Wine grape quality and quantity are affected by vine growing conditions during critical phenological stages. Field observations of vine growth stages are too sparse to fully capture the spatial ...variability of vine conditions. In addition, traditional grape yield prediction methods are time consuming and require large amount grape samples. Remote sensing data provide detailed spatial and temporal information regarding vine development that is useful for vineyard management. In this study, Landsat surface reflectance products from 2013 and 2014 were used to map satellite-based Normalized Difference Vegetation Index (NDVI) and leaf area index (LAI) over two Vitis vinifera L. cv. Pinot Noir vineyards in California, USA. The spatial correlation between grape yield maps and the interpolated daily time series (LAI and NDVI) was quantified. NDVI and LAI were found to have similar performance as a predictor of spatial yield variability, providing peak correlations of 0.8 at specific times during the growing season, and the timing of this peak correlation differed for the two years of study. In addition, correlations with maximum and seasonal-cumulative vegetation indices were also evaluated, and showed slightly lower correlations with the observed yield maps. Finally, the within-season grape yield predictability was examined using a simple strategy in which the relationship between grape yield and vegetation indices were calibrated with limited ground measurements. This strategy has a strong potential to improve the accuracy and efficiency of yield estimation in comparison with traditional approaches used in the wine grape growing industry.
The reliability of standard meteorological drought indices based on measurements of precipitation is limited by the spatial distribution and quality of currently available rainfall data. Furthermore, ...they reflect only one component of the surface hydrologic cycle, and they cannot readily capture nonprecipitation-based moisture inputs to the land surface system (e.g., irrigation) that may temper drought impacts or variable rates of water consumption across a landscape. This study assesses the value of a new drought index based on remote sensing of evapotranspiration (ET). The evaporative stress index (ESI) quantifies anomalies in the ratio of actual to potential ET (PET), mapped using thermal band imagery from geostationary satellites. The study investigates the behavior and response time scales of the ESI through a retrospective comparison with the standardized precipitation indices and Palmer drought index suite, and with drought classifications recorded in the U.S. Drought Monitor for the 2000–09 growing seasons. Spatial and temporal correlation analyses suggest that the ESI performs similarly to short-term (up to 6 months) precipitation-based indices but can be produced at higher spatial resolution and without requiring any precipitation data. Unique behavior is observed in the ESI in regions where the evaporative flux is enhanced by moisture sources decoupled from local rainfall: for example, in areas of intense irrigation or shallow water table. Normalization by PET serves to isolate the ET signal component responding to soil moisture variability from variations due to the radiation load. This study suggests that the ESI is a useful complement to the current suite of drought indicators, with particular added value in parts of the world where rainfall data are sparse or unreliable.
Operational estimation of spatio-temporal continuously daily evapotranspiration (ET), and the components evaporation (E) and transpiration (T), at river basin scale is very useful for developing ...sustainable water resource strategies, particularly in regions of limited water supplies. In this study, multi-year all-weather daily ET, E and T were estimated using MODIS-based (Dual Temperature Difference) DTD model under different land covers in the Heihe river basin in China, with a total area of approximately 143 × 103 km2. The remotely sensed ET was validated using ground measurements from large aperture scintillometer systems, with a source area of several kilometers, over grassland, cropland and riparian shrub-forest land cover. The results showed that the remotely sensed ET produced mean absolute percent differences (MAPD) of around 20% with the ground measurements during the growing season under clear sky conditions, but the model performance deteriorated for cloudy days. However, the daily ET product gave reasonable estimates for croplands with an MAPD value of about 20% and the estimates of T/ET and E/ET in good agreement with ground measurements. The DTD model also significantly outperformed other remote sensing-based models being applied globally. Based on these results the DTD model is considered reliable for monitoring crop water use and stress and to develop efficient irrigation strategies.
•Daily ET, E and T were estimated at regional scale.•Modeled MODIS ET was validated using LAS observations over different land covers.•Land surface heterogeneity was evaluated with Landsat data.
