Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards
Abstract
:1. Introduction
1.1. TSEB2T Model
1.2. TSEB2T Main Inputs
1.2.1. Leaf Area Index (LAI)
1.2.2. Canopy Height (hc)
1.2.3. Fractional Cover (fc) and Canopy Width (wc):
1.2.4. wc/hc Ratio
2. Materials and Methods
2.1. Study Area and Data Sources
2.2. Data Processing
2.2.1. Thermal Data
2.2.2. Optical Data
2.3. Energy Balance Closure Adjustment Methods for EC
2.4. Contextual Spatial Domain
2.5. TSEB2T Inputs
2.5.1. Leaf Area Index (LAI)
2.5.2. Canopy Height (hc)
2.5.3. Fractional Cover (fc) and Canopy Width (wc)
2.5.4. wc/hc Ratio
2.6. Goodness-of-Fit Statistics
3. Results and Discussion
3.1. TSEB2T Contextual Spatial Domains Validation
3.1.1. EC Footprint Estimation
3.1.2. Statistical Performance
3.2. Contextual Spatial Domain Aggregations Effects
3.2.1. The Effect of Model Grid Size on TSEB2T Inputs
3.2.2. Contextual Spatial Domain Effect on Field-Scale LE Estimation
3.2.3. Contextual Spatial Domain Effect on LE Statistical Characteristics
3.2.4. Effects of Model Grid Size on LE
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
References
- Melesse, A.M.; Weng, Q.; Thenkabail, P.S.; Senay, G.B. Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling. Sensors 2007, 7, 3209–3241. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Nieto, H.; Kustas, W.P.; Torres-Rúa, A.; Alfieri, J.G.; Gao, F.; Anderson, M.C.; White, W.A.; Song, L.; Alsina, M.D.M.; Prueger, J.H.; et al. Evaluation of TSEB turbulent fluxes using different methods for the retrieval of soil and canopy component temperatures from UAV thermal and multispectral imagery. Irrig. Sci. 2018, 37, 389–406. [Google Scholar] [CrossRef] [Green Version]
- Cammalleri, C.; Anderson, M.; Gao, F.; Hain, C.R.; Kustas, W.P. A data fusion approach for mapping daily evapotranspiration at field scale. Water Resour. Res. 2013, 49, 4672–4686. [Google Scholar] [CrossRef]
- McKee, M.; Torres-Rua, A.F.; Aboutalebi, M.; Nassar, A.; Coopmans, C.; Kustas, W.P.; Gao, F.; Dokoozlian, N.; Sanchez, L.; Alsina, M.M. Challenges that beyond-visual-line-of-sight technology will create for UAS-based remote sensing in agriculture (Conference Presentation). In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV; SPIE-The International Society for Optical Engineering: Bellingham, WA, USA, 2019; Volume 11008, p. 110080J. [Google Scholar]
- Torres-Rue, A.F.; Aboutalebi, M.; Wright, T.; Nassar, A.; Guillevic, P.; Hipps, L.; Gao, F.; Jim, K.; Alsina, M.; Coopmans, C.; et al. Estimation of Surface Thermal Emissivity In A Vineyard For UAV Microbolometer Thermal Cameras Using NASA Hytes Hyperspectral Thermal, And Landsat And Aggieair Optical Data. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV; SPIE: Baltimore, MD, USA, 2019; pp. 15–17. [Google Scholar]
- Nassar, A.; Nieto, H.; Aboutalebi, M.; TorresRue, A.F.; McKee, M.; Kustas, W.P.; Prueger, J.H.; McKee, L.; Alfieri, J.G.; Hipps, L.; et al. Pixel Resolution Sensitivity Analysis for the Estimation of Evapotranspiration Using the Two Source Energy Balance Model and sUAS Imagery under Agricultural Complex Canopy Environments; American Geophysical Union (AGU): Washington, DC, USA, 2018. [Google Scholar]
