Monitoring Water Stress Research Articles

Colorimetric Technique for Monitoring Water Stress in Palm Oil Seedlings

Prevention of stress in the asymptomatic stage of the plants could result in improved crop management. In this study, the water and light stress of three oil palm seedlings (Elaeis Guineensis) was examined by assessing the leaves' colour using CIELAB colour space. The oil palm seedlings were subjected to water stress for 33 days and then to water and light stress from 55 to 78 day (for 25 days). The variation of the colour of the leaves due to water stress was discussed in detail. The approach used in this study to identify the drought stress may allow for differentiating mild environmental and severe drought stress in oil palm plants and may be used for remote field-scale estimation of plant stress resistance and health.

Twenty Years of Remote Sensing Applications Targeting Landscape Analysis and Environmental Issues in Olive Growing: A Review

The olive (Olea europaea L.) is an iconic tree linked to the birth of some of the most ancient civilizations and one of the most important cultivated tree species in the Mediterranean basin. Over the last few decades, given the high socio-economic importance of the olive sector, there has been much research involving remote sensing (RS) applications in olive growing, especially in precision agriculture. This review article is part of a review that aims to cover the past, from the 2000s onwards, and the most recent applications of remote sensing (RS) in olive growing to be able to include research and all topics related to the use of RS on olive trees. As far as the use of RS platforms such as satellites, aircraft, and unmanned aerial vehicles (UAVs) in olive growing is concerned, a review of the literature showed the presence of several works devoted to it. A brief introduction on the history of the olive tree and its distribution and cultivation around the world, together with a summary of the leading RS platforms (a good portion of which are satellites) used in olive research, anticipates the discussion of four topics about olive growing that have as their common thread positive (and non-positive) impacts on the environment: preservation of olive landscape and soil erosion, identification of olive groves, olive oil mill wastewater (OOMW) and relative environmental risks, irrigation water management and the use of RS platforms for water stress monitoring. The preservation of olive groves as an element of Mediterranean identity and strategic economic resource in agriculture depends on sustainable environmental management alongside technological advances brought by precision agriculture.

Continental-scale evaluation of three ECOSTRESS land surface temperature products over Europe and Africa: Temperature-based validation and cross-satellite comparison

High spatial resolution land surface temperature (LST, <100 m) is crucial for agricultural water management, crop water stress monitoring, fire mapping, urban heat island study and volcano eruption detection. LST retrievals from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) launched in June 2018, together with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, launched in 1999) and the Landsat series (since 1972), comprise the state-of-the-art high spatial resolution LST datasets publicly accessible. Recently, we generated the ECOSTRESS LST product over Europe and Africa using both the temperature and emissivity separation (TES) and split-window (SW) algorithms under the European ECOSTRESS Hub (EEH). Here, we validated the official Jet Propulsion Laboratory (JPL) TES (Collection 1), EEH TES and EEH SW ECOSTRESS LST products over Europe and Africa between August 1, 2018 and December 31, 2021 by comparing against the in-situ measurements at 9 sites over a wide variety of land cover types. Meanwhile, the validation results were compared with those obtained for ASTER and Landsat LST at the same sites for a thorough understanding of the consistency among these high spatial resolution LST products. The results reveal that the three ECOSTRESS LST products have consistent performances, with an overall RMSE around 2 K. A cold bias around 1 K exists for all three ECOSTRESS LST, which is presumably originated from the radiometric calibration of the sensor in Collection 1 data. The Landsat LST shows a similar accuracy, with an RMSE of 2.20 K and bias of 0.54 K. The EEHSW LST show the highest consistency with Landsat LST, possibly due to the identical emissivity correction process. The performance of ASTER LST is also similar, with an RMSE of 1.98 K and bias of 0.9 K. The precisions of all the LST products are around 1.5 K. Future recalibration of the ECOSTRESS Level 1 radiance data in Collection 2 is expected to further improve the accuracy of ECOSTRESS LST. Overall, this study supports the adaptation of LST retrieval algorithms for the future thermal missions.

