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Article

Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I)

1
Department of Applied Biology, Section of Crop Science, University of Helsinki, P.O. Box 27, FIN-00014 Helsinki, Finland
2
Remote Sensing and Photogrammetry Department, The Finnish Geodetic Institute, 02431 Masala, Finland
*
Author to whom correspondence should be addressed.
Remote Sens. 2010, 2(1), 76-114; https://doi.org/10.3390/rs2010076
Submission received: 14 October 2009 / Revised: 11 November 2009 / Accepted: 11 December 2009 / Published: 29 December 2009
(This article belongs to the Special Issue Microwave Remote Sensing)

Abstract

:
During 1996–2006 the Ministry of Agriculture and Forestry in Finland, MTT Agrifood Research Finland and the Finnish Geodetic Institute carried out a joint remote sensing satellite research project. It evaluated the applicability of composite multispectral SAR and optical satellite data for cereal yield estimations in the annual crop inventory program. Three Vegetation Indices models (VGI, Infrared polynomial, NDVI and Composite multispectral SAR and NDVI) were validated to estimate cereal yield levels using solely optical and SAR satellite data (Composite Minimum Dataset). The average R2 for cereal yield (yb) was 0.627. The averaged composite SAR modeled grain yield level was 3,750 kg/ha (RMSE = 10.3%, 387 kg/ha) for high latitude spring cereals (4,018 kg/ha for spring wheat, 4,037 kg/ha for barley and 3,151 kg/ha for oats).

Graphical Abstract

1. Introduction

Remote sensing has been extensively applied in world crop production estimations by the European Union, the United States of America and the Food and Agriculture Organization (FAO) of the United Nations. The advantage of applying satellite based remote sensing data is global coverage and equally calibrated data, which enables temporal and spatial comparison over the years in monitoring areas.
New precision farming techniques in agriculture can be used more accurately to assess crop growth and soil conditions during growing seasons [1,2,3,4,5]. New high accuracy GPS satellites used for position control, e.g., the new EU-funded GALILEO system [6], and new hyperspectral spectrometers operating over a wide range of wavelengths (λ = 400–2,400 nm) provide new possibilities and tools for cereal cultivation [1,7,8,9]. Major climatic events, such as global drought periods during the forthcoming climate change can be monitored with new generation satellites of the enhanced spectral resolution [10,11] . The Joint Research Centre of EU has applied JRC-FAPAR index (Fraction of Absorbed Photosynthetically Active Radiation) for monitoring large area vegetation stress states and biomass productivity during Pan-European drought periods [12,13,14,15,16,17]. In addition, JRC-FAPAR model with Radiative Transfer models (RT) and using SeaWiFS data from the OrbView-2 (AKA SeaStar) satellite has bee applied to identify key vegetation phenology events (start, end, length) during growing season.
Recently Harrison et al. [18] used the AFRCWHEAT2 crop model for scaling-up wheat phenological development in Europe. Moulin et al. [19] applied SPOT satellite data to validate the AFRCWHEAT2 and SAIL reflectance models to estimate wheat yield and biomass NPP (Net Primary Production).
In northern latitudes the cloudiness during the growing season seriously reduces the applicability of optical spectrum satellite data . However, new generation microwave based satellite systems (e.g., COSMO-SkyMed/Italy, ENVISAT/ESA/EU, ALOS/PALSAR, Radarsat-2 /Canada, TerraSAR-X/ Germany can map images through cloud coverage and during the night-time. The Japanese ADEOS II (Miradori II) and GOSAT/IBUKI (Greenhouse gases Observing SATellite) satellites can also measure atmospheric aerosol columns, vertical greenhouse gas profiles (CO2, CH4, NO2, N2O, O3, CFC-11, CFC-12) and water vapour . Global greenhouse gas estimates can be used in large area climate change studies to assess changes in biomass NPP production and cereal yield production [19, 20]. Recently multispectral remote sensing studies have focused on integrating both optical and microwave SAR data [1,21]. Recently McNairn et al. [1] integrated multispectral classification techniques with SAR and optical data in Canada. Cereal, grass and oil crop fields in four prairie testing sites were classified with Neural Network and Decision trees methodologies.
In Finland Kuittinen [22], Kuittinen et al. [23] and Karvonen et al. [24] have reviewed the yield estimation of spring wheat and barley (Hordeum vulgare L.) combined with Landsat and SPOT data for Nordic high latitude agricultural regions. Yara Co. (formerly Kemira GrowHow Finland) [25] has established in Finland a commercial Kemira Loris™ (LOcal Resource Information System) integrated expert system for farmers. Loris utilizes GPS based precision farming techniques with optimum fertilization and cultivation practises combined with infrared aerial photographs.
During 1996–2006 the Ministry of Agriculture and Forestry in Finland (MAFF), MTT Agrifood Research Finland (MTT), the Finnish Geodetic Institute (FGI) and the Technical Research Centre of Finland (VTT, Dept. of Space Research) performed a joint remote sensing satellite research project combined with ground truth measurements to evaluate the applicability of multispectral SAR and optical satellite data for cereal yield estimations in the annual crop inventory program. Both optical and composite SAR microwave Vegetation Indices models were validated to estimate spring cereal yield levels using solely optical and SAR satellite data (Composite Minimum Dataset).
In summary, new composite multispectral optical and SAR classification and modeling techniques provide new integrated tools for cereal yield estimations in national inventory programs [1,17,26,27,28]. In Finland the Ministry of Agriculture and Forestry performs the annual ground based crop inventory sampling to estimate cereal yield production and cultivation areas on national level. New classification and modeling techniques integrated with digitized Finnish Land Parcel Identification System (FLPIS) and Integrated Administration and Control System (IACS) can be used to support Finnish national crop inventory programs [29,30]. More accurate cereal inventory estimates will also support Finnish national CAP-policy (Common Agricultural Policy) goals and objectives on EU scale. These aspects are reviewed in Part II of this publication.
The aim of the present study was to estimate actual non-potential grain yield levels for high latitude spring cereals (spring wheat, barley and oats, Avena Sativa L.) in large area field conditions in southern Finland. The cereal theoretical maximum yielding capacity is limited by environmental and vegetation stresses (e.g., drought periods, nutrient deficiencies, pathogen epidemics) during growing season in actual field growing conditions. These stress factors result to reduced non-potential baseline yield levels (yb, kg/ha) on field parcel level.
The objectives of the present study were: (i) to construct a dynamic SatPhenClass phenological classification model, which classifies both optical and SAR satellite data based on cereal actual phenological development in both vegetative and generative phases (ii) to calibrate and validate multispectral Composite Vegetation Indices (VGI) models, which integrate both phenologically pre-classified optical (Models I–II) and microwave SAR data (Composite SAR and NDVI Model III), and finally (iii) VGI models were used to estimate cereal non-potential baseline yield (yb) levels in growing zones (I–IV) in southern Finland during 1996–2006.

2. Methodology and Study Area

2.1. System Analysis and Strategy

The research applied in this study focused on utilizing multispectral optical and microwave SAR data extracted from experimental sites in southern Finland. A dynamic SatPhenClass phenological algorithm was developed to classify measured satellite data based on phenological development of different spring cereals. The pre-classified SAR and optical satellite data were used as input for VGI models. The system analysis [31,32] of this study is depicted in Figure 1.
Figure 1. System Analysis and design diagram by satellite systems and VGI models [31,38].
Figure 1. System Analysis and design diagram by satellite systems and VGI models [31,38].
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The analysis strategy applied in this study is depicted in phases I–XI. Phases I–II (Section 2.2) illustrate satellite and ground truth data pool consisting of solely SAR and optical satellite data (Composite SAR and Optical Minimum Datasets 1996–2006). Correspondingly phase III (Section 2.4) explains geometric and radiometric calibration of SAR and optical data. Phases IV–V (Section 2.3.1, Section 3.2.1) illustrate SatPhenClass phenological classification model and LAI and ETS ground truth sampling methodology. Phases VI–VII (Section 2.3.2, Section 3.2.2) explain calibration of VGI models using Minimum Datasets without extensive agrometeorological ground truth data required by conventional dynamic crop models [33,34,35,36]. Phases VIII–IX (Section 2.5, Section 3.3) explain the validation of VGI models. Phases X–XI(a,b) (Section 3.2, Section 3.3) explain the estimation of baseline grain yield levels (yb) for different spring cereals resulting outputs for SAR & NDVI models (Phase XI(a), Part I) and for detailed optical VGI models (Phase XI(b), Part II).
In addition, Phase XI(c) scheme presents an alternative cereal specific LAI-bridge coupling system [23] reviewed in Part II. LAI-bridge coupling system utilizes optical satellite data as input data for dynamic crop models. Hodges [33] and Bowen [36] have reviewed the principles of dynamic crop models explaining phenological development and crop physiological processes (root and soil systems, photosynthesis, respiration and translocation of assimilates to heads). Dynamic crop models require extensive agrometeorological and phenological ground truth data for daily integration of biomass and yield accumulation [34,35].
In LAI-bridge coupling system, the GEMI-index (Global Environment Monitoring Index, [37]) is applied to estimate cereal LAI development (LAIsatellite) from optical reflectance data during growing season. A similar model, VGI GEMI (model IV) is used in this study in Part II. The LAI-bridge coupling method applies iterative Kalman filter (KF) algorithm [38] commonly applied in System Control theory, Markov chains and Bayesian estimations. The GEMI based LAI estimates (LAISatellite) and LAI values estimated by crop models (LAICropModel) are corrected in Kalman filter iterations with LAISatellite and LAICropModel variances to obtain optimized LAI (Final) values for non-potential baseline yield (yb) calculations. LAI-bridge coupling method was originally developed by Karvonen et al. [24] using SPOT and Landsat data as input for PotCropF [24] dynamic crop model used in Finnish cereal yield estimations. Later on, Kuittinen et al. [23] applied LAI-bridge coupling system with GEMI index by using SPOT and NOAA optical data as input for CropWatN [39] and WOFOST [40] crop models.
In this study (Part I) three Vegetation Indices (VGI) models were calibrated and validated with optical (NDVI) and microwave SAR satellite and ground truth (1996–2006) data. The satellite data were measured in five different experimental locations in southern Finland (Table 1, Figure 2). The detailed analysis strategy (Figure 1, Figure 3a,b) consisted of following specific procedures:
(a) Pre-classification of SAR and optical data with the SatPhenClass phenological classification algorithm for spring cereals (Figure 3a, b). The SatPhenClass algorithm exploits BBCH [41,42] and Zadoks [43] growth scales (Table 2) with four phenological classes for spring cereals (ap: BBCH 0–12, bp: BBCH 12–50, cp: BBCH 50–90, dp: BBCH > 90). Class ap corresponds to development period between sowing and two leaf stage with double ridge formation. Class bp corresponds to period between two leaf stage and ear emergence with maximum Leaf Area Index (LAImax) exposure with fully closed canopy. Class cp corresponds to period between ear emergence and anthesis with grain filling until full maturity. Finally, class dp corresponds to senescence and post-harvest phases.
(b) Calibration of Composite Vegetation Indices (VGI) models (models I–III in Part I, models IV–V in Part II) with pre-classified C-band SAR and optical data. Both linear and non-linear polynomial response functions in VGI models were applied for cereal baseline yield (yb) estimations.
(c) Validation of each calibrated VGI model using two independent data sources separately: (i) the MTT Agrifood Research Finland official variety trial data (1996–2006) and (ii) the MAFF (Ministry of Agriculture and Forestry in Finland) annual crop yield inventory data (1996–2006). MTT and MAFF validation datasets take into account temporal and spatial variation between years and locations.
(d) Estimating non-potential baseline grain yield levels (yb) for different spring cereals in actual field growing conditions in Southern Finland with calibrated VGI models and optical and SAR data.
Table 1. Sensors, datasets, growing zones, locations and soil classification [44].
Table 1. Sensors, datasets, growing zones, locations and soil classification [44].
Sensor
Type (1)
Publ.
part
Data
set
Growing
Zone (2)
MAFF Agriculture Advisory
Centre
(Figure 2 and Figure 4)
Location
(Figure 2)
Coarse soils (%)Clay soils (%)Organic & mould
(%)
Gravel with
coarse sand
Fine sandCoarse sandSiltSandy claySilt clayGyttja clay 3)
SAR & OpticalI/II1.1III (IV)Etelä-PohjanmaaLapua 23° 10' E,
62° 50' N
11.219.823.5
I/II1.2III (IV)Etelä-
Pohjanmaa
Seinäjoki
23° 10' E,
62° 50' N
15.510.120.525.3
I/II1.3III (IV)Etelä-
Pohjanmaa
Ilmajoki
23° 10' E,
62° 50' N
13.212.03.921.4
I/II2.1INylands
Svenska
Porvoo
25° 50' E,
60° 50' N
6.217.88.538.010.9
OpticalII3.1INylands
Svenska
Kirkkonummi
24° 30'E, 60° 10’N
9.427.58.541.45.4
II3.2IIHämeJokioinen
23° 50' E,
60° 50' N
(Kuuma
Exp. Area)
56.07.015.17.7
(70–80)
II3.3IIHämeMellilä
22° 20'E,
60° 50' N
7.48.313.228.636.0
(e) (1) For abbreviations refer to Table 7, Appendix B. (2) Growing zones (I–IV) are depicted in Figure 4a. (3) Gyttja clay contains peat and mud fractions.
Figure 2. Satellite and ground truth measurement locations in Finland (Original Data (C) NASA, visibleearth.nasa.gov/).
Figure 2. Satellite and ground truth measurement locations in Finland (Original Data (C) NASA, visibleearth.nasa.gov/).
Remotesensing 02 00076 g002