- Nassar, A.; Torres-Rue, A.F.; McKee, M.; Kustas, W.P.; Coopmans, C.; Nieto, H.; Hipps, L. Assessment of UAV Flight Times for Estimation of Daily High Resolution Evapotranspiration in Complex Agricultural Canopy Environments; Universities Council in Water Resources (UCOWR): Snowbird, UT, USA, 2019. [Google Scholar]
- Wu, H.; Li, Z.-L. Scale Issues in Remote Sensing: A Review on Analysis, Processing and Modeling. Sensors 2009, 9, 1768–1793. [Google Scholar] [CrossRef]
- El Maayar, M.; Chen, J.M. Spatial scaling of evapotranspiration as affected by heterogeneities in vegetation, topography, and soil texture. Remote. Sens. Environ. 2006, 102, 33–51. [Google Scholar] [CrossRef]
- Miu, M.; Zhang, X.; Dewan, M.A.A.; Wang, J. Aggregation and Visualization of Spatial Data with Application to Classification of Land Use and Land Cover. Geoinformatics Geostat. Overv. 2017, 5. [Google Scholar] [CrossRef] [Green Version]
- Brunsell, N.; Gillies, R. Scale issues in land–atmosphere interactions: Implications for remote sensing of the surface energy balance. Agric. For. Meteorol. 2003, 117, 203–221. [Google Scholar] [CrossRef]
- Giorgi, F. An Approach for the Representation of Surface Heterogeneity in Land Surface Models. Part I.; Theoretical Framework. Mon. Weather Rev. 1997, 125, 1885–1899. [Google Scholar] [CrossRef]
- Sharma, V.; Kilic, A.; Irmak, S. Impact of scale/resolution on evapotranspiration from Landsat and MODIS images. Water Resour. Res. 2016, 52, 1800–1819. [Google Scholar] [CrossRef] [Green Version]
- Ershadi, A.; McCabe, M.; Evans, J.; Walker, J. Effects of spatial aggregation on the multi-scale estimation of evapotranspiration. Remote. Sens. Environ. 2013, 131, 51–62. [Google Scholar] [CrossRef]
- Moran, M.S.; Humes, K.S.; Pinter, P.J. The scaling characteristics of remotely-sensed variables for sparsely-vegetated heterogeneous landscapes. J. Hydrol. 1997, 190, 337–362. [Google Scholar] [CrossRef]
- Kustas, W.; Norman, J. Evaluating the Effects of Subpixel Heterogeneity on Pixel Average Fluxes. Remote Sens. Environ. 2000, 74, 327–342. [Google Scholar] [CrossRef]
- Verburg, P.H.; Crossman, N.; Ellis, E.C.; Heinimann, A.; Hostert, P.; Mertz, O.; Nagendra, H.; Sikor, T.; Erb, K.-H.; Golubiewski, N.; et al. Land system science and sustainable development of the earth system: A global land project perspective. Anthropocene 2015, 12, 29–41. [Google Scholar] [CrossRef] [Green Version]
- Long, D.; Singh, V.P.; Li, Z. How sensitive is SEBAL to changes in input variables, domain size and satellite sensor? J. Geophys. Res. Space Phys. 2011, 116, 116. [Google Scholar] [CrossRef]
- Goodchild, M.F.; Gopal, S. The Accuracy of Spatial Databases; CRC Press: Boca Raton, FL, USA, 1989. [Google Scholar]
- Fritz, S.; See, L.; Rembold, F. Comparison of global and regional land cover maps with statistical information for the agricultural domain in Africa. Int. J. Remote Sens. 2010, 31, 2237–2256. [Google Scholar] [CrossRef]
- Bian, L.; Butler, R. Comparing effects of aggregation methods on statistical and spatial properties of simulated spatial data. Photogramm. Eng. Remote Sens. 1999, 65, 73–84. [Google Scholar]
