ANFIS (RMSE = 0.291 t/ha)
Figure 8: Grange total annual yield (t/ha).
From Figure 7 and Figure 8 it can be concluded that the precise estima- tion of growth rate can be used to calculate the total weekly and annual yield. However, the assumption of linear growth throughout the week is
not always true. Due to the strong relationship between the growth rate and weather conditions (e. g., daily temperature, precipitation) this issue
of linear (or constant) growth rate can be resolved by using GDD informa-
tion. In the next chapter theGDDinformation calculated from weather data
(daily: minimum, maximum and mean temperature) were combined with
theVIin order to analyse the influence of climate variability on the retrieval
4
F U S I O N O F R E M O T E S E N S I N G A N D W E AT H E R D ATA T O R E T R I E V E G R A S S L A N D B I O M A S S A N D G R O W T H R AT E
Climate is what we expect, weather is what we get. — Mark Twain
c h a p t e r p u b l i c at i o n:
This chapter has been submitted as a research article for publication in "In- ternational Journal of Applied Earth Observation and Geoinformation":
Ali, I.; Cawkwell, F.; Dwyer, E.; and Green, S.; 2016, "Synergetic use of re- mote sensing and weather data to retrieve grassland biomass and growth rate", International Journal of Applied Earth Observation and Geoinformation. [Submitted, (IF: 3.470)]
4.1 pa p e r—3
4.1.1 Ali, I.; Cawkwell, F.; Dwyer, E.; and Green, S.; 2016, "Synergetic use of remote sensing and weather data to retrieve grassland biomass and growth rate", International Journal of Applied Earth Observation and Geoinforma- tion. [Submitted, (IF: 3.470)]
The main abiotic factors which determine the growth potential of each veg- etation type or plant include climate and soil. Therefore, it is important to know the climatic requirements for plant species, and because of their different phenological characteristics, different plant species require a dif- ferent range of temperature and soil moisture. There is thus a very strong link between the current and future climate and its effects on plant phe-
nology. After analysing the climate data from the past century Khanduri
et al. (2008) have reported that the average length of the growing season
(in different parts of the world) has extended by 3.3 days per decade.
GDD are a measurement of the growth and development of plants dur-
ing the growing season. Based on the findings from the previous paper (Chapter 3), this paper presents the inclusion of climate variables (daily
minimum, maximum and mean temperature forGDD calculation) into the
model development (ANFIS: for details, see chapter 3, section 2.4.3) as a
proxy to predict and improve biomass and growth rate estimation.
In the literature, different methods for calculating GDD have been re-
4.1 paper—3 163
justing minimum, maximum and average temperature with respect to the selected base temperature. Based on these adjustments each method will
produce a different profile of accumulatedGDD, therefore it is important to
clearly define the criteria and conditions used to calculate the GDD. In this
study, three different methods of calculatingGDDare used and their perfor-
mance for predicting both grassland biomass (DM kg/ha) and growth rate (DM kg/ha/day) for the Grange study site is analysed.
Results show that the fusion of remote sensingVIand accumulated grow-
ing degree-days temperature has improved the biomass rate and yield esti- mation performance.
Test site 2 (Grange) MODIS Time Series
Daily: min, max and mean temperature Weekly grass biomass and growth rate In-situ data Weather data
Output
Residual Error EvaluaCon (R2, RMSE) ANFIS ModelBiomass and growth rate esCmaCon &
Discussion and conclusion Preprocessing 0 500 1000 1500 2000 0 10 2001−2005, 2007 (Method 1) GDD 0 500 1000 1500 2000 −5 10 2001−2005, 2007 (Method 2) GDD 0 500 1000 1500 2000 0 10 2001−2005, 2007 (Method 3) GDD
Growing Degree Days VegetaCon Indices Python processing chain Filtering 2001–2005, 2007
Figure 9: Graphical abstract of this paper: Ali, I.; Cawkwell, F.; Dwyer, E.; and Green, S.;2016, "Synergetic use of remote sensing and weather data to retrieve grassland biomass and growth rate", International Journal of Applied Earth Observation and Geoinformation. [Submitted, (IF: 3.470)]
C O N T R I B U T I O N S TAT E M E N T
Declaration of own contribution to the published (or intended for publication) sci- entific papers within my dissertation.
d i s s e r tat i o n t i t l e: Retrieval of grassland biophysical parameters us-
ing multitemporal optical and radar satellite data.
pa p e r–3: Ali, I.;Cawkwell, F.; Dwyer, E.; and Green, S.; 2016, "Synergetic
use of remote sensing and weather data to retrieve grassland biomass and growth rate", International Journal of Applied Earth Observation and Geoinformation. [Submitted, (IF: 3.470)]
o w n c o n t r i b u t i o n i n t h i s w o r k: Concept development (fully), Lit-
erature search (fully), Methods development (fully), Research design (fully), Data collection (mainly), Data pre-processing (fully), Data anal- ysis (fully), Construction of the manuscript (fully), Argumentation (fully), Critical revision of the article (mainly).
Iftikhar Ali, MSC April 17, 2016