CubeSats Enable High Spatiotemporal Retrievals of Crop-Water Use for Precision Agriculture

Remote sensing based estimation of evapotranspiration (ET) provides a direct accounting of the crop water use. However, the use of satellite data has generally required that a compromise between spatial and temporal resolution is made, i.e., one could obtain low spatial resolution data regularly, or high spatial resolution occasionally. As a consequence, this spatiotemporal trade-off has tended to limit the impact of remote sensing for precision agricultural applications. With the recent emergence of constellations of small CubeSat-based satellite systems, these constraints are rapidly being removed, such that daily 3 m resolution optical data are now a reality for earth observation. Such advances provide an opportunity to develop new earth system monitoring and assessment tools. In this manuscript we evaluate the capacity of CubeSats to advance the estimation of ET via application of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) retrieval model. To take advantage of the high-spatiotemporal resolution afforded by these systems, we have integrated a CubeSat derived leaf area index as a forcing variable into PT-JPL, as well as modified key biophysical model parameters. We evaluate model performance over an irrigated farmland in Saudi Arabia using observations from an eddy covariance tower. Crop water use retrievals were also compared against measured irrigation from an in-line flow meter installed within a center-pivot system. To leverage the high spatial resolution of the CubeSat imagery, PT-JPL retrievals were integrated over the source area of the eddy covariance footprint, to allow an equivalent intercomparison. Apart from offering new precision agricultural insights into farm operations and management, the 3 m resolution ET retrievals were shown to explain 86% of the observed variability and provide a relative RMSE of 32.9% for irrigated maize, comparable to previously reported satellite-based retrievals. An observed underestimation was diagnosed as a possible misrepresentation of the local surface moisture status, highlighting the challenge of high-resolution modeling applications for precision agriculture and informing future research directions.

[1]  A. Monin,et al.  Basic laws of turbulent mixing in the surface layer of the atmosphere , 2009 .

[2]  Jingfeng Xiao,et al.  A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA , 2005 .

[3]  Chiara Corbari,et al.  Limitations and improvements of the energy balance closure with reference to experimental data measured over a maize field , 2014 .

[4]  R. Houborg,et al.  A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data , 2018 .

[5]  Marvin E. Jensen,et al.  Beyond irrigation efficiency , 2007, Irrigation Science.

[6]  William P. Kustas,et al.  Daily evapotranspiration estimates from extrapolating instantaneous airborne remote sensing ET values , 2008, Irrigation Science.

[7]  Peter M. Atkinson,et al.  Downscaling in remote sensing , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[8]  Matthew F. McCabe,et al.  A multi‐decadal assessment of the performance of gauge‐ and model‐based rainfall products over Saudi Arabia: climatology, anomalies and trends , 2016 .

[9]  Enrique Playán,et al.  Contribution of Evapotranspiration Reduction during Sprinkler Irrigation to Application Efficiency , 2008 .

[10]  Raul Morais,et al.  Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry , 2017, Remote. Sens..

[11]  H. L. Penman.  Natural evaporation from open water, bare soil and grass , 1948, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[12]  P. Ceccato,et al.  Actual evapotranspiration in drylands derived from in-situ and satellite data: Assessing biophysical constraints , 2013 .

[13]  Matthew F. McCabe,et al.  Effects of spatial aggregation on the multi-scale estimation of evapotranspiration , 2013 .

[14]  S. Running,et al.  A review of remote sensing based actual evapotranspiration estimation , 2016 .

[15]  J. Famiglietti.  The global groundwater crisis , 2014 .

[16]  Hans Peter Schmid,et al.  Footprint modeling for vegetation atmosphere exchange studies: a review and perspective , 2002 .

[17]  E. Vivoni,et al.  Ecohydrology of water‐limited environments: A scientific vision , 2006 .

[18]  G. Fogg.  The state and movement of water in living organisms. , 1966, Journal of the Marine Biological Association of the United Kingdom.

[19]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[20]  M. Mccabe,et al.  Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data , 2008 .

[21]  Martha C. Anderson,et al.  Mapping daily evapotranspiration at Landsat spatial scales during the BEAREX’08 field campaign , 2012 .

[22]  J. Norman,et al.  Correcting eddy-covariance flux underestimates over a grassland , 2000 .

[23]  Hongbo Su,et al.  A New Evapotranspiration Model Accounting for Advection and Its Validation during SMEX02 , 2013 .

[24]  Ray D. Jackson,et al.  Estimation of Daily Evapotranspiration from one Time-of-Day Measurements , 1983 .

[25]  N. Zhang,et al.  Precision agriculture—a worldwide overview , 2002 .

[26]  Matthew F. McCabe,et al.  Adapting a regularized canopy reflectance model (REGFLEC) for the retrieval challenges of dryland agricultural systems , 2016 .

