Satellite and meteorological monitoring of agricultural conditions

Satellite and meteorological monitoring of agricultural conditions

Posted on 07 Feb 2018

The effect of quantities and distribution of precipitation on crop cultivation.

Precipitation is agriculture’s main source of moisture. This includes both wet and dry precipitation. Their quantity is one of the key meteorological factors in agricultural productivity.
But it is not simply a case of there being too little or too much precipitation. No less important is its spatial and temporal distribution which increases or decreases potential productivity. The optimal amount of precipitation varies both for different climatic regions and for different crops at various stages of their growth.
Let’s take the year that has passed, 2017, as an example:

India received a total of 841.3 millimeters of precipitation during the monsoon season from June 1 to September 30 that year.
But this monsoon was somewhat freakish. In some parts there was surplus rain, and in others it was insufficient. This cannot but lead to differences in crop productivity in different parts of the country.

India 08/17/2017. RGB. Data source – Modis

Surplus rains in Gujarat can have a beneficial effect on peanut and cotton cultivation.
While contrasting figures for both Madhya Pradesh and Haryana (26% lower than normal) cast doubt on the prospects for production of oil crops such as soya, rapeseed, mustard and sunflower.

India before 09/30/2017. NDVI. Data source – Modis

USA, Midwest

The current winter snow cover was sufficient for the Northern Plains and areas in the north of the Midwest, but in the Central and Southern Plains and the south of the Midwest, the layer of snow was very thin, which led to soil freezing.
In the opinion of analysts and meteorologists, such deep frost is likely to cause significant damage to those winter wheat crops without a sufficient protective layer of snow.
According to data from the National Meteorological Service, over the last three months in Kansas, Oklahoma and Texas, either little snow fell, or none fell at all. And now cold constitutes a threat to the harvest.

Snow cover in Kansas, Oklahoma and Texas on 13/12/2017. RGB. Data source – Aqua

This year, the US winter wheat harvest is already set to be rather poor, lighter even than last year when productivity fell to its lowest level since 1909.

Currently, there are various ways of assessing the condition of cultivated land which can visually display whether crops will receive a sufficient, or, on the contrary, surplus amount of moisture, for specific fields and plantations. These are meteorological data and satellite images in particular. We make it possible to combine both these approaches and for you to receive this information in the quickest and most visually comprehensive way possible.


Historical  temperature data for the Kansas region for the period 12/30/2017 to 01/03/2018 

NDVI for the same area in Kansas on 10/11/2017 and 06/13/2017 respectively. When using the palette, the blue color represents maximum vegetation, the red - minimum, and yellow and green - intermediate stage

The availability of accumulated historical data on field conditions depicted in these satellite images, of the NDVI, NVI and EVI vegetation indices reflecting the status of vegetation for any given season, as well as of meteorological data for these fields for any period of time provide a powerful analytical tool for decision-making in challenging situations where there are changes to the weather or sudden weather events likely to induce shock in crops. 

OpenWeather  are launching a new product Agro API aimed primarily at specialists developing agricultural services and addressing the specific requirements of this sector. 

As part of this product, we are providing an API for receiving weather data (current weather, forecasts and history), satellite data (current and historical) and weather and vegetation indices based upon this.  As well as the data we already provide in other products, here we have added specialized agricultural indices such as soil temperature and moisture, accumulated temperature, cumulative precipitation and satellite data: images from space and vegetation indices (EVI and NDVI) based upon them. Weather data can be requested for a polygon. Find out more here.