Effect of acquisition date and TM spectral bands on wheat,mustard and gram classification accuracies

Single date classification accuracies of wheat, mustard and gram were investigated using TM data of five acquisition dates (January 24, February 9, 16,25 and March 12, 1988) and four band-combinations (TM 234 TM 345, TM 1234 and TM 2345) over an irrigated, optimum fertility site in Hisar (Haryana). Accuracies for wheat and gram were lowest on January 24 for all band-combinations and improved with later acquisitions. An interaction between acquisition date and band combination was apparent as accuracies with most optimal combinations remained high over the period from February 9 to March 12 ,while those with sub optimal combinations fluctuated widely from one date to another. The band-combinations which included middle-infrared (TM 2345 and TM 345) showed highest accuracies irrespective of crop and acquisition date while band combination of TM 234 consistently had lowest accuracies.     

Evaluation of potential usefulness of irs-1c wifs simulated data for dryland rabi sorghum crop discrimination

Two band simulad WiFS data for five dates correspfonding to rabi sorghun growing season of 1993-94 has been generated for Aurangabad district of Maharashtra. Ground truth data has been used for supervised classificatioa of one date raw image and five date NDVI of simulated WiFS data and the results were compared with those derived from single date IRS LISS I data. Analysis of classification accuracies indicate that single date WIFS data gives slightly lower accuracy of 79 per cent against 81 per cent obtained for single date LISS I data. Overall accuracy for 5-date WiFS data is 96 per cent which shows that classification performance of five date WiFS NDVI data is far superior to the single date data of the IRS-IC WiFS as well as the IRS LISS I. The study thus shows the importance of temporal domain of data acquisition in sorghum crop discrimination, Growth profile for sorghum and other crop classes were generated from multidate WiFS derived NDVI data. Differences in growth profiles of sorghum vigour classes as well as amongst different crop types and forests corroborate the premise of better discrimination of crop types and their vigour on multidate remotely sensed data.     

District-level mustard crop estimation for rajasthan: improving precision by in-season digital stratification

The acreage and yield of mustard crop in Rajasthan shows year to year variation. In the present study CAPE, analysis by incorporating digital stratification with current season data and comparison of coefficient variation (CV) at district level using conventional stratification with previous season data was undertaken. The stratification approach using current year’s data for mustard acreage estimation was adopted during 1994-95 and 1995-96 crop seasons and regional CV of less than 2 per cent was attained. A comparison of CV at district level for the years 1994-95 and 1995-96 with those obtained in previous two seasons (1992-93, 1993-94) indicated considerable improvement in precision (lower CV) is 7 out of 11 study districts. Mustard acreage estimate for Bharatpur (1995-96) had CV of 10.1 percent when conventional approach (past year data) for stratification was used. However, with the use of current year data for stratification CV reduced to 4.4 per cent The study suggests that use of in-season data for stratification improves precision for acreage estimation of crops like mustard which has high year to year variation in area.     

Study of irrigation and crop water requirements and growth of two <Emphasis Type="Italic">Rabi</Emphasis> crops grown in a semi arid region using agrometeorology and remote sensing

Irrigation water requirements of wheat and mustard crops grown in Western Yamuna Canal Command area were estimated using FAO model CROPWAT with the help of agrometeorological and remote sensing data (1986–1998 and 2008). The variations in irrigation water requirements of these two crops were judged by calculating coefficient of Variations (CVs) of yearly data. Crop coefficient values were obtained through FAO (1993) method. Supervised Maximum Likelihood Classification (MXL) of IRS 1B image was done to estimate area under wheat and mustard in the canal command. Water need was calculated from amount of supply and water requirement for the whole area. Results showed that ET_crop values of both wheat and mustard varied very little over different years (CVs 4.7% and 5.6% respectively). Irrigation water requirements of both these crops were having relatively large variations (CVs 14.1% and 22.6% respectively) which were mainly because of high variations of their effective rainfall (CVs 61.1% and 69.2% respectively). In general, increase in amount of irrigation enhanced the growth performance of the wheat crop. Increase in distribution equity within soil associations slightly improved the growth performance of the wheat crop. Agro-climatic data merged with satellite image approximated the deficiency of applied irrigation amount (549.5 ha-m for wheat and 692.7 ha-m for mustard) as compared to requirement.     

Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series

The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types and land surface characteristics, the ability to discriminate land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may impede their application. The optimisation of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance (FI) measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Forest, Water, Settlement, Peatland) and Grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland based on a 9-year time-series of MODIS Terra 16 day composite data (MOD13Q1). Feature Importances for each acquisition period of the Enhanced Vegetation Index (EVI) and Normalised Difference Vegetation Index (NDVI) were calculated for both classification scenarios. In the general land cover classification, the months December and January showed the highest, and July and August the lowest separability for both VIs over the entire nine-year period. This temporal separability was reflected in the classification accuracies, where the optimal choice of image dates outperformed the worst image date by 13% using NDVI and 5% using EVI on a mono-temporal analysis. With the addition of the next best image periods to the data input the classification accuracies converged quickly to their limit at around 8–10 images. The binary classification schemes, using two classes only, showed a stronger seasonal dependency with a higher intra-annual, but lower inter-annual variation. Nonetheless anomalous weather conditions, such as the cold winter of 2009/2010 can alter the temporal separability pattern significantly. Due to the extensive use of the NDVI for land cover discrimination, the findings of this study should be transferrable to data from other optical sensors with a higher spatial resolution. However, the high impact of outliers from the general climatic pattern highlights the limitation of spatial transferability to locations with different climatic and land cover conditions. The use of high-temporal, moderate resolution data such as MODIS in conjunction with machine-learning techniques proved to be a good base for the prediction of image acquisition timing for optimal land cover classification results.     

Discriminating transplanted and direct seeded rice using Sentinel-1 intensity data

Improved rice crop and water management practices that make the sustainable use of resources more efficient are important interventions towards a more food secure future. A remote sensing-based detection of different rice crop management practices, such as crop establishment method (transplanting or direct seeding), can provide timely and cost-effective information on which practices are used as well as their spread and change over time as different management practices are adopted. Establishment method cannot be easily observed since it is a rapid event, but it can be inferred from resulting observable differences in land surface characteristics (i.e. field condition) and crop development (i.e. delayed or prolonged stages) that take place over a longer time. To examine this, we used temporal information from Synthetic Aperture Radar (SAR) backscatter to detect differences in field condition and rice growth, then related those to crop establishment practices in Nueva Ecija (Philippines). Specifically, multi-temporal, dual-polarised, C-band backscatter data at 20m spatial resolution was acquired from Sentinel-1A every 12 days over the study area during the dry season, from November 2016 to May 2017. Farmer surveys and field observations were conducted in four selected municipalities across the study area in 2017, providing information on field boundaries and crop management practices for 61 fields. Mean backscatter values were generated per rice field per SAR acquisition date. We matched the SAR acquisition dates with the reported dates for land management activities and with the estimated dates for when the crop growth stages occurred. The Mann-Whitney U test was used to identify significant differences in backscatter between the two practices during the land management activities and crop growth stages. Significant differences in cross-polarised, co-polarised and band ratio backscatter values were observed in the early growing season, specifically during land preparation, crop establishment, rice tillering and stem elongation. These findings indicate the possibility to discriminate crop establishment methods by SAR at those stages, suggesting that there is more opportunity for discrimination than has been presented in previous studies. Further testing in a wider range of environments, seasons, and management practices should be done to determine how reliably rice establishment methods can be detected. The increased use of dry and wet direct seeding has implications for many remote sensing-based rice detection methods that rely on a strong water signal (typical of transplanting) during the early season.     

Discrimination between Babul plantations and Mustard crop using Polarimetric C- and L- band data

Radar sensors can be used for large-scale vegetation mapping and monitoring using backscattering coefficients in different polarizations and wavelength bands. C-band space borne SAR is widely used for the classification of agricultural crops, but can only perform a limited discrimination of various tree species. This paper presents the results of discrimination between mustard crop and babul plantation (Prosopis sp.) using quad polarisation Radarsat 2 and ALOS PALSAR data. Study area is comprised of dense babul plantation along the canal, mustard crop on one side of the canal and Fallow land near to Ramgarh village of Jaisalmer district. Three bands of Radarsat (HH, HV and VV) acquired during peak mustard crop growth stage were integrated with four polarizations (HH, HV, VH and VV) of ALOS PALSAR acquired when crop cover was absent. Using only Radarsat data Jefferies-Matusita (JM) separability between mustard crop and babul plantation was found to be poor (710). Where as in the seven band combination the separability was observed to be high (1374). Among the different polarizations three layer combination, highest separability was observed using cross polarizations (HV and VH) of L-band with any one of the Radarsat Polarisation (HH/HV/VV). This combination of C- and L-band resulted in easy separation of mustard and babul plantation which was otherwise difficult using only Radarsat data.     

