Search Results (10)

This study investigates the impact of climate variability on wheat production in Algeria’s semi-arid interior plains from 2014 to 2024, aiming to curb the challenges of rainfed wheat cultivation, optimize irrigation, and improve water productivity. The Soil–Water–Atmosphere–Plant (SWAP) model-driven approach refined irrigation scheduling to mitigate climate-induced losses and improve resource efficiency. Using historical climate data, soil properties, and wheat growth observations from the experimental farm of the Technical Institute for Field Crops, the SWAP model was calibrated and validated using one-factor-at-a-time sensitivity analysis, achieving a coefficient of determination (R²) of 0.93 and a Normalized Root Mean Squared Error (NRMSE) of 17.75. Two drought-based irrigation indices, Soil Moisture Drought Index (SMDI) and Crop Water Stress Index (CWSI), guided adaptive irrigation strategies, showing a significant reduction in crop failure during drought periods. Results revealed a strong link between rainfall variability and wheat yield. Adopting a 9-day irrigation interval could increase water productivity to 18.91 kg ha⁻¹ mm⁻¹, enhancing yield stability under varying climatic conditions. The SMDI approach maintained soil moisture during extreme drought, while CWSI optimized water use in normal and wet years. This study integrates SMDI and CWSI into a validated irrigation framework, offering data-driven strategies to enhance wheat production resilience. Findings support sustainable water management and provide practical insights for policymakers and farmers to refine irrigation planning and climate adaptation, contributing to long-term agricultural sustainability. Full article

Drought is a severe disaster, increasingly exacerbated by climate change, and poses significant challenges worldwide, particularly in arid and semi-arid regions like Morocco. This study aims to assess and monitor drought using a multi-index approach to provide a comprehensive understanding of its spatio-temporal dynamics at both meteorological and agricultural levels. The research focuses on the Upper Oum Er Rabia watershed, which spans 35,000 km² and contributes approximately a quarter of Morocco’s renewable water resources. We propose a methodology that combines ERA5 temperature data from remote sensing with ground-based precipitation data to analyze drought characteristics. Three meteorological indices were utilized: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Reconnaissance Drought Index (RDI). Additionally, three remote-sensing indices were employed to capture agricultural drought: the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Crop Water Stress Index (CWSI), with a total of 528 NDVI and EVI images and 1016 CWSI images generated through Google Earth Engine (GEE), using machine-learning techniques. Trend analyses were conducted to monitor drought patterns spatio-temporally. Our results reveal that the three-month interval is critical for effective drought monitoring and evaluation. Among the indices, SPEI emerged as the most effective for capturing drought in combination with remote-sensing data, while CWSI exhibited the highest correlation with SPEI over the three-month period, outperforming NDVI and EVI. The trend analysis indicates a significant precipitation deficit, alongside increasing trends in temperature and evapotranspiration over both the short and long term. Furthermore, all drought indices (SPI, SPEI, and RDI) demonstrate an intensification of drought conditions. Adaptation strategies are essential for managing water resources in the Upper Oum Er Rabia watershed under these evolving climate conditions. Continuous monitoring of climate variables and drought indices will be crucial for tracking changes and informing future water management strategies. Full article

Pistachio (Pistacia vera L.) has earned recognition as a significant crop due to its unique nutrient composition and its adaptability to the growing threat of climate change. Consequently, the utilization of remote sensing techniques for non-invasive pistachio monitoring has become critically important. This research was conducted in two pistachio orchards located in Spain, aiming to assess the effectiveness of vegetation indices (VIs) in estimating nut yield and quality under various irrigation conditions. To this end, high-resolution multispectral and thermal imagery were gathered using a Micasense ALTUM sensor carried by a DJI Inspire 2 drone in order to calculate the NDRE (normalized difference red edge index), GNDVI (green normalized difference vegetation index), NDVI (normalized difference vegetation index), and CWSI (crop water stress index). Each orchard underwent two flights at distinct growth stages, totaling four flights. In June, NDRE-carbohydrates (r = 0.78) and CWSI-oleic (r = 0.77) showed the highest correlations, while in September, CWSI-carbohydrates (r = 0.62) and NDVI-iron (r = 0.54) Despite NDVI’s limitations due to saturation effects, all VIs had significant yield and quality correlations, with GNDVI proving most effective in both flights. CWSI correlated considerably on both dates in terms of several quality parameters (carbohydrate percentage, magnesium, iron, and fatty acids, namely palmitoyl, stearic, oleic, and linoleic), surpassing non-thermal indices. Finally, it is important to consider the impact of environmental factors, such as the location of the sun, when interpreting the CWSI, as it modifies the temperature distribution pattern within the canopy. This study supports the viability of remote sensing and vegetation indices as potential tools for enhancing the management of pistachio orchards. Full article