Ground Hyper-Spectral Remote-Sensing Monitoring of Wheat Water Stress during Different Growing Stages

Monitoring agricultural drought via ground hyper-spectral remote sensing has always been a hot topic in the fields of agriculture and meteorology. In this study, a greenhouse experiment was conducted on wheat subjected to water stress during its different growth stages, namely tillering, jointing, and milk maturity. An instrument (HOBO ware PRO) used to continuously measure soil moisture was employed to measure the soil water content (SWC). An analytical spectral device (ASD) was utilized to obtain the spectral curve of wheat subject to different water treatment methods. The canopy temperature was obtained using thermal infrared sensors (METER SI-400). The relationships between the SWC, wheat drought stage, canopy temperature, and spectral response characteristics were elucidated. The results showed that the significant differences in spectral characteristics were due to water stress during the different growth stages of wheat. Red-edge parameters of red-valley position (RVP) and red-edge position (REP) both changed by 21 nm for the tillering-stage drought and the jointing-stage drought; however, the RVP and REP values for the milk maturity stage drought and the treatment under no water stress changed by 2 nm. Further, it was proved that the red-edge blue-shift phenomenon was affected not only by the different wheat growth processes, but also by the water stress at different growth stages. Red-edge reflectance clearly reflects wheat water stress at different growth stages. From SWC and canopy temperature analysis results, SWC and canopy temperature had a significant difference between wheat drought at different growth stages, and the canopy temperature at the jointing stage drought had the strongest change. The water index (WI) based on eleven vegetation water indexes exhibited a good performance for distinguishing wheat water stress at different growth stages. In conclusion, ground-based hyperspectral remote sensing can provide a large amount of high temporal and spectral resolution data on vegetation and its surrounding environment, making it an important technical tool for wheat drought monitoring, which has a great significance on the monitoring and early warning of wheat drought, reducing drought-related yield losses, and ensuring food security.

Reconstruction of land surface temperature under cloudy conditions from Landsat 8 data using annual temperature cycle model

Land surface temperature (LST) is an important parameter in the processes of energy exchange and water cycle between the land surface and the atmosphere. The impact of cloud cover leads to spatially incomplete of thermal infrared (TIR)-based LST products, which seriously hinders the applications of LST products in various fields. Several methods have been developed to reconstruct LST under cloudy conditions in previous studies, but there is a lack of an effective method for the reconstruction of cloudy LST at the spatial resolution of Landsat pixel (30 m). In this study, a novel method was proposed to reconstruct LST under cloudy conditions from Landsat 8 data. The LST reconstruction method includes four main steps: (1) identification of cloud-free, cloud-shadow, cloud-obscured, and cloud-covered pixels by integrating the Fmask method with a cloud-shape matching method; (2) calculation of annual temperature cycle (ATC)-based reference LST by fitting an ATC model to all available Landsat 8 LST product during 2013-2020; (3) estimation of LST residual from spatially adjacent similar pixels; and (4) estimation of reconstructed LST in terms of the sum of ATC-based reference LST and LST residual. The performance of the LST reconstruction method was evaluated using Landsat 8 LST images under clear-sky conditions as reference data. The root mean squared error (RMSE) between reconstructed LST and Landsat 8 reference LST ranges from 0.9 K to 2.5 K. The LST reconstruction method was further applied to reconstruct actual Landsat 8 LST images under cloudy conditions. Compared with original Landsat 8 LST images, the spatial distribution of reconstructed LST images is more complete. The pattern of reconstructed LST images reflects the spatial variability of LST well. The accuracy of the LST reconstruction method was validated against in situ LST measurements at six SURFRAD (Surface Radiation Budget Network) sites. The overall bias and RMSE between reconstructed LST and in situ LST at all sites are approximately −0.3 K and 3.5 K, respectively. The LST reconstruction method has great potentials to improve the applications of Landsat LST product in urban thermal environment monitoring and crop water stress monitoring.

Drought impacts on tree carbon sequestration and water use - evidence from intra-annual tree-ring characteristics.

