National Inventory of Rabi Pulses in India Using Remotely Sensed Data

A scheme called National Food Security Mission was launched by Government of India in 2007 for wheat, rice and pulses crops. At the request of Ministry of Agriculture for monitoring intensification of pulses a project called Pulses Intensification was taken up in Rabi season 2012–2013. Reliable statistics using advanced methods is very important for variety of pulse crops. Remotely sensed data can help in pre-harvest area estimation of pulse crops. Pulses in India are grown as partly scattered and partly contiguous crop. Growth in scattered areas and poor vegetation canopy of some of the pulse crops poses a challenge in its identification and discrimination using remotely sensed data. National Inventory of Rabi pulse crops in major growing regions of northern and southern parts of India was attempted. Multi-date AWiFS data and multi-date NDVI products of AWiFS of Rabi season 2014–2015 were used to study spectral-temporal behavior of pulse crops. Pulse crops accuracies of more than 95 % was observed in contiguous areas and 50–80.77 % in scattered regions. All India area estimate of Rabi pulses for the year 2014–2015 was 8963.327 ‘000 ha.     

A novel method for enhancement of radiometric resolution using image fusion

The Resourcesat-I satellite is equipped with different types of sensors with varied characteristics. For the effective utilization of the available multi-sensor, multi-temporal, multi-spectral and multi-radiometric data from these sensors, fusion of digital image data has become a valuable technique. Image fusion enhances the information content and helps in better discrimination of various land cover types. The Resourcesat-1 has equipped with three sensors, AWiFS, LISS-III and LISS-IV, which are having identical spectral resolutions, with different spatial, radiometric and temporal resolutions. The spatial resolutions ratio of the data set for merging are required to be maximum of 1:6, where as the data sets (AWiFS and LISS-III) that are used in the current study are having the ratio of 1:2.5 approximately. A novel merging technique is designed, which retains the multi-spectral response of the input data in the output data. The merged data set provides the higher spatial and radiometric resolutions. In order to evaluate the fusion merits quantitatively, all the data sets are digitally classified and studied the output classes for homogeneity and clear discrimination. A comprehensive comparative study is carried out between the fused image and the LISS-III image based on the contingency matrix and the scatter plots, which demonstrates the strength of fused image for discriminating the object classes at 23.5 m spatial and 10-bit radiometric resolutions. The merged data set gives the improved classification accuracy.     

Evaluation of approaches for AWiFS multi-date registration

AWiFS onboard IRS-P6 belongs to the category of high-repetivity sensors based on large swath, but with ground trace based on narrow-swath sensor (LISS-III). This is useful for cloud removal as well as vegetation phenology studies. Such multi-date analysis has a prerequisite of accurate multi-date registration. This study investigates the accuracy of multi-date registration over a mixed plain and hilly terrain in northern India (29–31°N latitude and 77.5–79.5°E longitude; 200–4000 m.a.s.l.). Simple polynomial rectification, multi-date registration using ortho-correction technique on standard product (level-2) and radiometric product (level-1) as a function of number of ground control points (GCPs) and external Digital Elevation Model (DEM) were investigated. The results indicated that ortho-rectification on level-1 product provided better accuracy in comparison to simple rectification and ortho-rectification on level-2 product.     

Utilization of Resourcesat-1 data for improved crop discrimination

Subsequent to the launch of the state-of-art third generation Indian Remote Sensing satellite, Resourcesat-1, studies have been conducted to understand the capabilities of the on-board sensors for crop discrimination. The paper discusses the unique capabilities of the AWiFS, LISS-III and LISS-IV sensors in terms of their dimensionality, radiometry and spatial resolutions for crop discrimination and monitoring. The studies have indicated better crop discriminability especially using the short wave infrared data in 1.55–1.70 μm data among the spectrally confusing land cover classes, attributed to the relative differences of water contents. 10-bit radiometry of AWiFS data in four bands has been observed to be a better discriminant. Intrafield variability was very well captured by the LISS-IV data revealing the potential of data for applications like precision farming. The studies have revealed that potential of Resourcesat-1 data becoming the workhorse for several agricultural applications.     

