Crop water stress index derived from multi-year ground and aerial thermal images as an indicator of potato water status

Rud, Ronit; Cohen, Yafit; Alchanatis, V.; Levi, A.; Brikman, R.; Shenderey, C.; Heuer, Bruria; Markovitch, T.; Dar, Z.; Rosen, Carl J.; Mulla, D. J.; Nigon, Tyler J.

doi:10.1007/s11119-014-9351-z

Cited by 102 publications

(82 citation statements)

References 24 publications

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“…However, the drifting CWSI range can be anchored in advance with the help of auxiliary data, such as the air temperature. T dry can be predicted by T air + threshold temperature (e.g., 5 • C) in the empirical method [16,[20][21][22]. If the estimated T dry in GMM distribution is lower than T dry predicted by T air , it would indicate non-severe crop water stress.…”

Section: Discussionmentioning

confidence: 99%

“…Firstly, a temperature histogram is generated from TIR image subset of each sub-region. This study assumes that Twet can be taken from the coldest part of the histogram from the TIR image [16], and Tdry is also the temperature of a non-transpiring leaf, which can be derived from the highest part of the histogram. Table 2.…”

Section: Adaptive Andmentioning

confidence: 99%

“…Both quality and yield of crops can be enhanced temperature. Rud et al [16] compared three methods to calculate T wet -from WARS, a method based on energy balance, and a statistical histogram approach-and showed that all CWSI values have similar correlations with actual reference data. Since CWSI based on statistical analysis does not require either any meteorological data for the energy balance method or equipment for WARS, it reduces the time related to field work and the CWSI calculation complexity.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV)

et al. 2017

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Abstract:The capability to monitor water status from crops on a regular basis can enhance productivity and water use efficiency. In this paper, high-resolution thermal imagery acquired by an unmanned aerial vehicle (UAV) was used to map plant water stress and its spatial variability, including sectors under full irrigation and deficit irrigation over nectarine and peach orchards at 6.12 cm ground sample distance. The study site was classified into sub-regions based on crop properties, such as cultivars and tree training systems. In order to enhance the accuracy of the mapping, edge extraction and filtering were conducted prior to the probability modelling employed to obtain crop-property-specific ('adaptive' hereafter) lower and higher temperature references (T wet and T dry respectively). Direct measurements of stem water potential (SWP, ψ stem ) and stomatal conductance (g s ) were collected concurrently with UAV remote sensing and used to validate the thermal index as crop biophysical parameters. The adaptive crop water stress index (CWSI) presented a better agreement with both ψ stem and g s with determination coefficients (R 2 ) of 0.72 and 0.82, respectively, while the conventional CWSI applied by a single set of hot and cold references resulted in biased estimates with R 2 of 0.27 and 0.34, respectively. Using a small number of ground-based measurements of SWP, CWSI was converted to a high-resolution SWP map to visualize spatial distribution of the water status at field scale. The results have important implications for the optimal management of irrigation for crops.

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Section: Discussionmentioning

confidence: 99%

Section: Adaptive Andmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV)

et al. 2017

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“…The quadratic polynomial equation between CWSI and gs was the same with the results got by Aladenol and Madramootoo (2014) on bell pepper, but was different from the negatively linear relationship between CWSI and gs reported by Möller et al (2007) or Zia et al (2011). It might be because the data were collected on different sampling data, that might result in the difference in relations W1 70%θs1 65%θs1 60%θs1 70%θs2 75%θs2 80%θs3 70%θs3 W2 70%θs1 60%θs1 55%θs1 65%θs2 70%θs2 75%θs3 70%θs3 W3 70%θs1 55%θs1 50%θs1 60%θs2 65%θs2 70%θs3 70%θs3 W4 70%θs1 50%θs1 45%θs1 55%θs2 60%θs2 65%θs3 70%θs3 θ between CWSI and gs among sampling date as reported by Rud et al (2014) on potato. Based on the quadratic polynomial relationships between CWSI and leaf physiological indexes, the critical CWSI values for decline in Pn were higher than those for decline in Tr or gs.…”

