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

scholarly journals Remote Sensing for Crop Water Management: from Experiments to User-Driven Services

2016 ◽  
Author(s):  
Alfonso Calera ◽  
Isidro Campos ◽  
Anna Osann ◽  
Guido D´Urso ◽  
Massimo Menenti
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Meteorological Data ◽  
Irrigation Scheduling ◽  
Web Gis ◽  
Optical Remote Sensing ◽  
Crop Water ◽  
Biophysical Parameters ◽  
Water Requirements ◽  
Crop Water Requirements

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e. professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies. This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

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. .

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>

scholarly journals Crop Water Requirements in Egypt Using Remote Sensing Techniques

2014 ◽  
Vol 03 (02) ◽  
pp. 57-65 ◽  
Author(s):  
Mohammed A. El-Shirbeny ◽  
Abd-Elraouf M. Ali ◽  
Nasser H. Saleh
Keyword(s):  
Remote Sensing ◽  
Crop Water ◽  
Water Requirements ◽  
Crop Water Requirements ◽  
Remote Sensing Techniques

Combination of remote sensing and meteorological data for estimation of crop water requirements in the irrigated perimeter of Tadla in Morocco

2021 ◽  
Author(s):  
Jaouad El Hachimi ◽  
Abderrazak El Harti ◽  
Amine Jellouli
Keyword(s):  
Remote Sensing ◽  
Water Resources ◽  
Arid Regions ◽  
Landsat 8 ◽  
Climate Data ◽  
Crop Water ◽  
Water Requirements ◽  
Crop Water Requirements ◽  
Irrigated Perimeter ◽  
Semi Arid

<p>In arid and semi-arid regions, agriculture is an important element of the national economy, but this sector is a large consumer of water. In a context of high pressure on water resources (climate change, population growth, desertification, etc.), an appropriate management is required. The development of remote sensing tools: temporal, spatial and spectral resolution offers a better opportunity for hydro-agricultural management. The main objective of this study is to combine climate data with that of optical remote sensing in order to estimate crop water requirements in the irrigated perimeter of Tadla. In semi-arid regions, such as Tadla Plain, a large quantity of water is lost by evapotranspiration (ET). The objective of this study is to use a scientific approach based on the modulation of evaporative demand for the estimation of crop water requirements. This approach is based on the FAO-56 method using image data from the Sentinel-2A and Landsat-8 satellites, and climate data: surface temperature, air humidity, wind speed, global solar radiation and precipitation. It also allowed the spatialization of crop water requirements on a large area of irrigated crops during the 2016–2017 agricultural season. Maps of water requirements have been developed. They show the variability over time of crop development and their estimated water requirements. The results obtained constitute an important indicator of how water should be distributed over the area in order to improve irrigation efficiency and protection of water resources.</p>

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