Summary The impact of climate extremes on forest ecosystems is poorly understood but important for predicting carbon and water cycle feedbacks to climate. Some knowledge gaps still remain regarding how drought‐related adjustments in intra‐annual tree‐ring characteristics directly impact tree carbon and water use.In this study we quantified the impact of an extreme summer drought on the water‐use efficiency and carbon sequestration of four mature Norway spruce trees. We used detailed observations of wood formation (xylogenesis) and intra‐annual tree‐ring properties (quantitative wood anatomy and stable carbon isotopes) combined with physiological water‐stress monitoring.During 41 d of tree water deficit, we observed an enrichment in 13C but a reduction in cell enlargement and wall‐thickening processes, which impacted the anatomical characteristics. These adjustments diminished carbon sequestration by 67% despite an 11% increase in water‐use efficiency during drought. However, with the resumption of a positive hydric state in the stem, we observed a fast recovery of cell formation rates based on the accumulated assimilates produced during drought.Our findings enhance our understanding of carbon and water fluxes between the atmosphere and forest ecosystems, providing observational evidence on the tree intra‐annual carbon sequestration and water‐use efficiency dynamics to improve future generations of vegetation models.

Trunk growth rate frequencies as water stress indicator in almond trees

The continuous monitoring of water stress will increase the accuracy of the deficit irrigation scheduling. Almonds are very sensitive to water stress conditions and an important water consumer. Recently, a novel approach to the use of trunk growth rate (TGR) data has been proposed for olive trees. These works suggested the use of TGR frequencies to evaluate water status of the trees. The aim of the current work was to compare the seasonal pattern of three different indicators derived from the daily curves of trunk diameter fluctuations with midday stem water potential. During three consecutive seasons (2017–2019), an irrigation experiment was carried out in a mature almond orchard (cv Vairo) at Dos Hermanas (Seville, Spain). Four irrigation treatments replicated in four blocks were evaluated using the daily curves of midday stem water potential and trunk diameter fluctuations. The different irrigation treatments were: Control, full irrigated conditions; RDI-1 (irrigation scheduling based on midday stem water potential with a deficit irrigation during kernel filling [values around −1.2 MPa]); RDI-2(similar to RDI-1 but with a more severe water stress [−2 MPa]) and incomplete recovery after harvest due to limitation of the seasonal amount of water (around 100 mm); SDI, sustained deficit irrigation with a seasonal applied water equal to RDI-2. Trunk diameter fluctuations were measured with a wireless band dendrometer. The daily curves were processed to obtain three different indicators. Maximum daily shrinkage (MDS) was the difference between the daily maximum and minimum. Trunk growth rate (TGR) was the difference between two consecutive daily maximums. The frequencies of several ranges of TGR were compared with the midday stem water potential. Weekly frequencies of values greater than 0.3 mm day−1 decreased with the reduction of midday stem water potential, but the pattern changed greatly in different seasons. The weekly frequency of values between − 0.1 and 0 and between 0 and 0.1 mm day−1 were steadier in different seasons. Differences between seasons were related to growth pattern and yield.

Prediction of Water Stress Episodes in Fruit Trees Based on Soil and Weather Time Series Data

Water is a limited resource in arid and semi-arid regions, as is the case in the Mediterranean Basin, where demographic and climatic conditions make it ideal for growing fruits and vegetables, but a greater volume of water is required. Deficit irrigation strategies have proven to be successful in optimizing available water without pernicious impact on yield and harvest quality, but it is essential to control the water stress of the crop. The direct measurement of crop water status is currently performed using midday stem water potential, which is costly in terms of time and labor; therefore, indirect methods are needed for automatic monitoring of crop water stress. In this study, we present a novel approach to indirectly estimate the water stress of 15-year-old mature sweet cherry trees from a time series of soil water status and meteorological variables by using Machine Learning methods (Random Forest and Support Vector Machine). Time information was accounted for by integrating soil and meteorological measurements within arbitrary periods of 3, 6 and 10 days. Supervised binary classification and regression approaches were applied. The binary classification approach allowed for the definition of a model that alerts the farmer when a dangerous crop water stress episode is about to happen a day in advance. Performance metrics F2 and recall of up to 0.735 and 0.769, respectively, were obtained. With the regression approach a R2 of up to 0.817 was achieved.

Real-Time Assessment of Mandarin Crop Water Stress Index.

