Extracting Regional Pattern of Wheat Sowing Dates Using Multispectral and High Temporal Observations from Indian Geostationary Satellite

Monitoring of Agricultural crops using remote sensing data is an emerging tool in recent years. Spatial determination of sowing date is an important input of any crop model. Geostationary satellite has the capability to provide data at high temporal interval to monitor vegetation throughout the entire growth period. A study was conducted to estimate the sowing date of wheat crop in major wheat growing states viz. Punjab, Haryana, Uttar Pradesh (UP), Madhya Pradesh (MP), Rajasthan and Bihar. Data acquired by Charged Couple Detector (CCD) onboard Indian geostationary satellite INSAT 3A have continental (Asia) coverage at 1 km?×?1 km spatial resolution in optical spectral bands with high temporal frequency. Daily operational Normalized Difference Vegetation Index (NDVI) product from INSAT 3A CCD available through Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC) was used to estimate sowing date of wheat crop in selected six states. Daily NDVI data acquired from September 1, 2010 to December 31, 2010 were used in this study. A composite of 7 days was prepared for further analysis of temporal profile of NDVI. Spatial wheat crop map derived from AWiFS (56 m) were re-sampled at INSAT 3A CCD parent resolution and applied over each 7 day composite. The characteristic temporal profiles of 7 day NDVI composite was used to determine sowing date. NDVI profile showed decreasing trend during maturity of kharif crop, minimum value after harvest and increasing trend after emergence of wheat crop. A mathematical model was made to capture the persistent positive slope of NDVI profile after an inflection point. The change in behavior of NDVI profile was detected on the basis of change in NDVI threshold of 0.3 and sowing date was estimated for wheat crop in six states. Seven days has been deducted after it reached to threshold value with persistent positive slope to get sowing date. The clear distinction between early sowing and late sowing regions was observed in study area. Variation of sowing date was observed ranging from November 1 to December 20. The estimated sowing date was validated with the reported sowing date for the known wheat crop regions. The RMSD of 3.2 (n?=?45) has been observed for wheat sowing date. This methodology can also be applied over different crops with the availability of crop maps.     

Yield Estimation Model and Water Productivity of Wheat Crop (Triticum aestivum) in an Irrigation Command Using Remote Sensing and GIS

Crop yield estimation has an important role on economy development and its accuracy and speed influence yield price and helps in deciding the excess or deficit production conditions. The water productivity evaluates the irrigation command through water use efficiency (WUE). Remote sensing (RS) and geographical information system (GIS) techniques were used for crop yield and water productivity estimation of wheat crop (Triticum aestivum) grown in Tarafeni South Main Canal (TSMC) irrigation command of West Bengal State in India. One IRS P6 image and four wide field sensor (WiFS) images for different months of winter season were used to determine the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) for area under wheat crop. The temporally and spatially distributed spectral growth profile and AREASUM of NDVI (A_NDVI) and SAVI (A_SAVI) with time after sowing of wheat crop were developed and correlated with actual crop yield of wheat (Y_act). The developed relationships between A_SAVI and Y_act resulted high correlation in comparison to that of A_NDVI. Using the developed model the RS based wheat yield (Y_RS) predicted from A_SAVI varied on entire TSMC irrigation command from 22.67 to 33.13 q ha⁻¹ respectively, which gave an average yield of 26.50 q ha⁻¹. The RS generated yield based water use efficiency (WUE_YRS) for water supplied from canal of TSMC irrigation command was found to be 6.69 kg ha⁻¹ mm⁻¹.     

Wheat growth profile: satellite monitoring and crop yield modelling

The most important advantage of the low resolution National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (NOAA AVHRR) data is its high temporal frequency and high radiometric sensitivity which helps in vegetation detection in the visible and near-infrared spectral regions. In areas where most of the crop cultivation is in large contiguous areas, and if the AVHRR data are selected for time period such that the crop of interest is well discriminated from other crops, these data can be used for monitoring vegetative growth and condition very effectively. The present study deals with the application of AVHRR data for the monitoring of the wheat crop in its seventeen main growing districts of the Rajasthan state. The fourteen date AVHRR data covering the entire growth period have been used to generate the normalized difference vegetation index (NDV1) growth profile for the crop by masking the non-crop pixels following the two-date NDVI change method. The growth profile parameters and other derived parameters, such as post-anthesis senescence rate and areas under the entire growth profile or under selected growth periods have been related to the district average wheat yield through statistical regression models. Various methods adopted for wheat pixels masking have been critically evaluated. It is found that the wheat yield can be predicted well by the area under the profile in different growth periods.     