Unmanned aerial vehicle (UAV) remote sensing has become a readily usable tool for agricultural water management with high temporal and spatial resolutions. UAV-borne thermography can monitor crop water status near real-time, which enables precise irrigation scheduling based on an accurate decision-making strategy. The crop water stress index (CWSI) is a widely adopted indicator of plant water stress for irrigation management practices; however, dependence of its efficacy on data acquisition time during the daytime is yet to be investigated rigorously. In this paper, plant water stress captured by a series of UAV remote sensing campaigns at different times of the day (9h, 12h and 15h) in a nectarine orchard were analyzed to examine the diurnal behavior of plant water stress represented by the CWSI against measured plant physiological parameters. CWSI values were derived using a probability modelling, named ‘Adaptive CWSI’, proposed by our earlier research. The plant physiological parameters, such as stem water potential (ψ_stem) and stomatal conductance (g_s), were measured on plants for validation concurrently with the flights under different irrigation regimes (0, 20, 40 and 100 % of ETc). Estimated diurnal CWSIs were compared with plant-based parameters at different data acquisition times of the day. Results showed a strong relationship between ψ_stem measurements and the CWSIs at midday (12 h) with a high coefficient of determination (R² = 0.83). Diurnal CWSIs showed a significant R² to g_s over different levels of irrigation at three different times of the day with R² = 0.92 (9h), 0.77 (12h) and 0.86 (15h), respectively. The adaptive CWSI method used showed a robust capability to estimate plant water stress levels even with the small range of changes presented in the morning. Results of this work indicate that CWSI values collected by UAV-borne thermography between mid-morning and mid-afternoon can be used to map plant water stress with a consistent efficacy. This has important implications for extending the time-window of UAV-borne thermography (and subsequent areal coverage) for accurate plant water stress mapping beyond midday. Full article

Climate change entails increasingly frequent, longer, and more severe droughts, especially in some regions, such as the Mediterranean region. Under these water scarcity conditions, agricultural yields of important crops, such as garlic, are threatened. Finding better adapted cultivars to low water availability environments could help mitigate the negative agricultural and economic impacts of climate change. For this purpose, plant phenotyping protocols based on remote-sensing technologies, such as thermal imaging, can be particularly valuable since they facilitate screening and selection of germplasm in a cost-effective manner, covering a wide range of temporal and spatial scales. In this study, the use of a thermal index known as the crop water stress index (CWSI) was tested as a predictor of bulb biomass and for the assessment of inter-cultivar variability of five garlic cultivars in response to a gradient of soil volumetric water contents (VWCs). Three experimental assays, one in the 2018 season and two in 2019, covering a wide range of water availability levels were carried out. Different linear models were developed, with CWSI and VWCs as continuous predictors of bulb biomass, and the factor cultivar as a categorical predictor. The results support the existence of inter-cultivar variation in terms of sensitivity to water availability. The most productive cultivars under favorable conditions were also the most sensitive to water availability. In contrast, the cultivars with lower bulb production potential displayed lower sensitivity to water availability and higher stability across experimental assays. The results also support that CWSI, which was sensitive to inter-cultivar variability, is a good predictor of garlic bulb biomass. Therefore, CWSI can be a valuable tool for garlic phenotyping and cultivar screening. Full article

Precision agriculture aims at optimizing crop production by adapting management actions to real needs and requires that a reliable and extensive description of soil and crop conditions is available, that multispectral satellite images can provide. The purpose of the present study, based on activities carried out in 2019 on an agricultural area north of Ravenna (Italy) within the project LIFE AGROWETLANDS II, is to evaluate the potentials and limitations of freely available satellite thermal images for the identification of water stress conditions and the optimization of irrigation management practices, especially in agricultural areas and wetlands affected by saline soils and salt water capillary rise. Point field surveys and a very-high resolution thermal survey (5 cm) by an unmanned aerial vehicle (UAV) supported thermal camera were performed on a maize field tentatively at every Landsat-8 passage to check land surface temperature (LST) and canopy cover (CC) estimated from satellite. Temperature measured in the soil near ground surface and from UAV flying at 100 m altitude is compared with LST estimated from satellite measurements using three conversion methods: the top of atmosphere brightness temperature based on Landsat-8 band 10 (SB) corrected to account only for surface emissivity, the radiative transfer equation (RTE) for atmosphere effects correction, and the original split window method (SW) using both Thermal Infrared Sensor (TIRS) bands. The comparison shows discrepancies, due to extreme difference in resolution, the systematic hour of satellite passage (11 am solar time), and systematic differences between methods beside the unavoidable inaccuracy of UAV measurements. Satellite derived temperatures result usually lower than UAV measurements; SB produced the lowest values, SW the best (difference = −1.7 ± 1.7), and RTE the median (difference = −2.7 ± 1.6). The correlation between contemporary 30 m resolution temperature values of near pixels and corresponding tile-average temperatures was not significant, due to the purely numerical interpolation from the 100 m resolution TIRS images, whereas the time pattern along the season is consistent among methods, being correlation coefficient always greater than 0.85. Correlation coefficients among temperatures obtained from Landsat-8 by different methods are almost 1, showing that values are almost strictly related by a linear transformation. All the methods are useful to estimate water stress, since its associated Crop Water Stress Index (CWSI) is, from its definition, insensitive to linear transformation of temperatures. Actual evapotranspiration (ETa) maps are evaluated with the Surface Energy Balance Algorithm for Land (SEBAL) based on the three Landsat-8 derived LSTs; the higher is LST, the lower is ETa. Resulting ETa estimates are related with LST but not strictly, due to variation in vegetation cover and soil, therefore patterns result similar but not equivalent, whereas values are dependent on the atmosphere correction method. RTE and SW result in the best methods among the tested ones and the derived ETa values result reliable and appropriate to user needs. For real time application the Normalized Difference Moisture Index (NDMI), which can also be derived from more frequent Sentinel-2 passages, can be profitably used in combination or as a substitute of the CWSI. Full article

