Water Stress Index Research Articles

Advancements in Remote Sensing Imagery Applications for Precision Management in Olive Growing: A Systematic Review

This systematic review explores the role of remote sensing technology in addressing the requirements of sustainable olive growing, set against the backdrop of growing global food demands and contemporary environmental constraints in agriculture. The critical analysis presented in this document assesses different remote sensing platforms (satellites, manned aircraft vehicles, unmanned aerial vehicles and terrestrial equipment) and sensors (RGB, multispectral, thermal, hyperspectral and LiDAR), emphasizing their strategic selection based on specific study aims and geographical scales. Focusing on olive growing, particularly prominent in the Mediterranean region, this article analyzes the diverse applications of remote sensing, including the management of inventory and irrigation; detection/monitoring of diseases and phenology; and estimation of crucial parameters regarding biophysical parameters, water stress indicators, crop evapotranspiration and yield. Through a global perspective and insights from studies conducted in diverse olive-growing regions, this review underscores the potential benefits of remote sensing in shaping and improving sustainable agricultural practices, mitigating environmental impacts and ensuring the economic viability of olive trees.

Assessing the Potential for Photochemical Reflectance Index to Improve the Relationship between Solar-Induced Chlorophyll Fluorescence and Gross Primary Productivity in Crop and Soybean

Photosynthesis is influenced by dynamic energy allocation under various environmental conditions. Solar-induced chlorophyll fluorescence (SIF), an important pathway for dissipating absorbed energy, has been extensively used to evaluate gross primary productivity (GPP). However, the potential for photochemical reflectance index (PRI), as an indicator of non-photochemical quenching (NPQ), to improve the SIF-based GPP estimation, has not been thoroughly investigated. In this study, using continually tower-based observations, we examined how PRI affected the link between SIF and GPP for corn and soybean at half-hourly and daily timescales. The relationship of GPP to SIF and PRI is impacted by stress indicated by vapor pressure deficit (VPD) and crop water stress index (CWSI). Moreover, the ratio of GPP to SIF of corn was more sensitive to PRI compared to soybean. Whether in Pearson or Partial correlation analysis, the relationships of PRI to the ratio of GPP to SIF were almost all significant, regardless of controlling structural-physiological (stomatal conductance, vegetation indices) and environmental variables (light intensity, etc.). Therefore, PRI significantly affects the SIF–GPP relationship for corn (r > 0.31, p < 0.01) and soybean (r > 0.22, p < 0.05). After combining SIF and PRI using the multi-variable linear model, the GPP estimation has been largely improved (the coefficient of determination, abbreviated as R2, increased from 0.48 to 0.49 to 0.78 to 0.84 and the Root Mean Square Error, abbreviated as RMSE, decreased from 6.38 to 10.22 to 3.56 to 6.60 μmol CO2·m−2·s−1 for corn, R2 increased from 0.54 to 0.62 to 0.78 to 0.82 and RMSE decreased from 6.25 to 9.59 to 4.34 to 6.60 μmol CO2·m−2·s−1 for soybean). It suggests that better GPP estimations for corn and soybean can be obtained when SIF is combined with PRI.

Using the Hydraulic Properties of Zeolite to Grow Desert Willow—A Case Study to Rehabilitate Riparian Areas of Semi-Arid Environments

Plants in riparian areas are well known for their beneficial functions such as providing biodiverse habitats, maintaining water quality, and stabilizing streambanks. However, riparian plants are declining in semi-arid environments due to long-term drought, a decline in groundwater table, and an increase in soil salinity. A new technique using clinoptilolite zeolite (CZ) as a wicking material with minimum artificial irrigation to grow desert willow [Chilopsis linearis (Cav.) Sweet] under field conditions is introduced; desert willow is native to riparian regions of the southwestern United States. For this study’s experiment, desert willow seedlings were planted in boreholes filled with clinoptilolite zeolite (CZ) as a substrate and in situ riparian sandy loam soil (RS) as a control. The boreholes extended to the groundwater table at two distinctive depths, shallow (avg. depth = 1.21 m) and deep (avg. depth = 2.14 m). The plants’ viability was then assessed by measuring their midday water potential (Ψmd) as an indicator of water stress. There was no significant difference in Ψmd (p > 0.05) between the plants grown in CZ and RS (mean Ψmd = −0.91 vs. −0.81 MPa) where the groundwater was shallow and a significant difference (mean Ψmd = −0.75 vs. −2.03 MPa) where the groundwater was deep. The proposed method is promising as an alternative method for growing desert willow or other plants for riparian rehabilitation with no artificial irrigation. However, its effectiveness depends on groundwater being accessible at the base of the boreholes used for planting.

