Satellite NDVI Assisted Monitoring of Vegetable Crop Evapotranspiration in California’s San Joaquin Valley

Johnson, Lee F.; Trout, Thomas J.

doi:10.3390/rs4020439

Cited by 130 publications

(86 citation statements)

References 32 publications

(32 reference statements)

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“…We also found that reference ET o had an exponential relationship with R n (R 2 = 0.95) and therefore reconstructed Equation (13) and optimized the parameters to obtain the second empirical algorithm as shown below:…”

Section: Empirical Methods For Estimating Evapotranspirationmentioning

confidence: 99%

“…The ET results from the empirical methods were also compared with ET tower measurements. Equation (13) was chosen after model building and testing with all available spatial and temporal data: ET = ETo * (0.3108 * NDVI -0.0059 * ln(VPD) + 0.8315) (13) This statistical-based equation indicates that ET o and NDVI were positively related with ET as expected. VPD, however, negatively regulates evapotranspiration here, possibly as a proxy of water stress on ET o F [7].…”

Section: Empirical Methods For Estimating Evapotranspirationmentioning

confidence: 99%

“…Empirical algorithms were built to relate ET to remotely sensed vegetation indices and weather data [11][12][13][14]. These algorithms typically perform well in areas where they are calibrated, but the uncertainty usually increases when applied to other areas.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations

Jin

Kandelous

et al. 2017

Remote Sensing

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California growers face challenges with water shortages and there is a strong need to use the least amount of water while optimizing yield. Timely information on evapotranspiration (ET), a dominant component of crop consumptive water use, is critical for growers to tailor irrigation management based on in-field spatial variability and in-season variations. We evaluated the performance of a remote sensing-based approach, Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC), in mapping ET over an almond orchard in California, driven by Landsat satellite observations. Reference ET from a network of weather stations over well-watered grass (ET o ) was used for the internal calibration and for deriving ET at daily and extended time period, instead of alfalfa based reference evapotranspiration (ET r ). Our study showed that METRIC daily ET estimates during Landsat overpass dates agreed well with the field measurements. During 2009-2012, a root mean square error (RMSE) of 0.53 mm/day and a coefficient of determination (R 2 ) of 0.87 were found between METRIC versus observed daily ET. Monthly ET estimates had a higher accuracy, with a RMSE of 12.08 mm/month, a R 2 of 0.90, and a relatively small relative mean difference (RMD) of 9.68% during 2009-2012 growing seasons. Net radiation and Normalized Difference Vegetation Index (NDVI) from remote sensing observations were highly correlated with spatial and temporal ET estimates. An empirical model was developed to estimate daily ET using NDVI, net radiation (R n ), and vapor pressure deficit (VPD). The validation showed that the accuracy of this easy-to-use empirical method was slightly lower than that of METRIC but still reasonable, with a RMSE of 0.71 mm/day when compared to ground measurements. The remote sensing based ET estimate will support a variety of State and local interests in water use and irrigation management, for both planning and regulatory/compliance purposes, and it provides the farmers observation-based guidance for site-specific and time-sensitive irrigation management.

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Section: Empirical Methods For Estimating Evapotranspirationmentioning

confidence: 99%

Section: Empirical Methods For Estimating Evapotranspirationmentioning

confidence: 99%

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Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations

Jin

Kandelous

et al. 2017

Remote Sensing

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“…One of the most widely used approaches considers the relationship between vegetation indices (VI) derived from RS reflectance data and (actual) crop coefficients, either Kc or Kcb. The basis of this approach relies on the close correlation of several VI and various biophysical characteristics of the plants, e.g., leaf area index (LAI), ground cover fraction (fc), biomass, and physiological processes depending on light absorption by the canopy, including ET (e.g., [16][17][18][19]). With the VI approach, because these indices reflect the actual vegetation cover conditions, the estimated Kc or Kcb represent actual rather than potential Kc or Kcb.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Actual Crop Coefficients Using Remotely Sensed Vegetation Indices and Soil Water Balance Modelled Data