Despite covering only 2–6% of land, wetland ecosystems play an important role at the local and global scale. They provide various ecosystem services (carbon dioxide sequestration, pollution removal, water retention, climate regulation, etc.) as long as they are in good condition. By definition, wetlands are rich in water ecosystems. However, ongoing climate change with an ambiguous balance of rain in a temperate climate zone leads to drought conditions. Such periods interfere with the natural processes occurring on wetlands and restrain the normal functioning of wetland ecosystems. Persisting unfavorable water conditions lead to irreversible changes in wetland habitats. Hence, the monitoring of habitat changes caused by an insufficient amount of water (plant water stress) is necessary. Unfortunately, due to the specific conditions of wetlands, monitoring them by both traditional and remote sensing techniques is challenging, and research on wetland water stress has been insufficient. This paper describes the adaptation of the thermal water stress index, also known as the crop water stress index (CWSI), for wetlands. This index is calculated based on land surface temperature and meteorological parameters (temperature and vapor pressure deficit—VPD). In this study, an unmanned aerial system (UAS) was used to measure land surface temperature. Performance of the CWSI was confirmed by the high correlation with field measurements of a fraction of absorbed photosynthetically active radiation (R = −0.70) and soil moisture (R = −0.62). Comparison of the crop water stress index with meteorological drought indices showed that the first phase of drought (meteorological drought) cannot be detected with this index. This study confirms the potential of using the CWSI as a water stress indicator in wetland ecosystems. Full article

Accurate determination of plant water status is mandatory to optimize irrigation scheduling and thus maximize yield. Infrared thermography (IRT) can be used as a proxy for detecting stomatal closure as a measure of plant water stress. In this study, an open-source software (Thermal Image Processor (TIPCIP)) that includes image processing techniques such as thermal-visible image segmentation and morphological operations was developed to estimate the crop water stress index (CWSI) in potato crops. Results were compared to the CWSI derived from thermocouples where a high correlation was found ( $r_{Pearson}$ = 0.84). To evaluate the effectiveness of the software, two experiments were implemented. TIPCIP-based canopy temperature was used to estimate CWSI throughout the growing season, in a humid environment. Two treatments with different irrigation timings were established based on CWSI thresholds: 0.4 (T2) and 0.7 (T3), and compared against a control (T1, irrigated when soil moisture achieved 70% of field capacity). As a result, T2 showed no significant reduction in fresh tuber yield (34.5 ± 3.72 and 44.3 ± 2.66 t ha ${}^{-1}$ ), allowing a total water saving of 341.6 ± 63.65 and 515.7 ± 37.73 m ${}^3$ ha ${}^{-1}$ in the first and second experiment, respectively. The findings have encouraged the initiation of experiments to automate the use of the CWSI for precision irrigation using either UAVs in large settings or by adapting TIPCIP to process data from smartphone-based IRT sensors for applications in smallholder settings. Full article

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 (R²) 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 R² 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. Full article

Plant temperature is an indicator of stomatal conductance, which reflects soil moisture stresses. We explored the relationship between plant temperature and soil moisture to optimize irrigation schedules in a water-stress experiment using Firmiana platanifolia (L. f.) Marsili in an incubator. Canopy temperature, leaf temperature, and stomatal conductance were measured using thermal imaging and a porometer. The results indicated that (1) stomatal conductance decreased with declines in soil moisture, and reflected average canopy temperature; (2) the variation of the leaf temperature distribution was a reliable indicator of soil moisture stress, and the temperature distribution in severely water-stressed leaves exhibited greater spatial variation than that in the presence of sufficient irrigation; (3) thermal indices (Ig) and crop water stress index (CWSI) were theoretically proportional to stomatal conductance (gs), Ig was certified to have linearity relationship with gs and CWSI have a logarithmic relationship with gs, and both of the two indices can be used to estimate soil moisture; and (4) thermal imaging data can reflect water status irrespective of long-term water scarcity or lack of sudden rainfall. This study applied thermal imaging methods to monitor plants and develop adaptable irrigation scheduling, which are important for the formulation of effective and economical agriculture and forestry policy. Full article