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

2018 ◽  
Vol 10 (12) ◽  
pp. 1867 ◽  
Author(s):  
Bruno Aragon ◽  
Rasmus Houborg ◽  
Kevin Tu ◽  
Joshua B. Fisher ◽  
Matthew McCabe
Keyword(s):  
Remote Sensing ◽  
Eddy Covariance ◽  
Water Use ◽  
Spatial Resolution ◽  
Precision Agriculture ◽  
High Spatial Resolution ◽  
Assessment Tools ◽  
Optical Data ◽  
Crop Water ◽  
Crop Water Use

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. .

scholarly journals CubeSats deliver daily crop water use at 3 m resolution

2021 ◽  
Author(s):  
Bruno Jose Luis Aragon Solorio ◽  
Matteo G. Ziliani ◽  
Matthew F. McCabe
Keyword(s):  
Water Use ◽  
Spatial Resolution ◽  
Precision Agriculture ◽  
Water Security ◽  
Absolute Error ◽  
Accurate Information ◽  
Crop Water ◽  
Crop Water Use ◽  
The Earth ◽  
Eddy Covariance Measurements

<p>Precision agriculture needs accurate information on crop water use (via evaporation) at high spatiotemporal resolutions. Conventional satellite missions have traditionally required a compromise between having high spatial resolution retrievals occasionally; or coarse resolution captures regularly. The development of CubeSats is relaxing the need for such a compromise by monitoring the Earth at high spatiotemporal resolutions. Here, we show the results of using Planet’s daily CubeSat imagery to derive evaporation at 3 m spatial resolution over three agricultural fields in Nebraska USA. Our results indicate that the derived evaporation estimates can provide accurate information on crop water use when evaluated against eddy covariance measurements (r<sup>2</sup> of 0.86-0.89; mean absolute error between 0.06-0.08<sup></sup>mm/h) and deliver new insights to enhance water security efforts and in-field decision making.</p>

scholarly journals Evaluation of Evapotranspiration from Eddy Covariance Using Large Weighing Lysimeters

Agronomy ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 99 ◽  
Author(s):  
Jerry Moorhead ◽  
Gary Marek ◽  
Prasanna Gowda ◽  
Xiaomao Lin ◽  
Paul Colaizzi ◽  
...  
Keyword(s):  
Energy Balance ◽  
Eddy Covariance ◽  
Water Use ◽  
Growing Season ◽  
Irrigation Scheduling ◽  
Error Rates ◽  
Energy Balance Closure ◽  
Crop Water ◽  
Crop Water Use ◽  
Hourly Data

Evapotranspiration (ET) is an important component in the water budget and used extensively in water resources management such as water planning and irrigation scheduling. In semi-arid regions, irrigation is used to supplement limited and erratic growing season rainfall to meet crop water demand. Although lysimetery is considered the most accurate method for crop water use measurements, high-precision weighing lysimeters are expensive to build and operate. Alternatively, other measurement systems such as eddy covariance (EC) are being used to estimate crop water use. However, due to numerous explicit and implicit assumptions in the EC method, an energy balance closure problem is widely acknowledged. In this study, three EC systems were installed in a field containing a large weighing lysimeter at heights of 2.5, 4.5, and 8.5 m. Sensible heat flux (H) and ET from each EC system were evaluated against the lysimeter. Energy balance closure ranged from 64% to 67% for the three sensor heights. Results showed that all three EC systems underestimated H and consequently overestimated ET; however, the underestimation of H was greater in magnitude than the overestimation of ET. Analysis showed accuracy of ET was greater than energy balance closure with error rates of 20%–30% for half-hourly values. Further analysis of error rates throughout the growing season showed that energy balance closure and ET accuracy were greatest early in the season and larger error was found after plants reached their maximum height. Therefore, large errors associated with increased biomass may indicate unaccounted-for energy stored in the plant canopy as one source of error. Summing the half-hourly data to a daily time-step drastically reduced error in ET to 10%–15%, indicating that EC has potential for use in agricultural water management.

Remote sensing of crop water requirements in orange orchards using high-spatial-resolution sensors

2004 ◽  
Author(s):  
Salvatore Barbagallo ◽  
Simona Consoli ◽  
Guido D'Urso ◽  
Rosaria Giorgio Gaggia ◽  
Attilio Toscano
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Crop Water ◽  
Water Requirements ◽  
Crop Water Requirements

Evaluating crop water requirements and actual crop water use with center pivot irrigation system in Inner Mongolia of China

2020 ◽  
Author(s):  
Peejush Pani ◽  
Li Jia ◽  
Massimo Menenti ◽  
Guangcheng Hu ◽  
Chaolei Zheng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Water Use ◽  
Inner Mongolia ◽  
Crop Water ◽  
Water Requirements ◽  
Water Losses ◽  
Crop Water Use ◽  
Crop Water Requirements ◽  
Center Pivot

