CHAPTER 4

Evaluation of remote sensing techniques use for monitoring crop growth phases and promotion their applications in operational agrometeorology

Piotr Struzik1

(1) Satellite Remote Sensing Centre, Institute of Meteorology and Water Management, 14 P. Borowego Str., 30-215 Krakow, Poland, e-mail: piotr.struzik@imgw.pl

4.1 Introduction

Phenology which is derived from the Greek word “phaino” meaning to show or to appear, is the study of plant and animal life cycle events, which are triggered by periodical environmental changes, especially light and temperature. Seasons are the primary reason we see phenological changes. Seasons occur when there is an annual variation of climate, primarily caused by variations in the amount of solar energy hitting the surface of the earth at a specific location. Other factors, such as closeness to the ocean and general topography can also influence seasonal climate cycles at a regional level. As a result Phenology focuses on the chronology of periodic phases of living species development. Due to general difficulties with direct use of remote sensing for monitoring of animal life cycles, this paper will focus on plant phenology.

Phenological events and their inter-seasonal and inter-annual variations have important impacts on terrestrial ecosystems and human societies by altering global carbon, water and nitrogen cycles, crop production, duration of pollination season and distribution of diseases (Penuelas and Filella, 2001; Soudani et al., 2007). Monitoring crop phenology is required for understanding intra- and inter-annual variations of agroecosystems, as well as for improving yield prediction models.

Inter-annual variation in the local climate can be caused by several factors, including human induced factors. Monitoring the growing season length by monitoring phenological changes helps scientists better document and understand our changing climate from year-to-year, and over many decades.Seasonal changes are mainly driven by:

• Variations in day length or duration of sunlight• Precipitation• Temperature• Other life-controlling factors

Vegetation phenology, and it’s connection to climate, is an important variable in a wide variety of Earth and atmospheric science applications. In particular, in global change research accurate phenology models will become increasingly vital tools, enabling researchers to monitor and predict vegetation responses to the climatic variability.

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The presence or absence of a photosynthetically active canopy has dramatic effects on regional to global ecosystem simulation models [Running and Nemani, 1991; Goetz and Prince, 1996], coupled biosphere/atmosphere general circulation models (GCMs) [Sellers et al., 1996], and land surface parameterization schemes [Henderson-Sellers et al., 1993]. In these applications, the timing and the length of the growing season control the spatiotemporal dynamics of crucial carbon and water cycles and strongly influence latent/sensible heat transport [Schwartz, 1992]. Phenology is highly variable [Schmidt and Lota, 1980] and responsive to long-term variation in climate [Sparks and Carey, 1995].

With sufficient observations and understanding, phenology can be used as a predictor for other processes and variables of importance at local to global scales and could drive a varietyof ecological forecast models with both scientific and practical applications. Phenological data and models are used in agricultural production, integrated pest and invasive species management, drought monitoring, wildfire risk assessment, and treatment of pollen allergies.

4.2 Satellite data in phenological studies

Three main tools for monitoring of phonological events are: in situ observations, bioclimatic models and remote sensing (Schaber and Badeck, 2003; Fisher et al., 2007; Soudani et al., 2008). Field-based approaches are nearly impossible to extend to large areas as observations of vegetation phenology across large areas are expensive, time consuming and subject to uncertainty due to operator bias. At large scales, it is extremely difficult to obtain consistent field phenology observations across landcovers which represent ecosystem activity rather than species-level phenology. Bioclimatic models are often specie-specific and calibrated at local scales. Their applications at larger scales may not be accurate and depend on availability of vegetation maps and complete and consistent climate records used as forcing variables. To overcome this difficulty, numerous studies have used high-frequency coverage of the terrestrial biosphere with use of remote sensing techniques. Remote sensing has the advantages of being the only way of sampling at low-cost with good temporal repeatability over large and inaccessible regions.

