scholarly journals A New Evapotranspiration Model Accounting for Advection and Its Validation during SMEX02

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Yongmin Yang ◽  
Hongbo Su ◽  
Renhua Zhang ◽  
Jianjun Wu ◽  
Jianwei Qi
Keyword(s):  
Regional Scale ◽  
Local Scale ◽  
Heat Fluxes ◽  
Stress Index ◽  
Coefficient Of Determination ◽  
Remotely Sensed Data ◽  
Crop Water Stress Index ◽  
Scale Evaluation ◽  
Surface Heat Fluxes ◽  
Water Stress Index

Based on the crop water stress index (CWSI) concept, a new model was proposed to account for advection to estimate evapotranspiration. Both local scale evaluation with sites observations and regional scale evaluation with a remote dataset from Landsat 7 ETM+ were carried out to assess the performance of this model. Local scale evaluation indicates that this newly developed model can effectively characterize the daily variations of evapotranspiration and the predicted results show good agreement with the site observations. For all the 6 corn sites, the coefficient of determination (R2) is 0.90 and the root mean square difference (RMSD) is 58.52W/m2. For all the 6 soybean sites, theR2and RMSD are 0.85 and 49.46W/m2, respectively. Regional scale evaluation shows that the model can capture the spatial variations of evapotranspiration at the Landsat-based scale. Clear spatial patterns were observed at the Landsat-based scale and are closely related to the dominant land covers, corn and soybean. Furthermore, the surface resistance derived from instantaneous CWSI was applied to the Penman-Monteith equation to estimate daily evapotranspiration. Overall, results indicate that this newly developed model is capable of estimating reliable surface heat fluxes using remotely sensed data.

Monitoring Crop Water Status and Irrigation Needs Using Multispectral Airborne Sensors

HortScience ◽  
1995 ◽  
Vol 30 (4) ◽  
pp. 905D-905
Author(s):  
Thomas R. Clarke ◽  
M. Susan Moran
Keyword(s):  
Water Stress ◽  
Near Infrared ◽  
Water Status ◽  
Canopy Cover ◽  
Stress Index ◽  
Crop Water ◽  
Vegetative Cover ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Subsurface Drip

Water application efficiency can be improved by directly monitoring plant water status rather than depending on soil moisture measurements or modeled ET estimates. Plants receiving sufficient water through their roots have cooler leaves than those that are water-stressed, leading to the development of the Crop Water Stress Index based on hand-held infrared thermometry. Substantial error can occur in partial canopies, however, as exposed hot soil contributes to deceptively warm temperature readings. Mathematically comparing red and near-infrared reflectances provides a measure of vegetative cover, and this information was combined with thermal radiance to give a two-dimensional index capable of detecting water stress even with a low percentage of canopy cover. Thermal, red, and near-infrared images acquired over subsurface drip-irrigated cantaloupe fields demonstrated the method's ability to detect areas with clogged emitters, insufficient irrigation rate, and system water leaks.

An insight to the performance of crop water stress index for olive trees

2013 ◽  
Vol 118 ◽  
pp. 79-86 ◽  
Author(s):  
N. Agam ◽  
Y. Cohen ◽  
J.A.J. Berni ◽  
V. Alchanatis ◽  
D. Kool ◽  
...  
Keyword(s):  
Water Stress ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Olive Trees ◽  
Crop Water Stress

scholarly journals Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture

Agriculture ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. 116 ◽  
Author(s):  
Alessandro Matese ◽  
Salvatore Di Gennaro
Keyword(s):  
Water Stress ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Stress Index ◽  
Crop Water Stress Index ◽  
Practical Applications ◽  
Water Stress Index ◽  
Sensing Applications ◽  
Wide Range ◽  
Precision Viticulture

