Indicator Of Water Status Research Articles

Identification of plant water status indicators for fig

Identification of plant water status indicators for fig

Combining proximal and remote sensing to assess 'Calatina' olive water status.

Developing an efficient and sustainable precision irrigation strategy is crucial in contemporary agriculture. This study aimed to combine proximal and remote sensing techniques to show the benefits of using both monitoring methods, simultaneously assessing the water status and response of 'Calatina' olive under two distinct irrigation levels: full irrigation (FI), and drought stress (DS, -3 to -4 MPa). Stem water potential (Ψstem) and stomatal conductance (gs) were monitored weekly as reference indicators of plant water status. Crop water stress index (CWSI) and stomatal conductance index (Ig) were calculated through ground-based infrared thermography. Fruit gauges were used to monitor continuously fruit growth and data were converted in fruit daily weight fluctuations (ΔW) and relative growth rate (RGR). Normalized difference vegetation index (NDVI), normalized difference RedEdge index (NDRE), green normalized difference vegetation index (GNDVI), chlorophyll vegetation index (CVI), modified soil-adjusted vegetation index (MSAVI), water index (WI), normalized difference greenness index (NDGI) and green index (GI) were calculated from data collected by UAV-mounted multispectral camera. Data obtained from proximal sensing were correlated with both Ψstem and gs, while remote sensing data were correlated only with Ψstem. Regression analysis showed that both CWSI and Ig proved to be reliable indicators of Ψstem and gs. Of the two fruit growth parameters, ΔW exhibited a stronger relationship, primarily with Ψstem. Finally, NDVI, GNDVI, WI and NDRE emerged as the vegetation indices that correlated most strongly with Ψstem, achieving high R2 values. Combining proximal and remote sensing indices suggested two valid approaches: a more simplified one involving the use of CWSI and either NDVI or WI, and a more comprehensive one involving CWSI and ΔW as proximal indices, along with WI as a multispectral index. Further studies on combining proximal and remote sensing data will be necessary in order to find strategic combinations of sensors and establish intervention thresholds.

Thermal imaging from UAS for estimating crop water status in a Merlot vineyard in semi-arid conditions

AbstractThermal remote sensing indicators of crop water status can help to optimize irrigation across time and space. The Crop Water Stress Index (CWSI), calculated from thermal data, has been widely used in irrigation management as it has a proven association with evapotranspiration ratios. However, different approaches can be used to calculate the CWSI. The aim of this study is to identify the most robust method for estimating the CWSI in a commercial Merlot vineyard using high-resolution thermal imaging from Unoccupied Aerial Systems (UAS). To that end, three different methods were used to estimate the CWSI: Jackson’s model (CWSIj), Wet Artificial Reference Surface (WARS) method (CWSIw), and the Bellvert approach (CWSIb). A simpler indicator calculated as the difference between canopy and air temperature (Tc–Ta) was the benchmark to beat. The water status of a vine cultivar with anisohydric behavior (Merlot) in a vineyard in central Spain was assessed for two years with different agroclimatic conditions. Canopy temperature (Tc) was obtained from UAS flights at 9:00 h and 12:00 h solar hour over eight days during the irrigation period (June–August), and from vines under five different irrigation treatments. Stem water potential (SWP), stomatal conductance (gs), and leaf temperature (TL) were recorded at the time of the flights and compared with the thermal indices (CWSIj, CWSIw, CWSIb) and the benchmark indicator (Tc–Ta). Results show that the simpler indicator of water stress, Tc–Ta, performed better at identifying varying levels of crop hydration than CWSIb or CWSIw at 12:00 h. Under conditions of extreme aridity, the latter indices were less accurate than the physically-based CWSIj at 12:00 h, which had the highest correlation with SWP (r = 0.84), followed by the benchmark index Tc–Ta (r = 0.70 at 12:00). Considering the current climatic trends towards aridification, the CWSIj emerges as a useful operational tool, with robust performance across days and times of day. These results are important for irrigation management and could contribute to improving water use efficiency in agriculture.