Time series of remotely sensed data are an important source of information for understanding land cover dynamics. Vegetation dynamics can be defined over several time scales. In the short term, communities have seasonally driven phenologies which typically follow annual cycles. Between years, phenological markers (e.g., onset of greenness, length of growing season) may respond differently; these changes are affected by short-term climate fluctuations (e.g., temperature, rainfall) and/or anthropogenic forcing (e.g., groundwater extraction, urbanization) (Elmore et al., 2003;White et al., 2002). Over a longer time period, annual phenologies may shift as a result of climate changes and large scale anthropogenic disturbance (Myneni et al., 1997; Potter et al.,

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2003; Tucker et al., 2001). Differentiation of annual, inter-annual, and long-term phenological patterns are an important component of global ecosystems' monitoring and modelling (Reed et al., 1994; Schwartz, 1999) and may lead to better understanding of how and why land cover changes over time. The most common measure of the photosynthetic ‘greenness’ of vegetated land cover used to derive phenologies is the normalized difference vegetation index (NDVI) (Tucker & Sellers, 1986). Global NDVI have been collected since the early 1980s by Advanced Very High Resolution Radiometer (AVHRR) satellites. However, the full potential of long-term NDVI time series is often hampered by poor quality data caused by instrumentation problems, changes in sensor angle, sun angle, atmospheric conditions (e.g., clouds and haze), and ground conditions (e.g., snow cover), aging of satellite detectors. These problems tend to create data drop-outs (anomalously low NDVI values in time series) or data gaps, and make phenological markers difficult to identify (Reed et al., 1994).

At large scales, it is extremely difficult to obtain consistent field phenology observations across landcovers which represent ecosystem activity rather than species-level phenology. To overcome this difficulty, numerous studies have used high-frequency coverage of the terrestrial biosphere by the National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) to quantify ecosystem vegetation phenology. The normalized difference vegetation index (NDVI), calculated as (N-R) / (N+R), where N is the near infrared reflectance and R is the red reflectance, has been related to several biophysical parameters including chlorophyll density [Tucker et al., 1985], percent canopy cover [Yoder and Waring, 1994], absorbed photosynthetically active radiation [Myneni and Williams, 1994], leaf area index [Spanner et al., 1990b], and productivity [Prince et al., 1995]. Potentially, NDVI ranges from -1 to +1, but Earth surfaces are usually limited to -0.1-0.7 NDVI. Early in the history of satellite phenology research, Justice et al. [1985] used the NDVI to qualitatively assess the global phenology of numerous land cover types. Goward et al. [1985] demonstrated that the NDVI corresponds to known seasonality in the continental United States. Satellites were later used to interpret phenology as an indicator of land cover changes in South America [Stone et al., 1994] and to detect phenological dynamics. Quantitatively, a variety of methods have been used to identify dates of onset and offset from satellite data.

For determination and monitoring of actual state of vegetation most frequently used sensors are visible and near infrared ones. Due to continuation of satellite missions equipped with the same sensors, they cover quite long periods with frequent measurements. Other solution, at the moment still in development, is use of microwave sensors. Below are listed most extensively used data series.

Satellite sensors used for phenological studies: AVHRR 1981-present; (8-km) global coverage, 1989-present; (1-km) SPOT Vegetation 1998-present; 1-km resolution MODIS 2000-present; 250m, 500m, 1-km resolution TM/ETM Landsat 1984–2003 16 days, 10–30 m resolution SMMR data from 1978-1987, and SSM/I data from 1987 to 2005.

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Numerous studies have been implemented to detect and estimate vegetation phenology analogues at continental or regional levels such as:

the starting date of growing season (SGS), ending date of growing season (EGS), length of growing season (LGS).

Large number of studies was done, mainly using time-series of NDVI datasets derived from NOAA/AVHRR, TERRA/MODIS and SPOT/VEGETATION (Tab 4.1), focused in either developing methods for reconstructing a high-quality time-series datasets (Viovy et al., 1992; Roerink and Menenti, 2000; Jönsson and Eklundh, 2002; Zhang et al., 2003; Chen et al., 2004; Bradley et al., 2007) or exploring and evaluating the use of satellite derived phenological metrics for studying terrestrial ecosystems dynamics at different spatial and temporal scales (Myneni et al., 1997; Zhou et al., 2001; Lee et al., 2002; Yu et al., 2004; Zhang et al., 2004; Beck et al., 2006 and 2007; Heumann et al., 2007; Maignan et al., 2008). Most these studies were conducted in North America, Europe, Sahel and Sudan regions, Central Asia and China. Vegetation indices based on mentioned three data sources use different spectral coverage in visible and infrared part of spectrum.

