VICAL: Global Calculator to Estimate Vegetation Indices for Agricultural Areas with Landsat and Sentinel-2 Data

Abstract

The vegetation indices (VIs) estimated from remotely sensed data are simple and based on effective algorithms for quantitative and qualitative evaluations of the dynamics of biophysical crop variables such as vegetation cover, leaf area, vigor and development, and many others. Over the last decade, many VIs have been proposed and validated to enhance the vegetation signal by reducing the noise from effects produced either by the soil or by vegetation such as brightness, shadows, color, etc. VIs are commonly calculated from satellite images such as ones from Landsat and Sentinel-2 because of their medium resolution and free availability. However, despite the VIs being fairly simple algorithms, it can take hours to calculate them for an established agricultural area, mainly due to the pre-processing of the images (including atmospheric corrections, the detection of clouds and shadows), size and download time of the images, and the capacity of the computer equipment used. Time increases as the number of images increases. In this sense, the free to use Google Earth Engine (GEE) platform was here used to develop an application called VICAL to calculate 23 VIs map (VIs commonly used in agricultural applications) and time series of any agricultural area in the world with images (cloud-free) from Landsat and Sentinel-2 data. It was found that VICAL can calculate these 23 VIs accurately, and shows the potential of the GEE cloud-based tools using multispectral dataset to assess many spectral VIs. This tool is very beneficial for researchers with poor access to satellite data or in institutions with a lack of computational infrastructure to handle the large volumes of satellite datasets, since it is not necessary for the user writing a single line of code. The VICAL is open-access image analysis platform that can be modified to carry out more complex analysis or adapt it to a specific VI application.

Nitrogen Estimation in Fig Cultivation through Remote Sensing and Machine Learning

“…10.8 [26]. Additionally, indices from the VICAL platform (https://inifapcenidraspa.users.earthengine.app/view/vical) developed by Jiménez-Jiménez et al (2022) [27] were included.…”

Seminatural Grasslands: An Emblematic Challenge for Nature Conservation in Protected Areas

“…This improvement is credited to the satellites' frequent revisit times, superior spatial and spectral resolution, and the open policy implemented for data access. Sentinel-2 data are beneficial for vegetation monitoring [180] due to their spatial resolution, which varies from 10 to 60 m based on the bands, and their temporal resolution, which is approximately 2-3 days in Europe when both Sentinel-2 satellites are combined.…”

“…The Normalized Difference Vegetation Index (NDVI) was selected for its ease of interpretation and its established use in grassland monitoring and management [31,180]. It has been consistently used in empirical correlations with biomass measurements in pastures and meadows, forming the basis for estimating grass productivity [31,[181][182][183][184][185][186][187].…”

Modelos para la estimación del rendimiento de la caña de azúcar en Costa Rica con datos de campo e índices de vegetación

“…En este trabajo se utilizaron los siguientes índices de vegetación: el DVI (Difference Vegetation Index), el EVI (Enhanced Vegetation Index), el GNDVI (Green Normalized Difference Vegetation Index), el NDVI (Normalized Difference Vegetation Index), el RI (Redness Index), el RVI (Ratio Vegetation Index), el SAVI (Soil Adjusted Vegetation Index) y el SR (Simple Ratio) (Tabla 1). Estos índices de vegetación fueron seleccionados por su frecuente aplicación en investigaciones agrícolas (Jiménez-Jiménez et al, 2022).…”