Measurement and Scaling of Carbon Dioxide (CO<Subscript>2</Subscript>) Exchanges in Wheat Using Flux-Tower and Remote Sensing

The present study investigates the characteristics of CO₂ exchange (photosynthesis and respiration) over agricultural site dominated by wheat crop and their relationship with ecosystem parameters derived from MODIS. Eddy covariance measurement of CO₂ and H₂O exchanges was carried out at 10 Hz interval and fluxes of CO₂ were computed at half-hourly time steps. The net ecosystem exchange (NEE) was partitioned into gross primary productivity (GPP) and ecosystem respiration (R _e) by taking difference between day-time NEE and respiration. Time-series of daily reflectance and surface temperature products at varying resolution (250–1000 m) were used to derive ecosystem variables (EVI, NDVI, LST). Diurnal pattern in Net ecosystem exchange reveals negative NEE during day-time representing CO₂ uptake and positive during night as release of CO₂. The amplitude of the diurnal variation in NEE increased as LAI crop growth advances and reached its peak around the anthesis stage. The mid-day uptake during this stage was around 1.15 mg CO₂ m⁻² s⁻¹ and night-time release was around 0.15 mg CO₂ m⁻² s⁻¹. Linear and non-linear least square regression procedures were employed to develop phenomenological models and empirical fits between flux tower based GPP and NEE with satellite derived variables and environmental parameters. Enhanced vegetation index was found significantly related to both GPP and NEE. However, NDVI showed little less significant relationship with both GPP and NEE. Furthemore, temperature-greenness (TG) model combining scaled EVI and LST was parameterized to estimate daily GPP over dominantly wheat crop site. (R ² = 0.77). Multi-variate analysis shows that inclusion of LST or air temperature with EVI marginally improves variance explained in daily NEE and GPP.     

Study of the optimum time span for distinguishing <Emphasis Type="Italic">Avena ludoviciana</Emphasis> from wheat crop based on their spectral reflectance characteristics

The study to establish the optimum time span for distinguishing Avena ludoviciana from wheat crop based on their spectral signatures was carried out at Student’s Research Farm, Department of Agronomy during 2006–07 and 2007–08. The experimental sites during both the seasons were sandy loam in texture, with normal soil reaction and electrical conductivity, low in organic carbon and available nitrogen and medium in available phosphorus and potassium. The experiment was laid out in randomized block design with four replications and consisting of twelve treatments comprising 0, 10, 15, 25, 50, 75, 100, 125, 150, 200, 250 plants m⁻² and a pure Avena ludoviciana plot (T_max). The results revealed that in all the treatments irrespective of wheat and weeds, the red reflectance (%) value decreased from 34 to 95 DAS (days after sowing) in 2006–07 and 45 DAS to 100 DAS during 2007–08, and thereafter a sharp increase was observed in all the treatments. This trend might be due to increased chlorophyll index after 34 DAS as red reflectance was reduced by chlorophyll absorption. Among all the treatments, T_max (Pure Avena ludoviciana plot) had the highest red reflectance and T₀ (Pure wheat plot) had a lowest value of red reflectance during both the years. The highest value of IR reflectance was obtained at 95 DAS (2006–07) and 70 DAS (2007–08) in all the treatments. IR reflectance of wheat crop ranged between 24.61 and 61.21 per cent during 2006–07 and 27.33 and 67.3 per cent during 2007–08. However, IR reflectance values declined after 95 DAS and 70 DAS up to harvesting during 2006–07 and 2007–08. This lower reflectance may have been due to the onset of senescence. The highest RR and NDVI values were recorded under pure wheat treatment and minimum under pure weed plots. This may be due to dark green colour and better vigor of the wheat as compared to Avena ludoviciana. It was observed that by using RR and NDVI, pure wheat can be distinguished from pure populations of Avena ludoviciana after 34 DAS and different levels of weed populations can be discriminated amongst themselves from 68 DAS up to 107 DAS during both the years of investigation.     