Table 4.1. NOAA/AVHRR, TERRA/MODIS and SPOT/VEGETATION bandsAVHRR/3 SPOT VGT MODIS

red [nm] 580-680 610-680 620-670NIR [nm] 725-1000 780-890 841-876

The analysed temporal variability of vegetation indices allow for determination of metrics, which can be used for characterisation of phenological cycle and its individual parts (Fig. 4.1)

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Figure 4.1. Derivation of metrics related to phenological cycle from satellite derived NDVI (Source: K. B. Jones, 2005)

The most frequently used indices retrieved from temporal curves of NDVI are listed in Table 4.2 (source Pettorelli et al., 2006). Their biological meaning and commented limitations are also presented.

Table 4.2. Indices retrieved from temporal variation of NDVI ant their relation to vegetation cycle.

Index Type of measure

Definition Biological meaning

Comments

Inegated NDVI Overall productivityand biomass

Sum of positive NDVI values over a Niven period

Annual production ofvegetation

Not relevant when resource quality is AT least as important as quantity (e.g. highly selective foragers)

Annual Maximum NDVI

Overall productivityand biomass

Maximum value of the NVDI over a year

Annual production ofvegetation

Sensitive to false highs and noisecorrection

Relative annualrange of the NVDI

Interannual variabilityin productivity

(Maximum NDVIvalue-MinimumNDVI

Enables interannualcomparisons ofvegetation

Sensitivity of the range definition tooutliers in both

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value)/INDVI biomass directionsRate of increase/decrease of theNVDI

Phenological measure

Slope between two NDVI values at two defined dates, slopesof the fitted logistic curves to the NDVItime-series

Fastness of thegreening up (spring) or the senescence(fall) phases

Sensitive to false highs and noisescorrection

Dates of thebeginning or end of the growing season

Phenological measure

Dates estimated from threshold models or moving average procedures

Start of the green-up

Accuracy is linked to the temporal scale ofthe time-series considered (with the problem that higher temporal resolutionleads to more contaminated data)

Length of the‘green’ season

Phenological measure

Number of dayswhere NDVIO0;number of daysbetween theestimated date ofgreen-up and end ofthe growing season

In seasonalenvironments,number of dayswhen food isavailable

Sensitive to false highs and noisescorrection

Timing of theannual maximumNDVI

Phenological measure

Date when themaximum NDVIvalue occurs within ayear

Timing of themaximum availabilityof vegetation

Sensitive to false highs and noisecorrection

The NVDI is a vegetation index that has demonstrated its usefulness in many ecological studies. However, in some situations, other vegetation indexes might be more appropriate. The relationship between the NVDI and vegetation can be biased in sparsely vegetated areas (e.g. arid to semiarid zones in Australia) and dense canopies (e.g.

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Amazonian Forest (Heute, 1988). In sparsely vegetated areas with a leaf area index (LAI) of <3, the NVDI is influenced mainly by soil reflectance, whereas for LAI>6 (i.e. in densely vegetated areas), the relationship between the NVDI and NIR saturates (Asrar et al. 1984). Therefore, in sparsely vegetated areas, the soil-adjusted vegetation index SAVI (Heute, 1988) is recommended instead of the NVDI. However, the SAVI requires local calibration because it is difficult to predict how soil effects are manifested within large pixel areas, which aggregate soils and vegetation of many different types, each of which requires, in principle, separate calibration.

Another index that has appeared with MODIS is the Enhanced Vegetation Index EVI (Heute et al., 2002). This index provides complementary information about the spatial and temporal variations of vegetation, while minimizing many of the contamination problems present in the NDVI, such as those associated with canopy background and residual aerosol influences. Whereas the NDVI is chlorophyll sensitive and responds mostly to RED variations, the EVI is more NIR sensitive and responsive to canopy structural variations, including LAI, canopy type and architecture. This index is thus meant to take full advantage of the new state-of-the-art measurement capabilities of MODIS. Additionally, EVI does not become saturated as easily as the NDVI when viewing rainforests and other areas of the Earth with large amounts of Green material. However, EVI has been developed on MODIS data and so data are only available from 2000 onwards. Available MODIS based indices related to phenological cycle are listed in Table 4.3.

