Effects of irrigation regime and application of kaolin on canopy temperatures of sweet pepper and tomato

Ćosić, Marija; Stričević, Ružica; Djurović, Nevenka; Lipovac, Aleksa; Bogdan, Ivan; Pavlović, Marija

doi:10.1016/j.scienta.2018.04.035

Cited by 22 publications

(12 citation statements)

References 39 publications

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“…Detecting how much water is transpired by a crop canopy is critical for better allocating water resources and for scheduling irrigation [40][41][42]. The conventional field-based transpiration measurement techniques and most remotely sensed models rely heavily on ground-based auxiliary measurements, and many parameters are hardly obtained accurately [15].…”

Section: Discussionmentioning

confidence: 99%

“…To some extent, variations of transpiration rates directly reflect the ability of crops to regulate water loss and their ability to adapt to dry or wet environments [33], which is closely related to canopy temperature. Canopy temperature can be used to calculate a crop water stress index and stomatal conductance index, which can detect drought stress and support irrigation scheduling [41,42,54]. Therefore, evaluation of transpiration and canopy temperature is of great significance for monitoring plant water status and scheduling irrigation and understanding the crop transpiration and temperature under different irrigation schedules.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2018

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

et al. 2018

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“…where i and j are the i th and j th intervals of the update process of W RSAE1 and W RSAE2 , respectively, H (i,j) denotes the inverse of the Hessian matrix [17,18], and η denotes the step size of the update process accordingly. In this equation, η (i+1,j+1) can be determined iteratively by:…”

Section: Experimental Environment Of Sprinkler Irrigationmentioning

confidence: 99%

Adaptive Prediction of Water Droplet Infiltration Effectiveness of Sprinkler Irrigation Using Regularized Sparse Autoencoder–Adaptive Network-Based Fuzzy Inference System (RSAE–ANFIS)

Liang

Liu

Zou

et al. 2021

Water

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As the high productive efficiency of sprinkler irrigation is largely based on balanced soil moisture distribution, it is essential to study the exact effectiveness of water droplet infiltration, which provides a theoretical basis for rationally scheduling the circulation efficiency of groundwater in agricultural irrigation performance. This research carried out adaptive prediction of the droplet infiltration effectiveness of sprinkler irrigation by using a novel approach of a regularized sparse autoencoder–adaptive network-based fuzzy inference system (RSAE–ANFIS), for the purpose of quantifying actual water droplet infiltration and effectiveness results of precision irrigation in various environmental conditions. The intelligent prediction experiment we implemented could be phased as: the demonstration of governing equations of droplet infiltration for sprinkler irrigation modeling; the measurement and computation of probability densities in water droplet infiltration; innovative establishment and working analysis of RSAE–ANFIS; and the adaptive prediction of infiltration effectiveness indexes, such as average soil moisture depth increment (θ, mm), irrigation infiltration efficiency (ea, %), irrigation turn duration efficiency (et, mm/min), and the uniformity coefficient of soil moisture infiltration (Cu, %), which were implemented to provide a comprehensive illustration for the effective scheduling of sprinkler irrigation. Result comparisons indicated that when jetting pressure (Pw) was 255.2 kPa, the impinge angle (Wa) was 42.5°, the water flow rate (Fa) was 0.67 kg/min, and continuous irrigation time (Tc) was 32.4 min (error tolerance = ±5%, the same as follows), thereby an optimum and stable effectiveness quality of sprinkler irrigation could be achieved, whereas average soil moisture depth increment (θ) was 57.6 mm, irrigation infiltration efficiency (ea) was 62.5%, irrigation turn duration efficiency (et) was 34.5 mm/min, and the uniformity coefficient of soil moisture infiltration (Cu) was 53.6%, accordingly. It could be concluded that the proposed approach of the regularized sparse autoencoder–adaptive network-based fuzzy inference system has outstanding predictive capability and possesses much better working superiority for infiltration effectiveness in accuracy and efficiency; meanwhile, a high agreement between the adaptive predicted and actual measured values of infiltration effectiveness could be obtained. This novel intelligent prediction system has been promoted constructively to improve the quality uniformity of sprinkler irrigation and, consequently, to facilitate the productive management of sprinkler irrigated agriculture.

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“…Durante seu ciclo de vida as plantas são frequentemente submetidas a períodos de déficit hídrico do solo e da atmosfera (Pessarakli, 2005 Esses resultados corroboram com estudos similares (Ballester et al, 2013;Ćosić et al, 2018;O'Shaughnessy et al, 2011;Sezen et al, 2014;Zhang et al, 2017). hídrico, para fins de quantificação da lâmina de irrigação a ser aplicada.…”

Section: Resultsunclassified

“…A avaliação da temperatura do dossel é importante para o monitoramento do regime hídrico nas plantas, em virtude de sua alta demanda hídrica . O processo de transpiração foliar, fenômeno conhecido como resfriamento evaporativo, é condição natural para as plantas, no qual o calor é dissipado pela perda de vapor de água através dos estômatos (SINGH, 2004;ĆOSIĆ et al, 2018;.…”

Section: Introdução (Prefácio)unclassified

Imagens termais: detecção espacial de estresse hídrico nas plantas e suas relações fisiológicas com a produção agrícola

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Effects of irrigation regime and application of kaolin on canopy temperatures of sweet pepper and tomato

Cited by 22 publications

References 39 publications

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

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

Adaptive Prediction of Water Droplet Infiltration Effectiveness of Sprinkler Irrigation Using Regularized Sparse Autoencoder–Adaptive Network-Based Fuzzy Inference System (RSAE–ANFIS)

Imagens termais: detecção espacial de estresse hídrico nas plantas e suas relações fisiológicas com a produção agrícola

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