Study of crop discrimination by simulated IRS 1C LISS-III data

A study on crop discrimination was carried out using simulated IRS 1C LISS-III data produced using visible (to simulate B2, B3) and NIR (to simulate B4) channels from SPOT and middle infrared (MIR) channel (to simulate B5) from TM over a previously investigated test site, characterized by multiple crops and small fields, in Sabarkantha district (Gujarat). The separability amongst dominant kharif season crops, namely, cotton, groundnut, maize, pigeonpea, between crops and various natural vegetation classes was investigated using Jeffries-Matusita (JM) distance, a pair-wise inter-class separability measure. The study highlighted the capability of simulated LISS-III data to be useful in identifying and labelling small fields and the 4-band data set (B2345, i.e., simulated LISS-III) to significantly improve the separability amongst various crops and vegetation over two 3-band sets (B234, equivalent to SPOT)and B345.     

Results on potential use of simulated IRS-1C WiFS data for crop monitoring

Some of the basic requirements for cropping system analysis are updated information on crops grown, their phenological behaviour, method and duration of establishment and harvest, inter and intra crop variability, sequential cropping patterns. The next generation Indian Remote Sensing Satellite with high repeat cycle opens new possibility of crop surveys to derive such information. In this study, an attempt has been made to analyse cropping system at district level using simulated IRS-1C Wide Field Sensor (WiFS) data. Data acquired for nineteen dates during 1992–93 season for Bardhaman district, West Bengal has been used. It was feasible to derive accurate information on cropping pattern, crop rotation, crop duration, progress of harvest, crop growth profiles and annual crop acreage using multidate data. It was observed that even a seven to eight day interval of data acquisition during critical growth periods significantly affected classification and identification accuracy.     

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.     

Snow and forest cover assessment of uttaranachal state using irs 1c wifs data

In this study, IRS 1C WiFS data have been used for the assessment of two natural resources i.e. forest cover and snow cover. These two resources have a great role to play in various hydrological studies such as floods, soil erosion and water pollution etc. Therefore their assessment is very useful in various hydrological studies and management of these resources. The assessment of snow and forest cover have been made on the basis of multispectral classification and classification of NDVI images. Newly created Uttaranchal state has been taken as the study area. These two resources have been estimated for all the thirteen districts of the state separately. The forest cover area estimated in this study is compared with the available data sets of Forest Survey of India (FSI). The estimated forest is 52%, whereas the forest cover reported by the FSI is 44.5% of the total geographical area of the state. The snow cover is estimated for the period after winter season i.e. maximum snow cover and before next winter season i.e. minimum snow cover. It is found that one quarter of the state is under snow cover covering six districts of the state. As such no estimate of snow cover at regional scale has been made so far therefore comparison of the present assessment could not be made.     

Analysis of sorghum yield dependence on crop growth characteristics using noaa-avhrr data

Growth profiles of 1987-88 rabi sorghum crop cultivated in spatially extensive sites in six tehsils of Solapur and Ahmadnagar districts in Maharashtra have been generated using multidate NOAA AVHRR data based on crop growth equation suggested by Badhwar (1980). The sensitive parameters for sorghum yield modelling have been identified. The correlation of final grain yield with growth parameters shows that yield relationship is stronger when logarithmic senescence rate and timeintegrated logarithmic senescence rate are considered as the parameters instead of its value on any day during 30 days senescence period after attaining maximum vegetative cover.     

Evaluation of simulated IRS-1C WiFS data for concurrent monitoring of command areas at disaggregated level

Data of Wide Field Sensor (WiFS) to go onboard Indian Remote Sensing Satellite, IRS-1C, in December 1995, is simulated mainly from IRS IB LISS I data of Bhadra command area, Karnataka (India) during 1993–94 summer season, to evaluate its capability in concurrent monitoring of irrigated crops at disaggregated level Crop area, crop-growth profiles of homogeneous crops like paddy, as obtained from both simulated WiFS data and LISS I data are very close for almost all the distributary commands of Bhadra project Though non-paddy-crop groups could also be classified satisfactorily, the Workability with small-extent-individual crops like groundnut, garden and sugarcane is found to be less due to coarse resolution of WiFS data and hence the individual crops could not be separated out. This study proves the potential of WiFS in concurrent monitoring of fairly-large-extent irrigated crops at distributary level. The basic feasibility of WiFS had been established in an earlier work at broad level and this study demonstrates the feasibility of information extraction at distributary command level from WiFS data.     