2.2. Overview of Satellite and Ground Truth Sites

A general overview of satellite and ground truth experimental sites is displayed in Figure 2. A detailed description of the experimental sites is given by Kuittinen et al. [23]. Table 1 depicts the microwave SAR and optical data sources with publication division (I–II), growing zones and Agriculture Advisory Centres in Finland. A general scheme of the measuring satellite systems and calibration parameters used in this study is displayed in Tables 12 and 13 (Appendix C). The abbreviations applied in this study are given in Table 7 (Appendix B).
The satellite measurement and ground truth experiment sites (Lapua, Kirkkonummi , Jokioinen , Mellilä and Porvoo) with corresponding soil classifications [44] are depicted in Table 1 and Table 9 (Appendix B). The Lapua experimental site, consisting of Lapua, Ilmajoki and Seinäjoki experimental sites (Figure 2), was located near the Gulf of Bothnia on sandy clay type soils. Respectively Porvoo and Kirkkonummi experimental sites were located close to the Baltic Sea with humid marine climatic conditions. Jokioinen and Mellilä sites were located on inland plateaus with mainly clay type soils.

2.3. Calibration

2.3.1. Phenological classification algorithm (SatPhenClass) for satellite data

The calibrated optical reflectance and microwave SAR backscattering data (Table 11, Table 12 and Table 13, Appendix B.) measured from different satellite systems was classified with a phenological classification algorithm (SatPhenClass) for spring cereals. The detailed SatPhenClass classification model and VGI models applied in Parts I and II (detailed optical) are depicted in Figure 3a,b. The SatPhenClass classified satellite data were used in calibration of the VGI yield models (Figure 1). Figure 3a depicts the simplified ’Black Box’ diagram and Figure 3b depicts corresponding components on system analysis level. The pseudocode for the SatPhenClass-algorithm is presented in Appendix D.
Figure 3. (a) Simplified Black Box diagram for SatPhenClass satellite data classification and VGI models. (b) Satellite data classification diagram for spring cereals, ETS with modifications after Kontturi and Mukula & Rantanen [45,46].
Figure 3. (a) Simplified Black Box diagram for SatPhenClass satellite data classification and VGI models. (b) Satellite data classification diagram for spring cereals, ETS with modifications after Kontturi and Mukula & Rantanen [45,46].
Remotesensing 02 00076 g003
The radiometric and geometric corrected surface reflectance (rf) and backscattering (σ 0) data were used as input data for the dynamic SatPhenClass classification model using Julian date (DOY) as a driving variable. Figure 3a,b depict SatPhenClass-model diagram for spring cereal (wheat, barley and oats) classification using optical and microwave satellite data.

2.3.1.1. SatPhenClass Classification Categories

SatPhenClass algorithm yields four classification classes (ap, bp cp dp, Table 2) based on spring cereal DVS (Development stage) and corresponding satellite measurement Julian date [39,45]. The SatPhenClass classification model developed in this project utilizes BBCH numerical growth scale [41,42]. In addition, Zadoks, Feekes and Haun growth scales were used as scaling references [43,47]. The SatPhenClass model contains four BBCH threshold values for phenological classification: 0, 12, 50, and 90 (Figure 3b, phases VII–X, [43]).
Table 2. SatPhenClass Effective classifiers: Effective temperature sum (ETS) and LAImax values for high latitude cereals on growing zones I–IV [23,45].
Table 2. SatPhenClass Effective classifiers: Effective temperature sum (ETS) and LAImax values for high latitude cereals on growing zones I–IV [23,45].
Development period [35,50,51]BBCH
DVS
Class (Range) (1)
Estimated mean
Julian DOY
(JDay) (2)
ETS(Tb) minimum
requirements (dd) in
cultivation zones I–IV (3)
Observed
LAImax
(Li-Cor 2000)
X ± sd
Sowing-two-leaf &
double ridge stages
ap
(0–12)
165, 175Swh Tb 5°: 130 (e)–140 (l)
Brl Tb 5°: 130 (e)–140 (l)
Oats Tb 5°: 130 (e)–140(l)
Swh 2.12 ± 0.46
Brl 1.85 ± 0.89
Oats 1.54 ±0.65
Two leaf-ear emergence,
LAImax exposure with fully closed canopy structures
bp
(12–50)
225480 (Tb 4°)
Swh Tb 5°: 450 (e)–460 (l)
Brl Tb 5°: 800 (e)–950 (l)
Oats Tb 5°: 370 (e)–400(l)
Swh 4.27 ± 0.84
Brl 4.05 ± 0.58
Oats 3.44 ± 0.78
Ear emergence, anthesis-maturity, grain fillingcp
(50–90)
255399 (Tb 8°)Swh 3.87 ± 0.24
Brl 3.24 ± 0.87
Oats 2.14 ± 0.25
Senescence, post-harvest
phase
dp
(>90)
> 255
Sowing-maturityap, bp, cp165–2551050 ± 30, Tb
Swh Tb 5°: 970 (e)–1040 (l)
Brl Tb 5°: 800 (e)–950 (l)
Oats Tb 5°: 900 (e)–990(l)
(1) BBCH phenological development scale (Figure 3a, b) [41,42,43] (2) Mean values for JDayETS and JDayLAI (Figure 3b, sections IV–V) (3) Tb [°C] threshold baseline temperature (e)—Finnish early cultivars, (l)—Finnish late cultivars.
In class ap (Figure 3b, phase VII), when the BBCH value is below 12, the cereal plant is in vegetative phase between germination, two-leaf and double ridge stages.
In class bp (Figure 3b, phase VIII), the BBCH value is between 12 and 50. When BBCH value is between 12 and 40, the cereal leaf development and stem elongation occur. The booting of the uppermost flag leaf and the initialization of heading occur, when BBCH value is between 40 and 50. In addition, the Leaf Area Index maximum is usually observed in class bp.
In class cp (Figure 3b, phase IX), the BBCH value is between 50 and 90. The cereal inflorescence emergence occurs, when BBCH values are between 50 and 60. When the BBCH value is above 60, the cereal transition to generative grain filling phase begins with the initialization of anthesis and flowering. When the BBCH value is between 70 and 80, the development of grains in the head occurs with early milk, hard dough and fully ripened phases. Finally in class dp (Figure 3b, phase X), when the BBCH value is above 90, the cereal is in senescence phase. On the average, in Finnish long day growing conditions, the phenological class ap corresponds for May, class bp for June, class cp for July and dp for August and September.
The SatPhenClass phenological classification model enabled the temporal synchronization of different optical and microwave satellite data along with spring cereal phenological development. The classified optical and microwave SAR data can in turn be used as input for cereal VGI (Vegetation Indices) or dynamic crops models, like CERES-Wheat model [33,34,35,36,48]. In CERES-Wheat phenological submodel, the genotype x cultivar variation is taken into account by using genetic coefficients controlling cultivar photoperiodism, vernalization and phyllochron development. With SatPhenClass submodel, the cultivar and genotype level effects for phenological development were assumed to be constant. The genetic coefficients for high latitude Nordic spring cereals have been previously calibrated and assessed by Laurila [35] and later reviewed by Saarikko [49]. In Sweden Åfors et al. [50] have reviewed the use phenological growth scales for cereals grown in high latitude Nordic growing conditions. Recently Peltonen-Sainio et al. [51] revised the Feekes and Zadoks cereal growth scaling [43,47] for Finnish long day growing conditions.