Spatio-temporal monitoring of plant water status using optical remote sensing data and in situ measurements

Water content as an important physiological status variable in plants, is closely linked to transpiration, photosynthesis, water stress, and biomass productivity. Obtaining plant water information that provides sufficient spatial and temporal resolution for such applications remains a challenge. In this study, the data distribution space of fractional vegetation cover (FVC) and shortwave infrared transformed reflectance (STR) with linear and nonlinear edges was used to monitor plant water content. Four indicators of plant water status, equivalent water thickness (EWT), leaf water content (LWC), relative water content (RWC), and leaf water potential (LWP), were measured in a 10-ha corn field at the Karkaj Agricultural Research Station of Tabriz University, Tabriz, Iran. Furthermore, the Sentinel-2 Level 2 prototype processor (SL2P) was utilized to estimate EWT and compare the results with FVC-STR space. The FVC-STR space with nonlinear edges (FSNLE) provided better estimation accuracy of plant water status than the FVC-STR space with linear edges (FSLE). The root mean square errors of the EWT, LWC, RWC and LWP estimates for FSLE were 0.00306 g/cm2, 4.03 %, 6.56 %, and 0.38 bar, respectively, while those for FSNLE were 0.00303 g/cm2, 3.75 %, 5.57 %, and 0.37 bar, respectively. In addition, the R2 value for FSNLE was higher than that for FSLE (0.43–0.70 vs. 0.39–0.64). The RMSE and R2 for SL2P were 0.0041 g/cm2 and 0.401, respectively. Among the four measured indicators, the highest and lowest estimation accuracies in both FSLE and FSNLE were obtained with EWT and RWC, respectively. It can be concluded that FVC-STR space model based on Sentinel-2 imagery data provide acceptable accuracy for estimating plant water content. The FVC-STR space with nonlinear edges provided a better estimation accuracy for plant water indicators than the FVC-STR space with linear edges.

Relating microtensiometer-based trunk water potential with sap flow, canopy temperature, and trunk and fruit diameter variations for irrigated ‘Honeycrisp’ apple

Instrumentation plays a key role in modern horticulture. Thus, the microtensiomenter, a new plant-based sensor that continuously monitors trunk water potential (Ψtrunk) can help in irrigation management decisions. To compare the response of the Ψtrunk with other continuous tree water status indicators such as the sap flow rate, the difference between canopy and air temperatures, or the variations of the trunk and fruit diameter, all the sensors were installed in 2022 in a commercial orchard of ‘Honeycrisp’ apple trees with M.9 rootstocks in Washinton State (USA). From the daily evolution of the Ψtrunk, five indicators were considered: predawn, midday, minimum, daily mean, and daily range (the difference between the daily maximum and minimum values). The daily range of Ψtrunk was the most linked to the maximum daily shrinkage (MDS; R2 = 0.42), the canopy-to-air temperature (Tc-Ta; R2 = 0.32), and the sap flow rate (SF; R2 = 0.30). On the other hand, the relative fruit growth rate (FRGR) was more related to the minimum Ψtrunk (R2 = 0.33) and the daily mean Ψtrunk (R2 = 0.32) than to the daily range of Ψtrunk. All indicators derived from Ψtrunk identified changes in tree water status after each irrigation event and had low coefficients of variation and high sensitivity. These results encourage Ψtrunk as a promising candidate for continuous monitoring of tree water status, however, more research is needed to better relate these measures with other widely studied plant-based indicators and identify good combinations of sensors and threshold values.

Intra-annual dynamics of stem circumference variation and water status of four coniferous tree species (Pinus sylvestris, Picea abies, Larix decidua and Abies alba) under warmer and water-limited conditions

AbstractIn the paper, the intra-annual growth patterns and tree water balance of four different tree species (Pinus sylvestris, Picea abies, Larix decidua and Abies alba) were examined. Seasonal radial increment, tree water deficit (ΔW) and maximum daily shrinkage (MDS) were derived from the records obtained using high-resolution digital band dendrometers. The study area was located in Arboretum Borová hora (350 m a. s. l., Zvolen valley, Central Slovakia) characterised by a warmer climate (Picea abies) and warmer and drier climate (Abies alba, Larix decidua, Pinus sylvestris) compared to the sites of tree origins. Monitored species exhibited remarkably distinct growth and water balance patterns over the 2015 growing season characterised with the highly above normal temperature and uneven precipitation distribution. A. alba exhibited smooth continuous growth least affected by varying environmental conditions. Of all analysed species, only A. alba showed significant positive correlations of radial growth and ΔW with temperature and global radiation, despite environmental water limitations. The lowest cumulative growth, lower negative values of ΔW and greater MDS of L. decidua indicate a higher water limitation of this species. The results showed more pronounced sensitivity of P. sylvestris to increased temperature and drought. All monitored variables of environmental conditions, except precipitation, significantly influenced MDS values of all studied tree species. Based on 30 variables describing radial stem growth patterns and water status we identified large inter-species variability and discrete species-specific groups, while the indicators of growth and water status of L. decidua and P. sylvestris were similar and the most different patterns were observed between A. alba and L. decidua. The behaviour of P. abies was closer to A. alba than to the other two species.

