Subjects -> WATER RESOURCES (Total: 160 journals)
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 Irrigation ScienceJournal Prestige (SJR): 0.771 Citation Impact (citeScore): 2Number of Followers: 4      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1432-1319 - ISSN (Online) 0342-7188 Published by Springer-Verlag  [2469 journals]
• Early cascade rice irrigation shutoff (ECIS) conserves water: implications

Abstract: Abstract Cascade rice flood distribution (CASC), the predominate method used for rice irrigation in the lower Mississippi River basin (LMRB), is inherently water intensive owing to the need to overfill rice paddies to move irrigation water from one paddy to the next. The objectives of this research were to devise practices that make CASC more water efficient, assessing how early cascade rice irrigation shutoff (ECIS) impacts applied irrigation, run-off, and flood depth under LMRB rainfall conditions. This research used a conservation-of-mass model to show that using flood depth in the penultimate rice paddy to trigger irrigation shutoff in a 16-ha simulated rice field results in nominal irrigation water savings of 23% relative to CASC. This savings was reduced to 15% when supplemental irrigation was added to the last paddy at two critical stages of rice production. Field run-off estimates for ECIS were reduced by up to 78% relative to a CASC for both clay and silt loam soils, demonstrating how with ECIS the last paddy of a rice field acts as a ‘catch basin’ for excess up-field irrigation and uncaptured rainfall. Flood depth estimates for the last paddy resulting from ECIS resembled those of alternate wetting and drying flood management (AWD), suggesting that the agronomics developed for AWD could be used to help address production issues arising in the catch basin from ECIS. Success in coupling ECIS with irrigation automation technologies could reduce aquifer withdrawals across the rice producing areas of the LMRB.
PubDate: 2022-09-27

• Evaluation and development of empirical models for wetted soil fronts
under drip irrigation in high-density apple crop from a point source

Abstract: Abstract Accurate measurement of soil wetting pattern from the point source of drip irrigation system plays an important role for designing of the irrigation system. The study evaluated a novel empirical method for predicting soil wetted dimensions surrounding a drip emitter. The study conducted at Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, in a high-density apple orchard during 2018–2020. The field data were used to evaluate the five different semi-empirical models, namely, Al-Ogaidi (A-O), Malek and Peters (M–P), Amin and Ekhmaj (A–E), Jiusheng Li (J-L) and Schwartzman and Zur (S–Z). The model’s results were compared with field data for predicting the wetted pattern. The soil wetting front was measured using three different capacity emitters (2, 4, 8 lph) under a point source of a drip irrigation system. The results were evaluated on the basis of statistical comparisons [mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2)] between model-predicted and field-observed data. The newly developed empirical model has shown close agreement as compared to other models with MAE, RMSE, NSE and R2 for wetted soil width 0.205 (cm), 0.246 (cm), 0.996 and 0.997, respectively, and for wetted soil depth 0.421 (cm), 0.522 (cm), 0.992 and 0.993, respectively. The developed model accurately predicts the whole wetting pattern and performs well in reproducing from known experimental data. The study revealed that the higher the emitter discharge capacity, the more were the vertical soil wetting front advances with increased time duration of irrigation. The information of accurate wetting pattern of drip from a point source will be useful for the optimal design of drip irrigation systems.
PubDate: 2022-09-25

• Effects of microorganisms on clogging process and clogging substances
accumulation of drip irrigation emitters using the high-sediment water
sources

