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Prof. Yufang Jin
Department of Land, Air and Water Resources, University of California Davis, 133 Veihmeyer Hall, One Shields Ave, CA 95616-8627, USA

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0 Machine Learning
0 Remote Sensing
0 Eco-hydrology
0 Geospatial Technology
0 UAV applications

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Machine Learning

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Journal article
Published: 04 May 2021 in Water Resources Research
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Spatial estimates of crop evapotranspiration with high accuracy from the field to watershed scale have become increasingly important for water management, particularly over irrigated agriculture in semi‐arid regions. Here, we provide a comprehensive assessment on patterns of annual agricultural water use over California’s Central Valley, using 30‐m daily evapotranspiration estimates based on Landsat satellite data. A semi‐empirical Priestley‐Taylor approach was locally optimized and cross‐validated with available field measurements for major crops including alfalfa, almond, citrus, corn, pasture, and rice. The evapotranspiration estimates explained more than 70% variance in daily measurements from independent sites with an RMSE of 0.88 mm day‐1. When aggregated over the Valley, we estimated an average evapotranspiration of 820 ± 290 mm yr‐1 in 2014. Agricultural water use varied significantly across and within crop types, with a coefficient of variation ranging from 8% for Rice (1110 ± 85 mm yr‐1) to 59% for Pistachio (592 ± 352 mm yr‐1). Total water use in 2016 increased by 9.6%, as compared to 2014, mostly because of land‐use conversion from fallow/idle land to cropland. Analysis across 134 Groundwater Sustainability Agencies (GSAs) further showed a large variation of agricultural evapotranspiration among and within GSAs, especially for tree crops, e.g., almond evapotranspiration ranging from 339 ± 80 mm yr‐1 in Tracy to 1240 ± 136 mm yr‐1 in Tri‐County Water Authority. Continuous monitoring and assessment of the dynamics and spatial heterogeneity of agricultural evapotranspiration provide data‐driven guidance for more effective land use and water planning across scales.

ACS Style

A. J. Wong; Y. Jin; J. Medellín‐Azuara; K. T. Paw U; E. R. Kent; J. M. Clay; F. Gao; J. B. Fisher; G. Rivera; C. M. Lee; K. S. Hemes; E. Eichelmann; D. D. Baldocchi; S. J. Hook. Multi‐scale Assessment of Agricultural Consumptive Water Use in California’s Central Valley. Water Resources Research 2021, 1 .

AMA Style

A. J. Wong, Y. Jin, J. Medellín‐Azuara, K. T. Paw U, E. R. Kent, J. M. Clay, F. Gao, J. B. Fisher, G. Rivera, C. M. Lee, K. S. Hemes, E. Eichelmann, D. D. Baldocchi, S. J. Hook. Multi‐scale Assessment of Agricultural Consumptive Water Use in California’s Central Valley. Water Resources Research. 2021; ():1.

Chicago/Turabian Style

A. J. Wong; Y. Jin; J. Medellín‐Azuara; K. T. Paw U; E. R. Kent; J. M. Clay; F. Gao; J. B. Fisher; G. Rivera; C. M. Lee; K. S. Hemes; E. Eichelmann; D. D. Baldocchi; S. J. Hook. 2021. "Multi‐scale Assessment of Agricultural Consumptive Water Use in California’s Central Valley." Water Resources Research , no. : 1.