- Hong, S.-H.; Hendrickx, J.M.; Borchers, B. Up-scaling of SEBAL derived evapotranspiration maps from Landsat (30m) to MODIS (250m) scale. J. Hydrol. 2009, 370, 122–138. [Google Scholar] [CrossRef]
- Singh, R.K.; Senay, G.B.; Velpuri, N.M.; Bohms, S.; Verdin, J.P. On the Downscaling of Actual Evapotranspiration Maps Based on Combination of MODIS and Landsat-Based Actual Evapotranspiration Estimates. Remote Sens. 2014, 6, 10483–10509. [Google Scholar] [CrossRef] [Green Version]
- Li, X.; Liu, S.; Li, H.; Ma, Y.; Wang, J.; Zhang, Y.; Xu, Z.; Xu, T.; Song, L.; Yang, X.; et al. Intercomparison of Six Upscaling Evapotranspiration Methods: From Site to the Satellite Pixel. J. Geophys. Res. Atmos. 2018, 123, 6777–6803. [Google Scholar] [CrossRef]
- French, A.N.; Hunsaker, D.J.; Thorp, K.R. Remote sensing of evapotranspiration over cotton using the TSEB and METRIC energy balance models. Remote Sens. Environ. 2015, 158, 281–294. [Google Scholar] [CrossRef]
- Su, Z.; Pelgrum, H.; Menenti, M. Aggregation effects of surface heterogeneity in land surface processes. Hydrol. Earth Syst. Sci. 1999, 3, 549–563. [Google Scholar] [CrossRef] [Green Version]
- Guzinski, R.; Nieto, H. Evaluating the feasibility of using Sentinel-2 and Sentinel-3 satellites for high-resolution evapotranspiration estimations. Remote Sens. Environ. 2019, 221, 157–172. [Google Scholar] [CrossRef]
- Niu, H.; Zhao, T.; Wang, D.; Chen, Y. Evapotranspiration Estimation with UAVs in Agriculture: A Review. Preprints 2019. [Google Scholar] [CrossRef]
- Mauser, W.; Schädlich, S. Modelling the spatial distribution of evapotranspiration on different scales using remote sensing data. J. Hydrol. 1998, 212, 250–267. [Google Scholar] [CrossRef]
- The California Garden Web, University of California. Available online: http://cagardenweb.ucanr.edu/Growing_Grapes_in_the_California_Garden/?uid=1&ds=436 (accessed on 16 September 2019).
- Growing Fruit Trees in Maine, the University of Maine. Available online: https://extension.umaine.edu/fruit/growing-fruit-trees-in-maine/spacing/ (accessed on 1 October 2019).
- Norman, J.; Kustas, W.; Humes, K. Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agric. For. Meteorol. 1995, 77, 263–293. [Google Scholar] [CrossRef]
- Chirouze, J.; Boulet, G.; Jarlan, L.; Fieuzal, R.; Rodríguez, J.C.; Ezzahar, J.; Er-Raki, S.; Bigeard, G.; Merlin, O.; Garatuza-Payan, J.; et al. Intercomparison of four remote-sensing-based energy balance methods to retrieve surface evapotranspiration and water stress of irrigated fields in semi-arid climate. Hydrol. Earth Syst. Sci. 2014, 18, 1165–1188. [Google Scholar] [CrossRef] [Green Version]
- Alfieri, J.G.; Kustas, W.P.; Nieto, H.; Prueger, J.H.; Hipps, L.E.; McKee, L.G.; Gao, F.; Los, S. Influence of wind direction on the surface roughness of vineyards. Irrig. Sci. 2019, 37, 359–373. [Google Scholar] [CrossRef]
- Kustas, W.P.; Alfieri, J.G.; Nieto, H.; Wilson, T.G.; Gao, F.; Anderson, M.C. Utility of the two-source energy balance (TSEB) model in vine and interrow flux partitioning over the growing season. Irrig. Sci. 2019, 37, 375–388. [Google Scholar] [CrossRef]