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.     

Evaluation of RADARSAT ScanSAR synthetic aperture radar data for rice crop inventory and monitoring

Radarsat ScanSAR Narrow (SN2) data acquired on July 24 and August 17, 1997 were used to analyse the signature of rice crop in West Bengal, India. The analysis showed that the lowland practice of cultivation gives a distinct signature to rice due to the initial water background. The relatively stable backscatter from water bodies in temporal data enhanced the separability of rice fields from water using two date data. Around 94 per cent classification accuracy was achieved for rice crop using two date data. It was feasible to discriminate rice sub-classes based on their planting period like early and late crop. The analysis indicates the suitability of ScanSAR data for large area rice crop monitoring as it has a wide swath of 300 km.     

Evaluation of EO-1 hyperion data for agricultural applications

The present study was carried out to evaluate the satellite-based hyperspectral data available from Hyperion onboard EO-1 of NASA for agricultural applications. The study was carried out for Daurala block of Meerut district, using data of March 2005. The preliminary data analysis showed that there are 196 usable bands out of a total of 242 bands. Principal component (PC) analysis showed that about 99% of the information explained in 10 PCs. The atmospherically corrected reflectance, derived from satellite data had good agreement with the ground reflectance, observed using handheld spectroradiometer, with r² ranging from 0.85 to 0.98. A set of twenty most usable bands was selected by the criteria of maximum contribution to first five PCs and the band combinations with least inter-band correlations.     

Comparative performance of per-pixel classifiers using ers-1 sar data for classification of rice crop

Three-date ERS-1 SAR data acquired on August 24, September 28 and November 2, 1995, was used to classify rice crop in a predominant rice growing region of West Bengal. India, Artificial neural network, maximum likelihood, decision rute and K-Means clustering classifiers were used. Classification accuracy was evaluated from the error matrix of same set of training and validating pixels. Rice classification accuracy improved significantly using neural network classifier. The decision rule based classifier performed equally good for most of the sites, indicating the feasibility of deriving a common rule based algorithm for large area application. Law aecuracy was observed for maximum likelihood classifier.     

Assessing the suitability of data from Sentinel-1A and 2A for crop classification

Sentinel-1A C-SAR and Sentinel-2A MultiSpectral Instrument (MSI) provide data applicable to the remote identification of crop type. In this study, six crop types (beans, beetroot, grass, maize, potato, and winter wheat) were identified using five C-SAR images and one MSI image acquired during the 2016 growing season. To assess the potential for accurate crop classification with existing supervised learning models, the four different approaches namely kernel-based extreme learning machine (KELM), multilayer feedforward neural networks, random forests, and support vector machine were compared. Algorithm hyperparameters were tuned using Bayesian optimization. Overall, KELM yielded the highest performance, achieving an overall classification accuracy of 96.8%. Evaluation of the sensitivity of classification models and relative importance of data types using data-based sensitivity analysis showed that the set of VV polarization data acquired on 24 July (Sentinel-1A) and band 4 data (Sentinel-2A) had the greatest potential for use in crop classification.     

Interpretation and analysis of multispectral data for aricultural crop cover types-role of crop spectral response and crop sectral signature

Airborne multispectral data obtained over mono and multiple cropping systems of small farming agriculture was studied for two cropping seasons for a possible development of crop spectral signatures and to utilize such signatures for interpretation of multispectral data and for assessing agricultural potentials of a region. In multiple cropping system, the unique crop spectral response exhibited by crop species at specific growth stages facilitated interpretation and analysis of multispectral data with the knowledge of crop phenology. For resolving spectral confusion between crop species due to growtn stages of different crop species, temporal data were observed to be useful. Development and use of crop spectral sigrature for interpretation and analysis multispectral data related to mono cropping system were found to be relatively less complex and offer great promise because of minimum spectral confusion.     