The use of plant-based indicators and other conventional means to detect the level of water stress in crops may be challenging, due to their difficulties in automation, their arduousness, and their time-consuming nature. Non-contact and non-destructive sensing methods can be used to detect the level of water stress in plants continuously and to provide automatic sensing and controls. This research aimed at determining the viability, efficiency, and swiftness in employing the commercial Workswell WIRIS Agro R infrared camera (WWARIC) in monitoring water stress and scheduling appropriate irrigation regimes in mandarin plants. The experiment used a four-by-three randomized complete block design with 80–100% FC water treatment as full field capacity and three deficit irrigation treatments at 70–75% FC, 60–65% FC, and 50–55% FC. Air temperature, canopy temperature, and vapor pressure deficits were measured and employed to deduce the empirical crop water stress index, using the Idso approach (CWSI(Idso)) as well as baseline equations to calculate non-water stress and water stressed conditions. The relative leaf water content (RLWC) of mandarin plants was also determined for the growing season. From the experiment, CWSI(Idso) and CWSI were estimated using the Workswell Wiris Agro R infrared camera (CWSIW) and showed a high correlation (R2 = 0.75 at p < 0.05) in assessing the extent of water stress in mandarin plants. The results also showed that at an altitude of 12 m above the mandarin canopy, the WWARIC was able to identify water stress using three modes (empirical, differential, and theoretical). The WWARIC’s color map feature, presented in real time, makes the camera a suitable device, as there is no need for complex computations or expert advice before determining the extent of the stress the crops are subjected to. The results prove that this novel use of the WWARIC demonstrated sufficient precision, swiftness, and intelligibility in the real-time detection of the mandarin water stress index and, accordingly, assisted in scheduling irrigation.

Utilizing Optical Satellite Imagery to Monitor Temporal and Spatial Changes of Crop Water Stress: A Case Study in Alfalfa

Since the 1980s, thermal imagery has been used to assess crop water stress. The increase in the temporal resolution of optical satellite sensors (in the range of 400–2500 nm) and the better spatial resolution compared to the thermal imagery call for the definition of a new way for crop water stress monitoring. Hence, we are suggesting a new method utilizing spectral indices from three subsequent images to address this challenge. This method predicts the current water stress with the two past images and compares it to the current stress: if the existing conditions are better than the predicted stress, the crop is not under stress and has sufficient water for development. To evaluate the suggested method, we downloaded Sentinel-2 images and compared the stress found with that method to the leaf area index, leaf water potential, and yield from seven alfalfa growth cycles. The results outline the ability of the new optical stress index to depict spatial and temporal changes in the alfalfa water stress and especially illustrated the changes in the crop water stress over the growth cycle and after each irrigation. This new method needs to be validated with different crops and satellite sensors to verify its success.

Plant water stress monitoring and control system

Precision irrigation is an important component of the water-saving approaches in agriculture and is one of the methods for increasing water-saving efficiency. Detecting water stress in crops accurately is the basis of precision irrigation techniques. In this study, the crop water stress monitoring system was developed to detect the acoustic emission (AE) of plants, associating the three factors of soil, crops, and weather in real-time. The automatic monitoring system, which was developed using the virtual instrument platform, not only affects the water stress and the drought of the atmosphere but can also be used to control the greenhouse environment automatically. The substance subjected to testing was a potted tomato plant (Lycopersicon esculentum). Environmental parameters are monitored in this system using a virtual instrument data acquisition system based on Peripheral Component Interconnect - Data acquisition (PCI-DAQ). The AE sensor was used to fix tomato stems at 1/3 and 2/3 of their overall height in order to detect AE information, which serves as an indirect indicator of the plant's water condition. This system, which could test and record water requirement information for the crops, has been demonstrated to be stable, nondestructive, and easily manipulable. The results showed that the cohesion between water molecules was weak under water stress conditions. The water flow fracture of the conduits resulted in cavitation by rapidly expanding gas bubbles. The cavitation event caused a rapid relaxation of the liquid tension that produced an AE of energy. It was found that crop hydraulic structure and anti-drought indicators were correlated with AE, which resulted from crop adaptation to the environment. The counts of AE change regularly, according to the variation of the environment's temperature, humidity, carbon dioxide consistency, and transpiration. Based on the relationship between AE and crop water stress conditions, the mathematical model of precision irrigation was obtained. As producing AE is a complex biological process, the future work is to build the different crops' information models in different growth periods. The data acquisition based on the AE information should be researched further.