Low Nitrogen Input Mitigates Quantitative but Not Qualitative Reconfiguration of Leaf Primary Metabolism in Brassica napus L. Subjected to Drought and Rehydration.

In the context of climate change and the reduction of mineral nitrogen (N) inputs applied to the field, winter oilseed rape (WOSR) will have to cope with low-N conditions combined with water limitation periods. Since these stresses can significantly reduce seed yield and seed quality, maintaining WOSR productivity under a wide range of growth conditions represents a major goal for crop improvement. N metabolism plays a pivotal role during the metabolic acclimation to drought in Brassica species by supporting the accumulation of osmoprotective compounds and the source-to-sink remobilization of nutrients. Thus, N deficiency could have detrimental effects on the acclimation of WOSR to drought. Here, we took advantage of a previously established experiment to evaluate the metabolic acclimation of WOSR during 14 days of drought, followed by 8 days of rehydration under high- or low-N fertilization regimes. For this purpose, we selected three leaf ranks exhibiting contrasted sink/source status to perform absolute quantification of plant central metabolites. Besides the well-described accumulation of proline, we observed contrasted accumulations of some "respiratory" amino acids (branched-chain amino acids, lysineand tyrosine) in response to drought under high- and low-N conditions. Drought also induced an increase in sucrose content in sink leaves combined with a decrease in source leaves. N deficiency strongly decreased the levels of major amino acids and subsequently the metabolic response to drought. The drought-rehydration sequence identified proline, phenylalanine, and tryptophan as valuable metabolic indicators of WOSR water status for sink leaves. The results were discussed with respect to the metabolic origin of sucrose and some amino acids in sink leaves and the impact of drought on source-to-sink remobilization processes depending on N nutrition status. Overall, this study identified major metabolic signatures reflecting a similar response of oilseed rape to drought under low- and high-N conditions.

Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach.

Climate change is affecting global viticulture, increasing heatwaves and drought. Precision irrigation, supported by robust water status indicators (WSIs), is inevitable in most of the Mediterranean basin. One of the most reliable WSIs is the leaf water potential (Ψleaf), which is determined via an intrusive and time-consuming method. The aim of this work is to discern the most effective variables that are correlated with plants' water status and identify the variables that better predict Ψleaf. Five grapevine varieties grown in the Alentejo region (Portugal) were selected and subjected to three irrigation treatments, starting in 2018: full irrigation (FI), deficit irrigation (DI), and no irrigation (NI). Plant monitoring was performed in 2023. Measurements included stomatal conductance (gs), predawn water potential Ψpd, stem water potential (Ψstem), thermal imaging, and meteorological data. The WSIs, namely Ψpd and gs, responded differently according to the irrigation treatment. Ψstem measured at mid-morning (MM) and mid-day (MD) proved unable to discern between treatments. MM measurements presented the best correlations between WSIs. gs showed the best correlations between the other WSIs, and consequently the best predictive capability to estimate Ψpd. Machine learning regression models were trained on meteorological, thermal, and gs data to predict Ψpd, with ensemble models showing a great performance (ExtraTrees: R2=0.833, MAE=0.072; Gradient Boosting: R2=0.830; MAE=0.073).

Impact of Gibberellic Acid on Water Status, Growth, and Development of Cape Gooseberry in Newly Reclaimed Sandy Lands within Arid Regions

