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

Reyes-González, Arturo; Kjaersgaard, Jeppe; Trooien, Todd P.; Hay, Christopher; Ahiablame, Laurent

doi:10.1155/2018/4525021

Cited by 48 publications

(36 citation statements)

References 51 publications

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“…Considering all the plots and statistics, NDVI was chosen for further processing, being much easier to compute, while it is region-independent, unlike SAVI [47]. Additionally, international literature always confirms the importance of NDVI as a remote sensing tool [9,11,[28][29][30][31][32]65,[68][69][70]. Figure 7d1 illustrates ET r F versus NDVI for the general case, corresponding to Equation (26), which is the formula used for the next steps of the study.…”

Section: Resultsmentioning

confidence: 99%

Hybrid Methodology for the Estimation of Crop Coefficients Based on Satellite Imagery and Ground-Based Measurements

Spiliotopoulos

Λουκάς

2019

Water

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The objective of the current study was the investigation of specific relationships between crop coefficients and vegetation indices (VI) computed at the water-limited environment of Lake Karla Watershed, Thessaly, in central Greece. A Mapping ET (evapotranspiration) at high Resolution and with Internalized Calibration (METRIC) model was used to derive crop coefficient values during the growing season of 2012. The proposed methodology was developed using medium resolution Landsat 7 ETM+ images and meteorological data from a local weather station. Cotton, sugar beets, and corn fields were utilized. During the same period, spectral signatures were obtained for each crop using the field spectroradiometer GER1500 (Spectra Vista Corporation, NY, U.S.A.). Relative spectral responses (RSR) were used for the filtering of the specific reflectance values giving the opportunity to match the spectral measurements with Landsat ETM+ bands. Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index 2 (EVI2) were then computed, and empirical relationships were derived using linear regression analysis. NDVI, SAVI, and EVI2 were tested separately for each crop. The resulting equations explained those relationships with a very high R2 value (>0.86). These relationships have been validated against independent data. Validation using a new image file after the experimental period gives promising results, since the modeled image file is similar in appearance to the initial one, especially when a crop mask is applied. The CROPWAT model supports those results when using the new crop coefficients to estimate the related crop water requirements. The main benefit of the new approach is that the derived relationships are better adjusted to the crops. The described approach is also less time-consuming because there is no need for atmospheric correction when working with ground spectral measurements.

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Section: Resultsmentioning

confidence: 99%

Hybrid Methodology for the Estimation of Crop Coefficients Based on Satellite Imagery and Ground-Based Measurements

Spiliotopoulos

Λουκάς

2019

Water

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“…The information required at a regional scale for hydrological applications is typically obtained through remote sensing techniques (RS) [10][11][12], which provide relatively frequent and spatially contiguous measurements for the global monitoring of surface biophysical variables that affect ET. Some of the most used approaches are: (i) for crop, ET at plot scale based on the product between a reference ET (ETo) and the crop coefficient (Kc) derived from RS-based vegetation indices, such as the well-known normalized difference vegetation index (NDVI) [20]; (ii) based on a surface energy balance (SEB), one-source Landsat-based SEB models such as SEBAL (Surface Energy Balance Algorithm for Land) [21] and METRIC (Mapping Evapotranspiration with High Resolution and Internalized Calibration) [22], S-SEBI (Simplified Surface Energy Balance Index) [23], and SEBS (SEB System) [24], two-source SEB models such as ALEXI (Atmosphere-Land EXchange Inverse) [25], DisALEXI (Disaggregated Atmosphere-Land EXchange Inverse) [26], and the Sim-ReSET model (Simple Remote Sensing EvapoTranspiration) [27]; (iii) Priestley-Taylor Methods such as GLEAM (Global Land Surface Evaporation: the Amsterdam Methodology) [28]; (iv) Land Surface Models (LSM) [29] such as Noah [30,31], Mosaic [32,33], VIC (Variable Infiltration Capacity) [34][35][36], and CLM (Common Land Model) [37]; (v) Water balance methods (basin-wide estimates) using GRACE (Gravity Recovery and Climate Experiment) satellites data [38,39]; (vi) the solution of the Penman-Monteith equation based on biophysical parameters derived from RS, as is the case for the MOD16 [3,40] product analyzed in this work.…”

Section: Introductionmentioning

confidence: 99%

Performance Assessment of MOD16 in Evapotranspiration Evaluation in Northwestern Mexico

