaration of Competing Interest
The authors report no declarations of interest.
References (46)
- et al.
Quantifying soybean evapotranspiration using an eddy covariance approach
Agr. Water Manage.
(2018) - et al.
Application of thermal imaging of wheat crop canopy to estimate leaf area index under different moisture stress conditions
Biosyst. Eng.
(2018) - et al.
Multi-scales and multi-satellites estimates of evapotranspiration with a residual energy balance model in the Muzza agricultural district in Northern Italy
J. Hydrol.
(2015) - et al.
High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision
Remote Sens. Environ.
(2013) - et al.
Partitioning evapotranspiration-testing the Craig and Gordon model with field measurements of oxygen isotope ratios of evaporative fluxes
J. Hydrol.
(2013) - et al.
Effects of spatial aggregation on the multi-scale estimation of evapotranspiration
Remote Sens. Environ.
(2013) - et al.
UAVs challenge to assess water stress for sustainable agriculture
Agr. Water Manage.
(2015) - et al.
Evaluation of canopy temperature depression, transpiration, and canopy greenness in relation to yield of soybean at reproductive stage based on remote sensing imagery
Agr. Water Manage.
(2019) - et al.
An overview of current and potential applications of thermal remote sensing in precision agriculture
Comput. Electron. Agr.
(2017) - et al.
Determination of daily evaporation and evapotranspiration of winter wheat and maize by large-scale weighing lysimeter and micro-lysimeter
Agr. Forest Meteorol.
(2002)
Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors
Remote Sens. Environ.
Spatiotemporal dynamics of leaf transpiration quantified with time-series thermal imaging
Agr. Forest Meteorol.
An improved methodology to measure evaporation from bare soil based on comparison of surface temperature with a dry soil surface
J. Hydrol.
Theoretical analysis of a remotely measurable soil evaporation transfer coefficient
Remote Sens. Environ.
High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard
Agr. Water Manage.
Partitioning of evapotranspiration in remote sensing-based models
Agr. Forest Meteorol.
Estimation of evapotranspiration and its partition based on an extended three-temperature model and MODIS products
J. Hydrol.
Use of high-resolution thermal infrared remote sensing and "three-temperature model" for transpiration monitoring in arid inland river catchment
J. Hydrol.
Salinity stress effects on transpiration and plant growth under different salinity soil levels based on thermal infrared remote (TIR) technique
Geoderma
Comparing different methods for determining forest evapotranspiration and its components at multiple temporal scales
Sci. Total Environ.
Is scale really a challenge in evapotranspiration estimation? A multi-scale study in the Heihe oasis using thermal remote sensing and the three-temperature model
Agr. Forest Meteorol.
Estimation of transpiration and canopy cover of winter wheat under different fertilization levels using thermal infrared and visible imagery
Comput. Electron. Agr.
Characterizing the footprint of eddy covariance system and large aperture scintillometer measurements to validate satellite-based surface fluxes
IEEE Geosci. Remote. Sens. Lett.
Cited by (13)
Insights into chickpea (Cicer arietinum L.) genotype adaptations to terminal drought stress: Evaluating water-use patterns, root growth, and stress-responsive proteins
2024, Environmental and Experimental BotanyEnhancing leaf area index and biomass estimation in maize with feature augmentation from unmanned aerial vehicle-based nadir and cross-circling oblique photography
2023, Computers and Electronics in AgricultureCalibrating UAV thermal sensors using machine learning methods for improved accuracy in agricultural applications
2023, Infrared Physics and TechnologyEstimation of sugar content in sugar beet root based on UAV multi-sensor data
2022, Computers and Electronics in AgricultureCitation Excerpt :UAV (Unmanned aerial vehicle) has become one of the most popular methods to obtain crop canopy features due to its speed, flexibility, low cost and high temporal and spatial resolution (Maes and Steppe, 2019; Feng et al., 2021). These canopy features obtained by UAV include spectral, structural, and thermal features and have been used in the estimation of various crop physical (Yan et al., 2019; Che et al., 2020), physiological (Zhu et al., 2020; Yang et al., 2021), and biochemical metrics (Osco et al., 2019; Hou et al., 2021). Spectral features of the canopy obtained by UAV equipped with multispectral and hyperspectral camera are widely used to estimate various physiological and biochemical metrics of crops.