High spatial ground resolution and highly flexible and timely control due to reduced planning time are the strengths of unmanned aerial vehicle (UAV) platforms for remote sensing applications. These characteristics make them ideal especially in the medium–small agricultural systems typical of many Italian viticulture areas of excellence. UAV can be equipped with a wide range of sensors useful for several applications. Numerous assessments have been made using several imaging sensors with different flight times. This paper describes the implementation of a multisensor UAV system capable of flying with three sensors simultaneously to perform different monitoring options. The intra-vineyard variability was assessed in terms of characterization of the state of vines vigor using a multispectral camera, leaf temperature with a thermal camera and an innovative approach of missing plants analysis with a high spatial resolution RGB camera. The normalized difference vegetation index (NDVI) values detected in different vigor blocks were compared with shoot weights, obtaining a good regression (R2 = 0.69). The crop water stress index (CWSI) map, produced after canopy pure pixel filtering, highlighted the homogeneous water stress areas. The performance index developed from RGB images shows that the method identified 80% of total missing plants. The applicability of a UAV platform to use RGB, multispectral and thermal sensors was tested for specific purposes in precision viticulture and was demonstrated to be a valuable tool for fast multipurpose monitoring in a vineyard.

scholarly journals Detecting Plant Stress Using Thermal and Optical Imagery From an Unoccupied Aerial Vehicle

2021 ◽  
Vol 12 ◽  
Author(s):  
Bonny Stutsel ◽  
Kasper Johansen ◽  
Yoann M. Malbéteau ◽  
Matthew F. McCabe
Keyword(s):  
Salinity Stress ◽  
Vegetation Index ◽  
Plant Stress ◽  
Infrared Camera ◽  
Water Security ◽  
Optical Data ◽  
Stress Index ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Management Interventions

Soil and water salinization has global impact on the sustainability of agricultural production, affecting the health and condition of staple crops and reducing potential yields. Identifying or developing salt-tolerant varieties of commercial crops is a potential pathway to enhance food and water security and deliver on the global demand for an increase in food supplies. Our study focuses on a phenotyping experiment that was designed to establish the influence of salinity stress on a diversity panel of the wild tomato species, Solanum pimpinellifolium. Here, we explore how unoccupied aerial vehicles (UAVs) equipped with both an optical and thermal infrared camera can be used to map and monitor plant temperature (Tp) changes in response to applied salinity stress. An object-based image analysis approach was developed to delineate individual tomato plants, while a green–red vegetation index derived from calibrated red, green, and blue (RGB) optical data allowed the discrimination of vegetation from the soil background. Tp was retrieved simultaneously from the co-mounted thermal camera, with Tp deviation from the ambient temperature and its change across time used as a potential indication of stress. Results showed that Tp differences between salt-treated and control plants were detectable across the five separate UAV campaigns undertaken during the field experiment. Using a simple statistical approach, we show that crop water stress index values greater than 0.36 indicated conditions of plant stress. The optimum period to collect UAV-based Tp for identifying plant stress was found between fruit formation and ripening. Preliminary results also indicate that UAV-based Tp may be used to detect plant stress before it is visually apparent, although further research with more frequent image collections and field observations is required. Our findings provide a tool to accelerate field phenotyping to identify salt-resistant germplasm and may allow farmers to alleviate yield losses through early detection of plant stress via management interventions.

On-the-go thermal imaging for water status assessment in commercial vineyards

2017 ◽  
Vol 8 (2) ◽  
pp. 520-524
Author(s):  
S. Gutiérrez ◽  
M. P. Diago ◽  
J. Fernández-Novales ◽  
J. Tardaguila
Keyword(s):  
Thermal Imaging ◽  
Water Status ◽  
Canopy Temperature ◽  
Stress Index ◽  
Coefficient Of Determination ◽  
Stem Water Potential ◽  
Water Stress Index ◽  
Status Assessment ◽  
Commercial Vineyard ◽  
East And West

The goal of this work was the assessment of commercial vineyard water status using on-the-go thermal imaging. On-the-go thermal imaging acquisition was conducted with a thermal camera operating at 1.20 m distance from the canopy, mounted on a quad moving at 5 km/h. Canopy temperature, cross water stress index (CWSI) and stomatal conductance index (Ig) were strongly and significantly correlated to stem water potential (Ψstem) in east and west side of the canopy. For CWSI, the values of the coefficient of determination (R2) were 0.88*** and 0.73*** for east and west sides, respectively. As regards the index Ig, its relationships with Ψstem showed R2=0.89*** and R2=0.77*** for east and west sides, respectively. These results are promising and evidence the potential of on-the-go thermal imaging to become a new tool to evaluate the vineyard water status.