<p>This paper proposes a new approach to estimate and map separately gross and net water requirements and actual crop water use by applying multi–spectral satellite data. Remote sensing information is witnessing a boom in the availability of high-resolution multi-spectral data with frequent revisit time, paving the path for improved assessment of precision agriculture and minimizing the wastage of irrigation water. In this study, we have tried to integrate multi-source remote sensing information with farmer’s irrigation practices to evaluate the water use and losses at farm-scale for center pivot irrigation systems (CPIS) in Inner Mongolia autonomous region of China. The region is practicing modernized irrigation methods to efficiently use groundwater. Crop gross water requirements are estimated by evaluating separately the net crop water requirements (CWR) and the water losses inherently from a CPIS, i.e. droplet evaporation to the air directly before they fell on the crop canopy during irrigation application (<em>E<sub>A</sub></em>) and canopy interception loss (<em>I<sub>c</sub></em>). The crop water requirement is estimated according to the FAO-56 method based on the Penman-Monteith equation. Actual crop water use is evaluated by estimating separately soil evaporation (<em>E<sub>S</sub></em>) and plant transpiration (<em>E<sub>T</sub></em>) by applying the ETMonitor model. High-resolution multi–spectral data acquired by Sentinel-2 MSI and Landsat-8 OLI together with meteorological forcing data and soil moisture retrievals were used to construct daily estimates of crop water requirements and actual use. Finally, the performance of irrigation scenarios was assessed by applying a performance indicator (IP), as the ratio between gross water requirement and the volume of irrigation applied, where values closer to unity referring to optimum utilization and minimum loss. Measurements of actual evapotranspiration by eddy covariance system were applied to evaluate the actual evapotranspiration estimates by the ETMonitor. Field experiments were also carried out to validate the estimated irrigation losses, i.e. <em>E<sub>A</sub></em> and <em>I<sub>C</sub></em>. The estimates were in good agreement with the ground observations, i.e. an R<sup>2</sup> of 0.64 – 0.80 for actual water use and 0.66 – 0.97 for water losses. The RMSE was 0.6 – 1.2 mm/day for actual daily water use and 0.64 – 1.55 mm water losses for each irrigation, respectively. The IP was estimated as 1.6 for the performance of CPIS as per the above definition. Overall, the study shows that CPIS has under-performed in minimizing water losses in the study area with losses of 25.4% per season of the total volume of water applied for wheat, and 23.7% per season for potato. This implies that the amount of water applied was largely insufficient to meet the gross water requirements, i.e. including losses.</p>

A thermal-based remote sensing modelling system for estimating crop water use and stress from field to regional scales

2016 ◽  
pp. 71-80 ◽  
Author(s):  
W.P. Kustas ◽  
M.C. Anderson ◽  
K.A. Semmens ◽  
J.G. Alfieri ◽  
F. Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Use ◽  
Crop Water ◽  
Crop Water Use ◽  
Modelling System

scholarly journals Mapping Daily Evapotranspiration at Field Scale Using the Harmonized Landsat and Sentinel-2 Dataset, with Sharpened VIIRS as a Sentinel-2 Thermal Proxy

2021 ◽  
Vol 13 (17) ◽  
pp. 3420
Author(s):  
Jie Xue ◽  
Martha C. Anderson ◽  
Feng Gao ◽  
Christopher Hain ◽  
Yun Yang ◽  
...  
Keyword(s):  
Water Use ◽  
Spatial Resolution ◽  
Value Added ◽  
Surface Reflectance ◽  
Crop Water ◽  
Spatiotemporal Resolution ◽  
Crop Water Use ◽  
Temporal Sampling ◽  
Reflectance Data ◽  
Sentinel 2

Accurate and frequent monitoring of evapotranspiration (ET) at sub-field scales can provide valuable information for agricultural water management, quantifying crop water use and stress toward the goal of increasing crop water use efficiency and production. Using land-surface temperature (LST) data retrieved from Landsat thermal infrared (TIR) imagery, along with surface reflectance data describing albedo and vegetation cover fraction, surface energy balance models can generate ET maps down to a 30 m spatial resolution. However, the temporal sampling by such maps can be limited by the relatively infrequent revisit period of Landsat data (8 days for combined Landsats 7 and 8), especially in cloudy areas experiencing rapid changes in moisture status. The Sentinel-2 (S2) satellites, as a good complement to the Landsat system, provide surface reflectance data at 10–20 m spatial resolution and 5 day revisit period but do not have a thermal sensor. On the other hand, the Visible Infrared Imaging Radiometer Suite (VIIRS) provides TIR data on a near-daily basis with 375 m resolution, which can be refined through thermal sharpening using S2 reflectances. This study assesses the utility of augmenting the Harmonized Landsat and Sentinel-2 (HLS) dataset with S2-sharpened VIIRS as a thermal proxy source on S2 overpass days, enabling 30 m ET mapping at a potential combined frequency of 2–3 days (including Landsat). The value added by including VIIRS-S2 is assessed both retrospectively and operationally in comparison with flux tower observations collected from several U.S. agricultural sites covering a range of crop types. In particular, we evaluate the performance of VIIRS-S2 ET estimates as a function of VIIRS view angle and cloud masking approach. VIIRS-S2 ET retrievals (MAE of 0.49 mm d−1 against observations) generally show comparable accuracy to Landsat ET (0.45 mm d−1) on days of commensurate overpass, but with decreasing performance at large VIIRS view angles. Low-quality VIIRS-S2 ET retrievals linked to imperfect VIIRS/S2 cloud masking are also discussed, and caution is required when applying such data for generating ET timeseries. Fused daily ET time series benefited during the peak growing season from the improved multi-source temporal sampling afforded by VIIRS-S2, particularly in cloudy regions and over surfaces with rapidly changing vegetation conditions, and value added for real-time monitoring applications is discussed. This work demonstrates the utility and feasibility of augmenting the HLS dataset with sharpened VIIRS TIR imagery on S2 overpass dates for generating high spatiotemporal resolution ET products.

Making sense of cosmic-ray soil moisture measurements and eddy covariance data with regard to crop water use and field water balance

2018 ◽  
Vol 204 ◽  
pp. 271-280 ◽  
Author(s):  
Enli Wang ◽  
Chris J. Smith ◽  
Ben C.T. Macdonald ◽  
James R. Hunt ◽  
Hongtao Xing ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Eddy Covariance ◽  
Water Use ◽  
Cosmic Ray ◽  
Crop Water ◽  
Crop Water Use ◽  
Making Sense ◽  
Field Water Balance
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