Journal article
Published: 08 April 2021 in International Journal of Environmental Research and Public Health
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Wildfires can be detrimental to urban and rural communities, causing impacts in the form of psychological stress, direct physical injury, and smoke-related morbidity and mortality. This study examined the area burned by wildfires over the entire state of California from the years 2000 to 2020 in order to quantify and identify whether burned area and fire frequency differed across Census tracts according to socioeconomic indicators over time. Wildfire data were obtained from the California Fire and Resource Assessment Program (FRAP) and National Interagency Fire Center (NIFC), while demographic data were obtained from the American Community Survey. Results showed a doubling in the number of Census tracts that experienced major wildfires and a near doubling in the number of people residing in wildfire-impacted Census tracts, mostly due to an over 23,000 acre/year increase in the area burned by wildfires over the last two decades. Census tracts with a higher fire frequency and burned area had lower proportions of minority groups on average. However, when considering Native American populations, a greater proportion resided in highly impacted Census tracts. Such Census tracts also had higher proportions of older residents. In general, high-impact Census tracts tended to have higher proportions of low-income residents and lower proportions of high-income residents, as well as lower median household incomes and home values. These findings are important to policymakers and state agencies as it relates to environmental justice and the allocation of resources before, during, and after wildfires in the state of California.

ACS Style

Shahir Masri; Erica Scaduto; Yufang Jin; Jun Wu. Disproportionate Impacts of Wildfires among Elderly and Low-Income Communities in California from 2000–2020. International Journal of Environmental Research and Public Health 2021, 18, 3921 .

AMA Style

Shahir Masri, Erica Scaduto, Yufang Jin, Jun Wu. Disproportionate Impacts of Wildfires among Elderly and Low-Income Communities in California from 2000–2020. International Journal of Environmental Research and Public Health. 2021; 18 (8):3921.

Chicago/Turabian Style

Shahir Masri; Erica Scaduto; Yufang Jin; Jun Wu. 2021. "Disproportionate Impacts of Wildfires among Elderly and Low-Income Communities in California from 2000–2020." International Journal of Environmental Research and Public Health 18, no. 8: 3921.

Journal article
Published: 03 March 2021 in Journal of Experimental Botany
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Flower opening and closure are traits of reproductive importance in all angiosperms because they determine the success of self- and cross-pollination. The temporal nature of this phenotype rendered it a difficult target for genetic studies. Cultivated and wild lettuce, Lactuca spp., have composite inflorescences that open only once. An L. serriola×L. sativa F6 recombinant inbred line (RIL) population differed markedly for daily floral opening time. This population was used to map the genetic determinants of this trait; the floral opening time of 236 RILs was scored using time-course image series obtained by drone-based phenotyping on two occasions. Floral pixels were identified from the images using a support vector machine with an accuracy >99%. A Bayesian inference method was developed to extract the peak floral opening time for individual genotypes from the time-stamped image data. Two independent quantitative trait loci (QTLs; Daily Floral Opening 2.1 and qDFO8.1) explaining >30% of the phenotypic variation in floral opening time were discovered. Candidate genes with non-synonymous polymorphisms in coding sequences were identified within the QTLs. This study demonstrates the power of combining remote sensing, machine learning, Bayesian statistics, and genome-wide marker data for studying the genetics of recalcitrant phenotypes.

ACS Style

Rongkui Han; Andy J Y Wong; Zhehan Tang; Maria J Truco; Dean O Lavelle; Alexander Kozik; Yufang Jin; Richard W Michelmore. Drone phenotyping and machine learning enable discovery of loci regulating daily floral opening in lettuce. Journal of Experimental Botany 2021, 72, 2979 -2994.

AMA Style

Rongkui Han, Andy J Y Wong, Zhehan Tang, Maria J Truco, Dean O Lavelle, Alexander Kozik, Yufang Jin, Richard W Michelmore. Drone phenotyping and machine learning enable discovery of loci regulating daily floral opening in lettuce. Journal of Experimental Botany. 2021; 72 (8):2979-2994.

Chicago/Turabian Style

Rongkui Han; Andy J Y Wong; Zhehan Tang; Maria J Truco; Dean O Lavelle; Alexander Kozik; Yufang Jin; Richard W Michelmore. 2021. "Drone phenotyping and machine learning enable discovery of loci regulating daily floral opening in lettuce." Journal of Experimental Botany 72, no. 8: 2979-2994.