Table 4.3. MODIS data products useful for phenological studies.

MODISProduct

Spatialresolution

Description

MOD09GQK 250 m Daily surface reflectance computer from MODIS bands 1 (620–670 nm) and 2 (841–876 nm).

MOD13Q1 NDVI

250 m Normalized difference vegetation index (NDVI) computed from MOD09GQK and composited from 16 days of data.

MOD13Q1EVI

250 m Enhanced vegetation index (EVI) computed from MOD09GQK and composited from 16 days of data.

MOD15A2LAI

1000 m Leaf area index (LAI, one sided) computed from 1 km surface reflectance and land cover definition using radiative transfer or empirical (backup) methods, 8 day composite.

MOD15A2FPAR

1000 m Fraction of photosythetically active radiation (FPAR) absorbed by the vegetation computed from 1 km surface reflectance and land cover definition using radiative transfer or empirical (backup)

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methods, 8 day composite.MOD43B4 1000 m Nadir BRDF-adjusted reflectance

composited from 16 days of data.

There are many complications, limitations and causes of error associated with satellite data, including sensor resolution and calibration (Vermote et al., 1995), digital quantization errors (Viovy et al. 1992), ground and atmospheric conditions (Tanre et al. 1992), and orbital and sensor degradation (Kaufmann et al., 2000). NDVI data sets are generally well-documented, quality-controlled data sources that have been pre-processed to reduce many of these problems (Smith et al. 1997, Tucker et al. 2005, James et al. 1994, Gutmann 1999). However, some noise is still present in the downloadable data sets and, therefore, NDVI time-series need to be smoothed before being used. Such noise is mainly due to remnant cloud cover, water, snow, or shadow, sources of errors that tend to decrease the NDVI values. False highs, although much less frequent (Vermote et al. 1995), can also occur at high solar or scan angles (in which case the numerator and denominator in the NDVI ratio are both near zero) or because of transmission errors, such as line drop-out. To minimize the problem of false highs, the downloadable products are generally based on low-angle observations wherever possible.

Satellite phenology: benefits & limitations: seasonal & internnual variability• global monitoring, 25+ years• spatial integrated climate change impacts• gaps from atmospheric disturbances• diagnostic: no information about future

Studies of vegetation dynamics usually process A VHRR daily images using a maximum value compositing technique [Holben, 1986]. Within a compositing period, usually 2 weeks but often longer in chronically cloud-covered areas, the maximum NDVI is selected on a pixel-by-pixel basis, resulting in a complete, hopefully cloud-free image pieced together from multiple overpasses. Pixels with extremely off-nadir satellite view angles are often eliminated. The main assumption of the maximum value compositing technique is that nonoptimal atmospheric, soil, view, and illumination angle conditions depress the NDVI and that the maximum NDVI in the composite period best represents vegetation status. Despite the considerable advantages of this methodology (reduction in cloud contamination and data volume), there is an inevitable loss in temporal resolution.

To identify a suite of optimal methods for generating a land surface phenology different data processing methods were developed. While no consensus exists on the optimal methodology, strong consensus exists with respect to several key issues:

1. Because of constraints imposed by spatial resolution, whatever measurement strategy is used will require effective methods to cloud screen or composite data in an optimized fashion. Specifically, the presence of clouds is a key limitation for

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optical remote sensing that limits the consistent use of daily data. Similarly, orbital paths limit daily acquisitions of passive microwave imagery, especially at lower latitudes. These constraints are particularly important because they influence the accuracy and precision with which phenological quantities can be estimated.

2. Solar zenith angles also impart their own seasonal signals onto a phenology signature and must be removed. Several studies have shown a seasonally invariant VI signal in western conifer stands due to the canceling effects of sun angle and vegetation phenology.