Revisiting crop water stress index based on potato field experiments in Northern Germany

Different types of the Crop Water Stress Index (CWSI) have been useful for water stress monitoring and irrigation management in semi-arid regions, however little research exists on its effective application in humid regions. This study aims to assess the effectiveness of three CWSI models (CWSIe - empirical, CWSIt - theoretical, CWSIh - hybrid) for crop water stress monitoring in an experimental field for potato crops in Northern Germany. Irrigation experiments with three treatments (optimum-OP, reduced-RD and no) were conducted in the summer of 2018 and 2019. Continuous canopy temperatures (Tc) for OP and RD irrigation treatments together with meteorological measurements were used to derive CWSI from the different models. Additionally, Visible/near infrared (VNIR) and Thermal Infrared (TIR) drone images were collected on several days during the growing season to create CWSI maps. The different CWSI models were correlated with volumetric soil water content (θ) measurements for comparison and relationships were established between CWSI and θ for prediction. Results showed that CWSI accurately estimates soil water content under atmospheric conditions similar to those in semi-arid regions. The predictive performance of different CWSI models were fairly good (R2 =0.57–0.63) (situation in 2019). CWSIe and CWSIh performed better than CWSIt. CWSI-θ relations calibrated in one year effectively predicted θ in another year with errors of 1.2–2.2% absolute soil water content. CWSIh could be a promising alternative to the traditional CWSI as it combines aspects of CWSIe (empirical upper limit) and of CWSIt (theoretical lower limit) which has advantages for operational use. Finally, the drone-based CWSI and θ maps (derived from the developed CWSI- θ relations) captured well the applied irrigation patterns and could help to decide when to irrigate and how much water to apply.

One decade (2011–2020) of European agricultural water stress monitoring by MSG-SEVIRI: workflow implementation on the Virtual Earth Laboratory (VLab) platform

ABSTRACT Cloud computing facilities can provide crucial computing support for processing the time series of satellite data and exploiting their spatio-temporal information content. However, dedicated efforts are still required to develop workflows, executable on cloud-based platforms, for ingesting the satellite data, performing the targeted processes, and generating the desired products. In this study, an operational workflow is proposed, based on monthly Evaporative Stress Index (ESI) anomaly, and implemented in cloud-based online Virtual Earth Laboratory (VLab) platform, as a demonstration, to monitor European agricultural water stress. To this end, daily time-series of actual and reference evapotranspiration (ETa and ET0), from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor, were used to execute the proposed workflow successfully on VLab. The execution of the workflow resulted in obtaining one decade (2011–2020) of European monthly agricultural water stress maps at 0.04˚ spatial resolution and corresponding stress reports for each country. To support open science, all the workflow outputs are stored in GeoServer, documented in GeoNetwork, and made available through MapStore. This enables creating a dashboard for better visualization of the results for end-users. The results from this study demonstrate the capability of VLab platform for water stress detection from time series of SEVIRI-ET data.

Assessment of peach trees water status and leaf gas exchange using on-the-ground versus airborne-based thermal imagery

The thermal region of the electromagnetic spectrum might provide valuable information for assessing plant water status. Nevertheless, the plant’s physiology and the scale of measurement, (e.g. sensor viewing geometry and the canopy aggregation) are critical for quantifiying and monitoring water stress. This study compares the Crop Water Stress Index (CWSI) of a peach orchard obtained using on-the-ground, and airborne-based canopy temperature (Tc). The temporal evolution of CWSI under mild water stress conditions was assessed for three different irrigation strategies (over-irrigation, OI; farmer irrigation, FI; and non-irrigation, NI). Two aerial campaigns per irrigation season (2017–2018) were performed with an airborne thermal sensor: a first flight under well-watered conditions, and a second flight once mild water stress was developed. At the time of the flights, Tc and net photosynthesis (Pn), stomatal conductance (gs) and stem water potential (Ψs) were measured on the ground with a hand-held thermal camera, a portable gas exchange system and a pressure chamber, respectively. The canopy temperature obtained from the hand-held thermal camera, averaging the sunlit and shaded parts of the canopy, agreed with that derived from the airborne measurements (Y=1.00X; RMSE= 1.97 K). The CWSI values calculated from both approaches detected peach water status under different irrigation strategies. In general, Ψs was better predicted from the aircraft (R² up to 0.72 for CWSI obtained from the aircraft versus R2 =0.51 for Tc ground measurements), whereas the use of ground measurements was preferred for estimating gs and Pn (R² up to 0.73 and 0.74 for Tc ground measurements versus R2 =45 and 0.56 for Tc and CWSI derived from the aircraft). Regardless the approach used for deriving Tc, and due to the consideration of different meteorological conditions (i.e different dates), CWSI provided a better relationship with Ψs than Tc, whereas the latter was more closely related with gs and Pn.