Journal article
Published: 25 February 2021 in Journal of Geophysical Research: Biogeosciences
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California's Sierra Nevada has experienced a large increase in wildfire activities over recent decades. This intensifying fire regime has coincided with a warming climate and increasing human activity, but the relative importance of the biophysical and anthropogenic drivers of wildfire remains unclear across this diverse landscape, especially at a finer spatial scale. We used multi‐source geospatial datasets of fire occurrence, and human, climatic and biophysical variables to examine the spatial pattern and controls on Sierra Nevada wildfires averaged from 1984 to 2017. The maximum entropy model driven by both biophysical and anthropogenic variables predicted the spatial distribution of fire probability well, with an area under the curve (AUC) score of 0.79. Model diagnostics revealed that aspects of the climate, including vapor pressure deficit (VPD), temperature, and burning index (difficulty of control), dominated the spatial patterns of fire probability across the whole Sierra Nevada region. The VPD was the leading control, with a relative contribution of 32.1%. Population density and fuel amount were also significant drivers, each accounting for 15.8% to 12.4% of relative contribution. VPD and burning index were the most important factors for fire probability in higher‐elevation forest, while population density was comparatively more important in the lower‐elevation forest regions of the Sierra Nevada. Our findings improved our understanding of the relative importance of various factors in shaping the spatial patterns of historical fire probability in the Sierra Nevada and across various sub‐ecoregions, providing insights for targeting spatially varying forest management strategies to limit potential future increases in wildfires. This article is protected by copyright. All rights reserved.

ACS Style

Bin Chen; Yufang Jin; Erica Scaduto; Max A. Moritz; Michael L. Goulden; James T. Randerson. Climate, Fuel, and Land Use Shaped the Spatial Pattern of Wildfire in California’s Sierra Nevada. Journal of Geophysical Research: Biogeosciences 2021, 126, 1 .

AMA Style

Bin Chen, Yufang Jin, Erica Scaduto, Max A. Moritz, Michael L. Goulden, James T. Randerson. Climate, Fuel, and Land Use Shaped the Spatial Pattern of Wildfire in California’s Sierra Nevada. Journal of Geophysical Research: Biogeosciences. 2021; 126 (2):1.

Chicago/Turabian Style

Bin Chen; Yufang Jin; Erica Scaduto; Max A. Moritz; Michael L. Goulden; James T. Randerson. 2021. "Climate, Fuel, and Land Use Shaped the Spatial Pattern of Wildfire in California’s Sierra Nevada." Journal of Geophysical Research: Biogeosciences 126, no. 2: 1.

Journal article
Published: 06 January 2021 in Remote Sensing
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Long-term record of fine spatial resolution remote sensing datasets is critical for monitoring and understanding global environmental change, especially with regard to fine scale processes. However, existing freely available global land surface observations are limited by medium to coarse resolutions (e.g., 30 m Landsat) or short time spans (e.g., five years for 10 m Sentinel-2). Here we developed a feature-level data fusion framework using a generative adversarial network (GAN), a deep learning technique, to leverage the overlapping Landsat and Sentinel-2 observations during 2016–2019, and reconstruct 10 m Sentinel-2 like imagery from 30 m historical Landsat archives. Our tests with both simulated data and actual Landsat/Sentinel-2 imagery showed that the GAN-based fusion method could accurately reconstruct synthetic Landsat data at an effective resolution very close to that of the real Sentinel-2 observations. We applied the GAN-based model to two dynamic systems: (1) land over dynamics including phenology change, cropping rotation, and water inundation; and (2) human landscape changes such as airport construction, coastal expansion, and urbanization, via historical reconstruction of 10 m Landsat observations from 1985 to 2018. The resulting comparison further validated the robustness and efficiency of our proposed framework. Our pilot study demonstrated the promise of transforming 30 m historical Landsat data into a 10 m Sentinel-2-like archive with advanced data fusion. This will enhance Landsat and Sentinel-2 data science, facilitate higher resolution land cover and land use monitoring, and global change research.