Abstract: Abstract The microorganisms in the water sources are closely related to drip irrigation emitter clogging and clogging substances accumulation. However, few studies quantitatively considered the role of microorganisms in the emitter clogging process, especially in the high-sediment water source. The coupling process and mechanism between solid particles and microorganisms are not clear yet. Based on these, the Yellow River water (CK), general high sediment water (GHS) and artificial simulated Yellow River water (SYR) was applied in an indoor controllable experiment to study the long-term dynamic drip irrigation emitter clogging process, microorganism growth and its secretions as well as clogging substances accumulation. The results showed that the presence of microorganisms could accelerate the clogging process, with smaller discharge ratio variation (Dra) and Christiansen coefficient of uniformity (CU), and higher accumulation of clogging substances. Among them, the Dra and CU of the SYR treatment were the smallest at the end of the experiment (14.7% and 30.5%, respectively) and those of the GHS treatment were the largest (21.2% and 33.9%, respectively). Meanwhile, the total amounts of phospholipid fatty acids (PLFAs), extracellular polymer substances (EPS) and dry weight (DW) in SYR treatment were the highest, which were 110.2%, 23.8% and 16.7% higher than those in GHS treatment, respectively. The clogging parameters and clogging substances accumulated from the CK treatment were in a state of in-betweenness. However, the differences between CK and SYR treatments did not reach a significant level, while they were both significantly different from GHS treatment (p < 0.05). This mainly resulted from the interactions between solid particles and microorganisms in the drip irrigation emitter clogging process. Ignoring the existence of microorganisms may overestimate the Dra and CU by 6.5% and 3.4%, respectively. This offered theoretical references to the application of high-sediment water in the drip irrigation system.
PubDate: 2022-09-24

• Eight-year comparison of agroeconomic benefits of open ditch and
subsurface pipe drainage in mulched drip irrigated saline–sodic
farmland

Abstract: Abstract Because of the presence of shallow water tables and consequent secondary salinization in irrigated areas of Xinjiang China, there is an urgent need for installation of drainage systems to control the salinity levels in the crop rootzone. The goal of this study was to compare the midterm effects of the open ditch (depth of 2.2 m) and subsurface pipe (depth of 2.2 m) drainages on soil salinity, drainage, groundwater, cotton biomass, yield, and economic benefits while using drip irrigation under mulch (plastic film). We conducted a field experiment for eight consecutive years (2012–2019) in Shawan County of Xinjiang, China. Our experimental results indicated that open ditch and subsurface pipe drainages each reduced total soil salinity, improved saline–sodic soils, and controlled groundwater level, which caused a significant increase in the cotton biomass and yield. The open ditch drainage treatment (ODDA) represented a better desalination effect than the subsurface pipe drainage treatment (SPDA) at 73% and 81%, respectively. The electrical conductivity and pH of ODDA and SPDA water samples decreased as the soil salinities decreased over time. We used the farmland conditions from 2012 as the baseline for our experiment and evaluated how these baseline conditions changed over time in response to these treatments. Compared to this baseline, the cotton yield of ODDA and SPDA treated farmland increased by 18.30 times and 19.96 times in 2019, respectively. The investment payback periods for ODDA and SPDA treatments were 7.59 and 6.34 years, respectively, and their returns on investment were 12% and 30%, respectively. The midterm economic benefits of subsurface pipe drainage were more prominent than those of open ditch drainage. These results provide a reference for improving, developing, and utilizing soil saline–sodic land, and the sustainable development of agriculture in arid areas.
PubDate: 2022-09-23

• Factors influencing usage of subsurface drainage to improve soil
desalination and cotton yield in the Tarim Basin oasis in China

Abstract: Abstract Soil salinization is a global issue that results in soil degradation and affects the sustainable development of irrigated agriculture. A 2-year study was conducted in 2018 and 2019 to identify the effect of subsurface drainage spacing on soil moisture, salt, cotton growth, and yield under the Tarim Basin oasis in China. The tests involved three subsurface drainage treatments, with a pipe spacing of 10 m (W10), 20 m (W20), and 30 m (W30), respectively, and a drainage-absent treatment (CK). Compared with CK, subsurface drainage reduced soil salinity, resulted in better uniform water distribution and reduced inorganic salt concentration in shallow soil solution. In addition to improving soil moisture and salinity conditions, subsurface drainage increased seedling emergence rate (28%), root vigor (23%), and chlorophyll content (44%) of cotton, which in turn led to increases in cotton plant height (18%), leaf area (33%), dry matter weight (32%), and reproductive organ weight (39%), thereby resulting in high cotton yield (45%). A path analysis revealed that under subsurface drainage, the seedling emergence rate of cotton had the greatest impact on cotton yield, and subsurface drainage contributed the most to the increase in cotton yield. It increased the seedling emergence rate by reducing soil salinity. Moreover, cotton yield and next-year soil arability increased with decreasing drainage pipe spacing, suggesting that it is advantageous to adopt a drainage pipe layout with small pipe spacing when economic costs are not a concern.
PubDate: 2022-09-17