Assessment of crop growth parameters of wheat under stress condition through ground based spectral data

A field experiment was conducted on wheat crop during rabi seasons of 1995–96, 1996–97 and 1997–98 to study the spectral response of wheat crop (between 490 to 1080 nm) under water and nutrient stress condition. An indigenously developed ground truth radiometer having narrow band in visible and near infrared region (490 – 1080 nm) was used. Vegetation indices derived using different band combinations and related to crop growth parameters. The near infrared spectral region of 710 – 1025 nm was found most important for monitoring stress condition. Relationship has been developed between crop growth parameters and vegetation indices. Leaf Area Index (LAI) and chlorophyll could be predicted by knowing different reflectance ratios at milking stage of crop with R² value of 0.78 and 0.89, respectively. Dry biomass (DBM), Plant Water Content (PWC) and grain yield are also significantly related with reflectance ratios at flowering stage of crop with R² value of 0.90, 0.98 and 0.74, respectively.     

Derivation of crop phenological parameters using multi-date SPOT-VGT-NDVI data: A case study for Punjab

Crop phenological parameters, such as the start and end time of the crop growth, the total length of the growing season, time of peak vegetation and rate of greening and senescence are important for planning crop management and crop diversification/intensification. Multi-temporal remote sensing data provides opportunity to characterize the crop phenology at regional level. This study was conducted during the kharif season of the year 2001–02 for Punjab. The ten-day Normalised Difference Vegetation Index (NDVI) composite products, with 1 km spatial resolution, available from the Vegetation sensor onboard SPOT4 were used for the study. Twenty-one temporal datasets from May 1, 2001 to November 21, 2001 were used. Logical modelling approach was followed to compute the minimum and maximum NDVI, the amplitude of NDVI, the threshold NDVI during sowing and harvest, the crop duration, integrated NDVI and skewness of profile. The analysis showed that before July beginning, in the whole of Punjab, sowing/planting was over. It was found that the crop emergence in the eastern part of the state started earlier than the western region. The maximum NDVI, which represented peak vegetative stage, was above 0.7 and occurred mostly during August. The duration of crops ranged between 90–140 days, with majority between 110–120 days. Total integrated NDVI in Punjab was generally above 60. Using principal component analysis and divergence analysis seven best metrics were selected for crop discrimination.     

Wheat crop production estimation using satellite data

This paper reports acreage, yield and production forecasting of wheat crop using remote sensing and agrometeorological data for the 1998–99 rabi season. Wheat crop identification and discrimination using Indian Remote Sensing (IRS) ID LISS III satellite data was carried out by supervised maximum likelihood classification. Three types of wheat crop viz. wheat-1 (high vigour-normal sown), wheat-2 (moderate vigour-late sown) and wheat-3 (low vigour-very late sown) have been identified and discriminated from each other. Before final classification of satellite data spectral separability between classes were evaluated. For yield prediction of wheat crop spectral vegetation indices (RVI and NDVI), agrometeorological parameters (ET_max and TD) and historical crop yield (actual yield) trend analysis based linear and multiple linear regression models were developed. The estimated wheat crop area was 75928.0 ha. for the year 1998–99, which sowed ?2.59% underestimation with land record commissioners estimates. The yield prediction through vegetation index based and vegetation index with agrometeorological indices based models were 1753 kg/ha and 1754 kg/ha, respectively and have shown relative deviation of 0.17% and 0.22%, the production estimates from above models when compared with observed production show relative deviation of ?2.4% and ?2.3% underestimations, respectively.     

Cotton yield prediction through spectral parameters

To predict the crop yield from spectral parameters, a field experiment was conducted on cotton crop during 1997-98 Kharif season on a sandy loam soil at the Punjab Agricultural Unjversity, Ludhiana. India. Spectral reflectance and agronomic measurements were made for cotton species (American and Desi cotton), sown on two dates (May 1 and May 29) under five nitrogen levels (0, 40, 80, 120 and 160 kg/ha). Regression analysis showed that growth variables had poor correlation with seed cotton yield for all three models, however, yield attributes were significantly and highly correlated for second degree model with seed cotton yield. The integrated Radiance Ratio (RR) and Normalized Difference Vegetation Index (NDVI) measured over time were significantly correlated quadratically with seed cotton yield on three time segment periods viz., 81–110, 111–140 and 141–200 DAS, but highest correlation values were obtained during 81–110 DAS, In American cotton, the highest correlation coefficient for RR and NDVI were 0.91 and 0.81, respectively; whereas for Desi cotton these values were 0.88 and 0.84, respectively.     