ACS Style

Bin Chen; Jing Li; Yufang Jin. Deep Learning for Feature-Level Data Fusion: Higher Resolution Reconstruction of Historical Landsat Archive. Remote Sensing 2021, 13, 167 .

AMA Style

Bin Chen, Jing Li, Yufang Jin. Deep Learning for Feature-Level Data Fusion: Higher Resolution Reconstruction of Historical Landsat Archive. Remote Sensing. 2021; 13 (2):167.

Chicago/Turabian Style

Bin Chen; Jing Li; Yufang Jin. 2021. "Deep Learning for Feature-Level Data Fusion: Higher Resolution Reconstruction of Historical Landsat Archive." Remote Sensing 13, no. 2: 167.

Journal article
Published: 17 December 2020 in Remote Sensing
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The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides remotely-sensed estimates of evapotranspiration at 70 m spatial resolution every 1–5 days, sampling across the diurnal cycle. This study, in partnership with an operational water management organization, the Eastern Municipal Water District (EMWD) in Southern California, was conducted to evaluate estimates of evapotranspiration under ideal conditions where water is not limited. EMWD regularly uses a ground-based network of reference evapotranspiration (ETo) from the California Irrigation Management Information System (CIMIS); yet, there are gaps in spatial coverage and questions of spatial representativeness and consistency. Space-based potential evapotranspiration (PET) estimates, such as those from ECOSTRESS, provide consistent spatial coverage. We compared ECOSTRESS ETo (estimated from PET) to CIMIS ETo at five CIMIS sites in Riverside County, California from July 2018–June 2020. We found strong correlations between CIMIS ETo and ECOSTRESS ETo across all five sites (R2 = 0.89, root mean square error (RMSE) = 0.11 mm hr−1). Both CIMIS and ECOSTRESS ETo captured similar seasonal patterns through the study period as well as diurnal variability. There were site-specific differences in the relationship between ECOSTRESS AND CIMIS, in part due to spatial heterogeneity around the station site. Consequently, careful examination of landscapes surrounding CIMIS sites must be considered in future comparisons. These results indicate that ECOSTRESS successfully retrieves PET that is comparable to ground-based reference ET, highlighting the potential for providing observation-driven guidance for irrigation management across spatial scales.

ACS Style

Gurjot Kohli; Christine Lee; Joshua Fisher; Gregory Halverson; Evan Variano; Yufang Jin; Daniel Carney; Brenton Wilder; Alicia Kinoshita. ECOSTRESS and CIMIS: A Comparison of Potential and Reference Evapotranspiration in Riverside County, California. Remote Sensing 2020, 12, 4126 .

AMA Style

Gurjot Kohli, Christine Lee, Joshua Fisher, Gregory Halverson, Evan Variano, Yufang Jin, Daniel Carney, Brenton Wilder, Alicia Kinoshita. ECOSTRESS and CIMIS: A Comparison of Potential and Reference Evapotranspiration in Riverside County, California. Remote Sensing. 2020; 12 (24):4126.

Chicago/Turabian Style

Gurjot Kohli; Christine Lee; Joshua Fisher; Gregory Halverson; Evan Variano; Yufang Jin; Daniel Carney; Brenton Wilder; Alicia Kinoshita. 2020. "ECOSTRESS and CIMIS: A Comparison of Potential and Reference Evapotranspiration in Riverside County, California." Remote Sensing 12, no. 24: 4126.