3. Because of its spectral reflectance and emittance properties, the presence of snow can significantly affect the remotely sensed variables, such as VIs, that are currently used to estimate phenology. Thus, effective methods are required to screen, and perhaps adjust, for the presence of snow.

4. While the meaning of land surface phenology in deciduous forest ecosystems, temperate agro-ecosystems, and sub-humid to humid grasslands is relatively clear, there are many environments in which the precise meaning is less clear. Mixed forests, which are geographically extensive and which contain a mix of deciduous and evergreen species is one example. Other examples include evergreen biomes, which do not manifest a seasonal amplitude of phenology that is comparable to deciduous systems, but which do nonetheless exhibit seasonal behavior in forest canopies and understories, and arid and semi-arid systems in which the phenology of vegetation can be subtle, rapid, and highly transient.

5. Because of the variability and complexity of land surface phenology at global scales, it may be necessary to develop suites of algorithms that are unique or tuned to different plant functional types or climate regimes.

Current approaches to measuring land surface phenology rely almost exclusively on moderate resolution optical data sources. There does not yet exist a large body of published research on the use of other sources, such as active and passive microwave image time series. However, it is likely that future efforts will increasingly focus on products that are based on data fusion using multiple data sources.

4.3 Methods used for satellite data applications in phenology

The satellite data and derived indices are able to present temporal variability of vegetation cover during the whole season. As a result monitoring of actual vegetation status is possible. In perfect case when satellite pixel covers homogeneous area or area with dominant species the curve of derived vegetation indices represents actual behaviour of specific plant (Fig. 4.2). On the figure below examples of annual variability of NDVI for individual phonological periods and for deciduous and coniferous trees are presented.

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Figure 4.2. Examples of temporal varation of NDVI due to phenological periods (source: L. Aurdal et.al, 2005)

Of course real satellite measurements suffers already mentioned disturbances, mainly decreasing measured values. Daily observations suffers problem of cloudiness, aerosols, solar-scan geometry etc.. As a result scattered shape of vegetation indices temporal variation must be processed before determining parameters based on the shape of annual curve.Most errors thus tend to decrease NDVI values. This unusual error structure, with high NDVI values being more trustworthy than low ones, breaks the assumptions of many standard statistical approaches. Further complications can arise because the error structure can vary in time and space. The most common approaches to smoothing NDVI time-series are presented in Table 4. For many purposes, the choice of smoothing method might not be crucial. For example, in a recent ecological study, similar estimates of spring phenology were obtained using either locally weighted regressions or a simple cumulative maximum throughout the season.

Example, how satellite data processing allow for further estimation of metrics related to phenology is presented on Fig. 4.3. Part A represent Best Index Slope Extraction from raw AVHRR data. Points represent 1 year of daily AVHRR observations from a 1 km deciduous broadleaf forest pixel. The solid line is the fitted BISE curve. On part B all NDVI curves from the site are averaged and transformed into a ratio from 0 to 1 Onset is observed at the yearday when NDVI ratio exceeds 0.5; offset is observed when NDVI

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Birch

Pine

ratio falls below 0.5. Striped area is the growing season; shaded area is the non growing season.

Figure 4.3. Satellite phenology: processing and detection – description in text (source: White et al., 1997)

A serious problem with the time series of satellite images is how to trans form the images into comparable units (Table 4.5). Several methods for deriving annual vegetation phonologies using smoothing functions are usually applied. Reed et al. (1994) used median smoothing to extract phenological markers from AVHRR NDVI data. Moody and Johnson (2001) applied a discrete Fourier transform to AVHRR NDVI data in southern California to derive an average annual phenology. Jakubauskas et al. (2002) used a similar method of harmonic analysis to identify crop types in southwest Kansas. Jonsson and Eklundh (2002) showed how asymmetric Gaussian functions can be used to model inter-annual phenologies in western Africa. Chen et al. (2004) described a method of reducing the impact of cloud contaminated pixels using a Savitzky–Golay filter. Zhang et al. (2003) used piecewise logistic functions to fit an annual phenology of moderate resolution imaging spectroradiometer (MODIS) data for the northeastern U.S. Fisher et al. (2006) used logistic functions to derive average annual phenology from Landsat data in New England.