Shoreline and coral reef ecosystem changes in gulf of Mannar, Southeast coast of India

Changes in shoreline, coral reef and seafloor have been mapped using remote sensing satellite data of IRS LISS-III (1998), IRS LISS-II (1988), Survey of India Topographic sheet (1969), Naval Hydrographic Chart (NHO) 1975 and bathymetry data (1999) with ARC-INFO and ARC-VIEW GIS. The analysis of multi-date shoreline maps showed that 4.34 and 23.49 km2 of the mainland coast and 4.14 and 3.31 km² areas of island coast have been eroded and accreted, respectively, in the Gulf of Mannar. The analysis of multi-date coral reef maps showed that 25.52 km² of reef area and 2.16 km² of reef vegetation in Gulf of Mannar have been lost over a period of ten years. The analysis of multi-date bathymetry data indicates that the depth of seafloor has decreased along the coast and around the islands in the study area. The average reduction of depth in seafloor has been estimated as 0.51m over a period of twenty four years. The increased suspended sediment concentration due to coastal and island erosion, and raised reef due to emerging of coast by tectonic movement are responsible for coral reef degradation in the Gulf of Mannar. Validation by ground truth has confirmed these results.     

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.     

Mapping a Specific Crop—A Temporal Approach for Sugarcane Ratoon

Mapping a specific crop using single date multi-spectral imagery remains a challenging task because vegetation spectral responses are considerably similar. The use of multi-temporal images helps to discriminate specific crops as the classifier can make use of the uniqueness in the temporal evolution of the spectral responses of the different vegetated classes. However, one major concern in multi-temporal studies is the selection of optimum dates for the discrimination of crops as the use of all available temporal dates can be counterproductive. In this study this concern was addressed by selecting the best 2, 3, 4… combinations dates. This was done by conducting a separability analysis between the spectral response of the class of interest (here, sugarcane-ratoon) and non-interest classes. For this analysis, we used time series LISS-III and AWiFS sensors data that were classified using Possibilistic c-Means (PCM). This fuzzy classifier can extract single class sub-pixel information. The end result of this study was the detection of best (optimum) temporal dates for discriminating a specific crop, sugarcane-ratoon. An accuracy of 92.8 % was achieved for extracting ratoon crop using AWiFS data whereas the optimum temporal LISS-III data provided a least entropy of 0.437. Such information can be used by agricultural department in selecting an optimum number of strategically placed temporal images in the crop growing season for discriminating the specific crop accurately.     

ALCM: Automatic land cover mapping

It may be quite important for resource management people to extract single land cover class, at sub-pixel level from multi-spectral remote sensing images of different areas in single step processing. It has been observed, that neural network can be trained to extract single land cover class from multi-spectral remote sensing images, but they have problems in setting various parameters and slow during training stage. This paper present single land cover class water, extraction from mixed pixels present in multiple multi-spectral remote sensing data sets of same bands of AWiFS sensor of Resoursesat-1 (IRS-P6) satellite from different areas. In this work fuzzy logic-based algorithm, which is independent of statistical distribution assumption of data, has been studied at sub-pixel level to handle mixed pixels. It has been found; possibilistic c-means (PCM) algorithm takes the possibilistic view, that the membership of a feature vector in a class has nothing to do with its membership in other classes. Due to this, it was observed that PCM can extract only one class, from remote sensing multi-spectral data and it has produced 93.7% and 97.1% overall sub-pixel classification accuracy for two different data sets of different places using LISS-III (IRS-P6) reference data of same dates as of AWiFS data.     

Study on the utility of IRS-P6 AWiFS SWIR band for crop discrimination and classification

This present study was conducted to find out the usefulness of SWIR (Short Wave Infra Red) band data in AWiFS (Advanced Wide Field Sensor) sensor of Resourcesat 1, for the discrimination of different Rabi season crops (rabi rice, groundnut and vegetables) and other vegetations of the undivided Cuttack district of Orissa state. Four dates multi-spectral AWiFS data during the period from 10 December 2003 to 2 May 2004 were used. The analysis was carried out using various multivariate statistics and classification approaches. Principal Component Analysis (PCA) and separability measures were used for selection of best bands for crop discrimination. The analysis showed that, for discrimination of the crops in the study area, NIR was found to be the best band, followed by SWIR and Red. The results of the supervised MXL classification showed that inclusion of SWIR band increased the overall accuracy and kappa coefficient. The ‘Three Band Ratio’ index, which incorporated Red, NIR and SWIR bands, showed improved discrimination in the multi-date dataset classification, compared to other SWIR based indices.     