Journal article
Published: 25 August 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Satellite-based active fire (AF) products provide opportunities for constructing continuous fire progression maps, a critical data set needed for improved fire behavior modeling and fire management.This study aims to investigate the geospatial interpolation techniques in mapping daily fire progression and assess the accuracy of the derived maps from multi-sensor active fire products. We focused on 42 large wildfires in Northern California from 2017 to 2018, where the USDA Forest Service National Infrared Operations (NIROPS) daily fire perimeters were available for comparison.The standard active fire products from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the combined products were used as inputs. We found that the estimated surfaces generated by the natural neighbor method with the combined MODIS and VIIRS active fire input layers performed the best, with $R^2$ of 0.7 0.31 and RMSE of 1.25 1.21 ( $10^3$ acres) at a daily time scale; the accuracy was higher when assessed at a two day rolling window, e.g., $R^2$ of 0.83 0.20 and RMSE of 0.74 0.94. Relatively higher spatial accuracy was found using the 375m VIIRS active fire product as inputs when interpolated with the natural neighbor method. Furthermore, locational pixel-based comparison showed 61% matched to a single day, and an additional 25% explained within 1 day of the estimation, revealing greater confidence in fire progression estimation at a 2-day moving time interval. This study demonstrated the efficacy and potential improvements of daily fire progression mapping at local and regional scales.

ACS Style

Erica Scaduto; Bin Chen; Yufang Jin. Satellite-Based Fire Progression Mapping: A Comprehensive Assessment for Large Fires in Northern California. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 5102 -5114.

AMA Style

Erica Scaduto, Bin Chen, Yufang Jin. Satellite-Based Fire Progression Mapping: A Comprehensive Assessment for Large Fires in Northern California. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):5102-5114.

Chicago/Turabian Style

Erica Scaduto; Bin Chen; Yufang Jin. 2020. "Satellite-Based Fire Progression Mapping: A Comprehensive Assessment for Large Fires in Northern California." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 5102-5114.

Journal article
Published: 12 March 2019 in Remote Sensing
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Rangelands cover ~23 million hectares and support a $3.4 billion annual cattle industry in California. Large variations in forage production from year to year and across the landscape make grazing management difficult. We here developed optimized methods to map high-resolution forage production using multispectral remote sensing imagery. We conducted monthly flights using a Small Unmanned Aerial System (sUAS) in 2017 and 2018 over a 10-ha deferred grazing rangeland. Daily maps of NDVI at 30-cm resolution were first derived by fusing monthly 30-cm sUAS imagery and more frequent 3-m PlanetScope satellite observations. We estimated aboveground net primary production as a product of absorbed photosynthetically active radiation (APAR) derived from NDVI and light use efficiency (LUE), optimized as a function of topography and climate stressors. The estimated forage production agreed well with field measurements having a R2 of 0.80 and RMSE of 542 kg/ha. Cumulative NDVI and APAR were less correlated with measured biomass ( R 2 = 0.68). Daily forage production maps captured similar seasonal and spatial patterns compared to field-based biomass measurements. Our study demonstrated the utility of aerial and satellite remote sensing technology in supporting adaptive rangeland management, especially during an era of climatic extremes, by providing spatially explicit and near-real-time forage production estimates.

ACS Style

Han Liu; Randy A. Dahlgren; Royce E. Larsen; Scott M. Devine; Leslie M. Roche; Anthony T. O’ Geen; Andy J.Y. Wong; Sarah Covello; Yufang Jin. Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite. Remote Sensing 2019, 11, 595 .

AMA Style

Han Liu, Randy A. Dahlgren, Royce E. Larsen, Scott M. Devine, Leslie M. Roche, Anthony T. O’ Geen, Andy J.Y. Wong, Sarah Covello, Yufang Jin. Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite. Remote Sensing. 2019; 11 (5):595.

Chicago/Turabian Style

Han Liu; Randy A. Dahlgren; Royce E. Larsen; Scott M. Devine; Leslie M. Roche; Anthony T. O’ Geen; Andy J.Y. Wong; Sarah Covello; Yufang Jin. 2019. "Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite." Remote Sensing 11, no. 5: 595.