2.3.1.2 LAI and ETS Ground Truth Sampling Methodology for the SatPhenClass Algorithm

During growing season crop phenological observations (Julian Day of Year, DOY), LAI measurements (Leaf Area Index), biomass, growing density and other ground truth parameters were recorded simultaneously with satellite measurement dates (JDaySAT, Table 9, Appendix B) in the experimental sites (Table 1). Kuittinen et al. [23] presented a detailed description for the ground truth sampling methodology. Portable Li-Cor 2000 Plant Canopy Analyzer [52] was used to measure spring cereal LAI values with corresponding Julian dates (JDayLAI) during the growing period.
The sampling methodology on experimental sites was applied after Kuittinen et al. [23] and is displayed in Figure 6 (Appendix A). Sampling plots were randomly selected from the field parcels in experimental areas (Table 1). Sampling plots were adjusted to Landsat pixel size (30 × 30 m). In each plot inside selected parcels, the LAI and biomass were measured using 10 points. The distance between the points varied between 20 and 30 m depending on the size of the parcel. The LAI value of each point was the mean of 9 LAI measurements around the original point integrated automatically by the Li-Cor 2000 Plant Canopy Analyzer. The average plant biomass on the field parcel was estimated by selecting randomly 20 plant samples. Late on, the dry-weights of the dried plant samples were measured. The growing density was determined by calculating the number of plants from the randomly selected 1 meter long sowing row with two replicates. After the harvest in the autumn, the final cereal grain yield levels (kg/ha corrected to 15% moisture content) were estimated by the farmers in the experimental areas. Yield samples were measured from the granary silos.
The Finnish Meteorological Institute provided Cumulative Effective Temperature Sum (ETS, Tb = 5 °C) measurements with corresponding Julian dates (JDayETS, Figure 4b). Kriging interpolation method was applied to estimate ETS values in experimental sites from the nearest weather stations (Figure 4c).
Figure 4. (a) Growing zones (I–V) and MTT Experimental Station locations in Finland, (b) ETS (Tb 5 °C) cumulative isolines and (c) Meteorological Weather Stations in Finland (Original Data (C) MTT Agrifood Research Finland, Finnish Meteorological Institute and Finfood Finland).
Figure 4. (a) Growing zones (I–V) and MTT Experimental Station locations in Finland, (b) ETS (Tb 5 °C) cumulative isolines and (c) Meteorological Weather Stations in Finland (Original Data (C) MTT Agrifood Research Finland, Finnish Meteorological Institute and Finfood Finland).
Remotesensing 02 00076 g004
The cumulative ETS(Tb =5 °C) and measured LAI values were used as categorical classifiers in the SatPhenClass algorithm [23,24]. The Julian dates of the measured satellite data (JDaySat) were compared with the estimated Julian dates for spring cereals using the SatPhenClass algorithm (Figure 3b, sections II–VI: LAI development and ETS accumulation). The LAImax-values for high latitude spring cereals (Table 2) with corresponding Julian dates (JDayLAI, Figure 3b, section V) were used to estimate the transition date from generative (class bp) to grain filling phase (class cp). The LAImax for spring cereals varied between 3.44 and 4.27 in bp phase and between 2.14 and 3.87 in cp phase. Table 2 depicts the cereal ETS temperature sum requirements in Finnish growing conditions for different phenological classes (ap–dp) with corresponding threshold temperatures (Tb) and typical Julian dates (JDayETS, Figure 3b, section IV). In growing zones I–IV, the ETS requirements for a spring cereals [53,54] vary over years and locations (Figure 4a). In Nordic high latitude long day growing conditions the growing season is generally defined as the period when mean air temperature exceeds +5 °C (Tb 5 °C, Table 2 [45]). The average thermal time requirement of 1050 ± 30° degree-days (dd, Tb 5 °C) from sowing to yellow ripening stage is considered adequate for spring cereal ideotypes grown in growing zones I–IV [23,55,56]. The corresponding averaged Finnish ETS isolines (Tb 5 °C) are presented in Figure 4b. According to Hakala [53], Hakala et al. [54] typical Julian dates for spring cereals in southern Finland are: Sowing on average on 14–16th of May (DOY 164–166), emergence 25–30th of May (DOY 175–180), anthesis on 5–15th of July (DOY 220–225), full maturity on 5–15th of August (250–255).
In the final stage the SatPhenClass algorithm (Figure 3b, sections IV–V: ETS and LAI Classifiers) compares the ETS and LAI classification results. The classification result is accepted only, if both submodels reach concord (Figure 3b, section VI: ETS and LAI Evaluation). The measured optical or microwave satellite data with corresponding Julian date (JDaySat) is finally set to corresponding cereal phenological class ap-dp shown in Figure 3b (graphical plot in left lower corner). The phenologically classified optical and SAR satellite data can be used as input for optical VGI infrared and NDVI models (Table 3, Figure 3b, XI: Output VGII–II) and for Composite NDVI and SAR model (Figure 3b, XII: Output VGIIII).
Table 3. Composite Vegetation Indices Models (I–III) with dependent baseline yield (yb) and independent variables [23,57,58,59](1),(2).
Table 3. Composite Vegetation Indices Models (I–III) with dependent baseline yield (yb) and independent variables [23,57,58,59](1),(2).
Model
(Type)
Model equation,
Table 12, Appendix C (1), (2)
Independent variables (2)Model name, description of derived satellite parameters used in regression equations
I
Optical
yb= rf3(ap,bp,cp) + rf4(ap,bp,cp)rf3(ap,bp,cp) rf4(ap,bp,cp)Polynomial infrared model
Calibrated reflectance (rfchannel) values for infrared (channel = 3) and near infrared (channel=4) during growing season.
- ap,bp,cp classes correspond on average to Zadoks crop phenological growth scale with cereals: ap: 0–12, bp: 12–50,
cp: 50–90, dp: >90 [43]. (2)
II
Optical
yb = NDVI (ap,bp,cp)
NDVI = (NIR(ap,bp,cp) RED(ap,bp,cp))/
(NIR(ap,bp,cp) + RED(ap,bp,cp))
NDVI (ap,bp,cp)Normalized Difference Vegetation Index (NDVI) model [57,58,59] (2).
-
Ratio between near infrared and infrared channel surface reflectance (rf) values
-
rf3 infrared (λ = 0.63–0.69 µm)
-
rf4 near infrared (λ = 0.76–0.90 µm)
-
Landsat NDVI = (rf4(ap,bp,cp) – rf3(ap,bp,cp))/(rf4(ap,bp,cp) + rf3(ap,bp,cp)) ,
-
SPOT NDVI = (rf3(ap,bp,cp) – rf2(ap,bp,cp))/(rf3(ap,bp,cp) + Rf2(ap,bp,cp))
III
SAR + optical
yb = NDVI (ap,bp,cp ) +
σ0 HH5GHz,(ap,bp,cp,dp) +
σ0 VV5GHz,(ap,bp,cp,dp) +
σ0 HV5GHz,(ap,bp,cp,dp) +
σ0 VH5GHz,(ap,bp,cp,dp)
NDVI (ap,bp,cp),
σ0 5GHz,(ap,bp,cp, dp)
Composite multispectral SAR and NDVI model for spring cereals (swh, oats, barley) using NDVI reflectance and microwave backscattering (σ0, f = 5.3, 5.4 GHz) data with horizontal (HH), vertical (VV), and cross-polarization (HV, VH) levels. Instruments: HUTSCAT Scatterometer, ERS/SAR, Radarsat/SAR, Envisat/ASAR (Table 10, [60,61,62,63]).
(1) Model equations presented in Table 12 (Appendix C), for abbreviations refer to Table 7. (2) Independent variables classified with SatPhenClass-algorithm (Figure 3a,b).

2.3.2. Composite Vegetation Indices (VGI) models I–III

Three different Composite Vegetation Indices (VGI) models (I–III, Table 3) were calibrated with linear and non-linear polynomial regression models (Equations 1–2, Appendix C). Calibrated optical reflectance data were used for Models I–II (Models I-Infrared, II-NDVI) and composite SAR and optical data for the Model III (Composite NDVI and SAR). The NDVI optical and SAR satellite data were pre-classified with SatPhenClass model into the vegetative pre-anthesis phenological classes (ap, bp) and into the generative post-anthesis and senescence classes (cp, dp, Figure 3b, phases IV–VI).
The VGI models (I–III) were calibrated with SAS© linear stepwise REG/Stepwise (STW) regression algorithm (Equation 1, Appendix C, [57,58]) and with non-linear polynomial RSREG regression algorithm (Equation 2, Appendix C) by using SAR and optical satellite data (Table 12, Appendix C). The non-linear RSREG algorithm takes into account (i) the general linear component, (ii) the polynomial quadratic response of non-linear variation (iii) the cross product effect of dependent variables and (iv) the total model component (Table 5). The statistical significance levels are given in Table 8 (Appendix B).
The polynomial infrared model (I) is a polynomial linear regression model, which estimates baseline yield production (yb, kg/ha) with infra red (rf3) and near infrared (rf4) channels. Respectively in VGI model II the NDVI Index [57,58,59] and in Model III the composite SAR and optical NDVI components were applied.

2.4. Calibration of Data

2.4.1. Microwave SAR backscattering (σ0 ) and optical reflectance data

The primary combined microwave SAR and optical dataset pool (Table 1) consisted of (i) microwave SAR/ASAR (ERS, Radarsat, Envisat) and (ii) optical (Landsat and SPOT) datasets from the Etelä-Pohjanmaa (Lapua, Seinäjoki and Ilmajoki sites), Nylands Svenska (Kirkkonummi) and Häme (Jokioinen) Agricultural Advisory and Rural Development Centres 1996–2006 (Table 1, Figure 2, [27,28]). The satellite measurement dates (JDaySAT) in ground truth measurement sites (Lapua, Kirkkonummi, Jokioinen, Mellilä and Porvoo) are depicted in Table 9 (Appendix B). The Technical Research Centre of Finland (VTT, Dept. of Space Research) and the Finnish Geodetic Institute digitized and performed the classification of field parcels from the satellite images [23,27,28]. The total cultivation area (Seinäjoki, Ilmajoki and Lapua, Mellilä, Porvoo, Kirkkonummi, Jokioinen) estimated from satellite images was 1,253 ha for all spring cereal crops.
The composite SAR and optical reflectance datasets were used for spring cereal yield modeling under actual non-potential field conditions. Water stress limited significantly cereal yield accumulation on clay type soils in Kuuma and Porvoo experimental areas during growing season (Table 1, Figure 2).
The SAR calibration details for ERS, Radarsat and Envisat data have been previously published by Karjalainen and et al. [27,28] and by Matikainen et al. [26]. The set of Radarsat-1 images consisted of scenes acquired with various beam modes of Radarsat-1 satellite. The Radarsat incidence look angles varied between 39–44° in the experimental sites (Table 1). Envisat ASAR images were acquired in dual-polarization mode, the VV and VH polarization channels were used in calibration of VGI models (Table 11 and Table 12, Appendix B).
In addition, two older auxiliary optical [23] and microwave (HUTSCAT scatterometer, Table 10, Appendix B) [61,63] datasets consisting from Mellilä and Porvoo sites (Nylands Svenska Agric. Advisory Centre, Figure 2) were used in calibration of SatPhenClass phenology and VGI models (I–III, Table 3). The Porvoo experimental site (Kullogård experimental farm, Figure 5) was used for both optical Landsat and SPOT and microwave HUTSCAT measurements for spring cereals.
Figure 5. Landsat/TM satellite image from the Porvoo field experimental area. The image location was used for optical Landsat, SPOT and microwave HUTSCAT measurements. Porvoo River on the right corner of the image.
Figure 5. Landsat/TM satellite image from the Porvoo field experimental area. The image location was used for optical Landsat, SPOT and microwave HUTSCAT measurements. Porvoo River on the right corner of the image.
Remotesensing 02 00076 g005
The helicopter mounted HUTSCAT (Helsinki Univ. of Technology, Lab. Of Space Technology [63]) is a monostatic scatterometer system measuring with C- (f = 5.4 GHz) and X-bands (f = 9.8 GHz) with horizontal (HH), vertical (VV) and cross-polarizations levels (VH, HV). In this study only C-band data from HUTSCAT was used for the calibration of VGI models. Both the transmitter and receiver are mounted in the same flight rack under the helicopter [63]. The technical configuration of HUTSCAT resembles SAR/ASAR spectrometers currently onboard ERS, Radarsat and ENVISAT satellites (Table 10, Appendix B). The technical configuration of the monostatic HUTSCAT scatterometer has been previously published by Hallikainen et al. [61] and Koskinen et al. [62]. The HUTSCAT calibration details have been published Hyyppä et al. [63].
The Finnish Geodetic Institute applied ortho-rectification and radiometric correction for the optical Landsat and SPOT satellite data (Table 1, [23]). The optical calibration procedure for different Vegetation Indices models was originally presented by Price [57] and later revised and utilized for cereals by Flowers et al. [64], Prasad et al. [65], Maas [66,67,68] and Maas and Dunlap [69]. Table 11 (Appendix B) presents calibration parameter values applied in optical calibration equations 3–5 (Appendix C), which convert binary digital count (dc) values from satellite optical sensors into surface reflectance values (rf). The Landsat and SPOT raw data coded as 8 bit binary values (digital counts, dc) were converted to integer values between 0–255 (surface reflectance, rf ) with higher values indicating higher reflectance radiation to satellite sensors from ground cover and atmosphere. The satellite digital count-value (dc) to spectral radiance (R) transformation was performed after Equation 3. The spectral radiance —values were transformed into surface reflectance (rf) by means of solar equivalent radiance (S) (Equation 4). In addition, the sun zenith-angle during the observation time was taken into account (Equation 5).
The calibrated SAR backscattering and ortho-rectified and radiometric corrected optical reflectance data were applied in the calibration of VGI models. Total of 18 SAR and cloud free optical satellite images (Landsat5 /TM and SPOT /HRV2) were obtained during the 1996–2006 campaign. Images were temporarily well dispersed along the growing season between May, June, July and August (Table 11 and Table 12, Appendix C). Additional details of the optical calibration procedure and corresponding calibration parameters are presented in Electronic Supplementary Information Appendix (ESI).

2.5. Validation

2.5.1. Validation of Composite Vegetation Indices (VGI) Models

After the calibration procedure for VGI Composite models (I–III)), the validation procedure was applied to test the VGI Composite models for cereal non-potential baseline grain yield (yb, kg/ha) predictions. Yield differences were calculated as absolute values (kg/ha) and per cent (%) differences vs. observed MAFF annual yield inventory [29,70] and MTT Official Variety Trial grain yield estimates in corresponding sites and years [55,56,71]. MAFF and MTT validation datasets with over 3º latitude variation were used to assess cereal baseline yield variation (yb) in growing zones I–IV (Figure 4). In addition, the Polynomial Infrared and NDVI models (Models I, II) were analyzed with soil*species and soil*cultivar covariance categories. The MTT Official Variety Trial yield data for one Finnish malting barley cultivar (cv. Inari) and for two Finnish spring wheat cultivars (cv. Manu, Satu) in cultivation zones I–IV were used in analyzing soil*cultivar covariances (Table 1, Figure 4a).