In newly reclaimed sandy lands, plants face substantial environmental challenges, affecting their productivity, yield, and quality. Gibberellic acid (GA3) is a plant growth regulator physiologically involved in plant responses to abiotic stresses. As agricultural activities expand in desert regions, applications of GA3 could help address adverse plant growth and developmental effects. Here, we investigated the impact of exogenously applied GA3 on the growth of Cape gooseberry (Physalis peruviana L.) in newly reclaimed sandy lands in the arid Nubaria region of the West Delta of Nile, Egypt. Different GA3 concentrations of 100, 150, 200, and 250 ppm were foliar-applied to the plants. The application of GA3 in our study significantly improved the vegetative growth, plant height, leaf and branch count, and the fresh weight and yield of Cape gooseberry plants. Fruit weight, quality soluble solids, and leaf chlorophyll content were also improved. The most pronounced effects were achieved with concentrations of GA3 at 200 and 250 ppm, with the 200-ppm concentration displaying superiority in most parameters. Notably, GA3 treatments enhanced relative water content (RWC), an indicator of water status in arid conditions. Maintaining optimal RWC is crucial for essential processes like photosynthesis, promoting growth, and productivity. This research underscores GA3’s potential in enhancing Cape gooseberry growth, yield, and quality, providing a practical strategy for mitigating environmental challenges in arid regions, a concern exacerbated by climate change.

Using Soil Water Status Sensors to Optimize Water and Nutrient Use in Melon under Semi-Arid Conditions

Nowadays, agriculture must satisfy the growing demand for food, and increasing its sustainability, from an environmental, economic, and social point of view, is the only way to achieve this. The objective of this study was to increase the water and nutrient use efficiency of a melon crop during two consecutive seasons under commercial conditions, growing under semi-arid area. For this purpose, two treatments were studied: (i) a farmer treatment (FRM), fertigated at ~100% of crop evapotranspiration (ETc) during the whole growing season; and (ii) a precision irrigation treatment (PI), irrigated by adjusting, between flowering and ripening, the weekly farmer irrigation to minimize the leaching below the root system. The threshold for allowable soil water depletion in the active root uptake zone was set at 20–30%. The cumulative water savings in each year relative to the FRM treatment ranged between 30 and 27% for 2020 and 2021, respectively. Yield was not negatively affected, with no differences in fruit load (fruit per m) or fruit weight (kg) between irrigation treatments, although higher yields were obtained in the second year due to seasonal changes. The crop water status indicators evaluated (stem water potential, net photosynthesis, and stomatal conductance) were not affected by the irrigation treatments. Water and nitrogen productivity, on average, increased by 45.5 and 54.4% during the experimental period, respectively; the average PI ascorbic acid content increased by 33.4%.

Threshold Values of Plant Water Status for Scheduling Deficit Irrigation in Early Apricot Trees

Irrigated agriculture is facing a serious problem of water scarcity, which could be mitigated by optimizing the application of regulated deficit irrigation (RDI) strategies. For this reason, the aim of our study was to determine irrigation thresholds based on direct water status indicators of apricot trees under RDI to maximize water productivity. Three treatments were tested: (i) Control (CTL), irrigated at 100% of the crop evapotranspiration (ETc) during the entire crop cycle; (ii) RDI1, irrigated as CTL, except during fruit growth stages I–II when irrigation was reduced by 20% of CTL, and during late post-harvest, with an irrigation threshold of a moderate water stress of −1.5 MPa of stem water potential (Ψs); and (iii) RDI2, irrigated as RDI1, but during late post-harvest using a severe water stress threshold of −2.0 MPa of Ψs. As the irrigation scheduling of RDI1 and RDI2 did not affect yield and fruit quality, the crop water productivity was increased by 13.2 and 25.6%, respectively. This corresponded to 1124 and 2133 m3 ha−1 of water saved for RDI1 and RDI2. A water stress integral of 30.2 MPa day during post-harvest could be considered optimal since when 41 MPa day was accumulated, vegetative growth was reduced by 35%. The non-sensitive periods to water deficit were delimited by the accumulation of growing degree days (GDD) from full bloom, the end of fruit growth stages I–II corresponded to an accumulation of 640 °C GDD, and the beginning of the late post-harvest to an accumulation of 1840 °C GDD.

Estimating soil and grapevine water status using ground based hyperspectral imaging under diffused lighting conditions: Addressing the effect of lighting variability in vineyards