Geostatistical and visualization analysis of crop suitability for diversification in sub-mountain area of Punjab,North-West India

This study presents a Geographic Information System (GIS)-based geostatistical and visualization analysis of crop suitability in two blocks of sub-mountain area of Punjab under diversification programme. It combines the limitation approach of land capability classification, productivity potential evaluation procedure and crop suitability evaluation framework of FAO. Two blocks from the sub mountain Siwalik region of Punjab viz., Mahalpur and Garhshankar were selected. This study evaluates the capabilities of the study area for traditional crops like wheat, paddy and maize, and recently introduced crops like sugarcane, sunflower, pea, rapeseed-mustard, potatoes and kinnow for agricultural diversification. The suitability of the crops has been worked out at the village level. About 35–40 per cent of total area mostly in Siwallik hills is not fit for growing any type of crop. Sandy texture, uneven topography, moderately steep slopes and excessive drainage are responsible for unsuitability of this area. The GIS based suitability analysis for traditional crops as well as for new crops, under diversification of agriculture has been undertaken. The geostatistical analysis points towards suitability of relatively large areas for new crops like sunflower, potato, pea (green) and sugarcane. Forty three and 14 per cent of total area has been found highly suitable and suitable respectively for growing green pea - a cash crop. Thirty three per cent of total area is suitable for growing kinnow fruit. The success of diversification programme is subject to logical government policy in terms of providing cold storage, food processing facility and marketing infrastructure.     

Application of ENVISAT ASAR Data in Mapping Rice Crop Growth in Southern China

This research letter presents preliminary results of mapping rice crop growth using ENVISAT advanced synthetic aperture radar (ASAR) alternating polarization HH/HV data. Four ASAR HH/HV images were collected in the early rice-growth cycle in the test site in 2006, and the temporal response of ASAR data to the rice field was analyzed. The height and biomass of rice were measured during acquisition of ASAR data, and empirical relationships were established between the backscattering coefficient and these two parameters. Based on the temporal variation of the radar response, a method for mapping a rice growth area was developed using the combination of ASAR HH and HV polarization data between two acquisition dates. The results confirm that C-band SAR data have great potential in the development of an operational system for monitoring rice crop growth in Southern China.     

Wheat crop inventory using high spectral resolution irs-p3 mos-b spectrometer data

Modular Optoelectronic Scanner (MOS-B) spectrometer data over parts of Northern India was evaluated for wheat crop monitoring involving (a) sub pixel wheat fractional area estimation using spectral unmixing approach and (b) growth assessment by red edge shift at different phenological stages. Red shift of 10 nm was observed between crown root initiation stage to flowering stage. Wheat fraction estimates using linear spectral unmixing on Feb. 13, 1999 acquisition of MOS-B data had high correlation (0.82) with estimates from Wide Field Sensor (WiFS) data acquired on same date by IRS-P3 platform. It was observed that five bands (4,5,8,12,13 MOS-B bands) are sufficient for signature separability of major land cover classes viz. wheat, urban, wasteland, and water based on purely spectral separability criterion using Transformed Divergence (T.D.) approach. Higher number of bands saturated the T.D. values. In contrast, performance of sub pixel fractional area estimation using unmixing decreased drastically for eight bands (4,5,6,7,8,9,12,13 MOS-B bands) chosen from optimal band selection criteria in comparison to full set of 13 bands. The relative deviation between area estimated from Wifs and MOS-B increased from 1.72 percent when all thirteen bands were used in unmixing to 26.10 percent for the above eight bands.     

Utility of satellite data for land evaluation in land use planning for a part of Northern Karnataka

The study reported herein deals with the utility of satellite remote sensing techniques for land evaluation for agricultural land use planning. False colour composite of Landsat imagery in the scale of 1:250,000 was visually interpreted for physiography that formed the base for mapping soil and land resources in the field. The small-scale soil map thus prepared has thirteen map units with association of soil families. Soil and land resource units shown on these small-scale maps were evaluated for their suitability for growing sorghum crop by matching the relevant land qualities against the land requirements for sorghum. The land evaluation carried out for growing sorghum crop in the study area revealed that about 38.6 per cent is highly suitable (S1), 31.5 per cent moderately suitable (S2) and 24.5 per cent marginally suitable (S3). An area of about 5.4 per cent is not suitable, of which 3.0 per cent is currently not suitable (N1) and 2.4 per cent permanently not suitable for growing sorghum crop.     