A Case Study on Forewarning of Yellow Rust Affected Areas on Wheat Crop Using Satellite Data

Objective of this study was to identify stripe rust affected areas of wheat crop as well as evaluation of remote sensing (RS) derived indices. Moderately low temperature and high humidity favour the growth of yellow rust. Most affected areas of Punjab are the foothill districts such as Gurdaspur, Hoshiarpur and Ropar. Occurrence of yellow rust is possible when maximum temperature for day is below 15 °C and Temperature difference of day’s maximum and minimum temperature is less than 5 °C during the early growth of wheat. Forecast of the infestation was done using 3 days forecast of weather data obtained from Weather Research and Forecasting (WRF) model at 5 km resolution. Weather forecast used was obtained from Meteorological and Oceanographic Satellite Data Archival System (MOSDAC) site and post infestation, identification of specific locations were done using multi-date IRS AWiFS data. It is an attempt for early detection through 3 days advance forewarning of weather which will be handy tool for planners to expedite relief measures in case of epidemic with a more focused zones of infestation as well as for crop insurers to know the location and extent of damage affected areas.     

Deducing Rice Crop Dynamics and Cultural Types of Bangladesh Using Geospatial Techniques

Rice crop occupies an important aspect of food security and also contributes to global warming via GHGs emission. Characterizing rice crop using spatial technologies holds the key for addressing issues of global warming and food security as different rice ecosystems respond differently to the changed climatic conditions. Remote sensing has become an important tool for assessing seasonal vegetation dynamics at regional and global scale. Bangladesh is one of the major rice growing countries in South Asia. In present study we have used remote sensing data along with GIS and ancillary map inputs in combination to derive seasonal rice maps, rice phenology and rice cultural types of Bangladesh. The SPOT VGT S10 NDVI data spanning Aus, Aman and Boro crop season (1^st May 2008 to 30^th April 2009) were used, first for generating the non-agriculture mask through ISODATA clustering and then to generate seasonal rice maps during second classification. The spectral rice profiles were modelled and phenological parameters were derived. NDVI growth profiles were modelled and crop calendar was derived. To segregate the rice cultural types of Bangladesh into IPCC rice categories, we used elevation, irrigated area, interpolated rainfall maps and flood map through logical modelling in GIS. The results indicated that the remote sensing derived rice area was 9.99 million ha as against the reported area of 11.28 million ha. The wet and dry seasons accounted for 64% and 36 % of the rice area, respectively. The flood prone, drought prone and deep water categories account for 7.5%, 5.56% and 2.03%, respectively. The novelty of current findings lies in the spatial outcome in form of seasonal and rice cultural type maps of Bangladesh which are helpful for variety of applications.     

The Effect of Radiometric Resolution on the Retrieval of Leaf Area Index from Agricultural Crops

Motivated by the increasing availability of remote sensing data of high radiometric resolution, a study was conducted to determine whether high-resolution data (10 bits or more) yielded more accurate vegetation leaf area index (LAI) information than low-radiometric-resolution multispectral data (7-bit or less). The study evaluated the performance of simulated 12-bit LISS-III sensor data (derived from EO-1 hyperspectral Hyperion data) with original 7-bit LISS-III sensor data for the estimation of LAI of major agricultural crops (e.g., cotton, sugar cane, and rice). There was no significant improvement in the correlation coefficient encountered when using the high-radiometric-resolution (12-bit) LISS-III data versus the low-radiometric-resolution (7-bit) LISS-III data for the retrieval of LAI. The retrieval of LAI of agricultural crops met with moderate success, with overall correlation coefficients of around 0.55. These results suggest that satellite data of very high radiometric resolution may not be a required for remote measurement of LAI. Spectral bandwidth, band placement, and the method of retrieving biophysical parameters may be more important.     

