Improving Remote Sensing Derived Dry Matter Productivity by Water Limitation Factor
Durgun, Yetkin Ozum1; Gilliams, Sven1; Tychon, Bernard2; Djaby, Bakary2

Crop condition monitoring throughout the growing season and crop yield forecasting are both important to estimate the seasonal production. It is critical to make accurate and timely evaluation of reduced crop production particularly for countries with agriculture dependent economy. Early yield-loss estimation can avoid catastrophic situations, and help in defining the directions and making decisions. Different methods have been developed to estimate crop yields by means of satellite data, and one commonly applied is the development of empirical relationships between crop yield and remote sensing indicators (Bastiaanssen & Ali, 2003). Today, remote sensing technology provides timely assessment of changes in growth and development of agricultural crops (Doraiswamy et al., 2003).

When growing, crop converts solar energy into chemical energy through the photosynthesis process during which water and CO2 (and O2) are exchanged into the atmosphere. Dry matter productivity (DMP) derived from remote sensing is the net result of this photosynthetic activity. As a definition, DMP is an increase in crop dry matter biomass. Thus, it is an important biophysical indicator. Monteith (1972) formulated the first Radiation Use Efficiency Model to estimate Net Primary Production (NPP), a variation of DMP. According to the model, the biomass accumulation of the plant is correlated with the amount of absorbed radiation (APAR) and the actual efficiency of converting atmospheric CO2 into plant tissue (εACT) as


The Remote Sensing research unit of VITO started to produce DMP estimate images on a regular basis since around 2000 (Eerens, 2010). VITO calculates Monteith variant DMP as in Equation 1.

In the current version of DMP, APAR is formed by the incident solar radiation, fraction of absorbed photosynthetic radiation and fraction of photosynthetically active radiation. εACT is calculated by vegetation type specific maximum radiation use efficiency (εRUE) and includes reduction factors linked to temperature and CO2 fertilisation effect. In the formula, εRES is added to emphasise the fact that some important factors are clearly omitted (Eerens, 2010). Water stress is one of these factors. The objective of this study is to add a water stress reduction factor into the formula and to estimate water limited version of DMP.

In many places in the world, the most important factor for crop production is water availability (Senay & Verdin, 2003). Drought indices are powerful indicators to monitor water-limited areas. Furthermore, evapotranspiration is an important component of the water cycle and it is directly connected to the surface energy budget (Ghilain et al., 2011). In this study, water-limited version of DMP will be calculated by adding a supplementary efficiency, a drought index. The selected drought indices use actual evapotranspiration and potential evapotranspiration in their formula, such as water stress coefficient, aridity anomaly, water deficit and crop specific drought indices will be compared. Additionally, each efficiency used in the current DMP formula will be investigated. For instance, εRUE is taken as a constant though the radiation use efficiency differs for C3 and C4 plants by about 50% (Sheehy et al., 2000; Hibberd et al., 2008). The study areas are the main agricultural provinces or agricultural regions of Belgium and Morocco.

As a first group of dataset, the products of MSG-SEVIRI sensor will be used in the study which are actual evapotranspiration, radiation and temperature. The second group is potential evapotranspiration dataset extracted from operational forecasts of ECMWF. Last but not least, the fAPAR product of SPOT-Vegetation sensor will be used.

In spring 2013, ESA's new microsatellite PROBA-V and in 2014 Sentinel-3 will be launched. Both of these satellites will be the successors of SPOT-Vegetation sensor. Data continuity is crucial for agricultural monitoring. In case this study shows that water-limited version of DMP might be a solution to the yield forecasting problems as a better remote sensing indicator, products of PROBA-V and Sentinel-3 will replace the product of SPOT-Vegetation for the future applications of water-limited version of DMP. In conclusion, water is a controlling factor both for crop growth and yields. Considering the current climatic conditions, it is expected that measuring the real time water availability for the vegetation and responding on-time will become more and more important issues globally.


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