2.5.2. Validation Data

The calibrated VGI yield models (I–III) were validated with two independent cereal yield datasets consisting of (i) the Ministry of Agriculture and Forestry Official Inventory Statistics 1994–2006 data for cereal yields [29,30] and (ii) the MTT Agrifood Research Finland Official Variety trial results 1994–2006 (Figure 4, [56,71]). The MAFF validation dataset consisted yield inventory statistics from Etelä Pohjanmaa (Averaged III–IV Zone) Agricultural Advisory Centre. The MTT Official Variety Trial dataset consisted of yield trials from the Ylistaro (Zone III) and Ruukki (Zone IV) MTT Experimental Stations Averaged to Zone III–IV. Zones III–IV were pooled together because the satellite and ground truth measurement sites in Lapua, Seinäjoki and Ilmajoki were located in the middle of 1,100 ETS(Tb = 5 °C) isoline (Figure 4b).

3. Results

3.1. Cereal Canopy Soil Backscattering Covariance Variation

The SAR (Radarsat, ERS), ASAR (Envisat) and HUTSCAT backscattering variance (σ0) between different spring cereals in Lapua, Seinäjoki and Porvoo experimental sites is presented in Table 4. In addition, SAR backscattering horizontal and vertical polarization levels (HH, VV) and the cross-polarization (VH/HV) of the monostatic HUTSCAT scatterometer are presented. The Radarsat horizontal (HH) backscattering (σ0) variation between malt (MBrl) and fodder barley (FBrl) is presented in Radarsat sensor column. Table 4 depicts cereal canopy*soil backscattering covariance during generative phenological phases cp and dp (anthesis, grain full maturity and canopy senescence, Table 2) grown on clay, coarse and fine sand type soils.
Both the soil type and soil*cereal canopy covariances expressed extensive variation with SAR backscattering signal (σ 0) on clay type soils, which were dominant in experimental areas in zones I–IV (Table 1). According to Henderson & Lewis [60] the vertically oriented components (VV) with cereals, especially the stems, interact effectively with vertically polarized signals resulting increased backscattering attenuation. Oat species with more planophile canopy and panicle inflorescence structures have different backscattering polarization properties from corresponding properties of wheat and barley, which have more erectophile head and canopy structures [23,28,72].
On sandy clay soils the HUTSCAT backscattering signal (σ0) varied between –6 dB and –29 dB. On fine and coarse sands the Envisat and ERS SAR signal amplitude varied between –17 dB (Envisat in VV, VH dual polarization mode) and –8 dB (ERS) respectively. In the Porvoo experimental area the topsoil (5–10 cm) and the subsoil (<10 cm) consisted of fine and coarse sandy alluvium deposits from the Porvoo river (Figure 5) and gyttja clays containing minor fractions of organic compounds (mould, peat and mud) and silt. In Lapua and Seinäjoki areas the major soil type was sandy clay with minor fractions of organic mould and humus fractions (Table 1).
The SAR backscattering signals indicated extensive topsoil*cereal canopy layer covariance interaction especially after the anthesis (phenological stage cp) and during grain filling, yellow ripening and canopy senescence (dp). When analyzing the variation between different SAR sensors the HUTSCAT backscattering signal (f = 5.4 GHz, Table 10, Appendix B) varied with wheat, barley and oat cereals between –29 dB (oats* cp*VH*coarse sandy clay) and –6 dB (wheat*cp*VV*coarse and fine sand clay, barley*cp*VV*fine sand and gyttja clay). The ERS signal (f = 5.3 GHz) varied between –8 dB (barley*dp*VH*fine and coarse sandy clay) and –10 dB (barley*cp*VV* sandy clay, oats*cp*VV*sandy clay). The Radarsat signal (f = 5.3 GHz) varied between –13 dB (malt barley*cp*HH* fine and coarse sandy clay) and –10 dB (wheat*dp*HH*sandy clay). The Envisat signal (f = 5.3 GHz) varied between—16 dB (oats*dp*VH* fine and coarse sandy clay) and –9 dB (barley*dp*VV* sandy clay).
The helicopter mounted HUTSCAT signal amplitude varied more significantly when compared to other backscattering signals (Envisat, ERS, Radarsat) measured on clay and sandy soils. In addition, the HUTSCAT C-band measurement frequency was slightly higher (f=5.4 GHz) than with Envisat, ERS and Radarsat (f = 5.3 GHz).
Table 4. SAR backscattering variation (σ 0, f=5.3–5.4 GHz) on sand and clay type soils with different polarization levels during anthesis (cp), full grain maturity and canopy senescence (dp). (1),(2)
Table 4. SAR backscattering variation (σ 0, f=5.3–5.4 GHz) on sand and clay type soils with different polarization levels during anthesis (cp), full grain maturity and canopy senescence (dp). (1),(2)
SARS
ensor
Main
soil
Type
S.wheat
[dB] (X±Sd)
Barley 1)
[dB] (X±Sd)
Oats
[dB] (X ± Sd)
DVS3)cp
(July)
dp
(Aug.)
cp
(July)
dp
(Aug.)
cp
(July)
dp
(Aug.)
HUT
SCAT
Scattero
Meter
Sandy
clay
VV/
HH
VH/HV 3)VV/
HH
VH/
HV 3)
VV/
HH
VH/
HV 3)
VV/
HH
VH/
HV 3)
VV/
HH
VH/
HV 3)
VV/
HH
VH/
HV 3)
–6.40
(±0.84)/
–16.21
(±0.32)
–20.26
(±0.35)/
–20.16
(±0.14)
–10.53
(±0.24)/
–10.09
(±0.23)
–20.71
(±0.15)/
–20.05
(±0.13)
–5.78
(±1.79)/
–12.27
(±1.72)
–22.85
(±0.90)/
–22.97
(±0.87)
–11.26
(±1.79)/
–10.21
(±1.72)
–22.54
(±0.67)/
–22.48
(±0.49)
–9.75
(±1.58)/
–18.52
(±1.87)
–21.09
(±0.90)/
–21.31
(±0.73)
–13.39
(±1.47)/
–11.62
(±1.97)
–21.54
(±0.76)/
–21.17
(±0.58)
Envisat
ASAR
Fine,
coarse
sand
VVVHVVVHVVVHVVVHVVVHVVVH
–12.47
(±1.17)
–17.52
(±0.95)
–10.09
(±1.41)
–14.81
(±1.18)
–11.45
(±2.17)
–17.16
(±1.59)
–9.45
(±1.38)
–14.59
(±1.51)
–11.08
(±2.51)
–17.22
(±1.47)
–11.37
(±1.37)
–16.25
(±1.87)
Radarsat SAR
(1),(2)
Fine,
coarse
sand
HHHHMBrl
HH 2)
FBrl
HH 2)
MBrl
HH 2)
FBrl
HH 2)
HHHH
–12.27
(±2.28)
–10.48
(±0.79)
–13.88
(±1.25)
–12.57
(±1.58)
–12.28
(±2.27)
–11.39
(±1.28)
–12.64
(±1.65)
–11.23
(±0.97)
ERS
SAR
Fine,
coarse
sand
VVVVVVVV
–10.24
(±1.17)
–8.12
(±2.11)
–10.24
(±1.28
–9.28
(±1.02)
(1)MBrl- Malt barley, FBrl—feed barley (2) Soil types in Table 1 (3) HUTSCAT σ0 VH/HV cross-polarization
When analyzing the cereal variation for the wheat, the SAR signal varied between –25 dB (HUTSCAT *cp*VH* fine and coarse sandy clay) and –6 dB (HUTSCAT *dp*VH* sandy clay). The wheat VH cross-polarization amplitude was higher when compared with VV vertical levels both in anthesis (cp) and full maturity (dp) on clay and sandy soils. The wheat HH horizontal signal amplitude was higher in stage cp compared to dp.
The oat SAR amplitude varied between –29 dB (HUTSCAT *cp*VH* sandy clay) and –6 dB (HUTSCAT *dp*VH* sandy clay). The oats VH cross-polarization amplitude was higher when compared with VV vertical levels both in anthesis (cp) and full maturity (dp) on clay and sandy soils. Both the oats horizontal and vertical (HH, VV) signal amplitudes were higher in anthesis stage (cp) compared to maturity stage (dp).
With barley the SAR amplitude varied between –22 dB (HUTSCAT *cp*VH* sandy clay) and –5 dB (HUTSCAT *cp*VV* sandy clay). Especially the barley experimental plots with clay and peat topsoil layers in Porvoo (Table 1, Figure 2) were affected by excessive water from melting snow in the beginning of the growing season (ap) causing the topsoil to reach the full soil water capacity. Later on during mid-summer, drought periods especially in generative phases (cp, dp), caused the soil moisture content to recede close to wilting point. These factors affected the translocation of assimilates to the head with a reduction effect on final grain filling. The barley VH cross-polarization amplitude was higher when compared with VV vertical levels both in anthesis (cp) and full maturity (dp) on clay and sandy soils. Both the barley horizontal (HH) and vertical (VV) σ 0 signal amplitudes were higher in anthesis stage (cp) than in full maturity stage (dp). The malt barley (MBrl) σ 0 amplitude with Radarsat varied between –13 dB (malt barley*cp*HH* fine and coarse sandy clay) and –12 dB (malt barley*dp*HH*sandy clay). Respectively with fodder barley (FBrl) the Radarsat signal varied between—12 dB (feed barley*cp*HH* fine and coarse sandy clay) and –11 dB (feed barley*dp*HH* sandy clay).