Journal article
Published: 20 July 2018 in Agricultural and Forest Meteorology
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Recent prolonged droughts in California have emphasized the urgent need to implement more efficient water management practices for high value tree crops. Accurate estimation of evapotranspiration (ET), a main component of consumptive water use, is critical for improving management of micro-irrigated pistachio orchards grown in the San Joaquin Valley of California. We estimated ET of three mature commercial pistachio orchards on non-saline and increasingly saline soils in 2015 and 2016, using the Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) method and Landsat 8 satellite observations. Based on a comparison with field observations at 8 sites, we modified the parameterizations of the momentum roughness length and net radiation for pistachio tree crops and reduced the uncertainty of daily ET estimates. When compared with field data, the recalibrated METRIC ET estimates had an R2 of 0.59, a mean absolute error of 1.1 mm/day, and a RMSE of 1.4 mm/day during Landsat overpass dates (n = 72). The METRIC ET map captured the temporal dynamics and spatial heterogeneity both within and among the orchards. The mean annual crop season estimated ET (mid-March to mid-October in 2016) with remote sensing decreased by 32% from 1064 ± 99 mm in the non-salt affected control orchard to 725 ± 82 mm in the orchard with the highest level of soil-water salinity. The ET reduction was consistent with canopy volume differences among the study orchards, as shown by summer Normalized Difference Vegetation Index (NDVI) from Landsat observations, e.g., 0.72 ± 0.06 in the control vs. 0.52 ± 0.06 in the most saline orchard. The available energy was controlled mostly by canopy features and explained 64% of daily ET variation among all Landsat pixels and satellite overpass days. The normalized differenced water index (NDWI) could be considered as an important parameter to capture the partitioning of available energy for ET (R2 = 0.38), suggesting that the lower soil osmotic potential in saline orchards further reduced crop ET.

ACS Style

Yufang Jin; Ruyan He; Giulia Marino; Michael Whiting; Eric Kent; Blake L. Sanden; Mae Culumber; Louise Ferguson; Cayle Little; Stephen Grattan; Kyaw Tha Paw U; Luis O. Lagos; Richard L. Snyder; Daniele Zaccaria. Spatially variable evapotranspiration over salt affected pistachio orchards analyzed with satellite remote sensing estimates. Agricultural and Forest Meteorology 2018, 262, 178 -191.

AMA Style

Yufang Jin, Ruyan He, Giulia Marino, Michael Whiting, Eric Kent, Blake L. Sanden, Mae Culumber, Louise Ferguson, Cayle Little, Stephen Grattan, Kyaw Tha Paw U, Luis O. Lagos, Richard L. Snyder, Daniele Zaccaria. Spatially variable evapotranspiration over salt affected pistachio orchards analyzed with satellite remote sensing estimates. Agricultural and Forest Meteorology. 2018; 262 ():178-191.

Chicago/Turabian Style

Yufang Jin; Ruyan He; Giulia Marino; Michael Whiting; Eric Kent; Blake L. Sanden; Mae Culumber; Louise Ferguson; Cayle Little; Stephen Grattan; Kyaw Tha Paw U; Luis O. Lagos; Richard L. Snyder; Daniele Zaccaria. 2018. "Spatially variable evapotranspiration over salt affected pistachio orchards analyzed with satellite remote sensing estimates." Agricultural and Forest Meteorology 262, no. : 178-191.

Journal article
Published: 08 September 2017 in Remote Sensing
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A five-year drought in California led to a significant increase in tree mortality in the Sierra Nevada forests from 2012 to 2016. Landscape level monitoring of forest health and tree dieback is critical for vegetation and disaster management strategies. We examined the capability of multispectral imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) in detecting and explaining the impacts of the recent severe drought in Sierra Nevada forests. Remote sensing metrics were developed to represent baseline forest health conditions and drought stress using time series of MODIS vegetation indices (VIs) and a water index. We used Random Forest algorithms, trained with forest aerial detection surveys data, to detect tree mortality based on the remote sensing metrics and topographical variables. Map estimates of tree mortality demonstrated that our two-stage Random Forest models were capable of detecting the spatial patterns and severity of tree mortality, with an overall producer’s accuracy of 96.3% for the classification Random Forest (CRF) and a RMSE of 7.19 dead trees per acre for the regression Random Forest (RRF). The overall omission errors of the CRF ranged from 19% for the severe mortality class to 27% for the low mortality class. Interpretations of the models revealed that forests with higher productivity preceding the onset of drought were more vulnerable to drought stress and, consequently, more likely to experience tree mortality. This method highlights the importance of incorporating baseline forest health data and measurements of drought stress in understanding forest response to severe drought.