Evaluation of irrigation management in a Canal Command area based on Agrometeorology and Remote Sensing

Efficacy of irrigation management of wheat and mustard crops grown in Western Yamuna Canal Command area was determined in the present study from agro-climatic data merged with Maximum Likelihood Classified (MXL) satellite image and from irrigation scheduling efficiencies obtained through FAO model CROPWAT. For computing irrigation scheduling efficiencies, amount of water supplied at different growth stages, soil water depletion and crop water need have been taken into account. Agro-meteorological data in combination with MXL classified crop map approximated the deficiency of applied irrigation amount compared to requirement. Irrigations at 35-80 Days After Sowing (DAS) for two times of applications, 30-60-90 DAS for three, 21-50-80-110 DAS in case of four and 20-45-70-90-120 DAS in case of five irrigations have yielded better scheduling efficiencies for wheat than other times of applications in all soil associations.     

Multi temporal ERS-1 SAR data for identification of rice crop

Microwave sensors having all-weather capabilities provide an opportunity to monitor rice grown in monsoon season. An attempt has been made to identify rice crop using multitemporal ERS-1 SAR data in C-band (5.3 GHz). Data acquired on August 15 (D1), September 19 (D2), October 24 (D3) and November 28 (D4) 1993 were taken. Combinations of data acquired on different dates were used for identification of rice crop. Single-date IRS-1B LISS II data in visible and NIR bands acquired on October 23, 1993 was also used for comparison of estimated rice area. Analysis of the results has shown that a combination of SAR data acquired at the tillering (August), booting (September) and heading (October) stages of rice crop enabled identification and area estimation of rice crop grown under lowland conditions. Single-date SAR data acquired in the month of October was found to be better for identification of rice compared to other dates.     

Crop Evapotranspiration Estimation for Wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.) Using Remote Sensing Data in Semi-Arid Region of Maharashtra

Penman–Monteith method adapted to satellite data was used for the estimation of wheat crop evapotranspiration during the entire growth period using satellite data together with ground meteorological measurements. The IRS-1D/IRS-P6 LISS-III sensor data at 23.5 m spatial resolution for path 096 and row 059 covering the study area were used to derive, albedo, normalized difference vegetation index, leaf area index and crop height and then to estimate wheat crop evapotranspiration referred to as actual evapotranspiration (ETact). The ETact varied from 0.86 to 3.41 mm/day during the crop growth period. These values are on an average 16.40 % lower than wheat crop potential evapotranspiration (ETc) estimated as product of reference crop evapotranspiration estimated by Penman–Monteith method and lysimetric crop coefficient (Kc). The deviation of ETact from ETc is significant, when both the values were compared with t test for paired two sample means. Though the observations on ETact were taken from well maintained unstressed experimental plot of 120 × 120 m size, there was significant deviation. This deviation could be attributed to, the satellite images representing the actual crop evapotranspiration as function crop canopy biophysical parameters, condition of the crop stand, climatic and soil conditions and the microclimate variation over area of one hectare. However, Penman–Monteith method represents a flat rate of specific growth stage of the crop.     

Comparison of vegetation indices for practicable homology

Vegetation indices are widely used to assess quantitatively the biophysical characteristics of vegetation from remote sensing measurements. Different indices have their own advantages in retrieving vegetation information. It is very difficult to precisely attribute any vegetation index to any particular vegetation biophysical parameter. This study examines the correlations among different vegetation indices derived from a set of mustard, gram and wheat fields at three different phenological growth stages. The results are presented as correlation matrices along with correlation scatter plots. Homologous (equi-magnitude) vegetation information is represented by NDVI, PVI and AtRVI for wheat crop with leaf area index less than 1.     