Table 4.5. Selected procedures for smoothing temporal series of NDVI values.

Procedure Short descriptionMaximum Value Compositing (MVC)

NDVI values are temporally or spatially aggregated. The highest NDVI value for the period and area considered is retained

Curve-fitting Polynomial or Fourier functions are fitted to NDVI time-series

Step-wise logistic regression

A series of piecewise logistic functions are used to represent intra-annual vegetation dynamics. Four key transition dates are estimated: green-up, maturity (the date at which plant green leaf area ismaximal), senescence and dormancy

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Best Index Slope ExtractionMetod (BISE)

NDVI observations are judged as trustworthy or not depending on whether the rate-of-change in the NVDI is plausible

Asymmetric Gaussian functions

Use of Gaussian functions for curve fitting

Savitzky-Golay filter local polynomial least squares fit, within a moving window

Weightedleast-squares linear regression

A sliding-window combination of piecewise linear approximations to the NDVI time-series, putting more weight on ‘local peaks’ (NDVI values higher than the preceding and followingobservations). Tuning parameters are the weights affected to the local peaks and window widths

HANTS algorithm Iterative FFT algorithm for

4.4 Examples of successful application of satellite data for phenological studies

There is large number of published applications of remote sensing data in phenological studies covering different aspects of this problem, stating from available data sources, methods for data processing and phenology metrics extraction and finishing on multiannual studies presenting spatial and temporal changes of phenological cycle. It’s hard even to list all publications on this topic. As a result only several cases were selected and shortly presented below for betted proof of opportunities which are given when satellite data are applied in phenological studies.

4.4.1. Determination of Start of Season with use different techniques for satellite data smoothing (Fontana et al., 2008)

The retrieved from satellite data phenological stages: Start of Season, length of growing season, End of Season for Alpine environment were compared to ground observations. Different techniques were applied for satellite data processing. Retrieved results for three methods (Fig. 4.4) used proven good agreement both between methods and with ground measurements.

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Figure 4.4. Comparison of methods of NDVI processing regarding phenological stages detection

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Best Index Slope Extraction, daily NDVI values analysed – retieved SOS: 18 May 2002

Savitzky-Golay smoothing, 10 day MVC NDVI valueas analysed - retieved SOS: 17 May 2002

Fourier smoothing, 10 day MVC NDVI valueas analysed - retieved SOS: 15 May 2002

4.2. Variation of phenological stages in China (Piao et al., 2006)

In this study, possible impact of recent climate changes on growing season duration in the temperate vegetation of China was analysed, using the Advanced Very High Resolution Radiometer (AVHRR)/Normalized Difference Vegetation Index (NDVI) biweekly time-series data collected from January 1982 to December 1999. The phenological stages: onset of green-up, onset of dormancy and resulted season length were calculated from NDVI temporal curves, both for whole area and for selected individual crops. Clear trend for increase of season length was identified (Fig. 4.5).

Figure 4.5. Interannual variations in the onset dates of (a) green-up, (b) vegetation dormancy, and (c) growing season duration for the entire study area from 1982 to 1999.

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4.3. Comparison of phenology trends by land cover class: a case study in the Great Basin, USA (B. Bradley et al., 2008)

The relative trends of land cover classes may hold clues about differential ecosystem response to environmental forcing. Identification of phenological variability and 10-year trends for the major land cover classes in the Great Basin was analysed by authors. This case study involved two steps: a regional, phenology-based land cover classification and an identification of phenological variability and 10-year trends stratified by land cover class. The analysis used a 10-year time series of AVHRR/NOAA satellite data. The phenology-based regional classification was more detailed and accurate than national or global products. Phenological variability over the 10-year period is high, with substantial shifts in timing of start of season of up to 9 weeks. The mean long-term trends of montane land cover classes were significantly different from valley land cover classes due to a poor response of shrubland and pinyon-juniper woodland to the early 1990s drought. The differential response during the 1990s suggests that valley ecosystems may be more resilient and montane ecosystems more susceptible to prolonged drought (Fig. 4.6 – 4.7).

Figure 4.6. Average annual phenologies of Great Basin land cover classes retrieved from series of satellite NDVI data.

