scholarly journals UAV Multispectral Imagery Combined with the FAO-56 Dual Approach for Maize Evapotranspiration Mapping in the North China Plain

2019 ◽  
Vol 11 (21) ◽  
pp. 2519 ◽  
Author(s):  
Jiandong Tang ◽  
Wenting Han ◽  
Liyuan Zhang
Keyword(s):  
Water Stress ◽  
Vegetation Index ◽  
Estimation Method ◽  
Crop Coefficient ◽  
Irrigation Scheduling ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Condition ◽  
Crop Water Stress

As the key principle of precision farming, variation of actual crop evapotranspiration (ET) within the field serves as the basis for crop management. Although the estimation of evapotranspiration has achieved great progress through the combination of different remote sensing data and the FAO-56 crop coefficient (Kc) method, lack of the accurate crop water stress coefficient (Ks) at different space–time scales still hinder its operational application to farmer practices. This work aims to explore the potential of multispectral images taken from unmanned aerial vehicles (UAVs) for estimating the temporal and spatial variability of Ks under the water stress condition and mapping the variability of field maize ET combined with the FAO-56 Kc model. To search for an optimal estimation method, the performance of several models was compared including models based on Ks either derived from the crop water stress index (CWSI) or calculated by the canopy temperature ratio (Tc ratio), and combined with the basal crop coefficient (Kcb) based on the normalized difference vegetation index (NDVI). Compared with the Ks derived from the Tc ratio, the CWSI-based Ks responded well to water stress and had strong applicability and convenience. The results of the comparison show that ET derived from the Ks-CWSI had a higher correlation with the modified FAO-56 method, with an R2 = 0.81, root mean square error (RMSE) = 0.95 mm/d, and d = 0.94. In contrast, ET derived from the Ks-Tc ratio had a relatively lower correlation with an R2 = 0.68 and RMSE = 1.25 mm/d. To obtain the evapotranspiration status of the whole maize field and formulate reasonable irrigation schedules, the CWSI obtained by a handheld infrared thermometer was inverted by the renormalized difference vegetation index (RDVI) and the transformed chlorophyll absorption in reflectance index (TCARI). Then, the whole map of Ks can be derived from the VIs by the relationship between CWSI and Ks and can be taken as the basic input for ET estimation at the field scale. The final ET results based on multispectral UAV interpolation measurements can well reflect the crop ET status under different irrigation levels, and greatly help to improve irrigation scheduling through more precise management of deficit irrigation.

scholarly journals An Empirical Approach for Detecting Crop Water Stress Using Multispectral Airborne Sensors

1997 ◽  
Vol 7 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Thomas R. Clarke
Keyword(s):  
Water Stress ◽  
Vegetation Index ◽  
Near Infrared ◽  
Water Status ◽  
Irrigation Scheduling ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Airborne Sensors ◽  
Crop Water Stress

Irrigation scheduling can be improved by directly monitoring plant water status rather than depending solely on soil water content measurements or modeled evapotranspiration 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, which uses hand-held infrared thermometers as tools for scheduling irrigations. However, substantial error can occur in partial canopies when a downward-pointing infrared thermometer measures leaf temperature and the temperature of exposed, hot soil. To overcome this weakness, red and near-infrared images were combined mathematically as a vegetation index, which was used to provide a crop-specific measure of vegetative cover. Coupling the vegetation index with the paired radiant surface temperature from a thermal image, a trapezoidal two-dimensional index was empirically derived capable of detecting water stress even with a low percentage of canopy cover. Images acquired with airborne sensors over subsurface drip-irrigated muskmelon (Cucumis melo L.) fields demonstrated the method's ability to detect areas with clogged emitters, insufficient irrigation rate, and system water leaks. Although the procedure needs to be automated for faster image processing, the approach is an advance in irrigation scheduling and water stress detection technology.

Crop water stress index relationship with soybean seed, protein and oil yield under varying irrigation regimes in a Mediterranean environment

2020 ◽  
pp. 1-13
Author(s):  
Christos Vamvakoulas ◽  
Ioannis Argyrokastritis ◽  
Panayiota Papastylianou ◽  
Yolanda Papatheohari ◽  
Stavros Alexandris
Keyword(s):  
Water Stress ◽  
Seed Yield ◽  
Seed Protein ◽  
Soybean Seed ◽  
Irrigation Scheduling ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Crop Water Stress

