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

Abstract

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.

Maize Crop Coefficient Estimation Based on Spectral Vegetation Indices and Vegetation Cover Fraction Derived from UAV-Based Multispectral Images

“…Similar works have reported lower R 2 values with satellite images, Spiliotopoulos & Loukas [69] obtained an R 2 of 0.86 of the Kc-EVI2 relationship from Landsat 7 ETM images. Kamble et al [4] calculated Kc values from MODIS imagery using a simple linear regression model Kc -NDVI with an R 2 of 0.83.…”

Hydrodynamic Border Irrigation Model: Comparison of Infiltration Equations

“…The initial moisture content determines the irrigation time, optimal irrigation flow, and irrigation sheet that should be supplied in the plot to bring the soil to field capacity [25]. The CROPWAT software [32] can be used as an alternative to calculate the crops' needs from climatic data of the area and the crop phenological stage [33], if the latter cannot be provided by weather stations near the study area.…”

Spatiotemporal Drought Assessment Based on Gridded Standardized Precipitation Index (SPI) in Vulnerable Agroecosystems

“…There are several review articles in the international literature presenting and comparing various drought indices [30][31][32][33][34][35][36][37][38]. There are also many extensive studies on drought forecasting based on such indices [39][40][41][42][43][44][45][46][47]. Remote sensing methodologies can also play an important role in the computation of drought indices.…”

“…This connection has been achieved using vegetation indices (VIs) which have been introduced in the literature using a combination of surface reflectance values between two or more spectral bands related to satellite systems. Among over a hundred Vis, only a small part has been systematically studied [47]. One of the most prevalent remote sensing indices for the assessment of drought-among others-is the Normalized Difference Vegetation Index [48,49].…”

“…NDVI or similar indices like Vegetation Condition Index (VCI) derived from satellite systems can provide satisfactory temporal and spatial coverage over the Earth, making the related computations potentially easier and relatively less expensive than conventional ones [46,48,49]. The SPI has been selected to be adopted for the study area because it has already been tested globally with very reliable results [47,[50][51][52]. The SPI is based on a probability distribution function and is a standardized index, and as a result, it can be applied to various climate types all over the world.…”