Deriving Crop Phenology Metrics and Their Trends Using Times Series NOAA-AVHRR NDVI Data

In this study, an attempt has been made to derive the spatial patterns of temporal trends in phenology metrics and productivity of crops grown, at disaggregated level in Indo-Gangetic Plains of India (IGP), which are helpful in understanding the impact of climatic, ecological and socio-economic drivers. The NOAA-AVHRR NDVI PAL dataset from 1981 to 2001 was stacked as per the crop year and subjected to Savitzky-Golay filtering. For crop pixels, maximum and minimum values of normalized difference vegetation index (NDVI), their time of occurrence and total duration of kharif (June-October) and rabi (November–April) crop seasons were derived for each crop year and later subjected to pixel-wise regression with time to derive the rate and direction of change. The maximum NDVI value showed increasing trends across IGP during both kharif and rabi seasons indicating a general increase in productivity of crops. The trends in time of occurrence of peak NDVI during kharif dominated with rice showed that the maximum vegetative growth stage was happening early with time during study period across most of Punjab, North Haryana, Parts of Central and East Uttar Pradesh and some parts of Bihar and West Bengal. Only central parts of Haryana showed a delay in occurrence of maximum vegetative stage with time. During rabi, no significant trends in occurrence of peak NDVI were observed in most of Punjab and Haryana except in South Punjab and North Haryana where early occurrence of peak NDVI with time was observed. Most parts of Central and Eastern Uttar Pradesh, North Bihar and West Bengal showed a delay in occurrence of peak NDVI with time. In general, the rice dominating system was showing an increase in duration with time in Punjab, Haryana, Western Uttar Pradesh, Central Uttar Pradesh and South Bihar whereas in some parts of North Bihar and West Bengal a decrease in the duration with time was also observed. During rabi season, except Punjab, the wheat dominating system was showing a decreasing trend in crop duration with time.     

Analysis of Seasonal Variability of Vegetation and Methane Concentration over India using SPOT-VEGETATION and ENVISAT-SCIAMACHY Data

This paper highlights the spatial and temporal variability of atmospheric columnar methane (CH₄) concentration over India and its correlation with the terrestrial vegetation dynamics. SCanning IMaging Absorption spectrometer for Atmospheric CHartographY (SCIAMACHY) on board ENVIronmental SATellite (ENVISAT) data product (0.5° × 0.5°) was used to analyze the atmospheric CH₄ concentration. Satellite Pour l'Observation de la Terre (SPOT)-VEGETATION sensor’s Normalized Difference Vegetation Index (NDVI) product, aggregated at 0.5° × 0.5° grid level for the same period (2004 and 2005), was used to correlate the with CH₄ concentration. Analysis showed mean monthly CH₄ concentration during the Kharif season varied from 1,704 parts per billion volume (ppbv) to 1,780 ppbv with the lowest value in May and the highest value in September. Correspondingly, mean NDVI varied from 0.28 (May) to 0.53 (September). Analysis of correlation between CH₄ concentration and NDVI values over India showed positive correlation (r = 0.76; n = 6) in Kharif season. Further analysis using land cover information showed characteristic low correlation in natural vegetation region and high correlation in agricultural area. Grids, particularly falling in the Indo-Gangetic Plains showed positive correlation. This could be attributed to the rice crop which is grown as a predominant crop during this period. The CH₄ concentration pattern matched well with growth pattern of rice with the highest concentration coinciding with the peak growth period of crop in the September. Characteristically low correlation was observed (r = 0.1; n = 6) in deserts of Rajasthan and forested Himalayan ecosystem. Thus, the paper emphasizes the synergistic use of different satellite based data in understanding the variability of atmospheric CH₄ concentration in relation to vegetation.     