Reduced Irrigation during Orchard Establishment Conserves Water and Maintains Yield for Three Cider Apple Cultivars

Irrigation water productivity is a priority for sustainable orchard management as water resources become more limiting. This study evaluated reduced irrigation (RI) as a management strategy for cider apple (Malus domestica Borkh.) production in 2019 and 2020 in northwestern Washington, which has a Mediterranean climate and averages 14.1 cm of precipitation from June to September. RI was evaluated on three cider apple cultivars, Dabinett, Porter’s Perfection, and Golden Russet, in their third and fourth leaf. Stem water potential (stem ψ) was measured weekly throughout the growing season to monitor water stress and implement the RI treatment: irrigation would be applied when stem ψ values dropped below −1.5 MPa, a threshold indicative of moderate water stress in apples. Soil water potential was monitored throughout the season, vegetative growth was assessed by measuring shoot length and non-destructive imaging of the plant canopy using lateral photography, and yield, fruit quality, and juice quality were measured at harvest. Moderate water stress as indicated by stem ψ did not occur either year, thus irrigation was never applied to the RI treatment plots. There was a negative relationship between average stem ψ and both yield and air temperature (P &lt; 0.0001 for each); as yield increased by 5.9 kg per tree or temperature increased by 3.3 °C, stem ψ decreased by 0.1 MPa. The juice quality attributes of the three cultivars in this study were similar to their historic measures at this site and there were no differences due to irrigation treatment, likely because trees did not reach the threshold to induce physiological stress. Both years, trees in the RI treatment did not differ from the control treatment in vegetative growth, fruit yield, juice yield, or any juice quality attribute, but weight per fruit decreased by 7 g, and fruit firmness (measured only in 2020) increased by 2 N. Results from this study indicate that fruit yield and quality in an establishing orchard can be maintained when irrigation is reduced relative to crop water requirements that are estimated from a calculated water balance or relative to conventional grower practices for this region. This finding highlights the benefits of using plant water status to schedule irrigation.

Plant Water Stress Monitoring and Control System

Plant Water Stress Monitoring and Control System

Long-Term Assessment of Reference Baselines for the Determination of the Crop Water Stress Index in Maize under Mediterranean Conditions

Canopy temperature has been proposed as a relevant variable for crop water stress monitoring. Since crop temperature is highly influenced by the prevailing climatic conditions, it is usually normalized with indices such as the crop water stress index (CWSI). The index requires the use of two baselines that relate canopy temperature under maximum stress and non-water stress conditions with vapor pressure deficit (VPD). These reference baselines are specific to each crop and climatic region. In maize, they have been extensively studied for certain climatic regions but very little is known on their suitability to be used under Mediterranean-type conditions nor their temporal stability, both diurnally and between seasons. Thus, the objective of this work was to determine the reference baselines for maize grown under Mediterranean conditions, as well as its diurnal and long-term stability. An experiment was conducted for 3 years in a maize breeding field, under well-watered and water-stressed irrigation treatments. The determined reference baselines for computing CWSI in maize have shown to be stable in the long term but markedly influenced by the meteorological variations between 10–17 h UTC (Coordinated Universal Time). These results indicate that several reference baselines should be used for CWSI computing throughout the abovementioned time interval. The CWSI values calculated for well-watered and water-stressed maize breeding plots using the reference baselines derived in this study were successfully correlated with other physiological indicators of plant water stress.