- Bigeard, G.; Coudert, B.; Chirouze, J.; Er-Raki, S.; Boulet, G.; Ceschia, E.; Jarlan, L. Evapotranspiration monitoring based on thermal infrared data over agricultural landscapes: Comparison of a simple energy budget model and a SVAT model. Hydrol. Earth Syst. Sci. Discuss. 2018, 2018, 1–44. [Google Scholar]
- Kustas, W.; Anderson, M. Advances in thermal infrared remote sensing for land surface modeling. Agric. For. Meteorol. 2009, 149, 2071–2081. [Google Scholar] [CrossRef]
- Yang, Y.; Qiu, J.; Zhang, R.; Huang, S.; Chen, S.; Wang, H.; Luo, J.; Fan, Y. Intercomparison of Three Two-Source Energy Balance Models for Partitioning Evaporation and Transpiration in Semiarid Climates. Remote Sens. 2018, 10, 1149. [Google Scholar] [CrossRef] [Green Version]
- Andreu, A.; Kustas, W.P.; Polo, M.J.; Carrara, A.; González-Dugo, M.P. Modeling Surface Energy Fluxes over a Dehesa (Oak Savanna) Ecosystem Using a Thermal Based Two-Source Energy Balance Model (TSEB) I. Remote Sens. 2018, 10, 567. [Google Scholar] [CrossRef] [Green Version]
- Yao, W.; Han, M.; Xu, S. Estimating the regional evapotranspiration in Zhalong wetland with the Two-Source Energy Balance (TSEB) model and Landsat7/ETM+ images. Ecol. Inform. 2010, 5, 348–358. [Google Scholar] [CrossRef]
- Anderson, M.; Gao, F.; Knipper, K.; Hain, C.; Dulaney, W.; Baldocchi, D.; Eichelmann, E.; Hemes, K.; Yang, Y.; Medellin-Azuara, J.; et al. Field-Scale Assessment of Land and Water Use Change over the California Delta Using Remote Sensing. Remote Sens. 2018, 10, 889. [Google Scholar] [CrossRef] [Green Version]
- Kustas, W.P.; Norman, J.M. A two-source approach for estimating turbulent fluxes using multiple angle thermal infrared observations. Water Resour. Res. 1997, 33, 1495–1508. [Google Scholar] [CrossRef]
- Norman, J.M.; Kustas, W.P.; Prueger, J.H.; Diak, G.R. Surface flux estimation using radiometric temperature: A dual-temperature-difference method to minimize measurement errors. Water Resour. Res. 2000, 36, 2263–2274. [Google Scholar] [CrossRef] [Green Version]
- Gao, F.; Kustas, W.P.; Anderson, M.C. A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land. Remote Sens. 2012, 4, 3287–3319. [Google Scholar] [CrossRef] [Green Version]
- Zheng, G.; Moskal, L.M. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors. Sensors 2009, 9, 2719–2745. [Google Scholar] [CrossRef] [Green Version]
- White, W.A.; Alsina, M.M.; Nieto, H.; McKee, L.G.; Gao, F.; Kustas, W.P. Determining a robust indirect measurement of leaf area index in California vineyards for validating remote sensing-based retrievals. Irrig. Sci. 2019, 79, 269–280. [Google Scholar] [CrossRef]
- Liu, K.; Zhou, Q.-B.; Wu, W.-B.; Xia, T.; Tang, H.-J. Estimating the crop leaf area index using hyperspectral remote sensing. J. Integr. Agric. 2016, 15, 475–491. [Google Scholar] [CrossRef] [Green Version]
- Johnson, L.F. Temporal stability of an NDVI-LAI relationship in a Napa Valley vineyard. Aust. J. Grape Wine Res. 2003, 9, 96–101. [Google Scholar] [CrossRef]
- Dobrowski, S.; Ustin, S.; Wolpert, J. Remote estimation of vine canopy density in vertically shoot-positioned vineyards: Determining optimal vegetation indices. Aust. J. Grape Wine Res. 2002, 8, 117–125. [Google Scholar] [CrossRef]