A Comparative Analysis of Phenological Curves for Major Crops in Kansas

The goal of this research was to conduct an initial investigation into whether a time-series NDVI reference curve library for crops over a growing season for one year could be used to map crops for a different year. Time-series NDVI libraries of curves for 2001 and 2005 were investigated to ascertain whether or not the 2001 dataset could be used to map crops for 2005. The 2005 16-day composite MODIS 250 m NDVI data were used to extract NDVI values from 1,615 field sites representing alfalfa, corn, sorghum, soybeans, and winter wheat. A k-means cluster analysis of NDVI values from the field sites was performed to identify validation sites with time-series NDVI spectral profiles characteristic of the major crop types grown in Kansas. After completing the field site refinement process, there were 1,254 field sites retained for further analysis, referred to as "final" field sites. The methods employed to evaluate whether the MODIS-based NDVI profiles for major crops in Kansas are stable from year-to-year involved both graphical and statistical analyses. First, the time-series NDVI values for 2005 from the final field sites were aggregated by crop type and the crop NDVI profiles were then visually assessed and compared to the profiles of 2001 to ascertain if each crop's unique phenological pattern was consistent between the two years. Second, separability within each crop class in the time-series NDVI data between 2001 and 2005 was investigated numerically using the Jeffries-Matusita (JM) distance statistic. The results seem to suggest that time-series NDVI response curves for crops over a growing period for one year of valid ground reference data may be useful for mapping crops for a different year when minor temporal shifts in the NDVI values (resulting from inter-annual climate variations or changes in agricultural management practices) are taken into account.     

Relation of wheat yield with parameters derived from a spectral growth profile

An attempt has been made to generate crop growth profiles using multi-date NOAA AVHRR data of wheat-growing season of 1987–88 for the districts of Punjab and Haryana states of India. A profile model proposed by Badhwar was fitted to the multi-date Normalised Difference Vegetation Index (NDVI) values obtained from geographically referenced samples in each district. A novel approach of deriving a set of physiologically meaningful profile parameters has been outlined and the relation of these parameters with district wheat yields has been studied in order to examine the potential of growth profiles for crop-yield modelling. The parameter ‘area under the profile’ is found to be the best estimator of yield. However, with such a parameter time available for prediction gets reduced. Combination of different profile parameters shows improvement in correlation but lacks the consistency for individual state data.     

Studies on spectral reflectance under normal and nitrogen,phosphorus and pest and disease stress condition in soybean (<Emphasis Type="Italic">Glycine max</Emphasis> L.)

The results emerged out of the studies on spectral reflectance under normal and nitrogen and phosphorus stress condition in soybean (Glycine max L.) conducted at Marathwada Agricultural University experimental farm, Parbhani duringkharif 2004–05 showed that crop growth and bio-physiological parameters viz., Height, chlorophyll, leaf area index and total biomass influenced by pest and disease and nutrient stress resulted in detectable spectral reflectance variation. Poor crop growth, reduced canopy cover, chlorophyll content and biomass production are the effects observed in nutrient deficient crops. These above changes in soybean crop were related to spectral indices (RVI and NDVI) that are resulted in discrimination of stressed and normal (non-stressed) soybean crop.     

Spectral models for estimation of chlorophyll content, growth and yield of wheat crop

Field experiments were conducted during 1998–99 and 1999–2000 at research farm of the Department of Agricultural Meteorology, CCS Haryana Agricultural University, Hisar. Five wheat cultivars: WH 542, PBW 343, UP 2338, Raj 3765 and Sonak were sown on 25^th November, 10^th and 25^th December with four nitrogen levels viz., no nitrogen. 50, 100 and 150% of recommended dose. Leaf area index, dry matter at anthesis, final dry biomass and grain yield were recorded in all the treatments. Chlorophyll and wax contents of wheat leaves were estimated at different growth stages. Multiband spectral reflectance was measured using hand-held radiometer. Spectral indices such as simple ratio, normalized difference, transformed vegetation index, perpendicular vegetation index and greenness index were computed using the multiband spectral data. Values of all the spectral indices were maximum in 25 November sown crop with maximum dose of nitrogen (180 kg N ha^-1). PBW 343 showed higher values of all the spectral indices in comparison with other cultivars. The spectral indices recorded during maximum leaf area index stage were correlated with crop parameters. Using stepwise regression, empirical models for chlorophyll, leaf area index, dry biomass and yield prediction were developed. The ’R²’ values of these models ranged between 0.87 and 0.95.     