• Dynamic responses of physiological indexes in maize leaves to different
spraying fertilizers at varying concentrations

Abstract: Abstract The dual role of nutrient uptake by plant roots and leaves is one of the main advantages of sprinkler fertigation, while an improper solution concentration suppresses plant physiology and even causes foliar burns. To explore the suitable solution concentrations of nitrogenous fertilizer, phosphate fertilizer and potassium fertilizer, field experiments were conducted at two sites in the North China Plain during the 2019 and 2021 growing seasons of summer maize. The foliar relative chlorophyll content (SPAD), foliar light energy conversion capacity (Fv/F0) and maximum light energy conversion efficiency (Fv/Fm) prior to and after fertilizer solution spraying were measured and compared. In the experiments, six urea concentrations (0.10 − 3.20%), eight monoammonium phosphate concentrations (0.03 − 4.80%) and seven potassium sulfate concentrations (0.10 − 4.80%) were tested during the jointing stage (V6), flare opening stage (V12), heading stage (VT) and filling stage (R2) of summer maize. The results showed that after spraying fertilizer solution, the spatiotemporal variability in Fv/F0 reached moderate from the weak spatiotemporal variability observed prior to spraying. The SPAD values reached moderate from the weak spatiotemporal variability only after spraying nitrogen fertilizer from V6 to VT and after spraying potassium fertilizer from V12 to R2. All the changes in the index variability suggested a great influence of foliar nutrient absorption on plant physiology. Averaged over 5 days following nutrient spraying during the whole season, the average increments synthesized by SPAD, Fv/F0 and Fv/Fm were 1.60, 1.33, and 1.21 times, and the average reductions were 0.62, 0.78 and 0.62 times, respectively. Depending on the fertilizer type and spraying opportunity, the influence of the fertilizer solution on plant physiology changed greatly. To maximize the relative chlorophyll content and photosynthetic capacity of foliar plants resulting from fertilizer solution spraying, the recommended urea solutions were 0.10 − 0.80%, 0.40%, 0.25 − 0.40% and 0.25 − 0.40% during the V6, V12, VT and R2 stages, respectively. For monoammonium phosphate, the suggested concentrations were 0.06 − 0.15%, 0.06 − 0.15%, 0.03 − 0.40% and 0.03 − 0.80%, respectively. Spraying potassium sulfate at a concentration of 0.10 − 0.40% during the V12 and VT stages would benefit plant growth.
PubDate: 2022-09-08

• From vine to vineyard: the GRAPEX multi-scale remote sensing experiment
for improving vineyard irrigation management

Abstract: Abstract This second special issue of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) further advances and expands upon the initial research findings of the first GRAPEX special issue on the measurement and remote sensing of vine water use and stress. This is a highly collaborative and interdisciplinary experiment, which involves USDA-ARS scientists, industry, and university researchers. The large scope of this research has allowed the development of new measurement and remote sensing tools and techniques to quantify vine evapotranspiration, moisture status, and stress, with the ultimate goal of improving irrigation management in California vineyards.
PubDate: 2022-09-07

• LAI estimation across California vineyards using sUAS multi-seasonal
multi-spectral, thermal, and elevation information and machine learning