Improving the prediction performance of leaf water content by coupling multi-source data with machine learning in rice (Oryza sativa L.)

BackgroundLeaf water content (LWC) significantly affects rice growth and development. Real-time monitoring of rice leaf water status is essential to obtain high yield and water use efficiency of rice plants with precise irrigation regimes in rice fields. Hyperspectral remote sensing technology is widely used in monitoring crop water status because of its rapid, nondestructive, and real-time characteristics. Recently, multi-source data have been attempted to integrate into a monitored model of crop water status based on spectral indices. However, there are fewer studies using spectral index model coupled with multi-source data for monitoring LWC in rice plants. Therefore, 2-year field experiments were conducted with three irrigation regimes using four rice cultivars in this study. The multi-source data, including canopy ecological factors and physiological parameters, were incorporated into the vegetation index to accurately predict LWC in rice plants.ResultsThe results presented that the model accuracy of rice LWC estimation after combining data from multiple sources improved by 6–44% compared to the accuracy of a single spectral index normalized difference index (ND). Additionally, the optimal prediction accuracy of rice LWC was produced using a machine algorithm of gradient boosted decision tree (GBDT) based on the combination of ND(1287,1673) and crop water stress index (CWSI) (R2 = 0.86, RMSE = 0.01).ConclusionsThe machine learning estimation model constructed based on multi-source data fully utilizes the spectral information and considers the environmental changes in the crop canopy after introducing multi-source data parameters, thus improving the performance of spectral technology for monitoring rice LWC. The findings may be helpful to the water status diagnosis and accurate irrigation management of rice plants.

Construction of yield loss indicators for cold vortex, light-temperature-water combined stress during the flowering period of rice in Northeast China.

The construction of a yield loss evaluation index for the cold vortex type light-temperature-water composite adversity during rice flowering period in Northeast China is important for elucidating the impacts of cold vortex type composite disasters on rice yield loss in middle and high latitude areas. Moreover, it can provide meteorological support to ensure safe production of high-quality japonica rice in China and contribute to regional disaster reduction and efficiency improvement. By combining growth period data, meteorological data, and yield data, we delineated and constructed the composite stress occurrence index of cold vortex type light-temperature-water at the flowering stage of japonica. We analyzed the relationship between factors causing disasters and yield structure, as well as the relationship between different yield structures and yield by employing BP neural network method. We further dissected the processes involved in the causation of combined disasters. Based on the K-means clustering method and historical typical disaster years, we quantified the critical thresholds and disaster grades, and established an evaluation index and model for assessing yield loss caused by combined stress from cold vortex type light-temperature-water. Finally, we examined the spatial and temporal variations of low temperature, abundant rainfall, and reduced sunlight during the flowering period in the three provinces of Northeast China. Results showed that the critical thresholds for light, temperature, and water stress index during the flowering stage of mild, moderate, and severe cold vortex types were [0, 0.21), [0.21, 0.32), and [0.32, 0.64], respectively. The rates of yield loss were [0, 0.03), [0.03, 0.08), and [0.08, 0.096], respectively. Based on the verification results of a total of 751 samples in 11 random years from 1961 to 2020, the percentage of stations for which the production reduction grade, as calculated by the composite index developed in this study, aligning with the actual production reduction grade was 63.7%, consistently exceeding 58.0% annually. Moreover, the proportion of sites with a similarity or difference level of 1 stood at 88.3%, surpassing 85.0% in each year. The index could effectively assess the extent of rice yield loss caused by cold vortex disasters in Northeast China.