Evaluation of Resistances for Bermudagrass Turf Crop Water Stress Index Models

1994 ◽  
Vol 86 (3) ◽  
pp. 574-581 ◽  
Author(s):  
H. R. Jalali‐Farahani ◽  
D. C. Slack ◽  
D. M. Kopec ◽  
A. D. Matthias ◽  
P. W. Brown
Keyword(s):  
Water Stress ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Crop Water Stress

Quantifying Wheat Water Stress with the Crop Water Stress Index to Schedule Irrigations

1994 ◽  
Vol 86 (1) ◽  
pp. 195-199 ◽  
Author(s):  
Donald J. Garrot ◽  
Michael J. Ottman ◽  
D.D. Fangmeier ◽  
Stephen H. Husman
Keyword(s):  
Water Stress ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Crop Water Stress

scholarly journals Leveraging Very-High Spatial Resolution Hyperspectral and Thermal UAV Imageries for Characterizing Diurnal Indicators of Grapevine Physiology

2020 ◽  
Vol 12 (19) ◽  
pp. 3216 ◽  
Author(s):  
Matthew Maimaitiyiming ◽  
Vasit Sagan ◽  
Paheding Sidike ◽  
Maitiniyazi Maimaitijiang ◽  
Allison J. Miller ◽  
...  
Keyword(s):  
Canopy Temperature ◽  
Crop Productivity ◽  
In Situ Measurements ◽  
Sensor Technology ◽  
Stress Index ◽  
Coefficient Of Determination ◽  
Neural Net ◽  
Water Stress Index ◽  
Very High

Efficient and accurate methods to monitor crop physiological responses help growers better understand crop physiology and improve crop productivity. In recent years, developments in unmanned aerial vehicles (UAV) and sensor technology have enabled image acquisition at very-high spectral, spatial, and temporal resolutions. However, potential applications and limitations of very-high-resolution (VHR) hyperspectral and thermal UAV imaging for characterization of plant diurnal physiology remain largely unknown, due to issues related to shadow and canopy heterogeneity. In this study, we propose a canopy zone-weighting (CZW) method to leverage the potential of VHR (≤9 cm) hyperspectral and thermal UAV imageries in estimating physiological indicators, such as stomatal conductance (Gs) and steady-state fluorescence (Fs). Diurnal flights and concurrent in-situ measurements were conducted during grapevine growing seasons in 2017 and 2018 in a vineyard in Missouri, USA. We used neural net classifier and the Canny edge detection method to extract pure vine canopy from the hyperspectral and thermal images, respectively. Then, the vine canopy was segmented into three canopy zones (sunlit, nadir, and shaded) using K-means clustering based on the canopy shadow fraction and canopy temperature. Common reflectance-based spectral indices, sun-induced chlorophyll fluorescence (SIF), and simplified canopy water stress index (siCWSI) were computed as image retrievals. Using the coefficient of determination (R2) established between the image retrievals from three canopy zones and the in-situ measurements as a weight factor, weighted image retrievals were calculated and their correlation with in-situ measurements was explored. The results showed that the most frequent and the highest correlations were found for Gs and Fs, with CZW-based Photochemical reflectance index (PRI), SIF, and siCWSI (PRICZW, SIFCZW, and siCWSICZW), respectively. When all flights combined for the given field campaign date, PRICZW, SIFCZW, and siCWSICZW significantly improved the relationship with Gs and Fs. The proposed approach takes full advantage of VHR hyperspectral and thermal UAV imageries, and suggests that the CZW method is simple yet effective in estimating Gs and Fs.

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