Abstract: Abstract In agriculture, leaf area index (LAI) is an important variable that describes occurring biomass and relates to the distribution of energy fluxes and evapotranspiration components. Current LAI estimation methods at subfield scale are limited not only by the characteristics of the spatial data (pixel size and spectral information) but also by the empiricity of developed models, mostly based on vegetation indices, which do not necessarily scale spatiality (among different varieties or planting characteristics) or temporally (need for different LAI models for different phenological stages). Widely used machine learning (ML) algorithms and high-resolution small unmanned aerial system (sUAS) information provide an opportunity for spatial and temporal LAI estimation addressing the spatial and temporal limitations. In this study, considering both accuracy and efficiency, a point-cloud-based feature-extraction approach (Full Approach) and a raster-based feature-extraction approach (Fast Approach) using sUAS information were developed based on multiple growing seasons (2014–2019) to extract and generate vine-scale information for LAI estimation in commercial vineyards across California. Three known ML algorithms, Random Forest (RF), eXtreme Gradient Boosting (XGB), and Relevance Vector Machine (RVM), were considered, along with hybrid ML schemes based on those three algorithms, coupled with different feature-extraction approaches. Results showed that the hybrid ML technique using RF and RVM and the Fast Approach with 9 input variables, called RVM-RFFast model, performs better than others in a visual and statistical assessments of the generated LAI being also computationally efficient. Furthermore, using the generated LAI products in the quantification of energy balance using the two-source energy balance Priestley-Taylor version (TSEB-PT) model and EC tower data, the results indicated excellent estimation of net radiation (Rn) and latent heat flux (LE), good estimation of surface heat flux (G), and poor estimation of sensible heat flux (H). Additionally, TSEB-PT sensitivity analysis performed by regenerating LAI maps based on the generated LAI map (from − 15% of the original LAI map to + 15% with a 5% gap) showed that LAI uncertainty had a major impact on G, followed by evapotranspiration partitioning (T/ET), H, LE, and Rn. When considering the annual growth cycle of grapevines, the impact of LAI uncertainty on the T/ET in the veraison stage was larger than in the fruit set stage.
PubDate: 2022-09-01

• Evapotranspiration uncertainty at micrometeorological scales: the impact
of the eddy covariance energy imbalance and correction methods

Abstract: Abstract Under ideal conditions, evapotranspiration (ET) fluxes derived through the eddy covariance (EC) technique are considered a direct measure of actual ET. Eddy covariance flux measurements provide estimates at a temporal frequency that allows examining sub-daily, daily, and seasonal scale processes and relationships between different surface fluxes. The Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project has collected micrometeorological and biophysical data to ground-truth new remote sensing tools for fine-tuning vineyard irrigation management across numerous sites since 2013. This rich dataset allows us to quantify the impact of different approaches to estimate daily ET fluxes, while accounting for energy imbalance. This imbalance results from the lack of agreement between the total available energy and turbulent fluxes derived by the EC technique. We found that different approaches to deal with this energy imbalance can lead to uncertainty in daily ET estimates of up to 50%. Over the growing season, this uncertainty can lead to considerable biases in crop water use estimates, which in some cases were equivalent to ~ 1/3rd of the total growing season applied irrigation We analyzed ET uncertainty relative to atmospheric meteorological, stability, and advective conditions, and highlight the importance of recognizing limitations of micrometeorological observational techniques, considered state of the art, to quantify ET for model validation and field-scale monitoring. This study provides a framework to quantify daily ET estimates’ uncertainty and expected reliability when using the eddy covariance technique for ground-truthing or model validation purposes.
PubDate: 2022-09-01

• Vine water status mapping with multispectral UAV imagery and machine
learning

Abstract: Abstract Optimizing water management has become one of the biggest challenges for grapevine growers in California, especially during drought conditions. Monitoring grapevine water status and stress level across the whole vineyard is an essential step for precision irrigation management of vineyards to conserve water. We developed a unified machine learning model to map leaf water potential ( $${\psi }_{\mathrm{leaf}}$$ ), by combining high-resolution multispectral remote sensing imagery and weather data. We conducted six unmanned aerial vehicle (UAV) flights with a five-band multispectral camera from 2018 to 2020 over three commercial vineyards, concurrently with ground measurements of sampled vines. Using vegetation indices from the orthomosaiced UAV imagery and weather data as predictors, the random forest (RF) full model captured 77% of $${\psi }_{\mathrm{leaf}}$$ variance, with a root mean square error (RMSE) of 0.123 MPa, and a mean absolute error (MAE) of 0.100 MPa, based on the validation datasets. Air temperature, vapor pressure deficit, and red edge indices such as the normalized difference red edge index (NDRE) were found as the most important variables in estimating $${\psi }_{\mathrm{leaf}}$$ across space and time. The reduced RF models excluding weather and red edge indices explained 52–48% of $${\psi }_{\mathrm{leaf}}$$ variance, respectively. Maps of the estimated $${\psi }_{\mathrm{leaf}}$$ from the RF full model captured well the patterns of both within- and cross-field spatial variability and the temporal change of vine water status, consistent with irrigation management and patterns observed from the ground sampling. Our results demonstrated the utility of UAV-based aerial multispectral imaging for supplementing and scaling up the traditional point-based ground sampling of $${\psi }_{\mathrm{leaf}}$$ . The pre-trained machine learning model, driven by UAV imagery and weather data, provides a cost-effective and scalable tool to facilitate data-driven precision irrigation management at individual vine levels in vineyards.
PubDate: 2022-09-01