A timely and appropriate level of water deficit is desirable in wine grape production to optimize fruit quality for winemaking. Thus, it is crucial to find a robust and rapid method to assess grapevine water stress in real time. Hyperspectral imaging (HSI) has the potential to detect changes in leaf water status, but the robustness and accuracy are limited in field applications. This study focused on developing ground based approaches for detecting soil and grapevine water status using HSI obtained in diffused lighting conditions. During the 2021 growing season, leaf water potential (ΨL), stomatal conductance (gs) on the selected leaves and volumetric soil moisture (θv) in the root zone were measured as water status indicators. Spectral data from diffused and direct sunlight conditions were obtained to construct models to estimate plant and soil water status indicators. Partial least squares (PLS) regression models were individually developed to estimate ΨL, gs, and θv using spectra obtained from direct/diffused lighting conditions, respectively. The results indicated that the ΨL estimation model using spectral data from diffused lighting performed better than that obtained using direct sunlight, indicated by a higher R2 (0.89 vs. 0.82), a lower RMSE (0.12 vs. 0.15 MPa) and a lower MAE (0.10 vs. 0.11 MPa). The model developed for estimating θv using spectral data under diffused lighting achieved superior performance to the one in direct sunlight in terms of R2, RMSE and MAE (0.90 vs. 0.89 and 1.56 vs. 1.59 %, 1.26 vs. 1.29 %). These results demonstrated that spectral data obtained under diffused lighting can improve model performance by providing a more uniform illumination. Ground based HSI was capable of high-resolution sensing of grapevine water status by estimating ΨL and gs and map variability within canopies.

Application of Remote Sensing in Detecting and Monitoring Water Stress in Forests

In the context of climate change, the occurrence of water stress in forest ecosystems, which are solely dependent on precipitation, has exhibited a rising trend, even among species that are typically regarded as drought-tolerant. Remote sensing techniques offer an efficient, comprehensive, and timely approach for monitoring forests at local and regional scales. These techniques also enable the development of diverse indicators of plant water status, which can play a critical role in evaluating forest water stress. This review aims to provide an overview of remote sensing applications for monitoring water stress in forests and reveal the potential of remote sensing and geographic information system applications in monitoring water stress for effective forest resource management. It examines the principles and significance of utilizing remote sensing technologies to detect forest stress caused by water deficit. In addition, by a quantitative assessment of remote sensing applications of studies in refereed publications, the review highlights the overall trends and the value of the widely used approach of utilizing visible and near-infrared reflectance data from satellite imagery, in conjunction with classical vegetation indices. Promising areas for future research include the utilization of more adaptable platforms and higher-resolution spectral data, the development of novel remote sensing indices with enhanced sensitivity to forest water stress, and the implementation of modelling techniques for early detection and prediction of stress.

Potential application of spectral indices for olive water status assessment in (semi-)arid regions: A case study in Khuzestan Province, Iran.

Spectral indices can be used as fast and non-destructive indicators of plant water status or stress. It is the objective of the present study to evaluate the feasibility of using several spectral indices including water index (WI) and normalized spectral water indices 1-5 (NWI 1-5) to estimate water status in olive trees in arid regions in Iran. The experimental treatments involved two olive cultivars (Koroneiki and T2) and four irrigation regimes (irrigated with 100%, 85%, 70%, and 55% estimated crop evapotranspiration [ETc]). The results obtained showed that olive trees subjected to the different irrigation regimes of 85%, 70%, and 55% ETc experienced soil water content (SWC) deficits by 4.5%, 12%, and 20.5% that of the control, respectively. Significant differences were observed among the treatments with respect to measured relative water content (RWC), SWC, and the spectral indices of WI and NWI 1-5. The normalized spectral indices combining NIR and NIR wavelengths were found more effective in tracking changes in RWC and SWC than those that combine NIR and VIS or VIS and VIS wavelengths, respectively. Spectral indices were closely and significantly associated with RWC () and SWC (). Among all the spectral indices investigated, NWI-2 showed the least consistent associations with RWC (ranging from 4-15% lower than the other indices examined) and SWC (ranging from 1-23% lower than the others). Based on the pooled data on spectral indices, RWC, and SWC collected during the study period, WI, NWI-1, NWI-4, and NWI-5 showed stronger correlations with RWC and SWC than did NWI-3 and NWI-2. In conclusion, the spectral indices of WI and NWI 1-5 measured at the leaf level are found useful as fast and non-destructive estimators of plant water stress in arid regions.