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Figure 4.7. Average start of season during the 1990s for Great Basin land cover classes.

Results from analysis did not indicated any significant trend in 10 years period, while strong dependence of water availability was presented (drought at the beginning of period and very wet year 1998).

4.4.4. Alpine phenology (Stöckli, et al., 2007)

The inter annual differences in vegetation seasons were retrieved by using AVHRR derived vegetation indices. Such a parameters like start of season, end of season, length of season and maximum values for 20 years period are well seen. Also differences between individual seasons can be compared (Fig. 4.8).

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Figure 4.8. Varability of vegetation indices for Alpine subdomain for period 1982-2001. Source: „Remote Sensing data assimilation for a prognostic phenology model”, Reto Stöckli,This Rutishauser, Lixin Lu, Scott Denning, Peter Thornton

4.4 Changes of length of season in temperate region

Long term monitoring of vegetation status make possible to detect temporal changes of beginning and finish of vegetation season. Example of such a studies for latitudes above 45 deg is presented on Fig. 4.9. During the period 1981-1994, both spring and autumn dates were shifted, resulting with longer vegetation season. Also maximal values of NDVI (Normalised Difference Vegetation Index) grown during mentioned 13 years.

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AVHRR observations suggest that the growing season increased between 1981 and 1994 by 10%, but questions remain, mainly with respect to calibration and inter-calibration of sequential satellite instruments. The main question is whether is it greening trend ? But other possibilities are: orbital drift, inter-sensor variation or simply noise in the channel data.

4.5 Difficulties in use of satellite data for long term phenological observations

Most of the operational satellites were created as weather rather than climate platforms. As a result, long term absolute accuracy of satellite measurements was not a crucial issue. In the measurement of the climate variable it is vital for understanding climate processes and changes. However, it is not as necessary for determining long-term changes or trends as long as the data set has the required stability. And, when it comes to building satellite instruments, stability appears to be less difficult to achieve than accuracy. The difficulty arises because of the many known and unknown systematic uncertainties that are to be accounted for in the calibration of the instruments. Although excellent absolute accuracy is not critical for trend detection, it is crucial for understanding climate processes and changes.

During creation of satellite based Climate Data Records unique challenges appears:

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Figure 4.9. Temporal changes of beginning and finish of vegetation season based on satellite measured NDVI. (Ohring et.al. 2004)

• the need to manage extremely large volumes of data;

• restrictions of spatial sampling and resolution;

• accounting for orbit drift and sensor degradation over time;

• temporal sampling;

• difficulty of calibrating after launch (e.g., vicarious or onboard calibration);

• the need for significant computational resources for reprocessing.

A chronic difficulty in creating a continuous, consistent climate record from satellite observations alone is that satellites and instruments have a finite lifetime of a few years and have to be replaced, and their orbits are not stable. Most important is proper calibration of satellite sensors during their entire time. This can be done by:

pre-launch calibration, post-launch vicarious calibration, intercalibration.

Nominal calibration involves determining the calibration of a single sensor on a single platform, and while this is considered standard prelaunch practice, it is important to calibrate the sensor in orbit as well. Vicarious calibration monitoring involves measuring a known target or comparing the satellite signal with simultaneous in situ, balloon, radiosonde, or aircraft measurements. These instruments should undergo vicarious calibration monitoring at regular intervals, regardless of on-board nominal calibration, to prevent drifting of the data over time due to orbital drift and drift in the observation time, which aliases the diurnal cycle onto the record. Satellite-to-satellite cross-calibration involves adjusting several same-generation instruments to a common baseline, and this is particularly important for long term studies, as each sensor will have slightly different baselines even if they are built to the same specifications (Ohring et al. 2002).

Without proper post-launch calibration, spurious trends in the data can occur. The problem is with selection of the objects with stable properties, used for vicarious calibration. Different objects with stable albedo are used for calibration of visible sensors, like deserts, ice caps, dense tropical vegetation or even Moon used for SeaWifs postlaunch calibration.

Differences between sensors of consecutive satellites requires intercalibraton of satellite data. Fig. 4.11 presents spectral response functions of first 2 channels of AVHRR instrument used for long term studies of vegetation anomalies (NOAA 6-16, MODIS/TERRA, SPOT and ADEOS satellites).