• Evaluating different metrics from the thermal-based two-source energy
balance model for monitoring grapevine water stress

Abstract: Abstract Precision irrigation management requires operational monitoring of crop water status. However, there is still some controversy on how to account for crop water stress. To address this question, several physiological, several physiological metrics have been proposed, such as the leaf/stem water potentials, stomatal conductance, or sap flow. On the other hand, thermal remote sensing has been shown to be a promising tool for efficiently evaluating crop stress at adequate spatial and temporal scales, via the Crop Water Stress Index (CWSI), one of the most common indices used for assessing plant stress. CWSI relates the actual crop evapotranspiration ET (related to the canopy radiometric temperature) to the potential ET (or minimum crop temperature). However, remotely sensed surface temperature from satellite sensors includes a mixture of plant canopy and soil/substrate temperatures, while what is required for accurate crop stress detection is more related to canopy metrics, such as transpiration, as the latter one avoids the influence of soil/substrate in determining crop water status or stress. The Two-Source Energy Balance (TSEB) model is one of the most widely used and robust evapotranspiration model for remote sensing. It has the capability of partitioning ET into the crop transpiration and soil evaporation components, which is required for accurate crop water stress estimates. This study aims at evaluating different TSEB metrics related to its retrievals of actual ET, transpiration and stomatal conductance, to track crop water stress in a vineyard in California, part of the GRAPEX experiment. Four eddy covariance towers were deployed in a Variable Rate Irrigation system in a Merlot vineyard that was subject to different stress periods. In addition, root-zone soil moisture, stomatal conductance and leaf/stem water potential were collected as proxy for in situ crop water stress. Results showed that the most robust variable for tracking water stress was the TSEB derived leaf stomatal conductance, with the strongest correlation with both the measured root-zone soil moisture and stomatal conductance gas exchange measurements. In addition, these metrics showed a better ability in tracking stress when the observations are taken early after noon.
PubDate: 2022-09-01

• Time-series clustering of remote sensing retrievals for defining
management zones in a vineyard

Abstract: Abstract Management zones (MZs) are efficient for applying site-specific management in agricultural fields. This study proposes an approach for generating MZs using time-series clustering (TSC) to also enable time-specific management. TSC was applied to daily remote sensing retrievals in a California vineyard during four growing seasons (2015–2018) using three datasets: evapotranspiration (ET), leaf area index (LAI), and normalized difference vegetation index (NDVI). Distinct MZs were delineated based on similarities in pixel-level temporal dynamics for each dataset, using dissimilarity index to determine the optimal number of clusters and compare TSC results. The differences between the cluster centers were calculated, along with the ratio between the centers’ differences and the range of each dataset, denoting the degree of difference between MZs centers. Similarity between MZs from each factor was quantified using Cramer’s V and Fréchet distances. Finally, an aggregated (multi-factor) MZ map was generated using multivariate clustering. The resulting MZs were compared to a 2016 yield map to determine the significance of differences between means and distribution among MZs. The findings show that LAI TSC achieved the best cluster separation. The NDVI and LAI MZs maps were nearly identical (Cramer’s V of 0.97), while ET showed weaker similarities to NDVI and LAI (0.61 and 0.62, respectively). Similar findings were observed for the Fréchet distances. The yield values were found to be significantly different among MZs for all TSC maps. TSC may be further utilized for defining within-field spatial variability and temporal dynamics for precision irrigation practices that account for spatial and temporal variability.
PubDate: 2022-09-01

• The vertical turbulent structure within the surface boundary layer above a
Vineyard in California’s Central Valley during GRAPEX