The two-source energy balance (TSEB) model using the land surface temperature (LST) as a key boundary has been used to estimate land surface evapotranspiration (ET) over various landcovers and ...environmental conditions. However, LST may not always provide an adequate boundary condition to simultaneously constrain the soil evaporation and plant transpiration especially under water limited conditions. A refinement to TSEB model by coupling surface soil moisture information to derive the soil and vegetation component temperatures and a new transpiration algorithm was developed (TSEB-SM). The TSEB-SM model was evaluated under a wide range of surface soil water content values and vegetation cover conditions and compared with the performance with the original TSEB model using only LST. While the results showed that the TSEB-SM model produced similar agreement in the fluxes and ET as the original TSEB for the cropland and grassland sites, TSEB-SM model performance was notably improved at the shrub-forest and desert steppe sites with a significant reduction in mean absolute percent difference in daily ET from nearly 65% to 25% and from approximately 50% to 40%, respectively. It also appears to be more reliable in partitioning ET into soil evaporation and plant transpiration when compared to the partitioning using the water use efficiency (uWUE) approach in combination with the eddy covariance measurements. With satellite data such as MODIS LST and leaf area index, and surface soil moisture retrievals from microwave satellite observations, the TSEB-SM model may potentially be a more reliable tool for monitoring regional ET partitioning under sparse canopy cover conditions.
•A modified TSEB model (TSEB-SM) couples surface soil moisture.•The TSEB-SM model computed reliable ET under sparse vegetation conditions.•The TSEB-SM model computed more reliable partitioning of E and T.
Due to the influence of evaporation on land‐surface temperature, thermal remote sensing data provide valuable information regarding the surface moisture status. The Atmosphere‐Land Exchange Inverse ...(ALEXI) model uses the morning surface temperature rise, as measured from a geostationary satellite platform, to deduce surface energy and water fluxes at 5–10 km resolution over the continental United States. Recent improvements to the ALEXI model are described. Like most thermal remote sensing models, ALEXI is constrained to work under clear‐sky conditions when the surface is visible to the satellite sensor, often leaving large gaps in the model output record. An algorithm for estimating fluxes during cloudy intervals is presented, defining a moisture stress function relating the fraction of potential evapotranspiration obtained from the model on clear days to estimates of the available water fraction in the soil surface layer and root zone. On cloudy days, this stress function is inverted to predict the soil and canopy fluxes. The method is evaluated using flux measurements representative at the watershed scale acquired in central Iowa with a dense flux tower network during the Soil Moisture Experiment of 2002 (SMEX02). The gap‐filling algorithm reproduces observed fluxes with reasonable accuracy, yielding ∼20% errors in ET at the hourly timescale, and 15% errors at daily timesteps. In addition, modeled soil moisture shows reasonable response to major precipitation events. This algorithm is generic enough that it can easily be applied to other thermal energy balance models. With gap‐filling, the ALEXI model can estimate hourly surface fluxes at every grid cell in the U.S. modeling domain in near real‐time. A companion paper presents a climatological evaluation of ALEXI‐derived evapotranspiration and moisture stress fields for the years 2002–2004.
California's Central Valley grows a significant fraction of grapes used for wine production in the United States. With increasing vineyard acreage, reduced water availability in much of California, ...and competing water use interests, it is critical to be able to monitor regional water use and evapotranspiration (ET) over large areas, but also in detail at individual field scales to improve water management within these viticulture production systems. This can be achieved by integrating remote sensing data from multiple satellite systems with different spatiotemporal characteristics. In this research, we evaluate the utility of a multi-scale system for monitoring ET as applied over two vineyard sites near Lodi, California during the 2013 growing season, leading into the drought in early 2014. The system employs a multi-sensor satellite data fusion methodology (STARFM: Spatial and Temporal Adaptive Reflective Fusion Model) combined with a multi-scale ET retrieval algorithm based on the Two-Source Energy Balance (TSEB) land-surface representation to compute daily ET at 30m resolution. In this system, TSEB is run using thermal band imagery from the Geostationary Environmental Operational Satellites (GOES; 4-km spatial resolution, hourly temporal sampling), the Moderate Resolution Imaging Spectroradiometer (MODIS) data (1km resolution, daily acquisition) and the new Landsat 8 satellite (sharpened to 30m resolution, ~16day acquisition). Estimates of daily ET generated in two neighboring fields of Pinot noir vines of different age agree with ground-based flux measurements acquired in-field during most of the 2013 season with relative mean absolute errors on the order of 19–23% (root mean square errors of approximately 1mmd−1), reducing to 14–20% at the weekly timestep relevant for irrigation management (~5mmwk−1). A model overestimation of ET in the early season was detected in the younger vineyard, perhaps relating to an inter-row grass cover crop. Spatial patterns of cumulative ET generally correspond to measured yield maps and indicate areas of variable crop moisture, soil condition, and yield within the vineyards that could require adaptive management. The results suggest that multi-sensor remote sensing observations provide a unique means for monitoring crop water use and soil moisture status at field-scales over extended growing regions, and may have value in supporting operational water management decisions in vineyards and other high value crops.