Decision-support system for precision regulated deficit irrigation management for wine grapes

Deficit irrigation is often used in the wine grape industry to balance grape yield for optimizing fruit quality for winemaking. Regulated deficit irrigation (RDI) is an irrigation management strategy which applies less water than the full water requirement in some growing phases (e.g. from fruit set to veraison). This study focused on developing a decision-support system for managing precision RDI in vineyards. The system consists of a soil moisture prediction model and an RDI scheduling model developed based on artificial neural networks (ANN). Initial soil moisture, weather variables, crop coefficient, and irrigation amount were used as inputs to the soil moisture prediction model. The output from this prediction model provides an indicator of future water status in soil and vines. Initial soil moisture, weather variables, crop coefficient and desired soil moisture target were used as inputs to the RDI scheduling model for regulating the amount of water applied to achieve a desired soil moisture target which is connected to the grapevine water stress target. Field data were collected weekly during the 2017 to 2021 growing seasons. Data from the first four seasons (2017–2020) was used to train the models, and the 2021 data was used to test the system’s performance. Validation test results showed that the soil moisture prediction model could predict the soil moisture in the following week with an R2 of 0.9325 and RMSE of 0.8609 % m3·m−3, and the RDI scheduling model could estimate the weekly irrigation water amount for maintaining a target soil moisture with an R2 of 0.9413 and RMSE of 8.8518 L per drip irrigation emitter. The results demonstrated that this system was capable of predicting the following-week soil moisture changes and creating an adequate weekly drip irrigation plan for controlling the soil moisture at desired levels. This system could potentially be a useful practical tool for managing RDI in vineyards with the aim of achieving the production goal of balanced yield and optimized fruit quality.

Assessment of trunk microtensiometer as a novel biosensor to continuously monitor plant water status in nectarine trees.

The objective of this work was to validate the trunk water potential (Ψtrunk), using emerged microtensiometer devices, as a potential biosensor to ascertain plant water status in field-grown nectarine trees. During the summer of 2022, trees were subjected to different irrigation protocols based on maximum allowed depletion (MAD), automatically managed by real-time soil water content values measured by capacitance probes. Three percentages of depletion of available soil water (α) were imposed: (i) α=10% (MAD=27.5%); (ii) α=50% (MAD=21.5%); and (iii) α=100%, no-irrigation until Ψstem reached -2.0 MPa. Thereafter, irrigation was recovered to the maximum water requirement of the crop. Seasonal and diurnal patterns of indicators of water status in the soil-plant-atmosphere continuum (SPAC) were characterised, including air and soil water potentials, pressure chamber-derived stem (Ψstem) and leaf (Ψleaf) water potentials, and leaf gas exchange, together with Ψtrunk. Continuous measurements of Ψtrunk served as a promising indicator to determine plant water status. There was a strong linear relationship between Ψtrunk vs. Ψstem (R2 = 0.86, p<0.001), while it was not significant between Ψtrunk vs. Ψleaf (R2 = 0.37, p>0.05). A mean gradient of 0.3 and 1.8 MPa was observed between Ψtrunk vs.Ψstem and Ψleaf, respectively. In addition, Ψtrunk was the best matched to the soil matric potential. The main finding of this work points to the potential use of trunk microtensiometer as a valuable biosensor for monitoring the water status of nectarine trees. Also, trunk water potential agreed with the automated soil-based irrigation protocols implemented.

A physical method for downscaling land surface temperatures using surface energy balance theory

Fine-resolution land surface temperature (LST) derived from thermal infrared remote sensing images is a good indicator of surface water status and plays an essential role in the exchange of energy and water between land and atmosphere. A physical surface energy balance (SEB)-based LST downscaling method (DTsEB) is developed to downscale coarse remotely sensed thermal infrared LST products with fine-resolution visible and near-infrared data. The DTsEB method is advantageous for its ability to mechanically interrelate surface variables contributing to the spatial variation of LST, to quantitatively weigh the contributions of each related variable within a physical framework, and to efficaciously avoid the subjective selection of scaling factors and the establishment of statistical regression relationships. The applicability of the DTsEB method was tested by downscaling 12 scenes of 990 m Moderate Resolution Imaging Spectroradiometer (MODIS) and aggregated Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST products to 90 m resolution at six overpass times between 2005 and 2015 over three 9.9 km by 9.9 km cropland (mixed by grass, tree, and built-up land) study areas. Three typical LST downscaling methods, namely the widely applied TsHARP, the later developed least median square regression downscaling (LMS) and the geographically weighted regression (GWR), were introduced for intercomparison. The results showed that the DTsEB method could more effectively reconstruct the subpixel spatial variations in LST within the coarse-resolution pixels and achieve a better downscaling accuracy than the TsHARP, LMS and GWR methods. The DTsEB method yielded, on average, root mean square errors (RMSEs) of 2.01 K and 1.42 K when applied to the MODIS datasets and aggregated ASTER datasets, respectively, which were lower than those obtained with the TsHARP method, with average RMSEs of 2.41 K and 1.71 K, the LMS method, with average RMSEs of 2.35 K and 1.63 K, and the GWR method, with average RMSEs of 2.38 K and 1.64 K, respectively. The contributions of the related surface variables to the subpixel spatial variation in the LST varied both spatially and temporally and were different from each other. In summary, the DTsEB method was demonstrated to outperform the TsHARP, LMS, and GWR methods and could be used as a good alternative for downscaling LST products from coarse to fine resolution with high robustness and accuracy.