3.2. Calibration Results

3.2.1. Testing the SatPhenClass Phenological Model Accuracy

The SatPhenClass classification results for spring cereals are depicted in Table 5. The SatPhenClass classification accuracy for spring cereals varied between 61% and 89% with corresponding classification error ranging between phenological classes (ap 21–28%, bp 11–22%, cp 14–28% and dp 25–39%) as calculated from the observed mean phenological Julian dates (DOY). The observed phenological Julian dates for spring cereals were averaged from ground truth experimental sites in cultivation zones I–IV (Figure 2 and Figure 4). In addition, the mean observed Julian dates for spring cereals from MTT experimental stations in cultivation zones I–III were used for phenological validation (Zone I-Pernaja and Inkoo, Zone II-Jokioinen, Zone III and IV-Ylistaro and Ruukki).
The early emergence in vegetative phase (ap, BBCH 0–12) in two leaf stage before double ridge induction and the senescence phase after full maturity and harvest (dp), BBCH > 90) were difficult to estimate. This increased the classification error in ap and dp classes [35]. In addition, the SatPhenClass classification result was accepted only, if both submodels (ETS and LAI comparisons) were in line (Figure 3b).
Table 5. VGI composite linearand non-linear model (I–III) non-potential baseline yield estimates (yb, kg/ha) for spring cereals [64,65].
Table 5. VGI composite linearand non-linear model (I–III) non-potential baseline yield estimates (yb, kg/ha) for spring cereals [64,65].
CropModelGrow.
Zone
Main
Soil
Type
Dataset,
(Model
Equation) (1)
R2lin(a)/
non-lin. polyn.(b),(2)
RMSE
lin(c)/
non-lin.
polyn. (d)(4)
Model
mean yield
(I–III)
kg/ha(2)
Cv
(%)
Pr > F
linear
Pr > F
quadratic
Pr > F
cross product
Pr > F
total
model
Sat Phen Class phonological classification error range (% DOY, Table 2) (3)
SwhI IRI–IIGyttja & Sandy clay2.1–3.3 (1.1)0.764(a)282.3(c)4219.06.66<.0001
***
<.0001
***
<.0001
***
<.0001
***
ap 21–29
bp 11–16
cp 14–19
dp 25–32
III–IVSandy clayII (1.2)0.794(a)42.46(c)3768.65.89
II NDVII–IIGyttja & Sandy clay2.1–3.3 (1.3)0.737(b)297.6(d)4219.07.02<.0001
***
<.0001
***
<.0001
***
<.0001
***
I–IIGyttja & Sandy clay2.1–3.3(1.4)0.732(a)300.1(c)3556.77.88
III SAR,NDVI(2)(a)III–IVSandy clay1.1–1.3 (5.1,6.1)0.723 E
0.731 R
302.1
300.8
4127.0
4213.0
3.78
5.56
<.0001
***
<.0001
***
<.0001
***
<.0001
***
BrlI IRI–IIGyttja & Sandy clay2.1–3.3(2.1)0.615(a)449.3(c)4395.010.3<.0001
***
<.0001
***
N.S.<.0001ap 24–26
bp 15–19
cp 17–21
dp 29–34
IINDVII–IIGyttja & Sandy clay2.1–3.3(2.2)0.611(b)449.6(d)4298.010.3<.0001
***
<.0001
***
0.0014
*
***
IIISAR,NDVI
(2)(a)
III–IVSandy clay1.1–1.3 (4.1,5.2,6.2)0.694 E
0.702 R
0.448 S
349.8
322.8
482.7
3750.0
3909.0
3835.0
3.12
6.92
7.22
<.0001
***
<.0001
***
<.0001
***
<.0001
***
OatI IRI,II,IVSandy clay1.1,2.1,3.3(2.5)0.760(a)55.0. (c)3740.01.58<.0001
***
<.0001
***
<.0001
***
<.0001ap 28–34
bp 17–22
cp 19–28
dp 36–39
I,IIGyttja & Sandy clay2.1,3.3(e)0.75655.13488.71.58<.0001
***
N.S.N.S.***
I,IIGyttja & Sandy clay2.1,3.3(e)0.056994.83462.028.7<.0001
***
<.0001
***
N.S.<.0001
III
SAR,
NDVI(2) (a)
III–IVSandy clay1.1–1.3
(4.2,5.3,6.3) (a)
0.617 E
0.624 R
0.417 S
389.7
483.6
584.2
2826.0
3038.0
2942.0
4.22
7.89
8.47
<.0001
***
<.0001
***
<.0001
***
<.0001
***
Cereal
Mean
0.627386.93752.27.47
(1) Table 1, Table 12 (Appendix C) (2) SAR-components: Envisat (E), Radarsat (R), ERS2 (S) SAR backscattering (σ 0)(3) SatPhenClass phenological classification accuracy error range (% DOY) (a) R2 linear (b) R2 non-linear polynomial equation (c) RMSE linear (d) RMSE(kg/ha) non-linear polynomial equation (e) Equation not shown

3.2.2. Testing the Composite VGI model (I–III) calibration performance

The spring cereal calibration results derived from phenologically pre-classified (SatPhenClass) SAR and reflectance data, are depicted in Table 5. It contains linear (REG/Stepwise) and non-linear polynomial (RSREG) VGI model (I–III, Table 3) baseline yield estimates (yb, kg/ha) with corresponding R2 and RMSE, Cv (%) and Anova F-test significance level estimates. The calibrated VGI model (I–III) equations are presented in Table 12 (Appendix C).
There was a large variation in VGI Composite model cereal yield responses between years and locations with averaged R2 0.627 and RMSE 387 kg/ha in growing zones I–IV in southern Finland (Table 1). The average modeled yield response varied between 3.5 t/ha and 4.2 t/ha with spring wheat, between 4.1 t/ha and 4.4 t/ha with barley and between 3.4 t/ha and 3.7 t/ha with oats. The coefficient of variation (Cv,%) varied between 1.6% and 28.7% with spring cereals, respectively the VGI model subcomponents (linear, quadratic and cross products) were significant on 0.1% error level.
The calibration results indicate that the R2 of the VGI modeled (I–III, Table 3) final grain yield accuracy varied significantly between different spring cereals. The overall R2 average for yield (kg/ha) prediction was for spring sown cereals 0.63 (mean response yield 3750 kg/ha, RMSE 387 kg/ha). According to calibration results obtained from the optical VGI models (I–II) and Composite VGI model (III), the R2 tends to stabilize on the 0.60–0.70 level. With relatively few observations (n < 500) the R2 tends to increase above 0.80, whereas with larger data sets (n > 1,000 observations) the R2 varies between 0.50 and 0.70. The overall R2 for wheat, barley and oats varied between 0.615 and 0.794 for Infrared model (I), between 0.611 and 0.737 for NDVI (II), and between 0.417 (oats using ERS data) and for 0.730 (wheat using Envisat and Radarsat data) for Composite SAR model (III). The total cultivation area used for the calibration of VGI models was 1253 hectares. The R2 varied for wheat ranged between 0.731 (SAR-model (IIII), RMSE 300 kg/ha) and 0.794 (Infrared model (I), RMSE 42 kg/ha), for barley between 0.448 (III) and 0.615 (I) and for oats between 0.417 (III) and 0.760 (I).
The Composite Model III (NDVI * SAR) calibration results for ERS, Radarsat and Envisat are presented in Table 5 (Model III SAR, NDVI). The R2 for the Composite model III varied for wheat between 0.723 (Envisat SAR-component, RMSE 302 kg/ha) and 0.731 (Radarsat SAR-component, RMSE 300 kg/ha), for barley between 0.694 (ERS component, RMSE 482 kg/ha) and 0.702 (Radarsat SAR component, RMSE 322 kg/ha) and for oats between 0.417 (ERS SAR component, RMSE 584 kg/ha) and 0.624 (Radarsat, RMSE 483 kg/ha). The corresponding optical calibration results for soil*species and soil (clay) * cultivar covariances with Infrared Polynomial Model I are reviewed in Part II.

3.3. Validation Results

3.3.1. Soil Species Covariance Validation with Composite Models

The validation results assessing both soil*species (Composite SAR and NDVI Model III) and soil cultivar covariance (Infrared NDVI Models I, II) categories for cereal baseline yield (yb) are depicted in Table 6 (Model Categories I–II).
In model category I (Table 6), the SAR Composite Model (III) validation results were compared and validated with averaged inventory estimates (kg/ha) from the MAFF Etelä-Pohjanmaa Agricultural Advisory Centre. The SAR and NDVI modeling results depict detailed soil*canopy interactions between cereal canopies and soil top cover (5–10 cm) with fine and coarse sand soil textures. The SAR composite model validation results take into account both the pre-anthesis ap and bp phenological phases (Figure 3b, phases I–II) using the NDVI component and post-anthesis and senescence phases (cp and dp, phase III) using the SAR component with HH and VV levels.
In model category II (Table 6), the soil cultivar covariance validation results are depicted. Two spring wheat (cv. Manu, Satu) and one malting barley (cv. Inari) cultivars grown on heavy clay type soils and currently cultivated in Finland were analyzed by using optical VGI Infrared and NDVI models [44,56,71]. The response mean yields (kg/ha) were compared with the MTT Official Variety Trial mean yield results correspondingly grown on clay type soils in cultivation zone I (Pernaja and Inkoo Exp. Stations), II (Jokioinen Exp. Station) and III–IV (Ylistaro and Ruukki Exp. Station, Figure 4a).
The Model III soil*species validation results (Model Category I, Table 6) using MAFF validation data, indicated with spring wheat an overestimation (DPMAFF,%) between +104% (Envisat) and 107% (Radarsat) from the observed MAFF inventory reference (100% [29]). With oat cereals, the underestimation was between 85% (Envisat) and 88% (ERS2). Respectively with barley between 97% (Envisat) and 112% (ERS) and more precisely with malt barley 96% underestimation (Radarsat) vs. fodder barley 102%. Respectively the yield difference calculated as kg/ha (DYMAFF) ranged between—177 kg/ha (swh, Envisat ASAR) and 484 kg/ha (Oats, Envisat ASAR). The mean composite model baseline yield (yb, kg/ha) was 4,170 kg/ha for wheat, 3,848 kg/ha for barley (3,909 for malting barley and 3,899 kg/ha for fodder barley) and 3,386 kg/ha for oats.
The Model I and II validation results (Model Category II, Table 6) using MTT validation data, indicated that both observed wheat and barley yield levels from the MTT official variety trials (kg/ha) exceeded corresponding modeled spring wheat and barley yield levels. The soil*species and soil*cultivar covariance validation averaged results with Infrared and NDVI models indicated an underestimated yield difference (DPMTT, %) by –10% in soil*species category and between –31% and –14% in soil*cultivar category. The modeled results for spring wheat and barley baseline yield levels (yb) in both categories were lower than the corresponding yield levels in MTT Official Variety trials.
Spring wheat and barley cereals in MTT field trials were grown on more optimal, non-limiting growing conditions. The optical VGI modeling results indicate a general underestimation of modeled vs. MTT observed yield levels. More specifically, the results indicate that yield difference (DPMTT,%) ranged between 69% [Clay*Swh*(cv. Satu)] and 92% (Barley*clay) from the baseline reference (100%). In both categories (soil*species, soil*cultivar) the VGI models underestimated the baseline yield levels (yb) when compared with the corresponding observed MTT yield levels. Especially cv. Satu yield level on clay type soils was clearly underestimated (–44.9% DPMTT). Respectively the yield difference calculated as kg/ha (DYMTT) varied between –363 kg/ha (barley*clay) and –1,427 kg/ha (Satu*clay).
Table 6. Cereal composite SAR and optical VGI model baseline yield (yb kg/ha) validation on different soil types vs. MAFF and vs. MTT observed values in the Etelä-Pohjanmaa Agric. Advisory Centre [30,56,71](1).
Table 6. Cereal composite SAR and optical VGI model baseline yield (yb kg/ha) validation on different soil types vs. MAFF and vs. MTT observed values in the Etelä-Pohjanmaa Agric. Advisory Centre [30,56,71](1).
Model Category
I
CropComposite modelCovariance CategorySAR
Sensor
Mean yb, kg/ha,
X ± Sd
MAFF mean estimate
kg/ha
(X ± Sd) (2)
DYMAFF Difference
kg/ha from
MAFF obs.(1) (5)
DPMAFF Difference
(%) from
MAFF obs.
(100 ref.) (4)
I (2)
Composite SAR & NDVI (II,III)
SwhII+IIIFine coarse sandy clay
(Table 1)
Envisat ASAR4127 ± 683950 ± 72–177.0 oe104.4
II+IIIRadarsat SAR4213 ± 413840 ± 86–373.0 oe109.7
Swh ave.4170 ± 543895 ± 78–275.0 oe107.1
Brl (general)II+IIIFine coarse sandy clay
(Table 1)
Envisat ASAR3750 ± 913880 ± 47130.0 ue96.6
II+IIIERS2 SAR3835 ± 983 420 ± 82–415.0 oe112.1
Brl maltII+IIIRadarsat SAR3909 ± 244050 ± 76141.0 ue96.5
Brl feed & fodderII+IIIRadarsat SAR3899 ± 323820 ± 82–79.0 oe102.7
Brl ave.3848 ± 743792 ± 72–55.7 oe101.8
OatsII+IIIFine coarse sandy clay
(Table 1)
Envisat ASAR2826 ± 853310 ± 54484.0 ue85.4
II+IIIERS2 SAR2942 ± 493 330 ± 54388.0 ue88.4
II+IIIRadarsat SAR3038 ± 233520 ± 81482.0 ue86.3
Oats ave.2935 ± 283386 ± 48451.3 ue86.7
Mean tot.3615 ± 123680 ± 4964.5 ue98.2
Model Category
II
SpeciesModelCovariance
Category
Soil type/
Cultivar(1)
Averaged
model I,II
yield yb
X ± Sd kg/ha
MTT mean
X ± Sd kg/ha (3)
DYMTT
Difference
kg/ha from
MTT obs (7)
DPMTT, Difference
(%) from
MTT obs (100 ref.) (6)
II (3)
Optical Infrared (I), NDVI (II)
SwheatI+IISoil*
Species
Sandy clay4240 ± 524645 ± 546–404*ue)91.3*ue)
BarleyI+IISandy clay4428 ± 484791 ± 29–363*ue)92.4*ue)
SwheatI+IISoil*CultivarSand clay*Manu4015 ± 624423 ± 72–408*ue)90.8*ue)
SwheatI+IISandy clay*Satu3181 ± 314608 ± 92–1427*ue)69.0*ue)
BarleyI+IISandy clay*Inari4749 ± 895483 ± 44–733*ue)86.6*ue)
(1) Yield estimates corrected to 15% moisture content (2) MAFF mean inventory estimate (3) MTT Official Variety Trial averaged results from cultivation zones I–IV (Figure 4a) (4) DPMAFF—difference (%) modeled vs. MAFF inventory estimate (5) DYMAFF - difference modeled vs. MAFF inventory estimate (6) DPMTT —difference (%) modeled vs. MTT estimate (7) DYMTT — difference (kg/ha) modeled vs. MTT estimate *(ue) —underestimated by the corresponding model vs. observed *(oe) —overestimated by the corresponding model vs. observed.

