Oil spills from offshore drilling and coastal refineries often cause significant degradation of coastal environments. Early oil detection may prevent losses and speed up recovery if monitoring of the initial oil extent, oil impact, and recovery are in place. Satellite imagery data can provide a cost-effective alternative to expensive airborne imagery or labor intensive field campaigns for monitoring effects of oil spills on wetlands. However, these satellite data may be restricted in their ability to detect and map ecosystem recovery post-spill given their spectral measurement properties and temporal frequency. In this study, we assessed whether spatial and spectral resolution, and other sensor characteristics influence the ability to detect and map vegetation stress and mortality due to oil. We compared how well three satellite multispectral sensors: WorldView2, RapidEye and Landsat EMT+, match the ability of the airborne hyperspectral AVIRIS sensor to map oil-induced vegetation stress, recovery, and mortality after the DeepWater Horizon oil spill in the Gulf of Mexico in 2010. We found that finer spatial resolution (3.5 m) provided better delineation of the oil-impacted wetlands and better detection of vegetation stress along oiled shorelines in saltmarsh wetland ecosystems. As spatial resolution become coarser (3.5 m to 30 m) the ability to accurately detect and map stressed vegetation decreased. Spectral resolution did improve the detection and mapping of oil-impacted wetlands but less strongly than spatial resolution, suggesting that broad-band data may be sufficient to detect and map oil-impacted wetlands. AVIRIS narrow-band data performs better detecting vegetation stress, followed by WorldView2, RapidEye and then Landsat 15 m (pan sharpened) data. Higher quality sensor optics and higher signal-to-noise ratio (SNR) may also improve detection and mapping of oil-impacted wetlands; we found that resampled coarser resolution AVIRIS data with higher SNR performed better than either of the three satellite sensors. The ability to acquire imagery during certain times (midday, low tide, etc.) or a certain date (cloud-free, etc.) is also important in these tidal wetlands; WorldView2 imagery captured at high-tide detected a narrower band of shoreline affected by oil likely because some of the impacted wetland was below the tideline. These results suggest that while multispectral data may be sufficient for detecting the extent of oil-impacted wetlands, high spectral and spatial resolution, high-quality sensor characteristics, and the ability to control time of image acquisition may improve assessment and monitoring of vegetation stress and recovery post oil spills.

April 20, 2010 marked the start of the British Petroleum Deepwater Horizon oil spill, the largest marine oil spill in US history, which contaminated coastal wetland ecosystems across the northern Gulf of Mexico. We used hyperspectral data from 2010 and 2011 to compare the impact of oil contamination and recovery of coastal wetland vegetation across three ecologically diverse sites: Barataria Bay (saltmarsh), East Bird’s Foot (intermediate/freshwater marsh), and Chandeleur Islands (mangrove-cordgrass barrier islands). Oil impact was measured by comparing wetland pixels along oiled and oil-free shorelines using various spectral indices. We show that the Chandeleur Islands were the most vulnerable to oiling, Barataria Bay had a small but widespread and significant impact, and East Bird’s Foot had negligible impact. A year later, the Chandeleur Islands showed the strongest signs of recovery, Barataria Bay had a moderate recovery, and East Bird’s Foot had only a slight increase in vegetation. Our results indicate that the recovery was at least partially related to the magnitude of the impact such that greater recovery occurred at sites that had greater impact.

In California, a significant percentage of the pistachio acreage is in the San Joaquin Valley on saline and saline-sodic soils. However, irrigation management practices in commercial pistachio production are based on water-use information developed nearly two decades ago from experiments conducted in non-saline orchards sprinkler-irrigated with good quality water. No information is currently available that quantify the effect of salinity or combined salinity and sodicity on water use of micro-irrigated pistachio orchards, even though such information would help growers schedule irrigations and control soil salinity through leaching. To fill this gap, a field research study was conducted in 2016 and 2017 to measure the actual evapotranspiration (ETa) from commercial pistachio orchards grown on non-saline and saline-sodic soils in the southern portion of the San Joaquin Valley of California. The study aimed at investigating the functional relations between soil salinity/sodicity and tree performance, and understanding the mechanisms regulating water-use reduction under saline and saline-sodic conditions. Pistachio ETa was measured with the residual of energy balance method using a combination of surface renewal and eddy covariance equipment. Saline and saline-sodic conditions in the soil adversely affected tree performance with different intensity. The analysis of field data showed that ETa, light interception by the tree canopy, and nut yield were highly and linearly related (r² > 0.9). Moving from non-saline to saline and saline-sodic conditions, the canopy light interception decreased from 75% (non-saline) to around 50% (saline) and 30% (saline-sodic), and ETa decreased by 32% to 46% relative to the non-saline orchard. In saline-sodic soils, the nut yield resulted around 50% lower than that of non-saline orchard. A statistical analysis performed on the correlations between soil physical-chemical parameters and selected tree performance indicators (ETa, light interception, and nut yield) revealed that the sodium adsorption ratio (SAR) adversely affected tree performance more than the soil electrical conductivity (ECe). Results suggest that secondary effects of sodicity (i.e., degradation of soil structure, possibly leading to poor soil aeration and root hypoxia) might have had a stronger impact on pistachio performance than did salinity in the long term. The information presented in this paper can help pistachio growers and farm managers better tailor irrigation water allocation and management to site-specific orchard conditions (e.g., canopy features and soil-water salinity/sodicity), and potentially lead to water and energy savings through improved irrigation management practices.