ACS Style

Sarah Byer; Yufang Jin. Detecting Drought-Induced Tree Mortality in Sierra Nevada Forests with Time Series of Satellite Data. Remote Sensing 2017, 9, 929 .

AMA Style

Sarah Byer, Yufang Jin. Detecting Drought-Induced Tree Mortality in Sierra Nevada Forests with Time Series of Satellite Data. Remote Sensing. 2017; 9 (9):929.

Chicago/Turabian Style

Sarah Byer; Yufang Jin. 2017. "Detecting Drought-Induced Tree Mortality in Sierra Nevada Forests with Time Series of Satellite Data." Remote Sensing 9, no. 9: 929.

Journal article
Published: 05 May 2017 in Remote Sensing
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California growers face challenges with water shortages and there is a strong need to use the least amount of water while optimizing yield. Timely information on evapotranspiration (ET), a dominant component of crop consumptive water use, is critical for growers to tailor irrigation management based on in-field spatial variability and in-season variations. We evaluated the performance of a remote sensing-based approach, Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC), in mapping ET over an almond orchard in California, driven by Landsat satellite observations. Reference ET from a network of weather stations over well-watered grass (ETo) was used for the internal calibration and for deriving ET at daily and extended time period, instead of alfalfa based reference evapotranspiration (ETr). Our study showed that METRIC daily ET estimates during Landsat overpass dates agreed well with the field measurements. During 2009–2012, a root mean square error (RMSE) of 0.53 mm/day and a coefficient of determination (R2) of 0.87 were found between METRIC versus observed daily ET. Monthly ET estimates had a higher accuracy, with a RMSE of 12.08 mm/month, a R2 of 0.90, and a relatively small relative mean difference (RMD) of 9.68% during 2009–2012 growing seasons. Net radiation and Normalized Difference Vegetation Index (NDVI) from remote sensing observations were highly correlated with spatial and temporal ET estimates. An empirical model was developed to estimate daily ET using NDVI, net radiation (Rn), and vapor pressure deficit (VPD). The validation showed that the accuracy of this easy-to-use empirical method was slightly lower than that of METRIC but still reasonable, with a RMSE of 0.71 mm/day when compared to ground measurements. The remote sensing based ET estimate will support a variety of State and local interests in water use and irrigation management, for both planning and regulatory/compliance purposes, and it provides the farmers observation-based guidance for site-specific and time-sensitive irrigation management.

ACS Style

Ruyan He; Yufang Jin; Maziar M. Kandelous; Daniele Zaccaria; Blake L. Sanden; Richard L. Snyder; Jinbao Jiang; Jan W. Hopmans. Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations. Remote Sensing 2017, 9, 436 .

AMA Style

Ruyan He, Yufang Jin, Maziar M. Kandelous, Daniele Zaccaria, Blake L. Sanden, Richard L. Snyder, Jinbao Jiang, Jan W. Hopmans. Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations. Remote Sensing. 2017; 9 (5):436.

Chicago/Turabian Style

Ruyan He; Yufang Jin; Maziar M. Kandelous; Daniele Zaccaria; Blake L. Sanden; Richard L. Snyder; Jinbao Jiang; Jan W. Hopmans. 2017. "Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations." Remote Sensing 9, no. 5: 436.