Quantifying Resource Nexus: Virtual Water Flows, Water Stress Indices, and Unsustainable Import Fraction in South Korea’s Grain Trade Landscape

This study delves into the intricate dynamics of South Korea’s grain production and trade, specifically scrutinizing water resource management and sustainability trends from 1991 to 2022. The primary objective was to assess the temporal evolution of South Korea’s agricultural virtual water trade in maize, soybeans and wheat. Employing the virtual water flows assessment method, water stress index, and an unsustainable import fraction (UIF) analysis, this research aimed to uncover key patterns and dynamics within the agricultural grain trade landscape. Utilizing comprehensive datasets encompassing grain trade, as well as water and land footprint data, this study assessed the intricate interplay between agricultural production, trade, and resource utilization. Results showed a reduction in local production for soybeans and a slight increase in domestic wheat production over the study period. In addition, the study revealed an overall increase in the virtual water imports associated with grain crops, with maize exhibiting the steepest upward trend in comparison with the other grains (wheat and soybeans). Furthermore, the study demonstrated that the import of maize contributed the highest amount of water and land savings, implying that this grain crop had the most significant impact on conserving local water and land resources. Environmental impact assessments, including water stress indices and the unsustainable import fractions, contributed to a comprehensive understanding of grain trade sustainability with concrete result values and insights, highlighting the intricate interplay of international grain trade and local water resource conservation in South Korea. By scrutinizing the virtual land and water dimensions of grain trade, this research offers valuable insights for policymakers and researchers striving to navigate the nexus of agriculture, trade, and resource management. The findings hold significance in the context of ensuring food security, optimizing resource allocation, and fostering sustainable agricultural practices in a dynamically evolving global landscape.

Infrared thermometry-based stress indices as indicators of yield performance and seasonal evapotranspiration in potato plants grown under different moisture and potassium regimes

As the demand for food continues to rise and water availability challenges intensify, the delicate equilibrium between efficient water usage and sustainable agricultural production has become increasingly pivotal. Recognizing the significance of water stress monitoring and nutrient management in the country's agricultural landscape, two years of field trials (2018–2019 and 2019–2020) were conducted at the Regional Research Station, Bidhan Chandra Krishi Viswavidyalaya, Gayeshpur, Nadia, to demonstrate the efficiency of canopy temperature-based indices for monitoring crop water stress in potato (cv. Kufri Jyoti) under varied irrigation (IW:CPE = 0.4, 0.8, 1.2 and 1.6) and potassium doses (K2O @ 100, 150 and 200 kg ha−1). The seasonal evapotranspiration of potato plants ranged from 121.0 mm to 320.9 mm during the crop growth period, and it exhibited a quadratic relationship with both the total dry biomass and the tuber yield. In 2019–2020, the highest water use efficiency (WUE) of 12.74 kg m−3 was obtained from the application of IW; CPE= 1.20 with the application of K2O @ 200 kg ha−1. In the following year, the highest WUE of 9.78 kg m−3 was obtained from irrigation at IW:CPE= 1.60, along with K2O application @ 200 kg ha−1. During a major part of the growth period, the canopy air temperature difference (CATD) was greater in the treatments in which irrigation was applied at IW:CPE = 0.4 and 0.8 than in those in which irrigation was applied at higher levels, i.e., IW:CPE = 1.2 and 1.6, implying the effectiveness of CATD as a stress indicator. The stress indices, stress degree days (SDD) and crop water stress index (CWSI) accumulated over the crop growing season showed decreasing trends with increasing irrigation and potassium doses. Both the accumulated SDD and accumulated CWSI showed a strong inverse relationship with tuber yield. The significant linear relationship between tuber yield and the seasonal accumulation of both infrared thermometry-based stress indices (i.e., SDD and CWSI) may help to predict crop yield in response to seasonal moisture stress and thus aid in strategic crop planning under the limited availability of irrigation.

Comparison of CWSI and Ts-Ta-VIs in moisture monitoring of dryland crops (sorghum and maize) based on UAV remote sensing