From 2011 to 2016, California experienced a millennial‐intensity drought, generating high levels of tree mortality. Remote sensing has been used to monitor the long‐term impacts of drought; however, discriminating dead from live trees in arid and semiarid deciduous woodlands is challenging. The goals of this study were to assess and map the spatial patterns of drought‐induced tree mortality in a blue oak (Quercus douglasii) woodland, a highly drought‐tolerant species forming savannas along the lower foothills surrounding California's Central Valley. Airborne hyperspectral imagery was used to identify the most important wavelength regions predicting drought‐induced blue oak mortality. The best metric to predict canopy stress was a normalized ratio using the spectral bands 937.53 and 1100.08 nm with a correlation with tree mortality of R² = 0.83. The image prediction of mortality for nine field plots found that 16 of 98 trees died (17.9%) during the drought. We further evaluated tree mortality in 82 independent plots, and we found the greatest image predictive accuracy for tree mortality between 1% and 10%. When applied at the landscape level, the regression‐based index found mortality ranged from 1% to more than 51% in oak stands with an average mortality of 10% over the entire study region. In addition, tree mortality at landscape level showed higher tree mortality in blue oaks on south‐facing aspects probably because of higher insolation rates and in sites with low potential for accumulated drainage.

Coastal ecosystems are greatly endangered due to anthropogenic development and climate change. Multiple disturbances may erode the ability of a system to recover from stress if there is little time between disturbance events. We evaluated the ability of the saltmarshes in Barataria Bay, Louisiana, USA, to recover from two successive disturbances, the DeepWater Horizon oil spill in 2010 and Hurricane Isaac in 2012. We measured recovery using vegetation indices and land cover change metrics. We found that after the hurricane, land loss along oiled shorelines was 17.8%, while along oil-free shorelines, it was 13.6% within the first 7 m. At a distance of 7–14 m, land loss from oiled regions was 11.6%, but only 6.3% in oil-free regions. We found no differences in vulnerability to land loss between narrow and wide shorelines; however, vegetation in narrow sites was significantly more stressed, potentially leading to future land loss. Treated oiled regions also lost more land due to the hurricane than untreated regions. These results suggest that ecosystem recovery after the two disturbances is compromised, as the observed high rates of land loss may prevent salt marsh from establishing in the same areas where it existed prior to the oil spill.

A surface energy balance model was conceived to estimate crop transpiration and soil evaporation in orchards and vineyards where the floor is partially wetted by micro-irrigation systems. The proposed surface energy balance model for partial wetting (SEB-PW) builds upon previous multiple-layer modelling approaches to estimate the latent, sensible, and soil heat fluxes, while partitioning the total evapotranspiration ( $ET$ ) into dry and wet soil evaporation ( $\mathsf{\lambda }{\mathrm{E}}_{soil}$ ) and crop transpiration ( $T$ ). The model estimates the energy balance and flux resistances for the evaporation from dry and wet soil areas below the canopy, evaporation from dry and wet soil areas between plant rows, crop transpiration, and total crop $ET$ . This article describes the model development, sensitivity analysis and a preliminary model evaluation. The evaluation shows that simulated hourly $ET$ values have a good correlation with field measurements conducted with the surface renewal method and micro-lysimeter measurements in a micro-irrigated winegrape vineyard of Northern California for a range of fractional crop canopy cover conditions. Evaluation showed that hourly $LE$ estimates had root mean square error ( $RMSE$ ) of 58.6 W m⁻², mean absolute error ( $MAE$ ) of 35.6 W m⁻², Nash-Sutcliffe coefficient ( $C_{NS}$ ) of 0.85, and index of agreement ( $d_a$ ) of 0.94. Daily soil evaporation ( $E_s$ ) estimations had $RMSE$ of 0.30 mm d⁻¹, $MAE$ of 0.24 mm d⁻¹, $C_{NS}$ of 0.87, and $d_a$ of 0.94. $E_s$ estimation had a coefficient of determination ( $r^2$ ) of 0.95, when compared with the micro-lysimeter measurements, which showed that $E_s$ can reach values from 28% to 46% of the total $ET$ after an irrigation event. The proposed SEB-PW model can be used to estimate the effect and significance of soil evaporation from wet and dry soil areas on the total $ET$ , and to inform water balance studies for optimizing irrigation management. Further evaluation is needed to test the model in other partially wetted orchards and to test the model performance during all growing seasons and for different environmental conditions.