Stability requirements are being met, or appear to be close to being met for solar irradiance, cloud cover, cloud temperature, cloud height, atmospheric temperature, total column water vapour, ozone, ocean colour, snow cover, and sea ice measurements. Long term data sets have been assembled for many of these variables by stitching together observations from successive satellites and exploiting satellite overlap periods to account for systematic differences between successive instruments. Time series of climate

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variables have been constructed from those series. Among the problems which occur using different satellite platforms is satellite drift causing a change in the local time of the observations during each satellite’s lifetime, especially for the NOAA satellites (Fig. 4.10).

Unfortunately, for many climate variables, current-observing systems cannot meet both accuracies and stabilities. In some cases, we don’t know whether current systems are adequate, and studies are needed to answer the question.

Figure 4.10. Drift of orbital parameters

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Figure 4.11. Differences in spectral characteristics on NOAA AVHRR sensors (source: Latifovic et. al. 2004)

Figure 4.12. Comparison of albedo measurements with and without vicarious calibration SOURCE: Rao and Chen, 1995.

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Cloud cover is a major constraint on optical remote sensing, whether it is space borne, airborne or ground-based observation, particularly in cloudy regions such as the United Kingdom presented as example (Armitage et al. 2007). The impact of cloud cover on operational applications that require a time series of images can be significant. Clouds provide a major impediment to passive remote sensing at visible and infrared wavelengths. The presence of scattered clouds can cause objects of interest to be obscured, or can cast shadows which causes problems when processing images. Total cloud cover prevents any observation of the ground from space borne sensors and can severely limit data collection from airborne sensors. Cloud cover frequencies have a major effect on climatological applications of remote sensing, particularly when regular repeat data collection is required.

The example (Fig. 4.13) clearly show that some regions are continuously obscured by clouds (only a few cloud free days in year). Determination of any surface parameters or

features with use of remote sensing is highly difficult.

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Figure 4.13. Spatial distribution of cloud-free imagery frequencies for whole year and selected individual months, derived from the MODIS Cloud Mask SDS product at the 95% certainly level, across the UK in 2005 (Armitage et. al. 2007)

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4.6 Ground remote sensing techniques in phenological studies

Remote sensing techniques are not limited to satellite observations. There are many examples of successful use of both ground mounted and airborne sensors in phenological observations. For this purpose can be used cameras operating in visible and near infrared parts of spectrum, frequently separately recording different parts of this spectrum. Presented on Fig. 4.14. example show variability of recorded values by 3 WebCams operating in Red, Green and Blue parts of spectrum during half a year. Specially Green component clearly shows beginning of greenness on observed area.

Figure 4.14. Phenological observations with use of WebCams (Richardson et al., 2007)

Second example concern phenological studies with use of camera observing savannas region. Based on continuous observations of RGB components, parameter G/(R+G+B) was determined and analysed for nearly 3 years period (2006 – 2008). Annual variability and similarity between individual seasons are presented on Fig. 4.15.

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Figure 4.15. Seasonality of WebCam Vegetation Index: Oak Savanna (source: http://nature.berkeley.edu/~yryu/)

4.7 Conclusions

Remote sensing present very interesting tool extensively used in phenological studies specially when large areas are analysed – global, continental or regional studies. Application of remote sensing requires knowledge of benefits and limitation of this data source and further selection of proper data processing techniques to minimize unwanted disturbing factors. The variety of techniques are well documented in publications and even tools for data processing and phenological parameters extraction are freely available (e.g. TIMESAT).

Long term databases containing satellite data and/or processed products are freely available since beginning of 80-ties. Such a long period described by continuous series of data registered by the same instruments allow for studies on phenological changes in temporal and spatial scales. Many studies present relation between global warming and individual phenological parameters.

Satellite data and combination of space and ground observations are promising tool for further studies on inter-seasonal and inter-annual variations of plant growth factors and implementation of those data in phenological models. In particular, in global change research, accurate phenology models will become increasingly vital tools, enabling researchers to monitor and predict vegetation responses to the climatic variability.

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