Period: 1999 – present

Sensor: SPOT-VGT and PROBA-V

Frequency: 10 days

Resolutions: 1km

Extent: Global

Data can be accessed here.

Algorithm description: Ten day composite of GDMP (gross dry matter productivity) and DMP (dry matter productivity). Both quantities are expressed in units of dry matter instead of kg of C (unit: kg of DM/ha/day).

The algorithm calculates GDMP based on Monteith theory of light use efficiency approach (Swinnen et al 2018). GDMP is calculated as a product of APAR (calculated as FAPAR * incident solar radiation) and actual light use efficiency parameter. This parameter is modified as a function of atmospheric CO2 concentration, air temperature and autotrophic respiration factor (constant 0.5).  Inclusion of autotrophic respiration converts gross productivity to net productivity (DMP).

Accuracy: Version 1 of this product suffered mainly due to data gaps. GDMP matched well with the Fluxnet GPP. However, poor comparison of DMP with tower derived NPP indicated that the source of error was mostly related to poor representation of autotrophic respiration.

Version 2 product with gap-filled FAPAR input data has much less data gaps than version 1. It was also found to have better accuracy than version 1. However R2 (0.54) and RMSE (58.63) are still only moderate (Swinnen et al 2018).

There is no strong positive or negative bias, but 1:1 comparison plots against measurements show lot of dispersed scatter.


  • Accuracy is poor when compared against Fluxnet data. However, there is also concern about spatial representativeness of flux tower measurements and calculations for autotrophic respiration. This would also have impact on derivation of light use efficiency values using Fluxnet data for calibration.
  • Autotrophic respiration is represented by a constant factor (0.5).
  • Algorithm does not include water, nutrient stress, pest and disease stress factors.
  • Biome specific parameterisation makes the algorithm prone to errors associated with the land cover classification.


Swinnen, E., van Hoolst, R. & Eerens, H. Algorithm Theorethical Basis Document: Dry Matter Productivity (DMP) Version 2. 1–53 (2018).

Swinnen, E., van Hoolst, R., Eerens, H. & Tote, C. Quality Assessment Report: Dry Matter Productivity (DMP) collection 1km Version 2. 1–78 (2017).