A two-year field experiment was conducted to determine the effect of water stress, including Crop Water Stress Index (CWSI), on seed, protein and oil yields, for two hybrids of drip-irrigated soybean in Central Greece. The experiment was set up as a split plot design with four replicates, five main plots (irrigation treatments) and two sub-plots (soybean hybrids, ‘PR91M10’ and ‘PR92B63’). Irrigation was applied to provide 100, 75, 50 and 25% of the crop evapotranspiration needs and 0% non-irrigated. Biomass weight, seed yield, oil and protein concentration were measured after harvest. To compute CWSI, lower and upper baselines were developed based on the canopy temperature measurements of I100 and I0 treatments, respectively. Deficit irrigation had a significant effect on biomass, seed, protein and oil yields. Hybrid PR92B63 was more responsive to irrigation and showed higher biomass, seed protein and oil yields, while the more sensitive hybrid PR91M10 had the ability to maintain productivity with increasing degrees of water stress. The rain-fed treatments significantly reduced biomass production and seed yield compared with the fully-irrigated ones. The highest and the lowest protein and oil yields were obtained in the I100 and I0 treatments respectively in both years and cultivars. Statistically significant exponential relationships were determined between CWSI and biomass, seed, protein and oil yields. Generally, CWSI could be used to measure crop water status and to improve irrigation scheduling of the crop and 0.10 for PR92B63 and 0.19 for PR91M10 could be offered as threshold values under the climatic conditions of the region.

scholarly journals Evaluation of yield, quality and crop water stress index of sugar beet under different irrigation regimes

2016 ◽  
Vol 17 (2) ◽  
pp. 571-578 ◽  
Author(s):  
Omid Bahmani ◽  
Ali Akbar Sabziparvar ◽  
Rezvan Khosravi
Keyword(s):  
Water Stress ◽  
Sugar Beet ◽  
Irrigation Scheduling ◽  
Sugar Yield ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Yield Quality ◽  
Crop Water Stress

This study was carried out to evaluate the use of the crop water stress index (CWSI) for irrigation scheduling of sugar beet for two years under the semi arid climate of Iran. Statistical relationships between CWSI and yield, quality parameters and irrigation water use efficiency (IWUE) were investigated. Irrigations were scheduled based on 100 (I100), 85 (I85), 70 (I50) and 0% (I0) of plant water requirement. CWSI values were calculated from the measurements of canopy temperatures by infrared thermometer, air temperatures and vapor pressure deficit values for all the irrigated treatments. The highest IWUE was found in I70 with 9.16 and 1.66 kg m−3 for the root and sugar yield, respectively, in 2013. A non-water stressed baseline (lower line) equation for sugar beet was measured from full irrigated plots as (Tc − Ta)ll = −0.832VPD + 2.1811; R2 = 0.6508. There was a high determination coefficient between CWSI with the root and sugar yield and IWUE. The CWSI could be used to determine the irrigation time of sugar beet, and 0.3 could be offered as a threshold value. Results indicated that the CWSI can be used to evaluate crop water stress and improve irrigation scheduling for sugar beet under semiarid conditions.

scholarly journals Application of a Daily Crop Water Stress Index to Deficit Irrigate Malbec Grapevine under Semi-Arid Conditions

Agriculture ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 492
Author(s):  
Krista C. Shellie ◽  
Bradley A. King
Keyword(s):  
Water Stress ◽  
Water Status ◽  
Irrigation Scheduling ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Irrigation Amount ◽  
Water Stress Index ◽  
Arid Conditions ◽  
Crop Water Stress

Precision irrigation of wine grape is hindered by the lack of an automated method for monitoring vine water status. The objectives of this study were to: Validate an automated model for remote calculation of a daily crop water stress index (CWSI) for the wine grape (Vitis vinifera L.) cultivar Malbec and evaluate its suitability for use in irrigation scheduling. Vines were supplied weekly with different percentages of evapotranspiration-based estimated water demand (ETc) over four growing seasons. In the fifth growing season, different daily CWSI threshold values were used to trigger an irrigation event that supplied 28 mm of water. All three indicators of vine water status (CWSI, midday leaf water potential (Ψlmd), and juice carbon isotope ratio (δ13C)) detected an increase in stress severity as the irrigation amount decreased. When the irrigation amount decreased from 100% to 50% ETc, 70% to 35% ETc, or the daily CWSI threshold value increased from 0.4 to 0.6, berry fresh weight and juice titratable acidity decreased, juice δ13C increased, the weekly CWSI increased, and Ψlmd decreased. Under the semi-arid conditions of this study, utilizing a daily CWSI threshold for irrigation scheduling reduced the irrigation amount without compromising the yield or changes in berry composition and remotely provided automated decision support for managing water stress severity in grapevine.

scholarly journals Evaluation of Infrared Canopy Temperature Data in Relation to Soil Water-Based Irrigation Scheduling in a Humid Subtropical Climate

2020 ◽  
Vol 63 (5) ◽  
pp. 1217-1231
Author(s):  
Bruno P. Lena ◽  
Brenda V. Ortiz ◽  
Andres F. Jiménez-Lópe ◽  
Álvaro Sanz-Sáez ◽  
Susan A. O’Shaughnessy ◽  
...  
Keyword(s):  
Water Stress ◽  
Soil Water ◽  
Irrigation Scheduling ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Time Threshold ◽  
Irrigation Treatments ◽  
Temperature Time ◽  
Crop Water Stress