Abstract: Abstract Water is already a limited resource in California, and meeting the competing water needs, there will be only more challenges in the coming decades. Thus, sustaining the production of wine grapes, which are among the highest value specialty crops in the state, requires water to be used efficiently as possible. At the same time, improving irrigation management in vineyards requires spatially distributed information regarding vine water use or evapotranspiration (ET) at the sub-field scale that can only be collected via remote sensing. However, due to their unique canopy structure, current remote sensing models may not accurately describe the underlying turbulent exchange controlling ET from vineyards. To address that knowledge gap, this study investigates the vertical turbulent structure over a vineyard in the Central Valley of California. Using data from a profile of sonic anemometers (2.5 m, 3.75 m, 5 m, and 8 m, above the surface) collected during 2017 as a part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX), this study characterized the relationship between the turbulent flow at different heights using spectral analysis. It was found that the turbulent structure is strongly influenced by the underlying canopy. It also showed that the characteristics of the vertical structure differ significantly from what would be expected over other types of crops because of the unique configuration of vineyards, i.e., the concentration of the biomass in the upper part of the canopy and wide inter-row spacing. As a result, surface energy balance modeling using remote sensing data will likely require modifications to formulations of the turbulent energy exchange of the inter-row-canopy system with the lower atmosphere to reliably estimate vine ET. An example of this effect is shown for the mean wind profile which deviates from predicted profile using classical Monin–Obukhov similarity theory (MOST) used in remote sensing-based energy balance models resulting in errors in heat flux exchange which in turn affects modeled ET.
PubDate: 2022-09-01

• The complementary uses of Sentinel-1A SAR and ECOSTRESS datasets to
identify vineyard growth and conditions: a case study in Sonoma County,
California

Abstract: Abstract The launch of NASA’s ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and the European Space Agency’s Sentinel-1A/B synthetic aperture radar (SAR) satellites provides the opportunity to advance a multi-sensor remote sensing approach to crop monitoring. While ECOSTRESS and Sentinel-1A/B have been used separately to assess vegetation conditions, a study that quantifies the synergistic usefulness of both to monitor crops has not been performed. This study assesses the complementary uses of Sentinel-1A SAR and ECOSTRESS land surface temperature (LST) and evapotranspiration (ET) datasets to assess vine growth and conditions in blocks located in Sonoma County, California for 2018. Results indicate Sentinel-1A SAR dual-polarization backscatter measurements ( $$\sigma_{{{\text{VV}}}}^{0}$$ and $$\sigma_{{{\text{VH}}}}^{0}$$ ) have different sensitivities to vine leafiness and moisture content, based on measured vineyard field data and radiometric modeling. SAR and modeled $$\sigma_{{{\text{VV}}}}^{0}$$ backscatter suggest higher sensitivity to surface conditions and trunk and cane moisture, while SAR and modeled $$\sigma_{{{\text{VH}}}}^{0}$$ backscatter indicate higher sensitivity to vine leafiness and canopy moisture. ECOSTRESS LST measurements were sharpened to a 30 m resolution using a data mining sharpener and ET measurements were generated with a retrieval algorithm approach for select dates. Spearman’s rank correlation and linear regressions analyses between SAR backscatter to ECOSTRESS datasets indicate stronger relationships between $$\sigma_{{{\text{VH}}}}^{0}$$ backscatter to LST and ET relative to $$\sigma_{{{\text{VV}}}}^{0}$$ backscatter. The results suggest Sentinel-1A SAR $$\sigma_{{{\text{VH}}}}^{0}$$ backscatter can provide indications of vine leaf volume and moisture state that can be related to LST and ET measurements, providing useful information for vineyard management.
PubDate: 2022-09-01

• Inter-annual variability of land surface fluxes across vineyards: the role
of climate, phenology, and irrigation management

Abstract: Abstract Irrigation and other agricultural management practices play a key role in land surface fluxes and their interactions with atmospheric processes. California’s Central Valley agricultural productivity is strongly linked to water availability associated with conveyance infrastructure and groundwater, but greater scrutiny over agricultural water use requires better practices particularly during extended and severe drought conditions. The future of irrigated agriculture in California is expected to be characterized neither by perpetual scarcity nor by widespread abundance. Thus, further advancing irrigation technologies and improving management practices will be key for California’s agriculture sustainability. In this study, we present micrometeorological observations from the Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. Daily, seasonal, and inter-seasonal surface flux patterns and relationships across five vineyards over three distinct California wine production regions were investigated. Vineyard actual evapotranspiration showed significant differences at the sub-daily and daily scale when comparisons across wine production regions and varieties were performed. Water use in vineyards in the Central Valley was about 70% greater in comparison to the vineyards at the North Coast area due to canopy size, atmospheric demand, and irrigation inputs. Inter-annual variability of surface fluxes was also significant, even though, overall weather conditions (i.e., air temperature, vapor pressure deficit, wind speed, and solar radiation) were not significantly different. Thus, not only irrigation but also other management practices played a key role in seasonal water use, and given these differences, we conclude that further advancing ground-based techniques to quantify crop water use at an operational scale will be key to facing California’s agriculture present and future water challenges.
PubDate: 2022-09-01