Evaluation of spectral bands and spatial resolution of LISS II and LISS III sensors on-board IRS satellites for crop identification

The present study evaluates the performance of Indian Remote Sensing (JRS) LISS Jl and LISS III data having spatial resolutions of 36 m and 23.5 m respectively in the Classification accuracy of rice, mustard and potato crops grown in West Bengal, India. The role of Middle infra-red (MIR.) band, of IRS 1C LISS III was also investigated in this context. The results indicated that in case of crop like rice which was grown over large contiguous fields, no significant change in classification accuracy was observed between LISS II and LISS III data. However, the accuracy increased by 5–7 per cent with the inclusion of MIR band mainly due to better separability between lowland rice and other hill vegetation. In case of crops like mustard and potato which were grown on small size or less contiguous fields, the classification accuracy increased by 5–8 per cent due to higher spatial resolution of LISS III. Inclusion of MIR band did not improve the accuracy of these crops.     

Optimization of spectral indices and long-term separability analysis for classification of cereal crops using multi-spectral RapidEye imagery

Crop monitoring using remotely sensed image data provides valuable input for a large variety of applications in environmental and agricultural research. However, method development for discrimination between spectrally highly similar crop species remains a challenge in remote sensing. Calculation of vegetation indices is a frequently applied option to amplify the most distinctive parts of a spectrum. Since no vegetation index exist, that is universally best-performing, a method is presented that finds an index that is optimized for the classification of a specific satellite data set to separate two cereal crop types. The η² (eta-squared) measure of association – presented as novel spectral separability indicator – was used for the evaluation of the numerous tested indices. The approach is first applied on a RapidEye satellite image for the separation of winter wheat and winter barley in a Central German test site. The determined optimized index allows a more accurate classification (97%) than several well-established vegetation indices like NDVI and EVI (<87%). Furthermore, the approach was applied on a RapidEye multi-spectral image time series covering the years 2010–2014. The optimized index for the spectral separation of winter barley and winter wheat for each acquisition date was calculated and its ability to distinct the two classes was assessed. The results indicate that the calculated optimized indices perform better than the standard indices for most seasonal parts of the time series. The red edge spectral region proved to be of high significance for crop classification. Additionally, a time frame of best spectral separability of wheat and barley could be detected in early to mid-summer.     

Estimating inter-annual variability in winter wheat sowing dates from satellite time series in Camargue,France

Crop simulation models are commonly used to forecast the performance of cropping systems under different hypotheses of change. Their use on a regional scale is generally constrained, however, by a lack of information on the spatial and temporal variability of environment-related input variables (e.g., soil) and agricultural practices (e.g., sowing dates) that influence crop yields. Satellite remote sensing data can shed light on such variability by providing timely information on crop dynamics and conditions over large areas. This paper proposes a method for analyzing time series of MODIS satellite data in order to estimate the inter-annual variability of winter wheat sowing dates. A rule-based method was developed to automatically identify a reliable sample of winter wheat field time series, and to infer the corresponding sowing dates. The method was designed for a case study in the Camargue region (France), where winter wheat is characterized by vernalization, as in other temperate regions. The detection criteria were chosen on the grounds of agronomic expertise and by analyzing high-confidence time-series vegetation index profiles for winter wheat. This automatic method identified the target crop on more than 56% (four-year average) of the cultivated areas, with low commission errors (11%). It also captured the seasonal variability in sowing dates with errors of ±8 and ±16 days in 46% and 66% of cases, respectively. Extending the analysis to the years 2002–2012 showed that sowing in the Camargue was usually done on or around November 1st (±4 days). Comparing inter-annual sowing date variability with the main local agro-climatic drivers showed that the type of preceding crop and the weather conditions during the summer season before the wheat sowing had a prominent role in influencing winter wheat sowing dates.     

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。     

An evaluation of Radarsat-2 individual and combined image dates for land use/cover mapping

Various land use/cover types exhibit seasonal characteristics which can be captured in remotely sensed imagery. This study examined how different seasons of Radarsat-2 data influence land use/cover classification accuracies for two study sites. Two dates of Radarsat-2 C-band quad-polarised images were obtained for Washington, DC, USA and Wad Madani, Sudan. Spectral signatures were extracted and used with a maximum likelihood decision rule for classification and thematic accuracies were then determined. Both despeckled radar and derived texture measures were examined. Thematic accuracies for the two despeckled image dates were similar with a difference of 3% for Washington and 6% for Sudan. Merging the despeckled images for both seasons increased overall accuracy by 2% for Washington and 9% for Sudan. Further combining the original radar for both seasons with derived texture measures increased overall accuracies by 9% for Washington and 16% for Sudan for final overall accuracy values of 73 and 82%.