Monitoring agricultural drought using remote sensing data is crucial for precision irrigation in modern agriculture. Utilizing unmanned aerial vehicle (UAV) remote sensing, we explored the applicability of an empirical crop water stress index (CWSI) based on canopy temperature and three-dimensional drought indices (TDDI) constructed from surface temperature (Ts), air temperature (Ta) and five vegetation indices (VIs) for monitoring the moisture status of dryland crops. Three machine learning algorithms (random forest regression (RFR), support vector regression, and partial least squares regression) were used to compare the performance of the drought indices for vegetation moisture content (VMC) estimation in sorghum and maize. The main results of the study were as follows: (1) Comparative analysis of the drought indices revealed that Ts-Ta-normalized difference vegetation index (TDDIn) and Ts-Ta-enhanced vegetation index (TDDIe) were more strongly correlated with VMC compared with the other indices. The indices exhibited varying sensitivities to VMC under different irrigation regimes; the strongest correlation observed was for the TDDIe index with maize under the fully irrigated treatment (r=−0.93). (2) Regarding spatial and temporal characteristics, the TDDIn, TDDIe and CWSI indices showed minimal differences Over the experimental period, with coefficients of variation were 0.25, 0.18 and 0.24, respectively. All three indices were capable of effectively characterizing the moisture distribution in dryland maize and sorghum crops, but the TDDI indices more accurately monitored the spatial distribution of crop moisture after a rainfall or irrigation event. (3) For prediction of the moisture content of single crops, RFR models based on TDDIn and TDDIe estimated VMC most accurately (R2>0.7), and the TDDIn-based model predicted VMC with the highest accuracy when considering multiple-crop samples, with R2 and RMSE of 0.62 and 14.26%, respectively. Thus, TDDI proved more effective than the CWSI in estimating crop water content.

How do spatial scale and seasonal factors affect thermal-based water status estimation and precision irrigation decisions in vineyards?

The crop water stress index (CWSI) is widely used for assessing water status in vineyards, but its accuracy can be compromised by various factors. Despite its known limitations, the question remains whether it is inferior to the current practice of direct measurements of Ψstem of a few representative vines. This study aimed to address three key knowledge gaps: (1) determining whether Ψstem (measured in few vines) or CWSI (providing greater spatial representation) better represents vineyard water status; (2) identifying the optimal scale for using CWSI for precision irrigation; and (3) understanding the seasonal impact on the CWSI-Ψstem relationship and establishing a reliable Ψstem prediction model based on CWSI and meteorological parameters. The analysis, conducted at five spatial scales in a single vineyard from 2017 to 2020, demonstrated that the performance of the CWSI- Ψstem model improved with increasing scale and when meteorological variables were integrated. This integration helped mitigate apparent seasonal effects on the CWSI-Ψstem relationship. R2 were 0.36 and 0.57 at the vine and the vineyard scales, respectively. These values rose to 0.51 and 0.85, respectively, with the incorporation of meteorological variables. Additionally, a CWSI-based model, enhanced by meteorological variables, outperformed current water status monitoring at both vineyard (2.5 ha) and management cell (MC) scales (0.09 ha). Despite reduced accuracy at smaller scales, water status evaluation at the management cell scale produced significantly lower Ψstem errors compared to whole vineyard evaluation. This is anticipated to enable more effective irrigation decision-making for small-scale management zones in vineyards implementing precision irrigation.

The effects of high temperature and low humidity on crop water stress index of seed pumpkin plants (Cucurbita pepo L.) in semi-arid climate conditions

This study aimed to evaluate the effects of high temperature and low humidity on the crop water stress index (CWSI) of seed pumpkin plants grown under semi-arid climate conditions to determine the optimum irrigation time. This research unveils the critical impact of high temperature and low humidity on seed pumpkin growth, emphasizing the vital role of the CWSI in optimizing irrigation strategies and seed yield. Moreover, the relationship between CWSI, physiological parameters, and seed yield of the pumpkin was investigated. The mean CWSI values in the I70 (0.40) and I35 (0.56) treatments were 42% and 100% higher, respectively than those in the full irrigation (I100) treatment (0.28). While the I70 treatment showed manageable water stress with minimal impact, the I35 treatment experienced severe stress, significantly reducing crop growth and yield. The mean seed yield (SY) in the I70 treatment increased to 1245.2 kg ha–1 compared to I35 (903.3 kg ha–1) but remained lower than I100 (1339.3 kg ha–1). The CWSI had negative correlations (p ≤ 0.01) with seed yield, chlorophyll content, and leaf area index, while it had positive correlations with water use efficiency and irrigation water use efficiency (p ≤ 0.01). This study showed that pumpkins could be grown successfully at 30% water deficit conditions, and a water deficit higher than 30% may cause a significant seed yield loss in semi-arid climate conditions. In addition, the results highlight the importance of optimal irrigation and CWSI monitoring for informed irrigation decisions and sustainable agricultural practices. Therefore, moderate water deficit (I70) can be adopted in pumpkin cultivation as an alternative to full irrigation.

