Evaluation and use of Resourcesat-I data for agricultural applications

Resourcesat-1 satellite offers a unique opportunity of simultaneous observations at three different spatial scales through LISS-IV, LISS-III^* (improved LISS-III) and AWiFS sensors from a common platform. The sensors have enhanced capabilities in terms of spectral, spatial and radiometric resolution as compared to earlier Indian Remote sensing Satellite sensors. This paper summarizes the results of various studies such as evaluation of sensor characteristics, inter-sensor comparison studies, derivation and validation of surface reflectance measurements, quantification of improvements due to Resourcesat-1 sensors, and their use for various agricultural applications. The studies presented in this paper demonstrate that suit of sensors onboard Resourcesat-1 satellite provides better prospects for several agricultural applications like crop identification, discrimination and crop inventory for some major Indian crops, than its predecessors on IRS satellites.     

Geometric and radiometric evaluation of Resouresat-1 sensors

Resourcesat-1, launched in October 2003, is the 10th in the series of Indian Remote Sensing satellites built by the Indian Space Research Organization. Resourcesat-1, also known as IRS-P6, provides continuity to applications developed using data from IRS-1C and IRS-1D satellites. It also offers newer applications owing to enhanced capabilities of the sensors. The satellite contains three different imaging sensors: LISS-IV, with a ground sampling distance (GSD) of 5.8 m; LISS-III, with a GSD of 23.5 m; and AWiFS, with a GSD of 56 m at nadir. This paper provides data quality evaluation of the Resourcesat-1 sensors in terms of geometric and radiometric qualities. It is found that the sensors onboard Resourcesat-1 spacecraft has met all the mission set specifications and will help to generate data products with the required image geolocation and radiometric quality.     

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.     

Entropy-based fuzzy classification parameter optimization using uncertainty variation across spatial resolution

In the past researchers have suggested hard classification approaches for pure pixel remote sensing data and to handle mixed pixels soft classification approaches have been studied for land cover mapping. In this research work, while selecting fuzzy c-means (FCM) as a base soft classifier entropy parameter has been added. For this research work Resourcesat-1 (IRS-P6) datasets from AWIFS, LISSIII and LISS-IV sensors of same date have been used. AWIFS and LISS-III datasets have been used for classification and LISS-III and LISS-IV data were used for reference data generation, respectively. Soft classified outputs from entropy based FCM classifiers for AWIFS and LISS-III datasets have been evaluated using sub-pixel confusion uncertainty matrix (SCM). It has been observed that output from FCM classifier has higher classification accuracy with higher uncertainty but entropy-based classifier with optimum value of regularizing parameter generates classified output with minimum uncertainty.     

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.     

In-season time series analysis of Resourcesat-1 AWiFS data for estimating irrigation water requirement

AWiFS sensor on board IRS-P6 (Resourcesat-1), with its unique features—wide swath and 5-day revisit capability provides excellent opportunities to carry out in-season analysis of irrigated agriculture. The study carried out in Hirakud command area, Orissa State indicated that the progression of rice crop acreage could be mapped through analysis of time series AWiFS data set. The spectral emergence pattern of rice crop was found useful to identify the period of rice transplantation and its variability across the command area. This information, integrated with agro-meteorological data, was used to quantify 10-daily canal-wise irrigation water requirement. A comparison with field measured actual irrigation supplies indicated an overall supply adequacy of 88% and showed wide variability at lateral canal level ranging between 18% and 109%. The supply pattern also did not correspond with the chronological variations associated with crop water requirement, supplies were 15% excess during initial part of season (December and January) and were 20.1% deficit during later part of season (February to April). Rescheduling the excess supplies of the initial period could have reduced the deficit to 15% during peak season. The study has demonstrated the usefulness of AWiFS data to generate the irrigation water requirement by mid-season, subsequent to which 38% supplies were yet to be allocated. This would support the irrigation managers to reschedule the irrigation water supplies to achieve better synchronization between requirement and supply leading to improved water use efficiency.     

Quantification of improvements by using AWiFS over WiFS data

The Advanced WiFS sensor of RESOURCESAT- 1 satellite offers significantly improved specifications compared to the WiFS sensor onboard IRS IC, P3 and ID satellites. The improvements are in terms of spatial resolution, radiometry (quantisation levels) and number of spectral bands. In the present study, an attempt has been made to quantify the gains due to these enhanced specifications. The study has been carried out in a predominantly agricultural area. For the study reported here, one set of overlapping data acquired on the same day by WiFS and AWiFS sensors has been selected. This eliminates the need of atmospheric correction/ normalization for comparison. The effect of spatial resolution has been studied by applying ISODATA spectral clustering algorithm with number of clusters set at three different levels, viz., 10, 20 and 30. They are assumed to mimic first, second and third level classification, respectively. Output images were filtered using 3 × 3 majority filter. Homogeneous polygons having area less than 1/2 and 1 pixel of WiFS were recorded. This indicates the minimum loss by using WiFS data. A relative gain of 10 – 15 % is observed due to improvement in spatial resolution. For comparison of radiometry, local variance measure was used. It was observed that local variance is much larger for AWiFS data in comparison with WiFS data. This indicates presence of enhanced local contrast, hence heterogeneity, in AWiFS data over WiFS data. Separability analysis has been carried out to demonstrate improvements due to two additional spectral bands (Green and SWIR).     