4. Discussion

4.1. Implications from SAR Soil*Canopy Interactions

Both the SAR backscattering (σ 0) signal and SAR and NDVI baseline yield (yb) levels varied significantly both in cereal soil*species and species*canopy covariance categories. In the species*soil covariance category SAR backscattering signal varied significantly especially on clay type soils with minor fractions of sand, silt and organic mould in the Porvoo, Mellilä, Kirkkonummi, Jokioinen and Lapua experimental areas (Table 1).
With spring wheat the VH cross-polarization amplitude was higher when compared with VV vertical levels both in anthesis (cp) and full maturity (dp) on clay and sandy soils. The wheat horizontal signal (HH) amplitude was higher in cp stage compared to dp.
The microwave Radarsat VH cross-polarization amplitude in barley plots was higher when compared with VV vertical levels both in anthesis (cp) and full maturity (dp) on clay and sandy soils. Both the barley horizontal and vertical (HH, VV) σ 0 signal amplitudes were higher in anthesis stage when compared to full maturity stage.
With oat cereals, the VH cross-polarization amplitude was higher when compared with VV vertical levels both in anthesis (cp) and full maturity (dp) on clay and sandy soils. Both the horizontal and vertical (HH, VV) signal amplitudes were higher in anthesis stage than in full maturity stage containing grain filling, yellow ripening and canopy senescence stages.
In the species*canopy covariance category level there was a significant variation both in backscattering amplitude and polarization properties between spring wheat, barley and oats. Especially the canopy structure of oat cereals differs morphologically from other spring cereals. Oat cereals with more planophile canopy and panicle inflorescence structures differ in polarization properties from those of wheat and barley with more erectophile head and canopy structures [23,60,72].

4.2. Implications from the SAR Composite Modeling Results for Baseline Yield Levels (Yb)

The SAR calibration results with Composite NDVI and SAR models indicated that averaged baseline yield (yb) response varied between 3.5 t/ha and 4.2 t/ha with spring wheat, between 4.1 t/ha and 4.4 t/ha with barley and between 3.4 t/ha and 3.7 t/ha with oats. Correspondingly the SAR validation results compared with MAFF data indicated that with spring wheat the overestimation was between +104% (Envisat) and 107% (Radarsat) from the observed MAFF inventory reference. Respectively with oats the underestimation was between 85% (Envisat) and 88% (ERS2). With barley the underestimation was 97% with Envisat and overestimation 112% with ERS. With malt barley the yield was underestimated (96%) with Radarsat data and overestimated with fodder barley (102%).
The use of Composite Multispectral model (III) combined with the SatPhenClass phenological classification algorithm for spring cereals provides a promising integrating technique for combining both microwave SAR and optical reflectance data. The Composite Multispectral model takes into account both the pre-anthesis phenological phases using the NDVI component and post-anthesis and senescence phases using the SAR component with backscattering polarization levels. In addition, the Composite multispectral model can be used in assessing the soil*canopy covariances between cereal canopies and soil top layers on different soil types.
The Composite model (III) calibration results indicate that the R2 tends to stabilize on the 60%–70% level similar to optical VGI models (I–II). The Composite model results indicated that the use of the Envisat ASAR additional cross-polarization component (VH) did not increase the R2 level compared to ERS (VV) and Radarsat (HH) with only one polarization level in the 5.3 GHz measurement spectrum. The Radarsat signal with the HH horizontal polarization component yielded highest R2 values with wheat, barley and oats. ERS signal with only one VV vertical polarization component produced consistently lower R2 values. In Finland Karjalainen et al. [28] reported SAR modeling results with the mean R2 of 0.55. The average crop height was used to estimate the amount of biomass using dual-polarization (VV/VH) Envisat SAR data in the Lapua and Seinäjoki experimental sites [28].
Recently McNairn et al. [1] reported the composite VV-VH SAR and optical data to be the most suitable for wheat, maize and soybean classification with over 85% overall accuracy (κ Kappa range 0.47–0.89) in Canadian growing conditions. McNairn et al. [1] applied three primary classification methodologies Neural Networks [73], Gaussian Maximum-Likelihood Classifier [21,74] and Decision trees [75] for crop classification using composite SAR and optical data. Respectively in this study the SatPhenClass classification accuracy for spring cereals varied between 61% and 89% in phenological classes (ap–dp). Especially the early vegetative phase before double-ridge induction (ap) [35,50,51] and post-harvest senescence (dp) phases were major source for error variation decreasing the overall classification accuracy. Highest classification accuracies (>80%) were obtained during the anthesis (bp) near the LAI maximum proximity.
The composite model validation results indicated an overestimation with wheat and feed barley cereals. On the contrary, an underestimation was noticed with oats and malting barley cereals by using different SAR/ASAR and NDVI sources. In summary, the results suggest the potential inclusion of horizontal polarization component (HH) in the SAR composite models. The oat canopy and head structures differ from those of wheat and barley potentially changing the backscattering polarization properties [23,28,60].

5. Conclusions

The cereal validation results obtained in this study suggest that spring cereal baseline yield estimates derived from Vegetation Indices Models tend to retain between the observed MAFF yield inventory estimates and MTT Agrifood Official Variety trial data results. The annual MAFF non-potential yield inventory estimates are obtained from suboptimal growing conditions on actual farm level, where as MTT Agrifood Official Variety trial results are obtained from more optimal growing conditions.
Composite multispectral models combined with SatPhenClass phenological classification algorithm provide a new promising integrating technique by combining SAR and optical data with the cereal phenological development both in vegetative and generative phases. This new methodology can be applied in future large area crop monitoring campaigns and also in national crop inventory programs for assessing grain yield production on the Agricultural Advisory Centre level. The use of VGI models combined with satellite based Minimum Datasets would significantly reduce the operational costs and time required for more accurate cereal inventory estimates when compared with the traditional ground based crop inventory systems. The implications of this study for Finnish national inventory system and CAP policy are reviewed in the second part of this publication.

6. Acknowledgements

We wish to express our sincere gratitude to the following persons: Director General, Risto Kuittinen (satellite data, general management), Eero Ahokas (calibration of datasets, GIS and coordinate systems, satellite data calibration) from The Finnish Geodetic Institute contributed significantly to this publication during the field experiments, modelling work and preparation of the manuscript.
Also the support of Eero Varis, Pirjo Mäkelä, Mervi Seppänen (University of Helsinki), Timo Mela (MTT Agrifood Research of Finland ), Tuomo Karvonen (Technical University of Helsinki), N. Hintikka (Ministry of Agriculture and Forestry/Information Centre), Tuomas Häme (VTT/Technical Research Centre of Finland ), Eija Pehu (World Bank, Washington), Tim Carter (The Finnish Environmental Centre), Hiroshi Koizumi (NIAES, Tsukuba/Tokyo, Japan), Stefen Maas (ARS/USDA/USA ), J. Meyer-Roux, A. Husson, P. Vossen (JRC/Ispra/Italy), A. Baudoin (INRA/Paris/France), T. Le Toan (CESR/ Toulouse/ France), E. Parmes, B. Veikkanen, K. Andersson (VTT/Technical Research Centre of Finland), M. Jaakkola and J. Paavilainen (Finnish National Board of Survey and Novosat Finland Co.) is gratefully acknowledged for their advises and support during the field experiments, modelling work and preparation of the manuscript. This project was funded by the Finnish National Board of Agriculture from the Maatilatalouden kehittämisrahasto fund and by the European Union AO (Announce of Opportunity) contract AOE-488 for ENVISAT.

7. Appendix. Electronic Supplementary Information (ESI) and additional Figures and Tables

Appendix A-D, figures and tables can be downloaded from the link: http://koti.armas.fi/~hlaurila/download/Pb4.
The SatPhenClass algorithm can be downloaded from the link: http://koti.armas.fi/~hlaurila/download/Pb4 file: SatPhenClass-Appendix.pdf.

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Appendix A. Additional Figures and Tables

Additional figures and tables can be downloaded from the link: http://koti.armas.fi/~hlaurila/download/Pb4
Figure 6. Ground truth sampling methodology and LAI measurements with portable Li-Cor 2000 Plant Canopy Analyzer [23,52].
Figure 6. Ground truth sampling methodology and LAI measurements with portable Li-Cor 2000 Plant Canopy Analyzer [23,52].
Remotesensing 02 00076 g006

Appendix B. Abbreviations, Significance Levels and Tables, Satellite Systems and Locations