Brown bear digging decreases tree growth: Implication for ecological role of top predators in anthropogenic landscapes.

Large carnivores have recently increased in number and recolonized in human-dominated landscapes; however, their ecological roles in these landscapes have not been well studied. In the Shiretoko World Heritage (SWH) site, brown bears have recolonized a previously abandoned mosaic landscape of natural forests and conifer plantations after land abandonment. We previously reported that the bears had recently begun to dig for cicada nymphs in association with the creation of larch plantations. As a result, this digging activity decreased soil nutrients. To deepen the understanding of the novel ecological role of brown bears within human-modified landscapes, we examined the impacts of brown bear digging on the growth of larch trees. We found that brown bear digging decreased fine root biomass of larch, soil water, and nitrogen availability. Brown bear digging negatively affected needle nitrogen content, but not carbon isotope ratios, a water stress index of trees. Tree ring data suggest that digging negatively affected the radial growth of larches. The results imply that digging decreases tree growth due to limited soil nitrogen uptake. Our findings indicate that the ecological roles of large carnivores may differ between natural and anthropogenic landscapes.

Water Stress Index and Stomatal Conductance under Different Irrigation Regimes with Thermal Sensors in Rice Fields on the Northern Coast of Peru

In the face of the climate change crisis, the increase in air temperature negatively impacts rice crop productivity due to stress from water scarcity. The objective of this study was to determine the rice crop water stress index (CWSI) and stomatal conductance (Gs) under different irrigation regimes, specifically continuous flood irrigation treatments (CF) and irrigations with alternating wetting and drying (AWD) at water levels of 5 cm, 10 cm, and 20 cm below the soil surface (AWD5, AWD10, and AWD20) in an experimental area of INIA-Vista Florida and in six commercial areas of the Lambayeque region using thermal images captured with thermal sensors. The results indicated that AWD irrigation generated more water stress, with CWSI values between 0.4 and 1.0. Despite this, the yields were similar in CF and AWD20. In the commercial areas, CWSI values between 0.38 and 0.51 were obtained, with Santa Julia having the highest values. Furthermore, a strong Pearson correlation (R) of 0.91 was established between the CWSI and Gs, representing a reference scale based on Gs values for evaluating water stress levels.

Screening cotton genotypes for their drought tolerance ability based on the expression level of dehydration-responsive element-binding protein and proline biosynthesis-related genes and morpho-physio-biochemical responses.

Drought stress adversely affects growth, development, productivity, and fiber quality of cotton (Gossypium hirsutum L). Breeding strategies to enhance drought tolerance require an improved knowledge of plant drought responses necessitating proper identification of drought-tolerant genotypes of crops, including cotton. The objective of this study was to classify the selected cotton genotypes for their drought tolerance ability based on morpho-physio-biochemical traits using Hierarchical Ward's cluster analysis. Five genotypes of cotton (Takfa 3, Takfa 6, Takfa 7, Takfa 84-4, and Takfa 86-5) were selected as plant materials, and were grown under well-watered (WW; 98 ± 2% field capacity) and water-deficit (WD; 50 ± 2% field capacity) conditions for 16days during the flower initiation stage. Data on morpho-physio-biochemical parameters and gene expression levels for these parameters were collected, and subsequently genotypes were classified either as a drought tolerant or drought susceptible one. Upregulation of GhPRP (proline-rich protein), GhP5CS (Δ1-pyrroline-5-carboxylate synthetase), and GhP5CR (Δ1-pyrroline-5-carboxylate reductase) in relation to free proline enrichment was observed in Takfa 3 genotype under WD condition. An accumulation of free proline, total soluble sugar, and potassium in plants under WD conditions was detected, which played a key role as major osmolytes controlling cellular osmotic potential. Magnesium and calcium concentrations were also enriched in leaves under WD conditions, functioning as essential elements and regulating photosynthetic abilities. Leaf greenness, net photosynthetic rate, stomatal conductance, and transpiration rate were also declined under WD conditions, leading to growth retardation, especially aboveground traits of Takfa 6, Takfa 7, Takfa 84-4, and Takfa 86-5 genotypes. An increase in leaf temperature (1.1 - 4.0°C) and crop water stress index (CWSI > 0.75) in relation to stomatal closure and reduced transpiration rate was recorded in cotton genotypes under WD conditions compared with WW conditions. Based on the increase of free proline, soluble sugar, leaf temperature, and CWSI, as well as the decrease of aboveground growth traits and physiological attributes, five genotypes were categorized into two cluster groups: drought tolerant (Takfa 3) and drought susceptible (Takfa 6, Takfa 7, Takfa 84-4, and Takfa 86-5). The identified drought-tolerant cotton genotype, namely, Takfa 3, may be grown in areas experiencing drought conditions. It is recommended to further validate the yield traits of Takfa 3 under rainfed field conditions in drought-prone environments.

