Estimating Crop Transpiration of Soybean under Different Irrigation Treatments Using Thermal Infrared Remote Sensing Imagery

Abstract

Temporal and spatial resolution of satellite images are coarse and cannot provide the real-time, meter-scale resolution monitoring required in many applications, such as precision agriculture. Since high resolution thermal infrared data provide one means to observe canopy temperature variance, we developed an algorithm (three-temperature model, 3T) to estimate transpiration rate at meter-scale pixels and detected transpiration variation for soybean under different upper irrigation limits: No irrigation, 35% of field capacity (FC), 55% of FC, and 75% of FC, denoted as W0, W1, W2, and W3, respectively. The spatial patterns of the transpiration rate indicated that heterogeneity is common in farmland. Transpiration rates in the wet treatment (i.e., W3) were consistently higher than that in the dry treatment (i.e., W0). Transpiration rates reached peak values at around 12:30–14:30 and most of values showed that W3 > W2 > W1 > W0, with 0.91 mm/h, 0.89 mm/h, 0.79 mm/h, and 0.62 mm/h during the reproductive period, respectively. In general, the transpiration rate of soybean increased with increasing irrigation quantities. With a higher irrigation total, soil water content increased gradually, and then the transpiration rate increased. Although land surface temperature decreased by only 8.57 K (Kelvin), 6.33 K, and 5.47 K, respectively, the transpiration rate increased by 78%, 60%, and 40%, respectively, for the W3, W2, and W1 treatment compared with the W0 treatment. The magnitude of transpiration change is greater than that of canopy temperature, but both parameters are strongly interrelated with each other through a non-linear correlation. Heterogeneity of canopy leaf temperature and transpiration is mainly due to physical and biological interactions. Understanding transpiration rate and canopy temperature heterogeneity under different irrigation treatments can not only help in scheduling irrigation, but also in enhancing water utilization efficiency in irrigated agriculture. The real-time monitoring of crop transpiration at meter-scale is of great importance for large irrigation systems, especially for precision irrigation, and will have great application prospects in the near future.

Research Progress and Perspective on Drought Stress in Legumes: A Review

“…Thermal infrared imaging (also called infrared thermography), which estimates leaf or canopy temperature, may also be employed to screen grain legumes for drought resistance [223]. Plant canopy temperature is a widely measured variable that is closely associated with canopy conductance at the vegetative stage and thus provides insight into plant water status [224]. Thermal infrared imaging for estimating conductance can be used at the whole plant or canopy level over time.…”

A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

“…In this regard, studies have focused on the use of remote sensing to study spatial variability in k c and ET c [101,[256][257][258]. Thermal and NIR imagery can be used to compute k c and ET c as transpiration rate is closely related to canopy temperature [259][260][261] and k c has been shown to correlate with canopy reflectance [101,255]. Various thermal indices, such as CWSI, canopy temperature ratio, canopy temperature above non-stressed, and canopy temperature above canopy threshold, can be used to estimate ET c , where CWSI-based ET c was found to be the most accurate [24].…”

“…In contrast, high temporal resolution satellites are coarse in spatial resolution for field-scale observations [25]. The daily or even instantaneous estimation of ET c at the field scale is crucial for irrigation scheduling and is expected to have great application prospects in the future [240,259,262,263]. In this regard, the future direction of satellite-based ET estimates may focus on temporal downscaling either by extrapolation of instantaneous measurement [264], interpolation between two successive observations [201], data fusion of multiple satellites [25,260], and spatial downscaling using multiple satellites [265][266][267][268].…”

“…Using high-resolution thermal and/or multispectral imagery, ET has been derived for horticultural crops, such as grapevines [270] and olives [271]. The seasonal monitoring of ET c at high spatial and temporal resolutions is of high importance for precision irrigation of horticultural crops in the future [259].…”

Real-Time Localization Approach for Maize Cores at Seedling Stage Based on Machine Vision

“…Implementing weeding, spraying, and navigation work through recognition and localization for crops, such as maize, has long been a research focus regarding the technology and equipment of precise agriculture [2][3][4]. The predecessors have distinguished weeds through localizing position of crops with machine vision technology [5][6][7], to implement weeding work [8,9] and determine crop diseases and position for spraying.…”