Aguilar

Flores

Crespo

et al. 2018

Water

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Evapotranspiration (ET) is the second largest component of the water cycle in arid and semiarid environments, and, in fact, more than 60% of the precipitation on earth is returned to the atmosphere through it. MOD16 represents an operational source of ET estimates with adequate spatial resolution for several applications, such as water resources planning, at a regional scale. However, the use of these estimates in routine applications will require MOD16 evaluation and validation using accurate ground-based measurements. The main objective of this study was to evaluate the performance of the MOD16A2 product by comparing it with eddy covariance (EC) systems. Additional objectives were the analysis of the limitations, uncertainties, and possible improvements of the MOD16-estimated ET. The EC measurements were acquired for five sites and for a variety of land covers in northwestern Mexico. The indicators used for the comparison were: root mean square error (RMSE), bias (BIAS), concordance index (d), and determination coefficient (R 2) of the correlation, comparing measured and modelled ET. The best performance was observed in Rayón (RMSE = 0.77 mm•day −1 , BIAS = −0.46 mm•day −1 , d = 0.88, and R 2 = 0.86); El Mogor and La Paz showed errors and coefficients of determination comparable to each other (RMSE = 0.39 mm•day −1 , BIAS = −0.04 mm•day −1 , R 2 = 0.46 and RMSE = 0.42 mm•day −1 , BIAS = −0.18 mm•day −1 , R 2 = 0.45, respectively). In most cases, MOD16 underestimated the ET values.

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“…Second, NDVI is used to derive a spatio-temporal crop coefficient (the Kc-NDVI method) for grassland and agricultural fields (e.g. 55 Mutiibwa and Irmak, 2013;Kamble et al, 2013;Reyes-González et al, 2018) or natural or mixed ecosystems (Maselli et al, 2014;Hunink et al, 2017). The Kc-NDVI method neglects the soil moisture driven controls on E(T) and this is one of the main drawbacks of using this method in natural vegetation (Glenn et al, 2010).…”

mentioning

confidence: 99%

“…The above mentioned studies often derive the NDVI from MODIS or AVHRR data which have a spatial resolution 70 of 250 m and 1 km (except for Reyes-González et al (2018); Kim et al (2006); Rahman et al (2001); Su (2002), who used airborne data or high resolution satellite data (Landsat or IKONOS)). The NDVI is often compared with ET derived from different flux towers with a footprint length of 100 to 1000 m (Kim et al, 2006), or a water balance model.…”

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Does NDVI explain spatial and temporal variability in sap velocity in temperate forest ecosystems?

Dijke

Mallick

Teuling

et al. 2018

Preprint

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Abstract. There is a need for a better understanding of the link between vegetation characteristics and tree transpiration to facilitate satellite derived transpiration estimation. Many studies use the normalized difference vegetation index (NDVI), a proxy for tree biophysical characteristics, to estimate evapotranspiration. In this study we investigated the link between sap velocity and 30 m resolution Landsat derived NDVI for twenty days during two contrasting precipitation years in a temperate deciduous forest catchment. Sap velocity was measured in the Attert catchment in Luxembourg in 25 plots of 20 × 20 m covering three geologies with sensors installed in 2–4 trees per plot. The results show that sap velocity and NDVI were significantly positively correlated in April, i.e., NDVI successfully captured the pattern of sap velocity during the phase of green-up. After green-up, a significant negative correlation was found during half of the studied days. During a dry period, sap velocity was uncorrelated to NDVI, but influenced by geology and aspect. In summary, in our study area, the correlation between sap velocity and NDVI was not constant, but varied with phenology and water availability. The same behaviour was found for the Enhanced Vegetation Index (EVI). This suggests that methods using NDVI or EVI to predict small-scale variability in (evapo)transpiration should be carefully applied and that NDVI and EVI cannot be used to scale sap velocity to stand level transpiration in temperate forest ecosystems.

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

Cited by 48 publications

References 51 publications

Hybrid Methodology for the Estimation of Crop Coefficients Based on Satellite Imagery and Ground-Based Measurements

Hybrid Methodology for the Estimation of Crop Coefficients Based on Satellite Imagery and Ground-Based Measurements

Performance Assessment of MOD16 in Evapotranspiration Evaluation in Northwestern Mexico

Does NDVI explain spatial and temporal variability in sap velocity in temperate forest ecosystems?

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