• Evaluation of satellite Leaf Area Index in California vineyards for
improving water use estimation

Abstract: Abstract Remote sensing estimation of evapotranspiration (ET) directly quantifies plant water consumption and provides essential information for irrigation scheduling, which is a pressing need for California vineyards as extreme droughts become more frequent. Many ET models take satellite-derived Leaf Area Index (LAI) as a major input, but how uncertainties of LAI estimations propagate to ET and the partitioning between evaporation and transpiration is poorly understood. Here we assessed six satellite-based LAI estimation approaches using Landsat and Sentinel-2 images against ground measurements from four vineyards in California and evaluated ET sensitivity to LAI in the thermal-based two-source energy balance (TSEB) model. We found that radiative transfer modeling-based approaches predicted low to medium LAI well, but they significantly underestimated high LAI in highly clumped vine canopies (RMSE ~ 0.97 to 1.27). Cubist regression models trained with ground LAI measurements from all vineyards achieved high accuracy (RMSE ~ 0.3 to 0.48), but these empirical models did not generalize well between sites. Red edge bands and the related vegetation index (VI) from the Sentinel-2 satellite contain complementary information of LAI to VIs based on near-infrared and red bands. TSEB ET was more sensitive to positive LAI biases than negative ones. Positive LAI errors of 50% resulted in up to 50% changes in ET, while negative biases of 50% in LAI caused less than 10% deviations in ET. However, even when ET changes were minimal, negative LAI errors of 50% led to up to a 40% reduction in modeled transpiration, as soil evaporation and plant transpiration responded to LAI change divergently. These findings call for careful consideration of satellite LAI uncertainties for ET modeling, especially for the partitioning of water loss between vine and soil or cover crop for effective vineyard irrigation management.
PubDate: 2022-09-01

• Detecting short-term stress and recovery events in a vineyard using
tower-based remote sensing of photochemical reflectance index (PRI)

Abstract: Abstract Frequent drought and high temperature conditions in California vineyards necessitate plant stress detection to support irrigation management strategies and decision making. Remote sensing provides a powerful tool to continuously monitor vegetation function across spatial and temporal scales. In this study, we utilized a tower-based optical-remote sensing system to continuously monitor four vineyard subplots in California’s Central Valley. We compared the performance of the greenness-based normalized difference vegetation index (NDVI) and the physiology-based photochemical reflectance index (PRI) to track variations of eddy covariance estimated gross primary productivity (GPP) during four stress events between July and September 2020. Our results demonstrate that NDVI was invariant during stress events. In contrast, PRI was effective at tracking the short-term stress-induced declines and recovery of GPP associated with soil water depletion and increased air temperature, as well as reductions in GPP from decreased PAR caused by smokey conditions from nearby fires. Canopy-scale remote sensing can provide continuous real-time data, and physiology-based vegetation indices such as PRI can be used to monitor variation of photosynthetic activity during stress events to aid in management decisions.
PubDate: 2022-09-01

• Influence of modeling domain and meteorological forcing data on daily
evapotranspiration estimates from a Shuttleworth–Wallace
model using Sentinel-2 surface reflectance data