Precise Drought Threshold Monitoring in Winter Wheat Using the Unmanned Aerial Vehicle Thermal Method

Accurate monitoring of crop drought thresholds at different growth periods is crucial for drought monitoring. In this study, the canopy temperature (Tc) of winter wheat (‘Weilong 169’ variety) during the three main growth periods was extracted from high-resolution thermal and multispectral images taken by a complete unmanned aerial vehicle (UAV) system. Canopy-air temperature difference (ΔT) and statistic Crop Water Stress Index (CWSIsi) indicators were constructed based on Tc. Combined experiment data from the field and drought thresholds for the ΔT and CWSIsi indicators for different drought levels at three main growth periods were monitored. The results showed a strong correlation between the Tc extracted using the NDVI-OTSU method and ground-truth temperature, with an R2 value of 0.94. The CWSIsi was more stable than the ΔT index in monitoring the drought level affecting winter wheat. The threshold ranges of the CWSIsi for different drought levels of winter wheat at three main growth periods were as follows: the jointing–heading period, where the threshold ranges for normal, mild drought, moderate drought, and severe drought are <0.30, 0.30–0.42, 0.42–0.48, and >0.48, respectively; the heading–filling period, where the threshold ranges for normal, and mild, moderate, and severe drought are <0.33, 0.33–0.47, 0.44–0.53, and >0.53, respectively; and the filling–maturation period, where the threshold ranges for normal, mild drought, moderate drought, and severe drought are <0.41, 0.41–0.54, 0.54–0.59, and >0.59, respectively. The UAV thermal threshold method system can improve the accuracy of crop drought monitoring and has considerable potential in crop drought disaster identification.

Optimization of multi-dimensional indices for kiwifruit orchard soil moisture content estimation using UAV and ground multi-sensors

Accurately estimating of soil moisture content (SMC) is essential for effective irrigation water management and optimizing plant water productivity. Recent advancements in multi-sensor platforms and ensemble learning (EL) algorithms such as the use of UAV-based multispectral (MS) and thermal infrared (TIR) images, have enabled precise mapping of SMC at the field level. The purpose of this study is to optimize the multi-dimensional (n-D) index set and EL model to achieve accurate inversion mapping of SMC in the 0–60 cm root zone at a kiwifruit orchard during the fruit growth period (May-Sep 2022). The n-D index set was computed using kiwifruit canopy MS and TIR data from UAV along with in-field temperature data (air temperature [Ta], kiwifruit canopy temperature [Tc], soil temperature [Ts], and Ta-Tc, Ta-Ts, Tc-Ts). The primary findings of this study are as follows: (1) The Three-Dimensional Drought Index (TDDI), Temperature Vegetation Drought Index (TVDI), and Crop Water Stress Index (CWSI) demonstrated superior performance in capturing temporal variations of root zone SMC compared to Normalized Difference Vegetation Index (NDVI) and Enhance Vegetation Index (EVI). (2) The optimal Categorical Boosting (Catboost) model with two optimal TDDIs (TDDI22: (Tc-Ts)-EVI10,6,2-CWSI space and TDDI7: Tc-NDVI7,3-CWSI space), exhibited exceptional performance for SMC estimation, with determination coefficient (R2) and root-mean-square error (RMSE) of 0.966 ± 0.010 and 0.046 ± 0.003%, respectively. (3) The planted-by-planted mapping performed well in estimating root zone SMC at different growth stages and under different irrigation treatments, with a correlation coefficient (R) of 0.936 ± 0.074 (p < 0.001). This study demonstrated that the integration of multi-sensor indicators and EL models can improve the accuracy of SMC estimation due to its robust adaptability to various field conditions. Furthermore, the planted-grid-based SMC mapping framework can be considered as a valuable approach for monitoring dynamic SMC, facilitating informed irrigation decisions at the individual planting grid.