Characterisation of Residue Burning from Agricultural System in India using Space Based Observations

Biomass burning is a global phenomenon with agriculture residue burning having a sizeable share. Biomass burning is a major source of emission of green house gases (GHGs). Thus the space-based observations of global distribution of fire form a key component of climate change studies. This study is a step towards understanding the spatio-temporal occurrence of agricultural residues burning in Indo-Gangetic plains of India using fire products from space borne satellites. The 3 years daily active fire data of MODIS (Aqua/Terra) from August, 2006 to July, 2009 have been used in this study. The data analysis showed that out of total fire events, around 69% contribution comes from agricultural areas and remaining (31%) comes from non-agricultural areas. This is mainly due to the intensive cultivation in this belt. The characterisation analysis revealed that, 84% of agriculture residues burning is from Rice-Wheat system (RWS) alone and remaining 16% in other types of crop rotations. The fire incidents were very high in October–December (55%) compared to that in March–May (36%), further indicating that burning of rice residue is more prevalent than that of wheat.     

Village level crop inventory using remote sensing and field survey data

This paper presents results of a pilot study in six villages located in the states of Haryana, Rajasthan and Madhya Pradesh, to evaluate accuracy of crop area at village level estimated by IRS - LISS-I1I data with respect to detailed field survey carried out by National Sample Survey Organization. The selected villages were located in Karnal, Kota and Bhopal districts which represented single dominant wheat crop as well as wheat-mustard and wheat-gram situation, respectively. Accuracy assessment of remote sensing based estimate with field survey of NSSO showed relative deviation in wheat estimate ranging from 3.72 percent for Mainmati village in Karnal district in Haryana to 22.65 percent fo Ranpur village in Kota district of Rajasthan. It was found that relative deviation in area estimation is inversely poportional to the crop proportion in that village. Observations of over estimation at low crop proportion and underestimation at higher crop proportion was explained by simple budgeting of relative proportion of ommision and commision errors. The study demonstrates that on the average, 90 percent crop area accuracy is possible with LISS-II1 data and the adopted approach.     

Spectral characteristics of sensors onboard IRS-1D and P6 satellites: Estimation and their influence on surface reflectance and NDVI

This paper reports the results of a modeling study carried out with two objectives, (1) to estimate and compare effective spectral characteristics (central wavelength, bandwidth and bandpass exo-atmospheric solar irradiance E_o) of various spectral channels of LISS-III, WiFS, LISS-III*, LISS-IV and AWiFS onboard Indian Remote Sensing Satellites IRS-ID and P6 using moment method based on the laboratory measurements of sensor spectral response, and (2) to quantify the influence of varying sensor spectral response on reflectance and Normalized Difference Vegetation Index (NDVI) measurements using surface reflectance spectra corresponding to different leaf area index conditions of crop target obtained through field experiment. Significant deviation of 4 to 14 nm in central wavelength and 1.6 to 14.07 nm in spectral width was observed for the corresponding channel of IRS sensors. Coefficient of variation of the order of 0.1 to 1.11% was noticed in E_o among various IRS sensors, which could induce a difference of 0.72 to 3.35% in the estimation of top of atmosphere reflectance for crop target. The variation in spectral response of IRS sensors implied a relative difference of the order of 0.91 to 3.38% in surface reflectance and NDVI measurements. Polynomial approximations are also provided for spectral correction that can be utilized for normalizing the artifacts introduced due to differences in spectral characteristics among IRS sensors.     

