Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin

Senay, Gabriel B.; Friedrichs, M.; Singh, R. K.; Velpuri, Naga Manohar

doi:10.1016/j.rse.2015.12.043

Cited by 170 publications

(118 citation statements)

References 46 publications

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“…Similar ET c maps at a field scale for agricultural crops including corn were reported by Farg et al [32], Zipper and Loheide [62], and Senay et al [63], they reported minimum and maximum ET c values at different crop growth stages, where the higher evapotranspiration rates were found at the mid-season growth stage and lowest evapotranspiration rates were found at early growth stage. that in those particular stages farmers can save irrigation water (grey wide column in the graph), because in those stages the crop needs small water requirements, due to the fact that the crop canopy is no yet fully developed.…”

Section: Et C Maps At a Field Scalesupporting

confidence: 81%

Estimation of Crop Evapotranspiration Using Satellite Remote Sensing-Based Vegetation Index

Reyes-González

Kjaersgaard

Trooien

et al. 2018

Advances in Meteorology

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Irrigation water is limited and scarce in many areas of the world, including Comarca Lagunera, Mexico. Thus better estimations of irrigation water requirements are essential to conserve water. The general objective was to estimate crop water demands or crop evapotranspiration (ET c ) at different scales using satellite remote sensing-based vegetation index. The study was carried out in northern Mexico (Comarca Lagunera) during four growing seasons. Six, eleven, three, and seven clear Landsat images were acquired for 2013, 2014, 2015, and 2016, respectively, for the analysis. The results showed that ET c was low at initial and early development stages, while ET c was high during mid-season and harvest stages. These results are not new but give us confidence in the rest of our ET c results. Daily ET c maps helped to explain the variability of crop water use during the growing season. Based on the results we can conclude that ET c maps developed from remotely sensed multispectral vegetation indices are a useful tool for quantifying crop water consumption at regional and field scales. Using ET c maps at the field scale, farmers can supply appropriate amounts of irrigation water corresponding to each growth stage, leading to water conservation.

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Section: Et C Maps At a Field Scalesupporting

confidence: 81%

Estimation of Crop Evapotranspiration Using Satellite Remote Sensing-Based Vegetation Index

Reyes-González

Kjaersgaard

Trooien

et al. 2018

Advances in Meteorology

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“…So, the SNR of Landsat 8 OLI is 6 to 10 times better than that of Landsat 7 ETM+ for the different spectral bands, which has allowed narrowing down of its spectral bandwidths to avoid atmospheric absorption to the reflected wave from water bodies [61,62]. An additional cirrus band of Landsat 8 (1.36-1.38 µm) and Sentinel-2 (1.365-1.385 µm) can be used to detect clouds, which is especially helpful for detecting high altitude clouds [62][63][64][65]. Some water indices were defined to be calculated by the surface reflectance data of each band, so the original TOA reflectance data needs to conduct atmospheric corrections to be converted into surface reflectance data, to remove the impacts of several factors such as atmospheric gases and aerosols in space.…”

Section: Comparison Of Different Sensors In Water Body Extractionmentioning

confidence: 99%

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Zhou

Dong

Xiao

et al. 2017

Water

189

105

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Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index-and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

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“…The Sentinel-2 twin satellites offered better spatial resolution (10-20-60 m versus 30 m), and better temporal resolution (5 days versus 16 days), compared with Landsat 8. However, the thermal bands of Landsat 8 does provided land surface temperature information [81] that was not available from Sentinel-2, and therefore, in cotton stem water potential estimation, Landsat imagery is still extremely valuable [82]. The surface temperature information and the surface reflectance information may provide different observing angles to detect cotton water status.…”

Section: Discussionmentioning

confidence: 99%

Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery

et al. 2020

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Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large scale. The aim of this study is to estimate cotton water stress at a high temporal frequency and at a large scale. In this study, we measured midday SWP samples according to the acquisition dates of Sentinel-2 images and used them to build linear-regression-based and machine-learning-based models to estimate cotton water stress during the growing season (June to August, 2018). For the linear-regression-based method, we estimated SWP based on different Sentinel-2 spectral bands and vegetation indices, where the normalized difference index 45 (NDI45) achieved the best performance (R 2 = 0.6269; RMSE = 3.6802 (-1*swp (bars))). For the machine-learning-based method, we used random forest regression to estimate SWP and received even better results (R 2 = 0.6709; RMSE = 3.3742 (-1*swp (bars))). To find the best selection of input variables for the machine-learning-based approach, we tried three different data input datasets, including (1) 9 original spectral bands (e.g., blue, green, red, red edge, near infrared (NIR), and shortwave infrared (SWIR)), (2) 21 vegetation indices, and (3) a combination of original Sentinel-2 spectral bands and vegetation indices. The highest accuracy was achieved when only the original spectral bands were used. We also found the SWIR and red edge band were the most important spectral bands, and the vegetation indices based on red edge and NIR bands were particularly helpful. Finally, we applied the best approach for the linear-regression-based and the machine-learning-based methods to generate cotton water potential maps at a large scale and high temporal frequency. Results suggests that the methods developed here has the potential for continuous monitoring of SWP at large scales and the machine-learning-based method is preferred.have devastating impacts on the environment [3]. For agriculture, drought can endanger food security which can have critical effects on the economy, geopolitics, and society [5]. Drought can also negatively affect agriculture production especially in arid and semiarid regions [6]. Therefore, detecting crop water stress is of vital importance to improve agricultural quality and productivity.Cotton is cultivated in arid and semiarid regions and is the most important fiber crop in the world [7]. Cotton is susceptible to water stress in all growth stages, particularly during the flowering and boll development stages [8]. Moreover, water deficit is an important factor limiting growth and development in cotton, including plant height, leaf and root size, as well as economic yield and fiber quality [8]. To better understand the cotton's response and adaption to water stress, continuous monitoring of cotton water status in large a...

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Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin

Cited by 170 publications

References 46 publications

Estimation of Crop Evapotranspiration Using Satellite Remote Sensing-Based Vegetation Index

Estimation of Crop Evapotranspiration Using Satellite Remote Sensing-Based Vegetation Index

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery

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