统计数据总量约束下全局优化阈值的冬小麦分布制图

大范围、长时间和高精度农作物空间分布基础农业科学数据的准确获取对资源、环境、生态、气候变化和国家粮食安全等问题研究具有重要现实意义和科学意义。本文针对传统阈值法农作物识别过程中阈值设置存在灵巧性差和自动化程度低等弱点,以中国粮食主产区黄淮海平原内河北省衡水市景县为典型实验区,首次将全局优化算法应用于阈值模型中阈值优化选取,开展了利用全局优化算法改进基于阈值检测的农作物分布制图方法创新研究。以冬小麦为研究对象,国产高分一号(GF-1)为主要遥感数据源,在作物面积统计数据为总量控制参考标准和全局参数优化的复合型混合演化算法SCE-UA (Shuffled Complex Evolution-University of Arizona)支持下,提出利用时序NDVI数据开展阈值模型阈值参数自动优化的冬小麦空间分布制图方法。最终,获得实验区冬小麦阈值模型最优参数,并利用优化后的阈值参数对冬小麦空间分布进行提取。通过地面验证表明,利用本研究所提方法获取的冬小麦识别结果分类精度均达到较高水平。其中冬小麦识别结果总量精度达到了99.99%,证明本研究所提阈值模型参数优化方法冬小麦提取分类结果总量控制效果良好;同时,与传统的阈值法、最大似然和支持向量机等分类方法相比,本研究所提阈值模型参数优化法区域冬小麦作物分类总体精度和Kappa系数分别都有所提高,其中,总体精度分别提高4.55%、2.43%和0.15%,Kappa系数分别提高0.12、0.06和0.01,这体现出SCE-UA全局优化算法对提高阈值模型冬小麦空间分布识别精度具有一定优势。以上研究结果证明了利用本研究所提基于作物面积统计数据总量控制以及SCE-UA全局优化算法支持下阈值模型参数优化作物分布制图方法的有效性和可行性,可获得高精度冬小麦作物空间分布制图结果,这对提高中国冬小麦空间分布制图精度和自动化水平具有一定意义,也可为农作物面积农业统计数据降尺度恢复重建和大范围区域作物空间分布制图研究提供一定技术参考。     

Full year crop monitoring and separability assessment with fully-polarimetric L-band UAVSAR: A case study in the Sacramento Valley,California

Spatial and temporal information on plant and soil conditions is needed urgently for monitoring of crop productivity. Remote sensing has been considered as an effective means for crop growth monitoring due to its timely updating and complete coverage. In this paper, we explored the potential of L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data for crop monitoring and classification. The study site was located in the Sacramento Valley, in California where the cropping system is relatively diverse. Full season polarimetric signatures, as well as scattering mechanisms, for several crops, including almond, walnut, alfalfa, winter wheat, corn, sunflower, and tomato, were analyzed with linear polarizations (HH, HV, and VV) and polarimetric decomposition (Cloude–Pottier and Freeman–Durden) parameters, respectively. The separability amongst crop types was assessed across a full calendar year based on both linear polarizations and decomposition parameters. The unique structure-related polarimetric signature of each crop was provided by multitemporal UAVSAR data with a fine temporal resolution. Permanent tree crops (almond and walnut) and alfalfa demonstrated stable radar backscattering values across the growing season, whereas winter wheat and summer crops (corn, sunflower, and tomato) presented drastically different patterns, with rapid increase from the emergence stage to the peak biomass stage, followed by a significant decrease during the senescence stage. In general, the polarimetric signature was heterogeneous during June and October, while homogeneous during March-to-May and July-to-August. The scattering mechanisms depend heavily upon crop type and phenological stage. The primary scattering mechanism for tree crops was volume scattering (>40%), while surface scattering (>40%) dominated for alfalfa and winter wheat, although double-bounce scattering (>30%) was notable for alfalfa during March-to-September. Surface scattering was also dominant (>40%) for summer crops across the growing season except for sunflower and tomato during June and corn during July-to-October when volume scattering (>40%) was the primary scattering mechanism. Crops were better discriminated with decomposition parameters than with linear polarizations, and the greatest separability occurred during the peak biomass stage (July-August). All crop types were completely separable from the others when simultaneously using UAVSAR data spanning the whole growing season. The results demonstrate the feasibility of L-band SAR for crop monitoring and classification, without the need for optical data, and should serve as a guideline for future research.     