HighlightsCorn response to irrigation was influenced by the precipitation distribution in 2018 and 2019, and that impacted the response of CWSI as an irrigation scheduling signaling method.CWSI was sensitive to changes in soil water storage, increasing due to crop evapotranspiration and decreasing after a precipitation or irrigation event.In 2018, both seasonal CWSI and yield were not different among the irrigation treatments, while in 2019, seasonal CWSI and yield were all statistically different among the treatments evaluated.Post analysis of canopy and air temperature indicated that the temperature-time threshold (TTT) method might not appropriately signal crop water stress in a humid environment.Abstract. Irrigation scheduling based on the crop water stress index (CWSI) and temperature-time threshold (TTT) methods is promising for semi-arid and arid climates. The objective of this study was to investigate if CWSI and TTT methods could be used as irrigation signaling tools for a humid environment in the southeastern U.S. Corn canopy temperature data were collected in Alabama in 2018 and 2019 using infrared leaf temperature sensors on a fully irrigated treatment and on two limited irrigation treatments. A set of three soil water sensors installed at 0.15, 0.3, and 0.6 m soil depth were used to prescribe irrigation time and amount. CWSI was sensitive to precipitation, irrigation, and plant water uptake. No statistical differences in CWSI or yield among the three irrigation levels were found in 2018 when precipitation was well distributed during the season. In contrast, during 2019 both CWSI and yield differed significantly among the three irrigation treatments. Precipitation events in 2019 were sparse compared to 2018; therefore, irrigation promoted greater differences in water availability between treatments. Inconsistencies observed in potential irrigation signaling using the TTT method with or without the inclusion of a limiting relative humidity algorithm indicate that the TTT method may not be a reliable irrigation signaling tool for humid environments. Keywords: Corn yield, Crop water stress index, Irrigation scheduling, Limiting relative humidity, Soil water depletion, Temperature-time threshold.

scholarly journals Data-Driven Models for Canopy Temperature-Based Irrigation Scheduling

2020 ◽  
Vol 63 (5) ◽  
pp. 1579-1592
Author(s):  
Bradely A. King ◽  
Krista C. Shellie ◽  
David D. Tarkalson ◽  
Alexander D. Levin ◽  
Vivek Sharma ◽  
...  
Keyword(s):  
Water Stress ◽  
Canopy Temperature ◽  
Irrigation Scheduling ◽  
Data Driven ◽  
Stress Index ◽  
Wine Grape ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Crop Water Stress

HighlightsArtificial neural network modeling was used to predict crop water stress index lower reference canopy temperature.Root mean square error of predicted lower reference temperatures was <1.1°C for sugarbeet and Pinot noir wine grape.Energy balance model was used to dynamically predict crop water stress index upper reference canopy temperature.Crop water stress index for sugarbeet was well correlated with irrigation and soil water status.Crop water stress idex was well correlated with midday leaf water potential of wine grape.Abstract. Normalized crop canopy temperature, termed crop water stress index (CWSI), was proposed over 40 years ago as an irrigation management tool but has experienced limited adoption in production agriculture. Development of generalized crop-specific upper and lower reference temperatures is critical for implementation of CWSI-based irrigation scheduling. The objective of this study was to develop and evaluate data-driven models for predicting the reference canopy temperatures needed to compute CWSI for sugarbeet and wine grape. Reference canopy temperatures for sugarbeet and wine grape were predicted using machine learning and regression models developed from measured canopy temperatures of sugarbeet, grown in Idaho and Wyoming, and wine grape, grown in Idaho and Oregon, over five years under full and severe deficit irrigation. Lower reference temperatures (TLL) were estimated using neural network models with Nash-Sutcliffe model efficiencies exceeding 0.88 and root mean square error less than 1.1°C. The relationship between TLL minus ambient air temperature and vapor pressure deficit was represented with a linear model that maximized the regression coefficient rather than minimized the sum of squared error. The linear models were used to estimate upper reference temperatures that were nearly double the values reported in previous studies. A daily CWSI, calculated as the average of 15 min CWSI values between 13:00 and 16:00 MDT for sugarbeet and between 13:00 and 15:00 local time for wine grape, were well correlated with irrigation events and amounts. There was a significant (p < 0.001) linear relationship between the daily CWSI and midday leaf water potential of Malbec and Syrah wine grapes, with an R2 of 0.53. The data-driven models developed in this study to estimate reference temperatures enable automated calculation of the CWSI for effective assessment of crop water stress. However, measurements taken under conditions of wet canopy or low solar radiation should be disregarded as they can result in irrational values of the CWSI. Keywords: Canopy temperature, Crop water stress index, Irrigation scheduling, Leaf water potential, Sugarbeet, Wine grape.

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