Abstract: Abstract Sustainable use of available water resources in viticulture can be aided by frequent high-resolution information on vineyard water status. Recently, a new Shuttleworth–Wallace evapotranspiration (ET) model, which uses a contextual framework to determine dry and wet extremes from the Sentinel-2 surface reflectance data (SW-S2), showed promising results when tested over a GRAPEX (Grape Remote-sensing Atmospheric Profile and ET eXperiment) site in California. However, current knowledge on its applicability across the climate gradient in California and how the selections of modeling domain and meteorological data influence model outputs are limited. This study expands the evaluation of the SW-S2 model across multiple domains and meteorological inputs covering all three GRAPEX sites over the 2018–2020 growing seasons. In comparison with flux tower observations, the size of the modeling domain did not have a strong influence on model performance, although the model performed marginally better under a larger domain (yielding root mean square error within 1.03–1.11 mm d−1 and mean biases within 2%). The source and quality of meteorological forcing data, in particular vapor pressure deficit (VPD) and wind speed (u), were found to have a strong influence on model output as indicated by the poor performance of the model with less accurate regional and coarse-scale gridded meteorological inputs. Results suggest that simple regression for local bias correction of VPD and u significantly improved model performance. Overall, this study supports future research aiming to merge outputs from more frequent spectral and less frequent thermal-based ET models and reduce latency in ET monitoring of California vineyards.
PubDate: 2022-09-01

• Spatial–temporal modeling of root zone soil moisture dynamics in a
vineyard using machine learning and remote sensing

Abstract: Abstract High-resolution spatial–temporal root zone soil moisture (RZSM) information collected at different scales is useful for a variety of agricultural, hydrologic, and climate applications. RZSM can be estimated using remote sensing, empirical equations, or process-based simulation models. Machine learning (ML) approaches for evaluating RZSM across numerous spatial–temporal scales are less generalizable than process-based models. However, data-driven ML approaches offer a unique opportunity to develop complex models of soil moisture without making assumptions about the processes governing soil water dynamics in a given study region. In this study, comparisons were made between two models, pySEBAL and EFSOIL, which were based on evaporation fraction (EF) and soil properties, and a data-driven model based on the Random Forest (RF) ensemble algorithm. These approaches were evaluated to demonstrate their capabilities for RZSM estimation. The EF obtained from Landsat images was used after validation with eddy covariance measurements as the major input to all three models, along with other meteorological and soil physical properties. The RF model was trained using in situ soil moisture data from Time Domain Reflectometry (TDR) sensors installed in a vineyard from 2018 to 2020. The predictor variables comprised of meteorological, soil properties, EF, and a vegetation index. The results reveal that there was a strong correlation between the in situ measured soil moisture and the RF predicted soil moisture at all sensor locations. Due to the complexity of the physical processes involved in soil water flow, the empirical models pySEBAL and EFSOIL were unable to reliably predict RZSM values at all monitored locations. The high RZSM predicted by pySEBAL demonstrated the presence of possible bias in the model’s algorithm used to estimate soil moisture. We also demonstrated that ML based on the RF algorithm may be used to predict spatially distributed RZSM when a few soil moisture ground measurements are combined with remote sensing to produce soil moisture maps.
PubDate: 2022-09-01

• Application of the vineyard data assimilation (VIDA) system to vineyard
root-zone soil moisture monitoring in the California Central Valley

Abstract: Abstract Efforts to apply gridded root-zone soil moisture (RZSM) products for irrigation decision-support in vineyards are currently hampered by the difficulty of obtaining RZSM products that meet required accuracy, resolution, and data latency requirements. In particular, the operational application of soil water balance modeling is complicated by the difficulty of obtaining accurate irrigation inputs and representing complex sub-surface water-flow processes within vineyards. Here, we discuss prospects for addressing these shortcomings using the Vineyard Data Assimilation (VIDA) system based on the assimilation of high-resolution (30-m) soil moisture information obtained from synthetic aperture radar and thermal-infrared (TIR) remote sensing into a one-dimensional soil water balance model. The VIDA system is tested retrospectively (2017–2020) for two vineyard sites in the California Central Valley that have been instrumented as part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Results demonstrate that VIDA can generally capture daily temporal variations in RZSM for vertical depths of 30–60 cm beneath the vine row, and the assimilation of remote sensing products is shown to produce modest improvement in the temporal accuracy of VIDA RZSM estimates. However, results also reveal shortcomings in the ability of VIDA to correct biases in assumed irrigation applications—particularly during well-watered portions of the growing season when TIR-based evapotranspiration observations are not moisture limited and, therefore, decoupled from RZSM. Prospects for addressing these limitations and plans for the near-real-time operational application of the VIDA system are discussed.
PubDate: 2022-09-01

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