Generating the 30-m land surface temperature product over continental China and USA from landsat 5/7/8 data

Generating a long-time-series, high-spatial-resolution land surface temperature (LST) product has considerable applications in monitoring water stress, surface energy and water balance at the field scale. This paper proposes an operational method to generate 30-m LSTs from thermal infrared (TIR) observations of Landsat series. Two key issues were addressed in the proposed method: one involved determining the land surface emissivity (LSE) by developing different LSE retrieval methods for specific land cover types; the other involved choosing an optimal reanalysis atmospheric profile for implementing the atmospheric correction of TIR data. After LSE determination and atmospheric correction, LST was resolved by inverting the radiative transfer equation. In situ measured LST and LSE data were used to validate the proposed method. The validation results based on the measurements from 24 sites showed that the absolute average bias of the LSE data estimated from Landsat 5/7/8 was generally within 0.01, and the standard deviations were all less than 0.002. The average biases of the retrieved LST at SURFRAD sites were 1.11/1.54/1.63 K, whereas the RMSEs were 2.72/3.21/3.02 K for Landsat 5/7/8, respectively. The average biases (RMSEs) of the retrieved LST at the BSRN and Huailai sites were 0.08 K (3.69 K) and 0.90 K (3.42 K) for Landsat 7 and Landsat 8, respectively. Furthermore, the validation results at the SURFRAD sites show that the precision and uncertainty of the retrieved Landsat 5/7/8 LSTs were all better than those of the USGS LSTs. Finally, we produced monthly composited LST maps for the Chinese landmass and continental United States using the retrieved Landsat 5/7/8 LSTs. This study provides guidance on how to estimate large-scale LSTs from satellite sensors with only one TIR channel. We will massively produce global LSTs from Landsat series TIR data and release them to the public in the next stage.

The impact of tree phenology on the response of irrigated avocado: The hysteretic nature of the maximum trunk daily shrinkage

The goal of this study was to check the feasibility of the use of dendrometers to monitor water stress levels in avocado trees (Persea americana Mill, cv. Hass) during the period of 2015–2017. This water stress was generated naturally during events of high vapor pressure deficit (VPD) that normally take place during the Israeli spring (May) and late autumn (September). It was also generated artificially by considering two irrigation regimes, one providing the crop water needs estimated using lysimeter data, and one applying 75% of that amount. The different water regimes and climatic conditions allowed us to investigate the impact of seasonal atmospheric changes on transpiration (T) and on the maximum trunk daily shrinkage (MDS) of the trees. The MDS captured well the differences between the irrigation treatments, showing over 100 µm increase at peak summer in response to the treatment providing 75% of the irrigation dose applied to the well-watered treatment. We found that T increased linearly with the daily average value of VPD up to around 1.3 kPa, above which the slope of the linear relationship decreased with the increase in VPD. This reduction of T induced moderate changes in MDS under high VPD. The relationships between MDS and VPD and between MDS and T depended on the season, exhibiting substantially lower MDS during the spring and early summer. The hysteretic nature of these relationships indicates that MDS values are related to tree phenology: a VPD of 1.3 kPa corresponds to an MDS of 50 µm in the first half of the year (flowering and fruit set) and to an MDS of 150 µm during the second half of the year (fruit growth and maturation). Finally, we found that MDS was highly correlated with the stem water potential, regardless of the irrigation treatment or time of year, and is, therefore, a reliable stress indicator in avocado trees as long as VPD and phenological stage of the tree are considered.

Intelligent thermal image-based sensor for affordable measurement of crop canopy temperature

Crop canopy temperature measurement is necessary for monitoring water stress indicators such as the Crop Water Stress Index (CWSI). Water stress indicators are very useful for irrigation strategies management in the precision agriculture context. For this purpose, one of the techniques used is thermography, which allows remote temperature measurement. However, the applicability of these techniques depends on being affordable, allowing continuous monitoring over multiple field measurement. In this article, the development of a sensor capable of automatically measuring the crop canopy temperature by means of a low-cost thermal camera and the implementation of artificial intelligence-based image segmentation models is presented. In addition, we provide results on almond trees comparing our system with a commercial thermal camera, in which an R-squared of 0.75 is obtained.