Table 7. Abbreviations used.
Table 7. Abbreviations used.
Definition, abbreviationUnit, (range)Description
XMean of sample
SdStandard deviation of sample (n)
CvCoefficient of variation (%) = Sd/X
SxStandard error of mean = Sd n
MSEMean squared error MSE ( X ¯ )   =   E ( ( X ¯ μ ) 2 ) = ( σ n ) 2
RMSERoot Mean Square Error , square root of MSE
R2Coefficient of determination
LSELeast-Square Estimation-algorithm
VGI with submodels (I-III)Vegetation Indices submodels (I-III: I—Infrared polynomial, II—NDVI, III—composite NDVI and backscattering model (Table 6)
ap, bp, cp, dpDVS (Phenological Development stage) four classification values in SatphenlClass algorithm (Table 3 a,b), used in VGI models: ap—vegetation stage class from emergence until 2 leaf and double ridge stages, bp—generative stage class until heading, cp—grain filling stage in generative phase between anthesis and full maturity, dp—senescence phase (Used only in microwave polynomial model III)
Rf (Ch,month)Landsat or SPOT calibrated reflectance values with index denoting channel and month during growing season (a–May, b–June, c–July); used in VGI Infrared model (I) as independent variables for crop*cultivar*soil covariance interaction and yield estimations (Table 3, Models. 1.1–7.3, Table 12 App. C),
* - general notation for covariance effects
DYMTTYield Difference (kg/ha) modelled (VGI)—observed MTT average (Table 11,14)
DRTMTTYield Difference Ratio, Modelled (VGI)/MTT Observed; over/underestimation (%) from the reference (100%) (Table 11, 14)
DYMAFFYield Difference (kg/ha) modelled (VGI)—obs. MAFF average (Table 10, 13).
DRTMAFFYield Difference Ratio, modelled (VGI)/MAFF Observed; over/underestimation (%) from the reference (100%) (Table 10,13)
Oe / ueOverestimated / Underestimated by the corresponding VGI model vs. observed (Table 12, Table 13 and Table 14).
spc., Cv.species, cultivar
spc.Spring sown cereal species (spring wheat, barley, oats)
SwhSpring wheat (Triticum aestivum L.) including cv. Heta, Kadett, Manu, Reno, Ruso, Satu, Tjalve
BrlBarley (Hordeum vulgare L.) including cv. Arra, Arttu, Artturi, Arve, Eero, Ida, Inari, Kustaa, Kymppi, Loviisa, Mette, Pohto, Pokko
OatsOats (Avena Sativa L.) including cv. Aarre, Salo
loc.Loc—Location: J-Jokioinen, K—Kirkkonummi, M-Mellilä, P—Porvoo, L—Lapua
Tbdegree (°C)Threshold temperature
Dddegree days
ETS(Tb)dd—degree daysCumulative temperature sum over threshold temperature (Tb = 5°)
PARMJ/d/m2
(10–20)
Photosynthetically Active Radiation (λ = 400–700 nm)
IR
NIR—Near IR
Mid IR
Thermal IR
MJ/d/m2infrared radiation (IR), λ = 630–690 nm
near infra, λ = 760–900 nm
mid infra, λ = 1.55–1.75 μm
thermal IR, λ = 10.4–12.5 μm
Rf(0.0– 1.0) Optical (λ = 400–700 nm) and infrared sensors (λ = 630–12.5 μm).Reflectance; reflected radiation from soil and vegetation canopies and measured by optical satellites [22,23,57,58,68,69]
σ0 (sigma zero)(–20–10 dB). Calibrated SAR (Synthetic Aperture radar) backscattering signal with microwave 5.4 GHz (C-band, λ = 5.7 cm) and 9.8 GHz (X-band) and polarization levels (HH, VV, VH, HV).Backscatter coefficient (sigma zero) for microwave backscattering signal, which is a combined signal reflected from soil and vegetation canopies [60,61,62,63,26,27,28].
Potential, non-limited yield,
yield potential
kg/haModelled maximum yield capacity (kg/ha) for a specific cultivar without limiting environmental stress factors during growing season (vegetation water stress, nutrient deficiencies, pathogen epidemics etc.)
Non-potential, limited yieldkg/haVGI modeled yield level (kg/ha) for a specific cultivar with limiting environmental stress factors during growing season reducing maximum yield capacity, see potential yield.
yb(spc, cv, soil type.)
spc=swh,brl,oats
kg/ha, 15% moisture contentBaseline yield for spring cereal species (swh, brl, oats).VGI (I-III) modeled cereal yield level (kg/ha) using time series for a specific cultivar under field conditions, see non-potential yield.Index denotes crop, cultivar and soil type; used as dependent variable in VGI models (Models. 1.1–7.3, Table 12, App. C).
NDVI%Normalized Difference Vegetation Index
SatphenlClassBBCH and Zadok’s scalingSatellite data classification algorithm based on cereal phenology
Minimum datasetExperimental dataset without ground truth or meteorological data, containing only optical or microwave satellite data
VGI with submodels (I-II)Vegetation Indices submodels (I-IIII: I—Infrared polynomial, II—NDVI, III—composite NDVI and backscattering model.
MAFFMinistry of Agriculture and Forestry in Finland
IIASAThe International Institute for Applied Systems Analysis [32]
Table 8. Statistical significance levels.
Table 8. Statistical significance levels.
Significance levels (α = reference probability) (1)
N.S.Statistically non-significant
0Moderately significant on 10% error level, α(0.10)
*Significant on 5% error level, α(0.05)
**Highly significant on 1% error level, α(0.01)
***Highly significant on 0.1% error level, α (0.001)
(1) Used in testing the p-value (Pr) in F-test
Table 9. Satellite measurements in Finnish experimental locations [23,26,28].
Table 9. Satellite measurements in Finnish experimental locations [23,26,28].
LocationDate
(Satellite measurement)
Satellite type, sensor and
Image no.
Incidence angle
(φ) –1)
Soil type4)
Lapua, Seinäjoki
23° 10 ' E,
62° 50' N
6.7.1994, 24.6.1995,
1996: 21.5., 3.7.
Optical:
SPOT XS 65 220
–21.3, –4.3,
10, 0.2
Clay (Sandy clay 56%), coarse
(Coarse sand 33.5%)
1997: 17.5.,7.6.,1.7Optical:
SPOT XS 65 220–221
9.3,5.9,–23.8
1996: 17.6, 30.7, 25.8, 25.9Microwave SAR:
ERS1
SAR5), f = 5.3 GHz, C band, (λ = 5.7 cm), VV polarization
2001: 6.5,16.5, 30.5, 6.6, 16.6, 23.6.,10.7, 17.7, 24.7, 3.8, 17.8, 27.8, 10.9, 17.9, 20.9, 27.9, 28.10.Microwave SAR:
Radarsat1
SAR, f = 5.3 GHz, C band, HH polarization
2003–2004: 15.6,18.6,21.6,28.6,4.7,7.7,14.7,23.7,02.8,11.8,24.8,15.9
2005–2006: Only ground truth observations 5)
Microwave ASAR:
ENVISAT
ASAR , f = 5.3 GHz, C-band), VV, HH, VV/HH, HV/HH, or VH/VV polarizations
Kirkkonummi
24° 30' E,
60° 10' N
1994: 6.6., 7.7Optical:
SPOT XS 73 227
24.5, –21.6Clay (2)
(gyttja clay
41.4%)
25.7.1994, 3.6.1995Optical:
Optical:
LANDSAT TM 189 18
23
15.6.1995, 13.7.1994Optical:
SPOT XS 73 227, 69 225
–2.2
Jokioinen
23° 50' E,
60° 50' N
25.7.1994Optical:
LANDSAT TM 189 18
–23.4Clay (Sandy clay 56%),
Kuuma exp. area
(70–80% organic
top layer, Table 3)
Mellilä
22° 20' E,
60°, 50' N
1989:24.5., 25.6., 27.7.Optical:
LANDSAT 5/TM
48.28, 50.39,
46.05
Clay (Sandy clay 29%,
gyttja clay 36%)(2)
Porvoo
25° 50 ' E,
60° 50' N
1990: 13.5., 21.6 29.7.Optical:
SPOT/HRV2/XS
50.01, 48.1Clay (2)
(gyttja clay 38%)
1990: 21.6Optical:
LANDSAT 5/TM
-
1990: 25.7, 24.8Microwave SAR:
HUTSCAT
Scatterometer (f = 5.4 GHz, C-band, 9.8 GHz, X-band), VV, HH, VH, HV polarizations(3)
1995: 25.6., 15.7.,
1996:25.7.
Optical:
SPOT XS 69 225
–1.6
14.6.1994Optical:
SPOT XS 65 220
5.2
(1) Used in Equation 2 (Appendix C) (2) Gyttja clay contains peat and mud fractions) (3) Helicopter mounted [61,62,63] (4) Main soil type classification with soil sub fractions (5) No Envisat data obtained in 2005–2006 because of re-programming of ASAR sensors and technical problems in ESA. Only ground truth phenological and LAI measurements measured in experimental areas.
Table 10. SAR and optical satellite systems used in remote sensing campaigns.
Table 10. SAR and optical satellite systems used in remote sensing campaigns.
Satellite typeNameSensorExperimental
locations & years
Reference
Microwave
ASAR
ENVISAT (3)ASAR(6), f = 5.3 GHz, C-band), VV, HH, VV/HH, HV/HH, or VH/VV polarizationsSeinäjoki, Lapua (2002–2004, Table 11).http://earth.esa.int,
http://envisat.esa.int/object/index.cfm?fobjectid=3772,
[27,28]
Microwave
Scatterometer
HUTSCAT(1),(6)Scatterometer (f = 5.4 GHz, C-band, 9.8 GHz, X-band), VV, HH, VH, HV polarizationsPorvoo, calibration data (1990, Table 11).www.space.hut.fi/research/equipment/ hutscat.html. [61,62,63]
Microwave
SAR
ERS12)SAR (5), f = 5.3 GHz, C band, (λ = 5.7 cm), VV polarizationSeinäjoki, Lapua (1995–1996, Table 11).http://earth.esa.int/ers, earth.esa.int/ers/sar,
[26,62]
Microwave
SAR
Radarsat1SAR(5), f = 5.3 GHz, C band, HH polarizationSeinäjoki, Lapua
(2001, Table 11).
Reference [27], Canadian Space Agency (CSA). ccrs.nrcan.gc.ca/, radar/spaceborne/radarsat1/index_e.php
OpticalLandsat 5Thematic Mapper (TM)
(λ = 450 nm–2.35 μm)
Porvoo, Mellilä, Kirkkonummi, Jokioinen, Lapua (1989–1997, Table 11).www.landsat.org, [76,22,23]
OpticalSPOT 2HRV2/XS
(λ = 450 nm–890 nm)
Porvoo, Mellilä, Kirkkonummi, Jokioinen, Lapua (1989–1997, Table 11).www.spot.com, www.spotimage.fr, [77,22,23]
Multi-
sensor
ADEOS1
Advanced Earth Observing Satellite4)
AVNIR, ILAS, RIS, IMG,TOMS: atmospheric
greenhouse gas (CO2, O3, CH4) columns (4)
(1996-1997, non-operational)
Collaboration with Prof. Hiroshi Koizumi, NIAES/ Tsukuba, Japan
NASDA/ JAXA, http//home.gna.org/adeos/
http://kuroshio.eorc.jaxa.jp/ADEOS,http://msl.jpl.nasa.gov/QuickLooks/ adeosQL.html
(1) HUTSCAT is helicopter mounted, see space.hut.fi/research/equipment/hutscat.html [63](2) European Remote Sensing satellite (ESA, European Space Agency)(3) ENVISAT (Environmental satellite, ESA) AOS (Announce of Opportunity) contract: AOE-488 for ENVISAT(4) ADEOS1 AOS contract: NASDA Contract 1062/Vegetation and Biology, MAFF, 5118/416/94(5) SAR, Synthetic Aperture Radar (6) Advanced Synthetic Aperture Radar(6) HUTSCATused for calibration verification purposes only (helicopter mounted)
Table 11. The optical calibration parameters of Landsat and SPOT—satellites [57].
Table 11. The optical calibration parameters of Landsat and SPOT—satellites [57].
Satellite/ sensor
(1),(2)
Sensor channel/λ (wave length)/typeDateSun elevation Angle (deg.)S(6)α(7)β(8)
TM1(1)450–520 nm/PAR(3)24.5.198948.286200.602–1.5
TM2(1)520–600 nm/PAR(3)25.6.198950.395771.17–2.8
TM3(1)630-690nm/infrared(3)27.7.198946.054930.806–1.2
TM4(1)760–900 nm/near infra21.6.199050.013320.815–1.5
TM5(1)1.55–1.75 μm /mid infra67.10.108–0.37
TM6(1)10.4–12.5 μm/thermal IR(4)n.a.(3)--
TM7(1)2.08–2.35 μm /mid infra24.50.057–0.15
HRV2/S1(2)500–590 nm / PAR(3)13.5.1990n.a.(5)587.01.221810
HRV2/S2(2)610–680 nm / infrared(3)29.7.199048.1502.01.225450
HRV2/S3(2)790–890 nm/near infrared331.01.297530
(1) Landsat5 / TM: Landsat (1987). Sensor: Thematic Mapper (λ = 450 nm–2.35 μm, resolution 30 × 30 m2 (2) SPOT / HRV2/XS: SPOT (1986). High Resolution Visible 2 (λ=500 nm–890 nm, resolution 20 × 20 m2). (3) PAR – Photosynthetically Active Radiation (λ = 400–700 nm) (4) Thermal infrared channel with ground resolution of 120 m. (5) n.a.—parameter not available (6) S—Equivalent solar radiance (7) α—Calibration gain coefficient (8) β—Calibration offset coefficient.

Appendix C. Equations

Statistical Analysis Equations

Equation 1 y(i) = β0 + βI × x(i) +...β(i+1) × x(i+1) + ε (linear REG/Stepwise [64])
Equation 2 y(i) = β01 × x(1)+ β2 × x(2)+β3 × x(1)2 + β4 × x(2)25 × x(1) × x(2) + ε (non-linear RSREG [65])
y(i) = dependent grain yield for spring cereals (wheat, barley, oats, kg/ha, 15% moisture content)
x(i) = independent ground truth and satellite (Table 3) variables in Vegetation Indices regression models
β0 = model intercept, βI = coefficient for independent variable x(i),
ε = error residual variation,
β1 × x(1)+ β2 × x(2)—the linear term,
β3 × x(1)2+ β4 × x(2)2—the squared effect term,
β5 × x(1) × x(2)—the cross product effect.