TVDI-based water stress coefficient to estimate net primary productivity in soybean areas

Net Primary Productivity (NPP) is a major parameter to assess carbon (C) increments by crops. Many models based on Light Use Efficiency (LUE) have been developed to estimate NPP in different regions. LUE in different ecosystems is reduced by environmental stressors, such as those caused by water restrictions. However, few studies have used remote sensing data to estimate water stress coefficients. Thus, our goal was to evaluate the performance of the Carnegie-Ames-Stanford-Approach (CASA) model using Temperature-Vegetation Dryness Index (TVDI) as a water stress coefficient to quantify NPP dynamics in agricultural ecosystems in northwestern Rio Grande do Sul state, in Brazil. Weather data from the ERA5 and surface weather stations were used to estimate NPP. Surface temperature (LST), Normalized Difference Vegetation Index (NDVI), estimated (EET) and potential (PET) evapotranspiration data were retrieved from Landsat/OLI and Terra/MODIS and used as input in the model. The CASA model was evaluated using ground-based data and then applied to the agricultural region of study. NPP data obtained using the CASA model and remote sensing data were in accordance with the observed data (RMSE less than 30 gC m−2 month−1 and r higher than 0,97), highlighting the model's efficiency in representing the temporal variations of NPP in the experimental area. The high correlation between simulated and observed data indicates that the TVDI is suitable for use as a water stress index. This work may serve as a baseline for future studies, which could explore the use of other indices and enhance NPP estimates.

Comparing Machine Learning Algorithms for Estimating the Maize Crop Water Stress Index (CWSI) Using UAV-Acquired Remotely Sensed Data in Smallholder Croplands

Monitoring and mapping crop water stress and variability at a farm scale for cereals such as maize, one of the most common crops in developing countries with 200 million people around the world, is an important objective within precision agriculture. In this regard, unmanned aerial vehicle-obtained multispectral and thermal imagery has been adopted to estimate the crop water stress proxy (i.e., Crop Water Stress Index) in conjunction with algorithm machine learning techniques, namely, partial least squares (PLS), support vector machines (SVM), and random forest (RF), on a typical smallholder farm in southern Africa. This study addresses this objective by determining the change between foliar and ambient temperature (Tc-Ta) and vapor pressure deficit to determine the non-water stressed baseline for computing the maize Crop Water Stress Index. The findings revealed a significant relationship between vapor pressure deficit and Tc-Ta (R2 = 0.84) during the vegetative stage between 10:00 and 14:00 (South Africa Standard Time). Also, the findings revealed that the best model for predicting the Crop Water Stress Index was obtained using the random forest algorithm (R2 = 0.85, RMSE = 0.05, MAE = 0.04) using NDRE, MTCI, CCCI, GNDVI, TIR, Cl_Red Edge, MTVI2, Red, Blue, and Cl_Green as optimal variables, in order of importance. The results indicated that NIR, Red, Red Edge derivatives, and thermal band were some of the optimal predictor variables for the Crop Water Stress Index. Finally, using unmanned aerial vehicle data to predict maize crop water stress index on a southern African smallholder farm has shown encouraging results when evaluating its usefulness regarding the use of machine learning techniques. This underscores the urgent need for such technology to improve crop monitoring and water stress assessment, providing valuable insights for sustainable agricultural practices in food-insecure regions.