- Fang, H. Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model. Remote Sens. Environ. 2003, 85, 257–270. [Google Scholar] [CrossRef]
- Sun, L.; Gao, F.; Anderson, M.C.; Kustas, W.P.; Alsina, M.M.; Sanchez, L.; Sams, B.; McKee, L.; Dulaney, W.; White, W.A.; et al. Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards. Remote Sens. 2017, 9, 317. [Google Scholar] [CrossRef] [Green Version]
- Kimes, D.; Knyazikhin, Y.; Privette, J.; Abuelgasim, A.; Gao, F. Inversion methods for physically-based models. Remote Sens. Rev. 2000, 18, 381–439. [Google Scholar] [CrossRef]
- Gonsamo, A.; Pellikka, P. The sensitivity based estimation of leaf area index from spectral vegetation indices. ISPRS J. Photogramm. Remote Sens. 2012, 70, 15–25. [Google Scholar] [CrossRef]
- Kustas, W.P.; Norman, J.M. Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover. Agric. For. Meteorol. 1999, 94, 13–29. [Google Scholar] [CrossRef]
- Nieto, H.; Kustas, W.P.; Alfieri, J.G.; Gao, F.; Hipps, L.E.; Los, S.; Prueger, J.H.; McKee, L.G.; Anderson, M.C. Impact of different within-canopy wind attenuation formulations on modelling sensible heat flux using TSEB. Irrig. Sci. 2019, 37, 315–331. [Google Scholar] [CrossRef]
- Kustas, W.P.; Anderson, M.C.; Alfieri, J.G.; Knipper, K.; Torres-Rua, A.; Parry, C.K.; Nieto, H.; Agam, N.; White, W.A.; Gao, F.; et al. The Grape Remote Sensing Atmospheric Profile and Evapotranspiration Experiment. Bull. Am. Meteorol. Soc. 2018, 99, 1791–1812. [Google Scholar] [CrossRef] [Green Version]
- Torres-Rua, A. Vicarious Calibration of sUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature. Sensors 2017, 17, 1499. [Google Scholar] [CrossRef] [Green Version]
- McKee, M.; Nassar, A.; Torres-Rua, A.F.; Aboutalebi, M.; Kustas, W. Implications of sensor inconsistencies and remote sensing error in the use of small unmanned aerial systems for generation of information products for agricultural management. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III; International Society for Optics and Photonics: Bellingham, WA, USA, 2018; Volume 10664, p. 1066402. [Google Scholar]
- Alfieri, J.G.; Kustas, W.P.; Prueger, J.H.; McKee, L.G.; Hipps, L.E.; Gao, F. A multi-year intercomparison of micrometeorological observations at adjacent vineyards in California’s Central Valley during GRAPEX. Irrig. Sci. 2019, 37, 345–357. [Google Scholar] [CrossRef]
- Agam, N.; Kustas, W.P.; Alfieri, J.G.; Gao, F.; McKee, L.M.; Prueger, J.H.; Hipps, L.E. Micro-scale spatial variability in soil heat flux (SHF) in a wine-grape vineyard. Irrig. Sci. 2019, 37, 253–268. [Google Scholar] [CrossRef]
- Kljun, N.; Calanca, P.; Rotach, M.W.; Schmid, H.P. A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP). Geosci. Model Dev. 2015, 8, 3695–3713. [Google Scholar] [CrossRef] [Green Version]
- Hassan-Esfahani, L.; Ebtehaj, A.M.; Torres-Rua, A.; McKee, M. Spatial Scale Gap Filling Using an Unmanned Aerial System: A Statistical Downscaling Method for Applications in Precision Agriculture. Sensors 2017, 17, 2106. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Moorhead, J.E.; Marek, G.W.; Colaizzi, P.D.; Gowda, P.H.; Evett, S.R.; Brauer, D.K.; Marek, T.H.; Porter, D.O. Evaluation of Sensible Heat Flux and Evapotranspiration Estimates Using a Surface Layer Scintillometer and a Large Weighing Lysimeter. Sensors 2017, 17, 2350. [Google Scholar] [CrossRef] [Green Version]