A leaf-mounted capacitance sensor for continuous monitoring of foliar transpiration and solar irradiance as an indicator of plant water status

A leaf-mounted sensor is described which detects condensing water vapour originating from leaf transpiration, taking advantage of a passive temperature gradient across the sunlit leaf and the underneath sensor plate, and simultaneously monitors incident solar radiation. The simple and low-cost device enables the qualitative assessment of plant water status by comparing the diurnal patterns of leaf transpiration and solar irradiance. A close correlation between condensation and irradiance occurs in conditions of unrestricted water supply, whereas a deviation of their course likely indicates a suboptimal plant water status.

Physiological basis to assess barley response to optimized regulated deficit irrigation for limited volumes of water (ORDIL)

Agriculture must improve the productivity of irrigation water due to several factors, such as global warming, the increasing water demand of other sectors or the protection of the environment. The “optimized regulated deficit irrigation for limited volumes of water” (ORDIL) methodology may contribute to reach this objective by optimizing the allocation of irrigation water during the growing cycle, when the available volume is lower than the crop irrigation requirements. ORDIL was applied to a barley crop in a 3-year (2015–2017) field test under the semiarid conditions of Albacete (Spain). The main aim was to assess the influence of ORDIL on the physiological response of barley. The specific objectives were: 1) Identify if stomatal conductance (gs), net assimilation rate (An), intrinsic water use efficiency (WUEi) and total dry matter (TDM) evolution can be used as early and sensitive indicators of barley water status and crop performance; 2) Provide a mechanistic basis to understand barley physiological response to deficit irrigation at the most sensitive stages and; 3) Evaluate barley physiological response to ORDIL and its relation with yield. Thus, five irrigation treatments were performed. One without deficit (ND), and four with limited volumes of irrigation water (100%, 90%, 80% and 70% of typical irrigation needs). According to the results, gs was a reliable variable to detect early water deficit in barley. Besides, critical thresholds for this variable were found to optimize irrigation and to avoid chronic physiological damages affecting the most sensitive and yield-related stages. In summary, the physiological approach applied in this study validates ORDIL methodology being useful for future irrigation scheduling and distribution improvements.

Estimating LAI for Cotton Using Multisource UAV Data and a Modified Universal Model

Leaf area index(LAI) is an important indicator of crop growth and water status. With the continuous development of precision agriculture, estimating LAI using an unmanned aerial vehicle (UAV) remote sensing has received extensive attention due to its low cost, high throughput and accuracy. In this study, multispectral and light detection and ranging (LiDAR) sensors carried by a UAV were used to obtain multisource data of a cotton field. The method to accurately relate ground measured data with UAV data was built using empirical statistical regression models and machine learning algorithm models (RFR, SVR and ANN). In addition to the traditional spectral parameters, it is also feasible to estimate LAI using UAVs with LiDAR to obtain structural parameters. Machine learning models, especially the RFR model (R2 = 0.950, RMSE = 0.332), can estimate cotton LAI more accurately than empirical statistical regression models. Different plots and years of cotton datasets were used to test the model robustness and generality; although the accuracy of the machine learning model decreased overall, the estimation accuracy based on structural and multisources was still acceptable. However, selecting appropriate input parameters for different canopy opening and closing statuses can alleviate the degradation of accuracy, where input parameters select multisource parameters before canopy closure while structural parameters are selected after canopy closure. Finally, we propose a gap fraction model based on a LAImax threshold at various periods of cotton growth that can estimate cotton LAI with high accuracy, particularly when the calculation grid is 20 cm (R2 = 0.952, NRMSE = 12.6%). This method does not require much data modeling and has strong universality. It can be widely used in cotton LAI prediction in a variety of environments.