Agri Punjab, Agriculture Department Agri Sindh, Agriculture Department University of Maryland Pakistan Space and Upper Atmosphere Research Commission U.S. Department of Agriculture Food and Agriculture Organization of the United Nations
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Remote Sensing Information in Pakistan

MODIS products

MODIS Normalized Difference Vegetation Index (NDVI) Time Series

In monitoring crop conditions for a specific region, remotely sensed vegetation index data are used to track the evolution of the growing season compared to reference long-term mean conditions. A global normalized difference vegetation index (NDVI) is produced from MODIS data, and is referred to as the "continuity index".

A NDVI time-series database, with a spatial resolution of 250 meters has been assembled using a 16-day compositing period, allowing for inter-annual comparisons of growing season dynamics.

The time-series data for Pakistan are accessible through the GLAM's web interface and analysis tool. From this stand-alone interface the analysts can query these data by pre-defined province/district areas, by interactive sub-setting, and by implementing crop/water masks to:

  • plot time-series graphs over the crop growing seasons to quickly assess crop conditions and anomalies
  • monitor current conditions
  • view spatially, NDVI anomalies comparing current conditions to previous year, or historical mean
  • plot histograms of current and historical NDVI data

These data and utilities are fundamental for crop yield forecasts and can serve as an early warning system for areas suffering from crop loss and food shortages.

SPOT Products

SPOT-VGT Normalized Difference Vegetation Index (NDVI) Time Series

This NDVI product is generated from the VEGETATION sensor onboard the Spot 4 satellite which was launched on March 24, 1998. It provides global coverage on an almost daily basis at a spatial resolution of 1 kilometer.

NDVI is a measure of the amount and vigor of vegetation on the land surface and NDVI spatial composite images are developed to more easily distinguish green vegetation from bare soils.

NDVI is calculated from satellite imagery according to this formula: (NIR - RED ) / (NIR + RED ) where, RED = the red portion of the electromagnetic spectrum and NIR = the near infrared portion.

For agricultural and vegetation condition monitoring, clouds are partially screened from NDVI images by producing Maximum Value Composites (MVC) over 10-day where the highest NDVI pixel value within the time period is retained under the assumption that it represents the maximum vegetation "greenness" during the period.

Physical values of NDVI range between -1.0 and 1.0 (unit less index). The NDVI values are rescaled such that they only occupy a byte. The physical range -0.1 to 0.92 is rescaled to the range 0 - 250, using the following formula:

Image value = (physical value + 0.1) * 250

To convert the image values back to physical values, the following formula is used:

Physical value = image value * 0.004 - 0.1

Higher values of NDVI indicate denser and healthier (higher green density) vegetation. NDVI values of 0.1 and below, for instance, typically correspond to areas with little to no vegetation (rocks, ice, desert). Moderate values (around 0.2 and 0.3) correspond to shrub and grasslands and high values (0.5 and above) typically correspond to dense vegetation like rainforests. Over the course of a growing season, we first see a steady increase in the NDVI values as the young, green vegetation grow (the growth makes the surface appear more and more green, which is reflected by the NDVI). This increase reaches a maximum value just before it drops suddenly at harvest time or when the plants die naturally, which can easily be explained by the harvesting of the healthy, green plants or their senescence, which makes the surface appear less green.

NDVI can be very beneficial for monitoring agricultural crops. In particular time-series are useful for estimating crop stage or growth period (i.e., planting, vegetative development, flowering, grain-filling, etc.) and observing when periods of dryness or drought stress occurred during the growing season.
example ndvi legend In general, when NDVI values begin to increase, it corresponds to the start of the growing season and the period of maximum NDVI approximately corresponds to the end of vegetative development or the beginning of the flowering stage for most climatic regions. It also is useful to monitor how long NDVI departures were below-average during the growing season and if any these dry periods occurred during critical crop stages that are especially sensitive to water stress, such as flowering or early ripening stages, for which several dry periods during the flowering and grain-filling stages can severely reduce potential grain yields.

Since NDVI is as an indicator of the amount of vegetation greenness vigor within a pixel, positive NDVI departures from average are shaded from light green to dark green to depict above-average vegetation conditions, and negative NDVI departures from average are shaded from light yellow to dark brown to depict below-average vegetation conditions.

The Pakistan's Crop Information Portal contains the complete archive of SPOT NDVI, 10-day composites from 1999 to current. Here is an example taken from the third decade of March 2008

spot ndvi example

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Sep 2, 2015 - A new amendment (no. 3) was signed by USDA and FAO to formalize a no-cost extension of the project GCP/PAK/125/USA from Oct 1, 2015 to Sep 30, 2016.

  recent updates

Aug 8, 2016. E-bulletin 11, Apr-Jun 2016.

Aug 5, 2016. E-learning courses on agriculture monitoring and statistics.

Aug 5, 2016. FAO's ECONET approach and land cover change in Pakistan.

Aug 5, 2016. Training on Carbon Sequestration.

Aug 5, 2016. CRS Punjab bulletins Apr 2016.

Apr 22, 2016. Cotton Production & Analytics.

Mar 1, 2016. Testing Sentinel-2 images for crop monitoring in Pakistan.

Feb 20, 2016. Crop Information Portal training.

Jan 20, 2016. Sugarcane Production & Analytics.

Jan 20, 2016. Training on REDD+ Technology for Forest Department Officers of Punjab.

Oct 29, 2014. Agriculture Information System project: roll-out workshop.

Aug 13, 2015. RS/GIS Training in Forest Management, Lahore, 4-8 May 2015.

FAO Pakistan website >>>>


Forum: "Pakistan Agriculture Sector". >>>>


SUPARCO, the National Space Agency of Pakistan >>>>


Government of Sindh: Agriculture Department >>>>


Agri Punjab >>>>

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