Satellite-based investigation of flood-affected rice cultivation areas in Chao Phraya River Delta,Thailand

The occurrence of catastrophic floods in Thailand in 2011 caused significant damage to rice agriculture. This study investigated flood-affected rice cultivation areas in the Chao Phraya River Delta (CRD) rice bowl, Thailand using time-series moderate resolution imaging spectroradiometer (MODIS) data. The data were processed for 2008 (normal flood year) and 2011, comprising four main steps: (1) data pre-processing to construct time-series MODIS vegetation indices (VIs), to filter noise from the time-series VIs by the empirical mode decomposition (EMD), and to mask out non-agricultural areas in respect to water-related cropping areas; (2) flood-affected area classification using the unsupervised linear mixture model (ULMM); (3) rice crop classification using the support vector machines (SVM); and (4) accuracy assessment of flood and rice crop mapping results. The comparisons between the flood mapping results and the ground reference data indicated an overall accuracy of 97.9% and Kappa coefficient of 0.62 achieved for 2008, and 95.7% and 0.77 for 2011, respectively. These results were reaffirmed by close agreement (R² > 0.8) between comparisons of the two datasets at the provincial level. The crop mapping results compared with the ground reference data revealed that the overall accuracies and Kappa coefficients obtained for 2008 were 88.5% and 0.82, and for 2011 were 84.1% and 0.76, respectively. A strong correlation was also found between MODIS-derived rice area and rice area statistics at the provincial level (R² > 0.7). Rice crop maps overlaid on the flood-affected area maps showed that approximately 16.8% of the rice cultivation area was affected by floods in 2011 compared to 4.9% in 2008. A majority of the flood-expanded area was observed for the double-cropped rice (10.5%), probably due to flood-induced effects to the autumn–summer and rainy season crops. Information achieved from this study could be useful for agricultural planners to mitigate possible impacts of floods on rice production.     

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.     

Synergistic use of multi-temporal Radarsat SAR and AWiFS data for <Emphasis Type="Italic">Rabi</Emphasis> rice identification

A national level project on kharif rice identification and acreage estimation is being carried out successfully for several states in the country. A similar methodology based on the temporal profile for identification and delineation of various land cover classes has been followed for the Rabi rice acreage estimation. To define rabi rice, rabi season in India starts from November — February to March — June. Though the main growing season is predominantly winter but the uncertainty of getting cloud free data during the season has resulted in the use of microwave data. A feasibility study was taken up for early forecasting of the rabi rice area using microwave data. Hierarchical decision rule classification technique was used for the identification of the different land cover classes. Land preparation, puddling and transplantation were the reasons for the specific backscatter of rice growing areas. The increase or decrease in the SAR backscatter due to progress in the crop phenology or due to delayed sowing respectively forms the basis for identifying the rice areas. In addition the potential of optical data of a later date has been utilized in the form of various indices from bands including MIR to distinctly separate the late sown areas and also the puddled areas from other areas. This study emphasizes the synergistic use of SAR and optical data for delineating the rabi rice areas which is of immense use in giving an early forecast.     

利用神经网络方法提取水稻种植面积—以湖北省双季早稻为例

在利用NOAA数据提取水稻种植面积的过程中，由于其地面分辨率较低，存在大量混合像元问题，使得提取精度不够理想，该文基于神经网络方法即可以提供多源数据的输入，又不受数据分布假设限制的特点，从NOAA图像演算最能反应朋稻分布信息的绿度指数（NDVI）和日夜温差值，将其重采样，然后加入对水稻生产区域有重要影响的土壤类型，土地利用类型及高程分布等信息，以TM图像作为准直值进行分类，获得较为理想的湖北省双季早稻种植面积。     

Combining the FCM Classifier with Various Kernels to Handle Non-linearity of Class Boundaries

This article presents the use of kernel functions in fuzzy classifiers for an efficient land use/land cover mapping. It focuses on handling mixed pixels obtained from a remote sensing image by considering non-linearity between class boundaries. It uses kernel functions combined with the conventional fuzzy c-means (FCM) classifier. Kernel-based fuzzy c-mean classifiers were applied to classify AWiFS and LISS-III images from Resourcesat-1 and Resourcesat-2 satellites. Optimal kernels were obtained from eight single kernel functions. Fractional images generated from high resolution LISS-IV image were used as reference data. Classification accuracy of the FCM classifier increased with 12.93%. Improvement in overall accuracy shows that non-linearity in the dataset was handled adequately. The inverse multiquadratic kernel and the Gaussian kernel with the Euclidean norm were identified as optimal kernels. The study showed that overall classification accuracy of the FCM classifier improved if kernel functions were included.