Evaluation of Crop Water Stress Index (CWSI) for High Tunnel Greenhouse Tomatoes under Different Irrigation Levels

An experiment was conducted to determine the effect of water stress on yield and various physiological parameters, including the crop water stress index for tomatoes in the Central Anatolian region of Turkey. For this purpose, the irrigation schedule used in this study includes 120%, 100%, 80%, and 60% (I120, I100, I80, I60) of evaporation from the gravimetrically. Water deficit was found to cause a stress effect in tomato plants, which was reflected in changes in plants’ morphological and pomological function (such as stem diameter, fruit weight, pH, titratable acidity, and total soluble solids). Irrigation levels had a significant effect on the total yield of tomatoes. The lowest water use efficiency (WUE) was obtained from the I60, while the highest WUE was found in the I100 irrigation level. The CWSI was calculated using an empirical approach from measurements of infrared canopy temperatures, ambient air temperatures, and vapor pressure deficit values for four irrigation levels. The crop water stress index (CWSI) values ranged from −0.63 to a maximum value of 0.53 in I120, from −0.27 to 0.63 in I100, from 0.06 to 0.80 in I80, and from 0.37 to 0.97 in I60. There was a significant relation between yield and CWSI. The yield was correlated with mean CWSI values, and the linear equation Total yield = −2398.9CWSI + 1240.4 can be used for yield prediction. The results revealed that the CWSI value was useful for evaluating crop water stress in tomatoes and predicting yield.

A comprehensive drought index based on spatial principal component analysis and its application in northern China.

In the background of the greenhouse effect, drought events occurred more frequently. How to monitor drought events scientifically and efficiently is very urgent at present. In this study, we employed the Vegetation Water Supply Index (VSWI), Temperature Vegetation Drought Index (TVDI), and Crop Water Stress Index (CWSI) as individual variables to construct a composite drought index (CDI) using spatial principal component analysis (SPCA). The validity of CDI was assessed using gross primary productivity (GPP), soil moisture (SM), Standardized Precipitation Evapotranspiration Index (SPEI), and Vegetation Condition Index (VCI). CDI was subsequently used for drought monitoring in northern China from 2011 to 2020. The results showed that (1) at a 99% confidence level, the Pearson correlation coefficients between CDI and GPP was 0.72, while the value between CDI and SM was 0.69, which indicated the relationship between SM, GPP, and CDI was significant. (2) We compared CDI with other variables such as Standardized Precipitation Evapotranspiration Index (SPEI) and Crop Drought Index (CDI) and found that the monitoring result of CDI was more sensitive, which indicated that the proposed CDI had a better effect in local drought monitoring. (3) The results of CDI showed that the drought status in the northern region during 2011-2020 lasted from March to October, and the high severe drought period generally occurs in March-May and September-October, with low severe drought in June-August.

Evaluation of canopy fraction-based vegetation indices, derived from multispectral UAV imagery, to map water status variability in a commercial vineyard

AbstractWater stress is a major factor affecting grapevine yield and quality. Standard methods for measuring water stress, such as midday stem water potential (ΨSWP), are laborious and time-consuming for intra-block variability mapping. In this study, we investigate water status variability within a 2.42-ha commercial Cabernet Sauvignon block with a standard vertical trellis system, using remote sensing (RS) tools, specifically canopy fraction-based vegetation indices (VIs) derived from multispectral unmanned aerial vehicle (UAV) imagery, as well as standard reference methods to evaluate soil and plant water status. A total of 31 target vines were monitored for ΨSWP during the whole growing season. The highest variability was at véraison when the highest atmospheric demand occurred. The ΨSWP variability present in the block was contrasted with soil water content (SWC) measurements, showing similar patterns. With spatial and temporal water stress variability confirmed for the block, the relationship between the ΨSWP measured in the field and fraction-based VIs obtained from multispectral UAV data was analysed. Four UAV flights were obtained, and five different VIs were evaluated per target vine across the vineyard. The VI correlation to ΨSWP was further evaluated by comparing VI obtained from canopy fraction (VIcanopy) versus the mean (VImean). It was found that using canopy fraction-based VIs did not significantly improve the correlation with ΨSWP (NDVIcanopyr = 0.57 and NDVImeanr = 0.53), however fractional cover (fcover) did seem to show a similar trend to plant water stress with decreasing canopy size corresponding with water stress classes. A subset of 14 target vines were further evaluated to evaluate if additional parameters (maximum temperature, relative humidity (RH), vapour pressure deficit, SWC and fractional cover) could serve as potential water stress indicators for future mapping. Results showed that the integration of NDVIcanopy and NDREmean with additional information could be used as an indicator for mapping water stress variability within a block.