Section: Discussionmentioning

confidence: 99%

“…Indices based on leaf or canopy temperature are widely used in crop water deficit diagnosis since 1970's with the advent of hand-held thermometers Jackson et al, 1981;Jones, 2004;Gontia and Tiwari, 2008;Peng et al, 2011), such as stress degree days (SDD) Patil et al, 2014), canopy temperature variability (CTV) (Clawson and Blad, 1982;Gonzalez-Dugo et al, 2006) and crop water stress index (CWSI). CWSI has been applied in many different crops, such as wheat (Yuan et al, 2004;Gontia and Tiwari, 2008;Li et al, 2010), cotton (Silva and Rao, 2005;O'shaughnessy et al, 2011), maize (Anda, 2009;Li et al, 2010;Taghvaeian et al, 2012), bean (Erdem et al, 2006b), and some vegetables (Cremona et al, 2004;Simsek et al, 2005;Erdem et al, 2010;Aladenola and Madramootoo, 2014;Rud et al, 2014) or fruits (Erdem et al, 2006a;Paltineanu et al, 2009). Early researchers mostly scanned several pots by hand-held infrared thermometer under field to detect the crop water status.…”

Section: Introductionmentioning

confidence: 99%

Diagnosing Crop Water Stress of Rice using Infra-red Thermal Imager under Water Deficit Condition

Xu¹,

YuPing²,

Liu³

et al. 2016

IJAB

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A thermal imager was used for measuring the canopy temperature to calculate crop water stress index (CWSI) of rice under water deficit condition. The CWSI varied diurnally with peaks appeared at noon, and soil water deficit led to higher CWSI values during noon. Transpiration rate (Tr), stomatal conductance (gs) and net photosynthetic rate (Pn) were high at low CWSI, and reduced with increasing CWSI. The relationship between CWSI and Pn, Tr or gs at noon was described by quadratic polynomial equations. At critical noon, CWSI values for the decline trend in Pn (0.303, 0.385 and 0.446 at tillering, panicle initiation to booting, milk to soft dough stage) were higher than for decline in Tr and gs. Assuming a 5% reduction in Pn from maximum is moderate water deficit, the critical CWSI values were 0.420, 0.472 and 0.536 at tillering, panicle initiation to booting and milk to soft dough stages. CWSI at 14:00 decreased significantly with increasing relative soil moisture contents. There was a slight difference between the linear relations under different vapor pressure deficit (VPD) conditions. The critical relative soil moisture contents for a 5% reduction in Pn were 1.57%, 1.18% and 1.27% higher under high VPD than low VPD conditions. It implied that rice water status was determined in conjunction with field soil moisture content and air aridity. The water deficit diagnosis based on canopy temperature tracked by thermal infrared imager is a promising method in reflecting the conjuncted function of soil moisture deficit and air aridity on crop water status.

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Monitoring water status in apple trees using a sensitive morning crop water stress index*

Mohamed

Osroosh²,

Peters

et al. 2020

Irrigation and Drainage

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The crop water stress index (CWSI) has shown to be a good indicator of water status in fruit trees. Conventional CWSI measured over solar noon is widely used to monitor plant water status. This study compared the theoretical CWSI averaged over morning hours, CWSImo, and over solar noon hours, CWSImd, of apple trees. This study also assessed their sensitivity to the changes in soil water status (soil water deficit [SWD, %] and soil water potential [SWP, kPa]) at different root zone depths. Four different types of commercial and experimental apple orchards with different characteristics growing under semi‐arid conditions in Washington State, United States, were chosen to assess CWSImo against CWSImd sensitivity to soil water status. In some of these locations, soil water status ranged from fully watered to severely stressed (low water deficit to high water deficit). Ground‐based thermal and microclimate measurements were recorded continuously at 10‐s intervals and acquired for the 15‐min average on a daily basis throughout the study period. The linear relationship between SWD and CWSImo at the effective root zone depth (average of 0–150 mm and 0–460 mm) resulted in a slightly higher correlation (R2 = 0.42–0.64, p < .001) compared to the traditional CWSImd (R2 = 0.32–0.60, p < 0.001). The correlation between CWSImo and SWP (R2 = 0.38–0.69, p < 0.001) was found to be larger than the correlation between CWSImo and SWD, while SWP was less correlated with CWSImd (R2 = 0.33–0.55, p < 0.001). Interestingly, a better relationship between soil water status and both CWSImo and CWSImd was observed using a nadir view orientation thermal measurement. The CWSImo showed higher sensitivity to SWP than SWD.

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Crop water stress index derived from multi-year ground and aerial thermal images as an indicator of potato water status

Cited by 102 publications

References 24 publications

Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV)

Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV)

Diagnosing Crop Water Stress of Rice using Infra-red Thermal Imager under Water Deficit Condition

Monitoring water status in apple trees using a sensitive morning crop water stress index*

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