Optical Satellite Data Calibration Equations

Equation 3 R   =   α * d c   +   β [57]
R = Spectral radiance (W·m-2·sr–1·µm–1)
dc = digital count value from the satellite sensor (0-255)
α = calibration gain coefficient (W·m–2·sr–1·µm–1·count–1)
β = calibration offset coefficient (W·m–2·sr–1·µm–1), sr = angle expressed as steradians
Equation 4 rf   =   R / ( S * cos ( φ )
rf = surface reflectance value (%—ratio between the radiation reflected from the soil cover and the incoming solar radiation)
R = spectral radiance (W·m–2·sr–1·µm–1)
S = equivalent solar radiance (W·m–2·sr–1·µm–1)
cos(ϕ) = Solar zenith-angle correction, calculated from the measuring time and the corresponding elevation or incidence angle (el): ϕ = (90-el), ϕ expressed in radians,
sr = angle expressed as steradians
Equation 5 S   =   [ 1 / w ] * d { Ω * ( eqv . solarradiance ) * [ Ω ] * Φ [ Ω ] }
Ω = satellite channel wavelength (µm)
w = satellite channel spectral bandwidth (µm)
Φ(Ω) = normalized wavelength response function of specific satellite channel
Baseline VGI (I-IV) linear(REG/Stepwise) and non-linear (RSREG) yield models for spring crops
Table 12. SAR and optical VGI (I-IV) linear (REG/Stepwise) and non-linear (RSREG) yield models for spring cereals(1),(2).
Table 12. SAR and optical VGI (I-IV) linear (REG/Stepwise) and non-linear (RSREG) yield models for spring cereals(1),(2).
Model
category
Factor :
Crop /Species 1)
Model
(I-IV)
R2RMSE
kg/ha
Model equation
I
Optical
Swh1.1 (I)0.764282.3yb(swh) = 4941.9 – 5455.9 × rf3ap – 1351.4 × rf4ap + 957.1 × rf3bp + 656.2 × rf4bp + 4742.1 × rf3cp – 4983.5 × rf4cp
Swh1.2 (I)0.79442.46yb(swh) = 985.93 + 13337.46 × rf3ap + 8355.88 × rf4ap – 387.19 × rf4bp + 255.58 × rf3cp
Swh1.3
(II, NDVI)
0.737297.6yb(swh,NDVI) = 4659.2 + 175.4 × nap – 25.6 × nbp – 3215.5 × ncp + 93.7 × nap2 – 19.1 × nbp × nap – 178.5 × nbp2 – 560.4 × ncp × nap + 3250.4 × ncp × nbp – 864.7 × ncp2
Swh1.4
(II, NDVI)
0.732300.1yb(swh,NDVI) = 4692.90 + 109.84 × nap – 210.38 × nbp – 969.22 × ncp
Swh1.5(5)
(III,GEMI)
0.704316.1Y(swh,GEMI) = 5074.1 – 766.2 × gap – 143.1 × gbp – 3378.2 × gcp + 4254.1 × gap2 + 1079.8 × gbp × gap – 1326.2 × gbp2 – 7264.8 × gcp × gap + 7717.2 × gcp × gbp – 2622.1 × gcp2
Swh1.6(5)
(III,GEMI)
0.570536.8yb(swh,GEMI) = 5506.31 + 7780.47 × gap – 12478.3 × gbp + 761.96 × gcp
Swh1.7(5)
(IV,PARND)
0.712311.6yb (swh, PARND)=4397.9+2736.9*pap-379.4*pbp-493.8*pcp-4235.1*pap2+ 2701.1*pbp*pap+605.8*pbp2 -2161.6*pcp*pap+883.1*pcp*pbp+493.9pcp2
Swh1.8(5)
(IV,PARND)
0.509406.3Y(swh, PARND)=4502.31+529.36*pap+2377.05*pbp-779.31*pcp
Brl2.1 (I)0.615449.3.Y(brl) = 5348.8 – 600.8 × rf3ap – 184.5 × rf4ap – 8562.4 × rf3bp – 2105.6 × rf4bp – 2766.9 × rf3cp – 1556.2 × rf4cp
Brl2.2
(II, NDVI)
0.611449.6Y(brl,NDVI) = 3481.5 –2 05.5 × nap–312.1 × nbp + 396.5 × ncp – 60.5 × nap2 – 584.5 × nbp × nap + 864.6 × nbp2 + 1081.4 × ncp × nap – 710.5 × ncp × nbp – 232.4 × ncp2
Brl2.3(5)
(III, GEMI)
0.614448.6Y(brl,GEMI)=5184.0 – 2802.9 × gap – 187.4 × gbp – 3457.9 × gcp – 374.8 × gap2 + 91.5 × gbp × gap + 1161.4 × gbp2 + 4817.1 × gcp × gap – 1932.1 × gcp × gbp + 2255.6 × gcp2
Brl2.4(5)
(IV,PARND)
0.587463.7Y(brl,PARND) = 4621.7 + 1852.8 × pap-6418.5 × pbp – 5850.4 × pcp + 48.9 × pap2 – 5952.3 × pbp × pap + 10853.0 × pbp2 –2040.4 × pcp × pap + 12879.0 × pcp × pbp + 6002.9 × pcp2
Oats2.5 (I)0.76055.0yb(oats) = 3457.4 – 3762.5 × rf3ap– 2135.8 × rf4ap+6643.1 × rf3bp+ 1566.5 × rf4bp+ 571.4 × rf3cp– 179.1 × rf4cp
II
Optical
Species*soil
Covariance
Crop /Species 1)ModelR2RMSEModel equation
Swh * sandy clay2.6 (I)0.764282.3yb(swheat,clay) = 6765.5 – 38407.0 × rf3ap – 14979.0 × rf4ap– 23698.0 × rf3bp + 9552.7 × rf4bp + 7261.4 × rf3cp – 22022.0 × rf4cp
Brl * sandy clay2.7 (I)0.1661382yb(barley,clay) = 7141.4-3842.1 × rf3ap-2612.4 × rf4ap – 3032.1 × rf3bp-11708.0 × rf4bp – 28637.0 × rf3cp – 1636.2 × rf4cp
III
Optical
Species*
Cultivar Cov.
Swh* cv. Manu3.1 (I)0.0891292yb (cv. Manu*clay) = 5696.5 + 2293.8 × rf3bp + 5387.9 × rf3cp – 736396.0 × (rf3bp)2
Swh * cv. Satu3.2 (I)0.0461031yb (cv. Satu*clay) = –9798.2 + 736440.0 × rf2bp – 9925014.0 × (rf2bp)2
Brl * cv. Inari3.3 (I)0.1441220yb (cv. Inari*clay) = 8336.8 – 71506.0 × rf3bp – 45627.0 × (rf3bp)2
IV
Microwave SAR
SensorCereal specieModel4)R2RMSEModel equation
ERS
SAR(4)
Brl4.1(III)0.448482.7yb (brl, ERS2) = 4345.7 + 109.4 × NDVIap – 211.6 × NDVIbp – 983.3 × NDVIcp–0.57 × VV(5GHz,cp) + 5.61 × VV(5GHz,dp)
Oats4.2(III)0.417584.2yb (oats, ERS2) = 3739.5 + 108.9 × NDVIap – 212.1 × NDVIbp – 938.8 × NDVIcp – 0.47 × VV(5GHz,cp) + 4.28*VV(5GHz,dp)
Radarsat SAR4)Swh5.1(III)0.731300.8yb (swh, Radarsat) = 4690.7 + 111.8 × NDVIap – 213.4 × NDVIbp – 982.6 × NDVIcp – 2.69 × HH(5GHz,cp) + 3.9 × HH(5GHz,dp)
Brl5.2(III)0.702322.8yb(brl,Radarsat) = 4430.1 + 109.4 × NDVIap – 211.6 × NDVIbp – 983.3 × NDVIcp – 0.52 × HH(5GHz,cp) + 5.07 × HH(5GHz,dp)
Oats5.3(III)0.624483.6yb (oats, Radarsat) = 3843.3 + 108.9 × NDVIap – 212.1 × NDVIbp – 983.8 × NDVIcp – 0.47 × HH(5GHz,cp) + 4.03 × HH(5GHz,dp)
Envisat
ASAR(4)
Swh6.1(III)0.723302.1yb(swh,Envisat) = 4701.1 + 108.2 × NDVIap – 208.8 × NDVIbp – 983.1 × NDVIcp – 3.9 × VH(5GHz,cp) + 17.4 × VV(5GHz,cp) – 3.1 × VH(5GHz,dp)+ 5.2 × VV(5GHz,dp)
Brl6.2(III)0.694349.8yb (brl, Envisat) = 4261.4 + 109.4 × NDVIap – 211.6 × NDVIbp – 983.3 × NDVIcp – 4.59 × VH(5GHz,cp) + 18.24 × VV(5GHz,cp) – 4.04 × VH(5GHz,dp) + 6.15 × VV(5GHz,dp)
Oats6.3(III)0.617389.7yb (oats, Envisat) = 3635.7 + 108.9 × NDVIap – 212.8 × NDVIbp– 983.8 × NDVIcp – 2.59 × VH(5GHz,cp) + 16.46 × VV(5GHz,cp) – 2.03 × VH(5GHz,dp) + 4.05 × VV(5GHz,dp)
HUTSCAT
Scattero
meter(3),(4)
Swh7.1(III)0.582416.8yb(swh,HUTSCAT) = 4258.4 + 109.4 × NDVIap – 198.2 × NDVIbp – 937.4 × NDVIcp + 5.2 × VV(5GHz,cp) + 18.4 × HH(5GHz,cp) – 2.9 × VH(5GHz,cp) – 16.4 × HV(5GHz,cp) + 4.4 × VV(5GHz,dp) + 12.4 × HH(5GHz,dp) – 2.3 × VH(5GHz,dp) – 14.4 × HV(5GHz,dp)
Brl7.2(III)0.518490.1yb (brl, HUTSCAT) = 4294.2 + 107.2 × NDVIap– 209.2 × NDVIbp – 928.2 × NDVIcp + 3.2 × VV(5GHz,cp) + 17.4 × HH(5GHz,cp) – 3.9 × VH(5GHz,cp) – 15.4 × HV(5GHz,cp) + 5.4 × VV(5GHz,dp) + 11.4 × HH(5GHz,dp) – 4.3 × VH(5GHz,dp) – 15.4 × HV(5GHz,dp)
Oats7.3(III)0.424544.2yb(oats,HUTSCAT) = 3782.5 + 106.8 × NDVIap – 207.2 × NDVIbp – 942.5 × NDVIcp + 4.2 × VV(5GHz,cp) + 15.2 × HH(5GHz,cp) – 5.9 × VH(5GHz,cp) – 17.1 × HV(5GHz,cp) + 4.8 × VV(5GHz,dp) + 11.8 × HH(5GHz,dp) – 4.3 × VH(5GHz,dp) – 12.2 × HV(5GHz,dp)
(1) For abbreviations refer to Table 9 (2) Independent variables classified with SatPhenClass-algorithm (Figure 3a,b). (3) HUTSCATused for calibration verification purposes only (helicopter mounted) (4) Only in Part I (SAR+Optical models) (5) Only in Part II (Optical models)

Appendix D. SatPhenClass Classification Algorithm for Satellite Data

Appendix figures and tables can be downloaded from the link: http://koti.armas.fi/~hlaurila/download/Pb4.
The SatPhenClass classification algorithm can be downloaded from the link: http://koti.armas.fi/~hlaurila/download/Pb4 file: SatPhenClass-Appendix.pdf

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Laurila, H.; Karjalainen, M.; Hyyppä, J.; Kleemola, J. Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I). Remote Sens. 2010, 2, 76-114. https://doi.org/10.3390/rs2010076

AMA Style

Laurila H, Karjalainen M, Hyyppä J, Kleemola J. Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I). Remote Sensing. 2010; 2(1):76-114. https://doi.org/10.3390/rs2010076

Chicago/Turabian Style

Laurila, Heikki, Mika Karjalainen, Juha Hyyppä, and Jouko Kleemola. 2010. "Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I)" Remote Sensing 2, no. 1: 76-114. https://doi.org/10.3390/rs2010076

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