- Imukova, K.; Ingwersen, J.; Hevart, M.; Streck, T. Energy balance closure on a winter wheat stand: Comparing the eddy covariance technique with the soil water balance method. Biogeosci. Discuss. 2015, 12, 6783–6820. [Google Scholar] [CrossRef]
- Mauder, M.; Genzel, S.; Fu, J.; Kiese, R.; Soltani, M.; Steinbrecher, R.; Zeeman, M.; Banerjee, T.; De Roo, F.; Kunstmann, H. Evaluation of energy balance closure adjustment methods by independent evapotranspiration estimates from lysimeters and hydrological simulations. Hydrol. Process. 2017, 32, 39–50. [Google Scholar] [CrossRef]
- Twine, T.; Kustas, W.; Norman, J.; Cook, D.; Houser, P.; Meyers, T.; Prueger, J.; Starks, P.; Wesely, M. Correcting eddy-covariance flux underestimates over a grassland. Agric. For. Meteorol. 2000, 103, 279–300. [Google Scholar] [CrossRef] [Green Version]
- Al-Juaidi, A.E.M.; Nassar, A.M.; Al-Juaidi, O.E.M. Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors. Arab. J. Geosci. 2018, 11, 765. [Google Scholar] [CrossRef]
- Sun, H.; Yang, Y.; Wu, R.; Gui, D.; Xue, J.; Liu, Y.; Yan, D. Improving Estimation of Cropland Evapotranspiration by the Bayesian Model Averaging Method with Surface Energy Balance Models. Atmosphere 2019, 10, 188. [Google Scholar] [CrossRef] [Green Version]
- Song, L.; Liu, S.; Kustas, W.P.; Zhou, J.; Xu, Z.; Xia, T.; Li, M. Application of remote sensing-based two-source energy balance model for mapping field surface fluxes with composite and component surface temperatures. Agric. For. Meteorol. 2016, 230, 8–19. [Google Scholar] [CrossRef] [Green Version]
- Kustas, W.P.; Nieto, H.; Morillas, L.; Anderson, M.C.; Alfieri, J.G.; Hipps, L.E.; Villagarcía, L.; Domingo, F.; Garcia, M. Revisiting the paper “Using radiometric surface temperature for surface energy flux estimation in Mediterranean drylands from a two-source perspective”. Remote Sens. Environ. 2016, 184, 645–653. [Google Scholar] [CrossRef] [Green Version]
- Carlson, T. An Overview of the “Triangle Method” for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery. Sensors 2007, 7, 1612–1629. [Google Scholar] [CrossRef] [Green Version]
- Hardin, P.J.; Jensen, R.R. Neural Network Estimation of Urban Leaf Area Index. GIScience Remote Sens. 2005, 42, 251–274. [Google Scholar] [CrossRef]
- Timmermans, W.J.; Kustas, W.P.; Anderson, M.C.; French, A.N. An intercomparison of the Surface Energy Balance Algorithm for Land (SEBAL) and the Two-Source Energy Balance (TSEB) modeling schemes. Remote Sens. Environ. 2007, 108, 369–384. [Google Scholar] [CrossRef]
Flight Date | Landsat Time PST | Afternoon PST | Midafternoon PST |
---|---|---|---|
09 August 2014 | 10:41 am | - | |
02 June 2015 | 10:43 am | 14:07 pm | - |
11 July 2015 | 10:35 am | 14:14 pm | |
02 May 2016 | - | 12:05 pm | 15:04 pm |
03 May 2016 | - | 12:48 pm | - |
ID | Micrometeorological Parameters | Instrument Name 1 | Elevation |
---|---|---|---|
1 | Water vapor concentration | Infrared gas analyzer (EC150, Campbell Scientific, Logan, Utah) | 5 m agl |