Evaluation of fuzzy-based classifiers for cotton crop identification

In this study, an evaluation of fuzzy-based classifiers for specific crop identification using multi-spectral temporal data spanning over one growing season has been carried out. The temporal data sets have been georeferenced with 0.3 pixel rms error. Temporal information of cotton crop has been incorporated through the following five indices: simple ratio (SR), normalized difference vegetation index (NDVI), transformed normalized difference vegetation index (TNDVI), soil-adjusted vegetation index (SAVI) and triangular vegetation index (TVI), to study the effect of indices on classified output. For this purpose, a comparative study between two fuzzy-based soft classification approaches, possibilistic c-means (PCM) and noise classifier (NC), was undertaken. In this study, advanced wide field sensor (AWiFS) data for soft classification and linear imaging self scanner sensor (LISS III) data for soft testing purpose from Resourcesat-1 (IRS-P6) satellite were used. It has been observed that NC fuzzy classifier using TNDVI temporal index – dataset 2, which comprises four temporal images performs better than PCM classifier giving highest fuzzy overall accuracy of 96.03%.     

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.     

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.     

Soil Resource Database at Village Level for Developmental Planning

A study was carried out in village Mohammadabad, Nalgonda district, Andhra Pradesh to demonstrate the potential of high-resolution remote sensing data in generating soil resource database at village level that would facilitate natural resource developmental planning. For this study, detail soil resource database was generated by interpreting IRS Resourcesat-1 LISS-IV + Cartosat-1 merged data and by undertaking soil survey and soil analysis. This database was integrated with cadastral map and was analysed for preparation of an action plan for the village that included recommendation on suitable crops, soil and water conservation measures and distribution of soil health card to farmers. The study indicates that the LISS-IV + Cartosat-1 merged satellite data helps in mapping soils at phase level and in delineation of more number of pure soil mapping units i.e. consociations at 1:10.000 scale. The plot-wise resource potential and constraints reveals that significant inter and intra-field soil variability exists. Soil fertility assessment indicates that the soils of the village are low to medium in fertility (available NPK Content) and micronutrients like copper, manganese and iron content is above the critical level for optimum growth of crops while zinc content is less than the critical level.     

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.     

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.     

Land use land cover classification of Orissa using multi-temporal IRS-P6 awifs data: A decision tree approach

An attempt has been made to understand the potential of temporal Advanced Wide Field Sensor (AWiFS) data aboard IRS-P6 (Resourcesat) to generate the land use land cover information along with the net sown area. The temporal data sets were georeferenced, converted to top of atmosphere reflectance and classified using decision tree classifier, See5. Results indicate that the temporal data set could give a better definition of training sites thereby resulting in good overall kappa (kappa = 0.8651) as well as individual classification accuracies. However, co-registration of temporal datasets accuracies also has got a significant influence on the classification accuracy. Temporal variation in cloud infestation and availability of appropriate data sets within the season (before harvest of the crop) has also affected the classification accuracy.     

Cotton Crop Discrimination Using Fuzzy Classification Approach

Crop growth information represented through temporal remote sensing data is of great importance for specific agriculture crop discrimination. In this paper, the effect of various indices was empirically investigated using temporal images for cotton crop discrimination. Five spectral indices SR (Simple Ratio), NDVI (Normalized Difference Vegetation index), TNDVI (Transformed Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and TVI (Triangular Vegetation Index) were investigated to identify cotton crop using temporal multi-spectral images. Data used for this study was AWIFS (coarser resolution) for soft classification and LISS-III (medium coarser) data for soft testing from Resourcesat-1 (IRS-P6) satellite. The mixed pixel (i.e. multiple classes within a single pixel) problem had been handled using soft computing techniques. Possibilistic fuzzy classification approach is used to handle mixed pixels for extracting single class of interest. The classification results with respect to various indices were compared in terms of image to image fuzzy overall classification accuracy. It was observed that temporal SAVI indices database with data set-2 outperformed other temporal indices database for cotton crop discrimination. Temporal SAVI indices database gave highest fuzzy overall accuracy of 93.12% with data set-2 in comparison to others.     