•Multi-scale, multi-sensor fusion methodology estimates vineyard evapotranspiration.•Combining Landsat 8, MODIS, and GOES data provides daily field scale ET estimates.•Modeled surface energy fluxes agree well with ground flux measurements.•Spatial distribution of ET corresponds with yield estimates.•ET overestimation in early season may be due to cover crop between vines.
The utilization of remotely sensed observations for light use efficiency (LUE) and tower-based gross primary production (GPP) estimates was studied in a USDA cornfield. Nadir hyperspectral ...reflectance measurements were acquired at canopy level during a collaborative field campaign conducted in four growing seasons. The Photochemical Reflectance Index (PRI) and solar induced chlorophyll fluorescence (SIF), were derived. SIF retrievals were accomplished in the two telluric atmospheric oxygen absorption features centered at 688 nm (O2-B) and 760 nm (O2-A). The PRI and SIF were examined in conjunction with GPP and LUE determined by flux tower-based measurements. All of these fluxes, environmental variables, and the PRI and SIF exhibited diurnal as well as day-to-day dynamics across the four growing seasons. Consistent with previous studies, the PRI was shown to be related to LUE (r^2 = 0.54 with a logarithm fit), but the relationship varied each year. By combining the PRI and SIF in a linear regression model, stronger performances for GPP estimation were obtained. The strongest relationship (r^2 = 0.80, RMSE = 0.186 mg CO2/m^2/s) was achieved when using the PRI and SIF retrievals at 688 nm. Cross-validation approaches were utilized to demonstrate the robustness and consistency of the performance. This study highlights a GPP retrieval method based entirely on hyperspectral remote sensing observations.
Reliable estimation of the surface energy balance from local to regional scales is crucial for many applications including weather forecasting, hydrologic modeling, irrigation scheduling, water ...resource management, and climate change research. Numerous models have been developed using remote sensing, which permits spatially distributed mapping of the surface energy balance over large areas. This study compares flux maps over a relatively simple agricultural landscape in central Iowa, comprised of soybean and corn fields, generated with three different remote sensing-based surface energy balance models: the Two-Source Energy Balance (TSEB) model, Mapping EvapoTranspiration at high Resolution using Internalized Calibration (METRIC), and the Trapezoid Interpolation Model (TIM). The three models have different levels of complexity and input requirements, but all have operational capabilities. METRIC and TIM make use of the remotely sensed surface temperature–vegetation cover relation to define key model variables linked to wet and dry hydrologic extremes, while TSEB uses these remotely sensed inputs to define component soil and canopy temperatures, aerodynamic resistances, and fluxes. The models were run using Landsat imagery collected during the Soil Moisture Atmosphere Coupling Experiment (SMACEX) in 2002 and model results were compared with observations from a network of flux towers deployed within the study area. While TSEB and METRIC yielded similar and reasonable agreement with measured heat fluxes, with root-mean-square errors (RMSE) of ∼50–75
W/m
2, errors for TIM exceeded 100
W/m
2. Despite the good agreement between TSEB and METRIC at discrete locations sampled by the flux towers, a spatial intercomparison of gridded model output (i.e., comparing output on a pixel-by-pixel basis) revealed significant discrepancies in modeled turbulent heat flux patterns that were largely correlated with vegetation density. Generally, the largest discrepancies, primarily a bias in
H, between these two models occurred in areas with partial vegetation cover and a leaf area index (LAI)
<
2.0. Adjustment of the minimum
LE assumed for the hot/dry hydrologic extreme condition in METRIC reduced the bias in
H between METRIC and TSEB, but caused a significant increase in bias in
LE between the models. Spatial intercomparison of modeled flux patterns over a variety of landscapes will be required to better assess uncertainties in remote sensing surface energy balance models, and to work toward an improved hybrid modeling system.