et al. 2015

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Abstract:A new procedure is proposed for estimating actual basal crop coefficients from vegetation indices (Kcb VI) considering a density coefficient (Kd) and a crop coefficient for bare soil. Kd is computed using the fraction of ground cover by vegetation (fc VI), which is also estimated from vegetation indices derived from remote sensing. A combined approach for estimating actual crop coefficients from vegetation indices (Kc VI) is also proposed by integrating the Kcb VI with the soil evaporation coefficient (Ke) derived from the soil water balance model SIMDualKc. Results for maize, barley and an olive orchard have shown that the approaches for estimating both fc VI and Kcb VI compared well with results obtained using the SIMDualKc model after calibration with ground observation data. For the crops studied, the correlation coefficients relative to comparing the actual Kcb VI and Kc VI with actual Kcb and Kc obtained with SIMDualKc were larger than 0.73 and 0.71, respectively. The corresponding regression coefficients were close to 1.0. The methodology herein presented and discussed allowed for obtaining information for the whole crop season, including periods when vegetation cover is incomplete, as the initial and development stages. Results show that the proposed methods are adequate for supporting irrigation management. OPEN ACCESSRemote Sens. 2015, 7 2374

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“…74 polygons of the fractional vegetation cover were generated from digital photographs taken with a multispectral camera mounted on a frame at nadir view pointed 2.3 m above the ground at the commercial agricultural fields of the San Joaquin Valley (in central California) on 11 dates mentioned above, except for one date when the Landsat acquisition preceded the ground observation by one day. For each date, 2-4 evenly spaced pictures were taken for an area of 100 m x 100 m with center location marked by a GPS (Johnson and Trout, 2012). These fractional measurements belonged to a diverse set of seasonal and perennial crops in various developmental stages, from emergence to full canopy that represented an agricultural scenario/environment in the RS data.…”

Section: Landsat Datamentioning

confidence: 99%

Sparse unmixing via variable splitting and augmented Lagrangian for vegetation and urban area classification using Landsat data

Kumar

Milesi

Nemani

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper, we explore the possibility of sparse regression, a new direction in unmixing, for vegetation and urban area classification. SUnSAL (Sparse unmixing via variable splitting and augmented Lagrangian) in both unconstrained and constrained forms (with the abundance non-negativity and abundance sum-to-one constraints) were used with a set of global endmembers (substrate, vegetation and dark objects) to unmix a set of computer simulated noise-free and noisy data (with Gaussian noise of different signal-to-noise ratio) in order to judge the robustness of the algorithm. The error in the fractional estimate was examined for varying noise power (variance): 2, 4, 8, 16, 32, 64, 128 and 256. In the second set of experiments, a spectrally diverse collection of 11 scenes of Level 1 terrain corrected, cloud free Landsat-5 TM data representing an agricultural setup in Fresno, California, USA were used. The corresponding ground data for validation were collected on the same days of satellite overpass. Finally in the third set of experiments, a clear sky Landsat-5 TM data for an area near the Golden Gate Bridge, San Francisco (an urbanized landscape), California, USA were used to assess the algorithm. The fractional estimates of the 30 m Landsat-5 TM data were compared with the fractional estimates of a high-resolution World View-2 data (2 m spatial resolution) obtained using a fully constrained least squares algorithm. The results were evaluated using descriptive statistics, correlation coefficient, RMSE, probability of success and bivariate distribution function, which showed that constrained model was better than unconstrained form.

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Satellite NDVI Assisted Monitoring of Vegetable Crop Evapotranspiration in California’s San Joaquin Valley

Cited by 130 publications

References 32 publications

Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations

Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations

Estimation of Actual Crop Coefficients Using Remotely Sensed Vegetation Indices and Soil Water Balance Modelled Data

Sparse unmixing via variable splitting and augmented Lagrangian for vegetation and urban area classification using Landsat data

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