Winter wheat yield forecasting in Ukraine based on Earth observation,meteorological data and biophysical models

Ukraine is one of the most developed agriculture countries and one of the biggest crop producers in the world. Timely and accurate crop yield forecasts for Ukraine at regional level become a key element in providing support to policy makers in food security. In this paper, feasibility and relative efficiency of using moderate resolution satellite data to winter wheat forecasting in Ukraine at oblast level is assessed. Oblast is a sub-national administrative unit that corresponds to the NUTS2 level of the Nomenclature of Territorial Units for Statistics (NUTS) of the European Union. NDVI values were derived from the MODIS sensor at the 250 m spatial resolution. For each oblast NDVI values were averaged for a cropland map (Rainfed croplands class) derived from the ESA GlobCover map, and were used as predictors in the regression models. Using a leave-one-out cross-validation procedure, the best time for making reliable yield forecasts in terms of root mean square error was identified. For most oblasts, NDVI values taken in April–May provided the minimum RMSE value when comparing to the official statistics, thus enabling forecasts 2–3 months prior to harvest. The NDVI-based approach was compared to the following approaches: empirical model based on meteorological observations (with forecasts in April–May that provide minimum RMSE value) and WOFOST crop growth simulation model implemented in the CGMS system (with forecasts in June that provide minimum RMSE value). All three approaches were run to produce winter wheat yield forecasts for independent datasets for 2010 and 2011, i.e. on data that were not used within model calibration process. The most accurate predictions for 2010 were achieved using the CGMS system with the RMSE value of 0.3 t ha⁻¹ in June and 0.4 t ha⁻¹ in April, while performance of three approaches for 2011 was almost the same (0.5–0.6 t ha⁻¹ in April). Both NDVI-based approach and CGMS system overestimated winter wheat yield comparing to official statistics in 2010, and underestimated it in 2011. Therefore, we can conclude that performance of empirical NDVI-based regression model was similar to meteorological and CGMS models when producing winter wheat yield forecasts at oblast level in Ukraine 2–3 months prior to harvest, while providing minimum requirements to input datasets.     

On spectral indices as a function of soil variability in safflower crop

Spectral indices as an indicator of physiological traits affecting safflower yield in relation to soil variability were evaluated in a two year experiment (1997–1999). Reflectance, biometric and phonological data were collected. Two indices namely normalized differential vegetation index (NDVI) and ratio of spectral reflectance in infrared region to red region (1R/R) were derived from radiometric observation. Yield data indicated significant difference in different soils. Temporal NDVI behaviour as a function of soil type was not prominent especially in early stages of crop growth. However NDVI at 75 days after sowing (DAS) was found to be relatively better indicator of plant status and yield. IR/R was relatively less effective in indicating the differential response of crop to soil types. Effect of soil and crop interaction on spectral indices was significant at 75 and 90 DAS, which was attributed to attainment of maximum leaf area and leaf area at these stages of growth. Regression analysis showed strong positive relationship between NDVI and leaf area, dry matter and yield. IR/R and leaf area had the strongest and consistent relationship (r = 0.96). A single regression equation accounted for yield variability in the dataset. Thus possible transformation of NDVI maps (satellite data) to LAI units and consequently applications like yield forecasting was indicated. Utility of spectra-temporal data as a pointer of plant development status and yield was also demonstrated.     

Observations on temporal red shift of wheat from multi-date spaceborne MOS-B spectrometer data

High spectral resolution spectroscopy enables to have detailed information on chemical and morphological status of crop. An attempt of using space platform for detecting red edge shift during different growth stages of wheat crop is reported. Study was conducted during rabi 1996–97 season using Modular Opto-Electronic Scanner MOS-B Imaging data onboard IRS-P3 satellite. Inverted Gaussian model was fitted for satellite derived reflectances between 650 and 870 nm to derive inflection wavelength and its subsequent change with crop stages i.e. red shift. Red shift of 10 nm observed from crown root initiation stage (703.8 nm) to peak vegetative stage (714.2 nm). A comparative study on temporal behaviour of vegetative indices like NDVI and ARVI with Red edge showed that latter is more atmospherically stable parameter. It is concluded that red edge shift which hitherto has been observed from ground and airborne sensors, can also be detected from space.