Savannas are among the most variable, complex and extensive biomes on Earth, supporting livestock and rural livelihoods. These water-limited ecosystems are highly sensitive to changes in both ...climatic conditions, and land-use/management practices. The integration of Earth Observation (EO) data into process-based land models enables monitoring ecosystems status, improving its management and conservation. In this paper, the use of the Two-Source Energy Balance (TSEB) model for estimating surface energy fluxes is evaluated over a Mediterranean oak savanna (dehesa). A detailed analysis of TSEB formulation is conducted, evaluating how the vegetation architecture (multiple layers) affects the roughness parameters and wind profile, as well as the reliability of EO data to estimate the ecosystem parameters. The results suggest that the assumption of a constant oak leaf area index is acceptable for the purposes of the study and the use of spectral information to derive vegetation indices is sufficiently accurate, although green fraction index may not reflect phenological conditions during the dry period. Although the hypothesis for a separate wind speed extinction coefficient for each layer is partially addressed, the results show that taking a single oak coefficient is more precise than using bulk system coefficient. The accuracy of energy flux estimations, with an adjusted Priestley–Taylor coefficient (0.9) reflecting the conservative water-use tendencies of this semiarid vegetation and a roughness length formulation which integrates tree structure and the low fractional cover, is considered adequate for monitoring the ecosystem water use (RMSD ~40 W m−2).
► Two source energy balance model calculates evaporation and transpiration. ► Soil and canopy component temperatures are required but seldom measured. ► Component temperatures can be measured or ...calculated from composite surface temperature. ► Measured composite temperature gave better results than measured component temperatures.
The two source energy balance model (TSEB) can estimate evaporation (E), transpiration (T), and evapotranspiration (ET) of vegetated surfaces, which has important applications in water resources management for irrigated crops. The TSEB requires soil (TS) and canopy (TC) surface temperatures to solve the energy budgets of these layers separately. Operationally, usually only composite surface temperature (TR) measurements are available at a single view angle. For surfaces with nonrandom spatial distribution of vegetation such as row crops, TR often includes both soil and vegetation, which may have vastly different temperatures. Therefore, TS and TC must be derived from a single TR measurement using simple linear mixing, where an initial estimate of TC is calculated, and the temperature – resistance network is solved iteratively until energy balance closure is reached. Two versions of the TSEB were evaluated, where a single TR measurement was used (TSEB-TR) and separate measurements of TS and TC were used (TSEB-TC-TS). All surface temperatures (TS, TC, and TR) were measured by stationary infrared thermometers that viewed an irrigated cotton (Gossypium hirsutum L.) crop. The TSEB-TR version used a Penman–Monteith approximation for TC, rather than the Priestley-Taylor-based formulation used in the original TSEB version, because this has been found to result in more accurate partitioning of E and T under conditions of strong advection. Calculations of E, T, and ET by both model versions were compared with measurements using microlysimeters, sap flow gauges, and large monolythic weighing lysimeters, respectively. The TSEB-TR version resulted in similar overall agreement with the TSEB-TC-TS version for calculated and measured E (RMSE = 0.7mmd−1) and better overall agreement for T (RMSE = 0.9 vs. 1.9mmd−1), and ET (RMSE = 0.6 vs. 1.1mmd−1). The TSEB-TC-TS version calculated daily ET up to 1.6mmd−1 (15%) less early in the season and up to 2.0mmd−1 (44%) greater later in the season compared with lysimeter measurements. The TSEB-TR also calculated larger ET later in the season but only up to1.4mmd−1 (20%). ET underestimates by the TSEB-TC-TS version may have been related to limitations in measuring TC early in the season when the canopy was sparse. ET overestimates later in the season by both versions may have been related to a greater proportion of non-transpiring canopy elements (flowers, bolls, and senesced leaves) being out of the TC and TR measurement view.
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