Retrieval of wheat leaf area index from AWiFS multispectral data using canopy radiative transfer simulation

Accurate representation of leaf area index (LAI) from high resolution satellite observations is obligatory for various modelling exercises and predicting the precise farm productivity. Present study compared the two retrieval approach based on canopy radiative transfer (CRT) method and empirical method using four vegetation indices (VI) (e.g. NDVI, NDWI, RVI and GNDVI) to estimate the wheat LAI. Reflectance observations available at very high (56 m) spatial resolution from Advanced Wide-Field Sensor (AWiFS) sensor onboard Indian Remote Sensing (IRS) P6, Resourcesat-1 satellite was used in this study. This study was performed over two different wheat growing regions, situated in different agro-climatic settings/environments: Trans-Gangetic Plain Region (TGPR) and Central Plateau and Hill Region (CPHR). Forward simulation of canopy reflectances in four AWiFS bands viz. green (0.52–0.59 μm), red (0.62–0.68 μm), NIR (0.77–0.86 μm) and SWIR (1.55–1.70 μm) were carried out to generate the look up table (LUT) using CRT model PROSAIL from all combinations of canopy intrinsic variables. An inversion technique based on minimization of cost function was used to retrieve LAI from LUT and observed AWiFS surface reflectances. Two consecutive wheat growing seasons (November 2005–March 2006 and November 2006–March 2007) datasets were used in this study. The empirical models were developed from first season data and second growing season data used for validation. Among all the models, LAI-NDVI empirical model showed the least RMSE (root mean square error) of 0.54 and 0.51 in both agro-climatic regions respectively. The comparison of PROSAIL retrieved LAI with in situ measurements of 2006–2007 over the two agro-climatic regions produced substantially less RMSE of 0.34 and 0.41 having more R² of 0.91 and 0.95 for TGPR and CPHR respectively in comparison to empirical models. Moreover, CRT retrieved LAI had less value of errors in all the LAI classes contrary to empirical estimates. The PROSAIL based retrieval has potential for operational implementation to determine the regional crop LAI and can be extendible to other regions after rigorous validation exercise.     

光学有效载荷高精度绝对辐射定标技术研究

随着多波段、多平台、高光谱分辨率遥感仪器的迅速发展,要求光谱辐射定标技术具有前所未有的高精度和长期测量稳定性。目前,传统辐射定标方法的精度已逐渐难以满足定量遥感应用的需求。本文提出了一种新型基于辐亮度标准探测器的高精度光谱辐射定标先进方法和技术,由于该方法可直接溯源至低温绝对辐射计,其绝对精度和长期稳定性优于传统的基于标准光源法,是提高中国目前光学遥感器实验室、星上和场地替代定标精度的有效途径。该方法已两次成功应用于中国“FY-2”气象卫星04和05星发射前外场定标,实际应用效果表明,它能够有效提高中国卫星遥感器的外场定标精度,对改善中国当前卫星遥感器各阶段定标精度,具有潜在的应用价值。     

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.     

Effect of Forest Fire on Snow Reflectance in Uttarakhand Region: An Analysis Using AWiFS and INSAT-3D Data

In the summer of 2016, fire broke out in the forests of Uttarakhand state. The effect of forest fire was thought to have severe impact on nearby glaciated region in terms of faster melting. It is understood that contamination and heat enhance the snowmelt, which reduces the reflectance in different part of EM spectrum. In order to assess the effect, AWiFS and IMAGER data from Resourcesat-1 and INSAT-3D, respectively, for the months of April and May of the years 2012, 2013, 2014 and 2016 were used to compare the reflectance of snow. It was observed that pre- and post-fire data of 2016 show drop in reflectance in comparison with earlier years. The change in reflectance for locations near to forest fire was significantly high in comparison with previous year images, whereas far-away locations did not show much change. This drastic drop may be attributed to deposition of black carbon on nearby locations in snow-covered area. AWS data of nearby Joshimath observatory were also analysed to avoid any anomalous change in temperature for the same duration.     

Multispectral sensor spectral resolution simulations for generation of hyperspectral vegetation indices from Hyperion data

The use of multispectral satellite sensors for generation of hyperspectral indices is restricted because of their coarse spectral resolutions. In this study, we attempted to synthesize a few of these hyperspectral indices, viz. RedEdge Normalized Difference Vegetation Index (NDVI₇₀₅), Plant Senescence Reflectance Index (PSRI) and Normalized-Difference-Infrared-Index (NDII), for crop stress monitoring at regional scale using multispectral images, simulated from Hyperion data. The Hyperion data were resampled and simulated to corresponding spatial and spectral resolutions of AWiFS, OCM-2 and MODIS sensors using their respective filter function. Different possible combinations of two bands (i.e. simple difference, simple ratio and normalized difference) were computed using synthetic spectral bands of each sensor, and were regressed with NDVI₇₀₅, PSRI and NDII. Models with highest correlation were selected and inverted on Hyperion data of another date to synthesize respective multispectral indices. Synthetic broad band indices of multispectral sensors with their respective narrow band indices of Hyperion were found to be in good agreement.