[27]  S. Seneviratne,et al.  Energy balance closure of eddy-covariance data: a multisite analysis for European FLUXNET stations. , 2010 .

[28]  Steven R. Evett,et al.  Nighttime Evapotranspiration from Alfalfa and Cotton in a Semiarid Climate , 2006 .

[29]  Matthew F. McCabe,et al.  High-Resolution NDVI from Planet's Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agriculture , 2016, Remote. Sens..

[30]  Olivier Merlin,et al.  Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil , 2018, Remote Sensing of Environment.

[31]  R. Dickinson,et al.  A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability , 2011 .

[32]  Yu Zhang,et al.  An Improvement of Roughness Height Parameterization of the Surface Energy Balance System (SEBS) over the Tibetan Plateau , 2013 .

[33]  T. Holmes,et al.  Global land-surface evaporation estimated from satellite-based observations , 2010 .

[34]  Matthew F. McCabe,et al.  The GEWEX LandFlux project: evaluation of model evaporation using tower-based and globally gridded forcing data , 2015 .

[35]  S. Lee,et al.  The CubeSat Approach to Space Access , 2008, 2008 IEEE Aerospace Conference.

[36]  Martha C. Anderson,et al.  A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation , 2007 .

[37]  Matthew F. McCabe,et al.  Daily Retrieval of NDVI and LAI at 3 m Resolution via the Fusion of CubeSat, Landsat, and MODIS Data , 2018, Remote. Sens..

[38]  Richard G. Allen,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Model , 2007 .

[39]  Thomas Foken,et al.  Documentation and Instruction Manual of the Eddy-Covariance Software Package TK3 (update) , 2011 .

[40]  Enrique Playán,et al.  Closure to “Contribution of Evapotranspiration Reduction during Sprinkler Irrigation to Application Efficiency” by A. Martínez-Cob, E. Playán, N. Zapata, J. Cavero, E. T. Medina, and M. Puig , 2010 .

[41]  Peter M. Atkinson,et al.  Downscaling remotely sensed imagery using area-to-point cokriging and multiple-point geostatistical simulation , 2015 .

[42]  M. Mccabe,et al.  Multi-site evaluation of terrestrial evaporation models using FLUXNET data , 2014 .

[43]  D. Baldocchi,et al.  Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites , 2008 .

[44]  Matthew F. McCabe,et al.  A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning , 2018 .

[45]  Martha C. Anderson,et al.  Advances in thermal infrared remote sensing for land surface modeling , 2009 .

[46]  Bingfang Wu,et al.  Estimating Evapotranspiration from an Improved Two-Source Energy Balance Model Using ASTER Satellite Imagery , 2015 .

[47]  P. Döll,et al.  Groundwater use for irrigation - a global inventory , 2010 .

[48]  Andreas Schumann,et al.  Global irrigation water demand: Variability and uncertainties arising from agricultural and climate data sets , 2008 .

[49]  Arnaud Carrara,et al.  Experimental validation of footprint models for eddy covariance CO2 flux measurements above grassland by means of natural and artificial tracers , 2017 .

[50]  Eric F. Wood,et al.  Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: Evaluation of three process-based approaches , 2011 .

[51]  J. Wallace.  Increasing agricultural water use efficiency to meet future food production , 2000 .

[52]  Matthew F. McCabe,et al.  The WACMOS-ET project – Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms , 2015 .

[53]  Matthew F. McCabe,et al.  Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors , 2006 .

[54]  Martha C. Anderson,et al.  A Two-Source Time-Integrated Model for Estimating Surface Fluxes Using Thermal Infrared Remote Sensing , 1997 .

[55]  Jiancheng Shi,et al.  The Future of Earth Observation in Hydrology. , 2017, Hydrology and earth system sciences.

[56]  Andreas Colliander,et al.  SMAP soil moisture improves global evapotranspiration , 2018, Remote Sensing of Environment.

[57]  J. Norman,et al.  Surface flux estimation using radiometric temperature: A dual‐temperature‐difference method to minimize measurement errors , 2000 .

[58]  D. Sharma,et al.  Determination of evapotranspiration for maize and berseem clover , 2002, Irrigation Science.

[59]  Ray D. Jackson,et al.  Remote Sensing Of Vegetation Characteristics For Farm Management , 1984, Other Conferences.

[60]  Paul D. Colaizzi,et al.  Applications of a thermal-based two-source energy balance model using Priestley-Taylor approach for surface temperature partitioning under advective conditions , 2016 .

[61]  Robert R. Gillies,et al.  A new look at the simplified method for remote sensing of daily evapotranspiration , 1995 .

[62]  Massimo Menenti,et al.  S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance , 2000 .

[63]  C. Priestley,et al.  On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters , 1972 .

[64]  H. Budzier,et al.  Calibration of uncooled thermal infrared cameras , 2015 .

[65]  H. Schmid,et al.  A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP) , 2015 .