2 | Wind velocity | Sonic anemometer (CSAT3, Campbell Scientific) | 5 m agl |
3 | Net radiation | 4-way radiometer (CNR-1, Kipp and Zonen, Delft, The Netherlands) | 6 m agl |
4 | Air temperature | Gill shielded temperature (Vaisala, Helsinki, Finland) | 5 m agl |
5 | Water vapor pressure | Humidity probe (HMP45C, Vaisala, Helsinki, Finland) | 5 m agl |
6 | Soil heat flux | Five plates (HFT-3, Radiation Energy Balance Systems, Bellevue, Washington) | −8 cm |
7 | Soil temperature | Thermocouples | −2 cm |
8 | Soil moisture | Soil moisture probe (HydraProbe, Stevens Water Monitoring Systems, Portland, Oregon) | −5 cm |
Spatial Domain | Fluxes | RMSE (W/m2) | NRMSE | MAE (W/m2) | MAPE (%) | NSE | R2 |
---|---|---|---|---|---|---|---|
3.6 m | Rn | 28 | 0.3 | 25 | 5 | 0.9 | 0.94 |
LE | 69 | 1.2 | 58 | 20 | 0.5 | 0.49 | |
H | 54 | 0.8 | 41 | 26 | 0.7 | 0.67 | |
G | 34 | 0.9 | 30 | 51 | 0.6 | 0.56 | |
7.2 m | Rn | 27 | 0.3 | 24 | 4 | 0.9 | 0.94 |
LE | 66 | 1.2 | 56 | 19 | 0.5 | 0.53 | |
H | 51 | 0.7 | 36 | 24 | 0.7 | 0.67 | |
G | 33 | 0.8 | 30 | 50 | 0.6 | 0.58 | |
14.4 m | Rn | 25 | 0.3 | 20 | 4 | 0.9 | 0.95 |
LE | 79 | 1.4 | 56 | 18 | 0.1 | 0.21 | |
H | 48 | 0.7 | 35 | 26 | 0.6 | 0.69 | |
G | 32 | 0.8 | 29 | 49 | 0.6 | 0.59 | |
30 m | Rn | 34 | 0.4 | 29 | 5 | 0.9 | 0.96 |
LE | 101 | 1.8 | 86 | 30 | 0.2 | 0.53 | |
H | 93 | 1.3 | 78 | 67 | −0.1 | 0.23 | |
G | 31 | 0.8 | 28 | 48 | 0.6 | 0.60 |
Flight | Spatial Domain | μ | σ | CV |
---|---|---|---|---|
09 August 2014 | 3.6 m | 0.91 | 0.56 | 0.61 |
7.2 m | 0.91 | 0.54 | 0.59 | |
14.4 m | 0.91 | 0.52 | 0.57 | |
30.0 m | 0.91 | 0.48 | 0.53 | |
02 June 2015 | 3.6 m | 0.57 | 0.38 | 0.66 |
7.2 m | 0.57 | 0.33 | 0.58 | |
14.4 m | 0.57 | 0.30 | 0.52 | |
30.0 m | 0.57 | 0.27 | 0.47 | |
11 July 2015 | 3.6 m | 0.52 | 0.39 | 0.75 |
7.2 m | 0.52 | 0.36 | 0.69 | |
14.4 m | 0.52 | 0.34 | 0.65 | |
30.0 m | 0.52 | 0.31 | 0.60 | |
02 May 2016 | 3.6 m | 0.06 | 0.11 | 1.90 |
7.2 m | 0.06 | 0.10 | 1.75 | |
14.4 m | 0.06 | 0.10 | 1.66 | |
30.0 m | 0.06 | 0.09 | 1.59 |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Nassar, A.; Torres-Rua, A.; Kustas, W.; Nieto, H.; McKee, M.; Hipps, L.; Stevens, D.; Alfieri, J.; Prueger, J.; Alsina, M.M.; et al. Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards. Remote Sens. 2020, 12, 342. https://doi.org/10.3390/rs12030342
Nassar A, Torres-Rua A, Kustas W, Nieto H, McKee M, Hipps L, Stevens D, Alfieri J, Prueger J, Alsina MM, et al. Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards. Remote Sensing. 2020; 12(3):342. https://doi.org/10.3390/rs12030342
Chicago/Turabian StyleNassar, Ayman, Alfonso Torres-Rua, William Kustas, Hector Nieto, Mac McKee, Lawrence Hipps, David Stevens, Joseph Alfieri, John Prueger, Maria Mar Alsina, and et al. 2020. "Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards" Remote Sensing 12, no. 3: 342. https://doi.org/10.3390/rs12030342
APA StyleNassar, A., Torres-Rua, A., Kustas, W., Nieto, H., McKee, M., Hipps, L., Stevens, D., Alfieri, J., Prueger, J., Alsina, M. M., McKee, L., Coopmans, C., Sanchez, L., & Dokoozlian, N. (2020). Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards. Remote Sensing, 12(3), 342. https://doi.org/10.3390/rs12030342