How are productivity maps generated?
Remote Sensing basics
Please review the following articles, to get familiar with the basics on Remote Sensing for Agriculture. This is recommended to understand how Productivity Maps are generated
- Remote Sensing Basics (extension.org)
- Precision Agriculture: Remote Sensing and Ground Truthing (University of Missoury Extension)
- Normalized Differential Vegetation Index (NDVI) Maps
Methodology - combining NDVI from past seasons
Productivity maps are generated based on the combination of Vegetation Index maps, through geo-statistical analysis. It's is an automated and objective, that allows to compare the field production and variability in different times, in a single map.
Remote sensing imagery is selected in key dates, when crops are vigorous, and Vegetation Index (NDVI) maps are generated on those dates. These layers are then normalized, and integrated in the productivity map. It's also possible to include information from previous yield monitor maps if available.
The following sample images and corresponding NDVI illustrate this process:
Landsat Image - Aug. 3, 2004
NDVI - Aug. 3, 2004
These images are integrated by analyzing each pixel, defining homogeneous production areas, and obtaining the productivity map.
When is the right time to select imagery?
NDVI values are closely related to phenological vegetation patterns. The phenological pattern describes the functioning of vegetation and allows understanding
its geographic variations, while the NDVI determines vegetative activity.
the following figures show the relationship between NDVI evolution and crop phenology, in different research experiences
A second aspect to consider is that in the maturity stage, and depending on the type of crop, there is a close correlation between NDVI values and actual yields. Thus, images to produce Productivity Maps are selected during that stage, to show yield variability.
A third aspect is that
Thus, the right time to...
The Crop History is very important to evaluate the history and variability of the fields, when processing and interpreting NDVI maps, Producitivity maps, or other layers derived from remote sensing processing. This information includes the following, for each of the last crop seasons: If there was any extraordinary event, such as hail, drought, floods, etc. The crop planted in each field, with planting and harvesting date.
Why is crop history important to analyze NDVI and Variability?
How often should Productivity Maps be updated?
La definición de ambientes se inicia en una campaña determinada pero es un proceso que permite mejorar continuamente especialmente al finalizar la primer campaña con la obtención de mapas de rendimiento. A partir de ellos se puede obtenerse información valiosa para retroalimentar el mapa de ambientes original.