[66]  Bruno Aragon,et al.  CubeSats in Hydrology: Ultrahigh‐Resolution Insights Into Vegetation Dynamics and Terrestrial Evaporation , 2017 .

[67]  Joshua B. Fisher,et al.  ET come home: potential evapotranspiration in geographical ecology , 2011 .

[68]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[69]  T. W. Horst,et al.  How Far is Far Enough?: The Fetch Requirements for Micrometeorological Measurement of Surface Fluxes , 1994 .

[70]  Gregoire Mariethoz,et al.  A space and time scale‐dependent nonlinear geostatistical approach for downscaling daily precipitation and temperature , 2015 .

[71]  Zeyong Hu,et al.  Actual and Reference Evapotranspiration in a Cornfield in the Zhangye Oasis, Northwestern China , 2017 .

[72]  Martha C. Anderson,et al.  The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources , 2017 .

[73]  Zhuguo Ma,et al.  Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China , 2014 .

[74]  Hans Peter Schmid,et al.  A Three-Dimensional Backward Lagrangian Footprint Model For A Wide Range Of Boundary-Layer Stratifications , 2002 .

[75]  Manuel Perez-Ruiz,et al.  Assessing a crop water stress index derived from aerial thermal imaging and infrared thermometry in super-high density olive orchards , 2017 .

[76]  Victor F. Rodriguez-Galiano,et al.  Downscaling Landsat 7 ETM+ thermal imagery using land surface temperature and NDVI images , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[77]  Martha C. Anderson,et al.  Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery , 2010 .

[78]  Matthew F. McCabe,et al.  Spatial and temporal patterns of land surface fluxes from remotely sensed surface temperatures within an uncertainty modelling framework , 2005 .

[79]  G. Mariéthoz,et al.  Demonstration of a geostatistical approach to physically consistent downscaling of climate modeling simulations , 2013 .

[80]  William P. Kustas,et al.  An intercomparison of the Surface Energy Balance Algorithm for Land (SEBAL) and the Two-Source Energy Balance (TSEB) modeling schemes , 2007 .

[81]  S. Polasky,et al.  Agricultural sustainability and intensive production practices , 2002, Nature.

[82]  M. Friedl,et al.  Diurnal Covariation in Soil Heat Flux and Net Radiation , 2003 .

[83]  Jiemin Wang,et al.  Assessment of Uncertainties in Eddy Covariance Flux Measurement Based on Intensive Flux Matrix of HiWATER-MUSOEXE , 2015, IEEE Geoscience and Remote Sensing Letters.

[84]  N. Verhoest,et al.  El Niño-La Niña cycle and recent trends in continental evaporation , 2014 .

[85]  N. U. Ahmed,et al.  Relations between evaporation coefficients and vegetation indices studied by model simulations , 1994 .

[86]  Joshua B. Fisher,et al.  Vegetation Water Use Based on a Thermal and Optical Remote Sensing Model in the Mediterranean Region of Doñana , 2018, Remote. Sens..

[87]  Niko E. C. Verhoest,et al.  Assimilation of Global Radar Backscatter and Radiometer Brightness Temperature Observations to Improve Soil Moisture and Land Evaporation Estimates , 2017 .

[88]  A. Goldstein,et al.  What the towers don't see at night: nocturnal sap flow in trees and shrubs at two AmeriFlux sites in California. , 2007, Tree physiology.

[89]  Chunlin Huang,et al.  Mapping daily evapotranspiration based on spatiotemporal fusion of ASTER and MODIS images over irrigated agricultural areas in the Heihe River Basin, Northwest China , 2017 .

[90]  Terry A. Howell,et al.  Comparison of five models to scale daily evapotranspiration from one-time-of-day measurements , 2006 .

[91]  K. Wittich,et al.  Area-averaged vegetative cover fraction estimated from satellite data , 1995 .

[92]  Giorgos Mallinis,et al.  On the Use of Unmanned Aerial Systems for Environmental Monitoring , 2018, Remote. Sens..

[93]  Chenghu Zhou,et al.  A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data , 2009, Sensors.

[94]  Matthew F. McCabe,et al.  The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets , 2015 .

[95]  David Krejci,et al.  A survey and assessment of the capabilities of Cubesats for Earth observation , 2012 .

[96]  T Vesala,et al.  Flux and concentration footprint modelling: state of the art. , 2008, Environmental pollution.

[97]  Alexander Loew,et al.  Evaluation of soil moisture downscaling using a simple thermal-based proxy – the REMEDHUS network (Spain) example , 2015 .

[98]  Simon J. Hook,et al.  ECOSTRESS, A NASA Earth-Ventures Instrument for studying links between the water cycle and plant health over the diurnal cycle , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[99]  H. Maas,et al.  An advanced radiometric calibration approach for uncooled thermal cameras , 2018 .