Characterizing Crop Water Use Dynamics in the Central Valley of California Using Landsat-Derived Evapotranspiration

Schauer, Matthew; Senay, Gabriel B.

doi:10.3390/rs11151782

Cited by 30 publications

(33 citation statements)

References 37 publications

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“…An understanding of the distribution and location of crops is essential information to enable rapid response to a biosecurity incursion, i.e., for the establishment of exclusion zones and the deployment of surveillance teams. Analysis of the location and area of crops facilitates water resource planning [5]. A better understanding of the temporal and spatial distribution of specific crop types can greatly assist in monitoring production spread and improve decision-making around varietal selection [6], harvest planning and decisions on spray application based on risk to neighboring crops and wildlife [7].…”

Section: Introductionmentioning

confidence: 99%

Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

Brinkhoff

Vardanega²,

Robson

2019

Remote Sensing

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Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently.

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Section: Introductionmentioning

confidence: 99%

Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

Brinkhoff

Vardanega²,

Robson

2019

Remote Sensing

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“…The Operational Simplified Surface Energy Balance (SSEBop) model was used in this study for estimating ET from Landsat imagery. Several studies on the SSEBop modeling and its applications in water-use estimation and analysis have been presented in recent years (Senay et al 2013, Velpuri et al 2013, Singh et al 2014, Schauer and Senay 2019. The model can be described based on the psychrometric principle for vapor pressure measurements, where the difference in land surface temperature between the study site location and a reference location can be directly related to the moisture status of the surface and plant.…”

Section: The Ssebop Modelmentioning

confidence: 99%

“…where ET daily is the total amount of daily actual ET (mm), and ET r is alfalfa reference ET (mm d −1 ). The ET r bias was corrected using a factor of 0.85 based on calibration results by Schauer and Senay (2019). Additional details on the SSEBop model can be obtained from Senay et al (2013Senay et al ( , 2017, and Senay (2018).…”

Section: The Ssebop Modelmentioning

confidence: 99%

“…This study evaluated the proposed approach for three overpass dates as a proof of concept. For generating time series of daily, monthly, seasonal and annual ET maps, a linear interpolation of ET f is needed to fill the gaps between satellite overpass dates and missing pixel values due to clouds and shadow (Senay et al 2019, Schauer andSenay 2019). The proposed approach was tested over an irrigated area, thus further evaluation is required for application to larger geographical areas including both irrigated and rainfed areas with several land covers using independent in-situ data.…”

Section: Challenges and Opportunities For Landsat-sentinel Data Fusiomentioning

confidence: 99%

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A novel approach for next generation water-use mapping using Landsat and Sentinel-2 satellite data

Singh

Khand

Kagone

et al. 2020

Hydrological Sciences Journal

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Evapotranspiration (ET) is needed in a range of applications in hydrology, climatology, ecology, and agriculture. Remote sensing-based estimation is the only viable and economical method for ET estimation over large areas. The current Landsat satellites provide images every 16 days limiting the ability to capture biophysical changes affecting ET. Thus, we explored the potential integration of Landsat 8 and Sentinel-2 data for estimating ET using a surface energy balance model. The results indicate the proposed Landsat-Sentinel data fusion approach substantially reduced relative errors from 48% to 10% on areawide and from 49% to 17% on pixel-wide compared to linear interpolation between two Landsat images. The proposed approach had a better agreement with expected actual ET maps across high-vegetation conditions than in low-vegetation conditions. The finer temporal resolution and better accuracy of ET maps based on Landsat-Sentinel integration is of great importance in managing limited water resources.

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“…Thus, Senay et al (2013) introduced the operational simplified surface energy balance (SSEBop) model, one of the simplest surface energy balance models, to directly solve for latent heat flux without requiring the estimation of heat flux. This method has been used for drought monitoring, famine early warning, and water mapping across different landscapes, at regional to continental scales (Alemu, Senay, Kaptue, & Kovalskyy, 2014; Olivera‐Guerra et al, 2017; Schauer & Senay, 2019; Senay et al, 2016; Singh et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Inferring the potential impact of human activities on evapotranspiration in the Tumen River Basin based on LANDSAT imagery and historical statistics

Lee

Lan

et al. 2020

Land Degrad Dev

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Understanding the spatial and temporal changes in actual evapotranspiration (ETa) caused by different human activities is important for water management in water‐sensitive areas, because ETa plays a major role in regulating the availability of water on land. Thus, ETa observation is particularly important for observing land degradation in the watershed. This study used the operational simplified surface energy balance (SSEBop) model to perform ETa estimations and observations in the Tumen River Basin (TRB), covering the border of China and Democratic People's Republic of Korea (DPRK), over the last 30 years by using eight periods of clear‐sky LANDSAT Thematic Mapper/Operational Land Imager (TM/OLI) and thermal infrared sensor (TIRS) data. To assess the reliability of the ETa estimations, multilayer soil temperatures from the China Meteorological Administration (CMA) Land Data Assimilation System Version 2.0 (CLDAS‐V2.0) were utilized. The highest R2 (.71) between ETa and soil temperature was observed in layers shallower than 10 cm; this value of R2 gradually decreased to .57 at a depth of 200 cm. The average ETa of the TRB declined from 1.65 mm in 1985 to 1.03 mm in 2017. The impact of human activities on water loss (ETa) was determined by comparing land cover changes observed by an object‐based image analysis (OBIA) approach and economic activities between 1994 and 2008. Varying economic activities and water depletion in different soil layers may have led to the variable ETa values between the northern (China) and southern (DPRK) part of the TRB during the last three decades.

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Characterizing Crop Water Use Dynamics in the Central Valley of California Using Landsat-Derived Evapotranspiration

Cited by 30 publications

References 37 publications

Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

A novel approach for next generation water-use mapping using Landsat and Sentinel-2 satellite data

Inferring the potential impact of human activities on evapotranspiration in the Tumen River Basin based on LANDSAT imagery and historical statistics

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