Calculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2

Mokhtari, Ali; Noory, Hamideh; Pourshakouri, Farrokh; Haghighatmehr, Parisa; Afrasiabian, Yasamin; Razavi, Maryam; Fereydooni, Fatemeh; Naeni, Ali Sadeghi

doi:10.1016/j.isprsjprs.2019.06.011

Cited by 47 publications

(31 citation statements)

References 55 publications

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“…The combination of available sensors also remains an excellent opportunity for improvement. For instance, Sentinel-2 has limitations towards estimating evapotranspiration and water stress due to its lack of any thermal bands, and as such data fusion with other complimentary sensors may generate opportunities to coordinate data with available thermal bands such as NASA's MODIS or Landsat-8 [97]. However, the spatial resolution of their thermal bands (1 km and 100 m, respectively) might be a downside for some agricultural applications that require more detail.…”

Section: Sentinel-2: Comparative Advantages and Future Workmentioning

confidence: 99%

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

et al. 2020

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The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in precision agriculture. Besides the Sentinel-2 A + B constellation technical features the open-access nature of the information they generate, and the available support software are a significant improvement for agricultural monitoring. This paper was motivated by the challenges faced by researchers and agrarian institutions entering this field; it aims to frame remote sensing principles and Sentinel-2 applications in agriculture. Thus, we reviewed the features and uses of Sentinel-2 in precision agriculture, including abiotic and biotic stress detection, and agricultural management. We also compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel-2 A + B constellation features. Contrasted with previous satellite image systems, the Sentinel-2 A + B twin platform has dramatically increased the capabilities for agricultural monitoring and crop management worldwide. Regarding crop stress monitoring, Sentinel-2 capacities for abiotic and biotic stresses detection represent a great step forward in many ways though not without its limitations; therefore, combinations of field data and different remote sensing techniques may still be needed. We conclude that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements. Current and future ways that Sentinel-2 can be utilized are also discussed.

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Section: Sentinel-2: Comparative Advantages and Future Workmentioning

confidence: 99%

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

et al. 2020

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“…Land Surface Temperature (LST) is an important component of the Earth's energy budget, closely linked to the partitioning between sensible and latent heat fluxes. As such, high-resolution satellite-derived LST is increasingly used in various applications related to the assessment of land surface conditions, including mapping the urban extent and the intensity of urban micro-climates, estimating high-resolution evapotranspiration for the management of water resources and assessing vegetation stress [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series

et al. 2020

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Land Surface Temperature (LST) is increasingly important for various studies assessing land surface conditions, e.g., studies of urban climate, evapotranspiration, and vegetation stress. The Landsat series of satellites have the potential to provide LST estimates at a high spatial resolution, which is particularly appropriate for local or small-scale studies. Numerous studies have proposed LST retrieval algorithms for the Landsat series, and some datasets are available online. However, those datasets generally require the users to be able to handle large volumes of data. Google Earth Engine (GEE) is an online platform created to allow remote sensing users to easily perform big data analyses without increasing the demand for local computing resources. However, high spatial resolution LST datasets are currently not available in GEE. Here we provide a code repository that allows computing LSTs from Landsat 4, 5, 7, and 8 within GEE. The code may be used freely by users for computing Landsat LST as part of any analysis within GEE.

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“…Land surface temperature (LST)-like near-surface air temperature-is a key variable in a wide variety of studies, since it is linked to land-atmosphere energy transfer and flux balances [1,2]. Thus, it is required for monitoring evapotranspiration and climate change [3,4], as well as for providing estimates of fire size and temperature [5,6], volcanoes and lava flow [7,8], and vegetation health [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Validation of Sentinel-3 SLSTR Land Surface Temperature Retrieved by the Operational Product and Comparison with Explicitly Emissivity-Dependent Algorithms

et al. 2021

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Land surface temperature (LST) is an essential climate variable (ECV) for monitoring the Earth climate system. To ensure accurate retrieval from satellite data, it is important to validate satellite derived LSTs and ensure that they are within the required accuracy and precision thresholds. An emissivity-dependent split-window algorithm with viewing angle dependence and two dual-angle algorithms are proposed for the Sentinel-3 SLSTR sensor. Furthermore, these algorithms are validated together with the Sentinel-3 SLSTR operational LST product as well as several emissivity-dependent split-window algorithms with in-situ data from a rice paddy site. The LST retrieval algorithms were validated over three different land covers: flooded soil, bare soil, and full vegetation cover. Ground measurements were performed with a wide band thermal infrared radiometer at a permanent station. The coefficients of the proposed split-window algorithm were estimated using the Cloudless Land Atmosphere Radiosounding (CLAR) database: for the three surface types an overall systematic uncertainty (median) of –0.4 K and a precision (robust standard deviation) 1.1 K were obtained. For the Sentinel-3A SLSTR operational LST product, a systematic uncertainty of 1.3 K and a precision of 1.3 K were obtained. A first evaluation of the Sentinel-3B SLSTR operational LST product was also performed: systematic uncertainty was 1.5 K and precision 1.2 K. The results obtained over the three land covers found at the rice paddy site show that the emissivity-dependent split-window algorithms, i.e., the ones proposed here as well as previously proposed algorithms without angular dependence, provide more accurate and precise LSTs than the current version of the operational SLSTR product.

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Calculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2

Cited by 47 publications

References 55 publications

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series

Validation of Sentinel-3 SLSTR Land Surface Temperature Retrieved by the Operational Product and Comparison with Explicitly Emissivity-Dependent Algorithms

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