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%.     

Evaluation of potential crop productivity based on remote sensing and agro-ecological zones around the world

Human diets strongly rely on wheat, maize, rice and soybean; research on the potential crop productivity of these four main crops could provide the basis for increasing global crop yields. The evaluation model of realistic potential crop productivity based on remote sensing and agro-ecological zones was proposed in this study to provide reliable reference data for world food security. The statistical data on these four main crops yields were obtained from the FAO. The model was used to investigate the potential production of four staple crops in the world. The distributions of the realistic potential productivity of four staple crops (winter wheat, maize, rice and soybean) were produced. In the main producing countries of the four staple crops, statistical analysis was conducted on the realistic potential productivity (RPP) of the four staple crops, the highest productivity (HP) during the period 1983–2011 and the gap between RPP and HP.     

Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines

This study developed an approach to map rice-cropping systems in An Giang and Dong Thap provinces, South Vietnam using multi-temporal Sentinel-1A (S1A) data. The data were processed through four steps: (1) data pre-processing, (2) constructing smooth time series VH backscatter data, (3) rice crop classification using random forests (RF) and support vector machines (SVM) and (4) accuracy assessment. The results indicated that the smooth VH backscatter profiles reflected the temporal characteristics of rice-cropping patterns in the study region. The comparisons between the classification results and the ground reference data indicated that the overall accuracy and Kappa coefficient achieved from RF were 86.1% and 0.72, respectively, which were slightly more accurate than SVM (overall accuracy of 83.4% and Kappa coefficient of 0.67). These results were reaffirmed by the government’s rice area statistics with the relative error in area (REA) values of 0.2 and 2.2% for RF and SVM, respectively.     

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.     

Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data

In the present study, Sentinel-1A Synthetic Aperture Radar analysis of time series data at C-band was carried out to estimate the winter wheat crop growth parameters. Five different date images were acquired during January 2015–April 2015 at different growth stages from tillering to ripening in Varanasi district, India. The winter wheat crop parameters, i.e. leaf area index, vegetation water content (VWC), fresh biomass (FB), dry biomass (DB) and plant height (PH) were estimated using random forest regression (RFR), support vector regression (SVR), artificial neural network regression (ANNR) and linear regression (LR) algorithms. The Ground Range Detected products of Interferometric Wide (IW) Swath were used at VV polarization. The three different subplots of 1 m² area were taken for the measurement of crop parameters at every growth stage. In total, 73 samples were taken as the training data-sets and 39 samples were taken as testing data-sets. The highest sensitivity (adj. R^2?=?0.95579) of backscattering with VWC was found using RFR algorithm, whereas the lowest sensitivity (adj. R^2?=?0.66201) was found for the PH using LR algorithm. Overall results indicate more accurate estimation of winter wheat parameters by the RFR algorithm followed by SVR, ANNR and LR algorithms.     

Comparison of ERS-SAR and spot data for sugar beet crop cover assessment

The present work was aimed to compare the abilities of radar and optical satellite data to estimate crop canopy cover, which is a key component of productivity estimates. Three ERS-1 SAR images were obtained of East Anglia (UK) in 1995 and one ERS-2 SAR image in 1996. The images covered a study area around the IACR Brooms Barn Sugar Beet Research Institute. Field data comprising radiometric and biophysical measurements of the crop canopy were collected in two fields from June 22 to August 3, 1995 to coincide with ERS-1 SAR overpass dates. In 1996, field data were collected in two fields from June 11 to July 29 on a weekly basis. A previously calibrated version of the water cloud model was inverted to estimate Leaf Area Index (LAI) from ERS-1 and ERS-2 SAR backscatter and soil moisture samples. Canopy cover was estimated from the radar-estimated LAI using a standard exponential relationship that has a well-established coefficient for sugar beet. Radio-metrically and atmospherically corrected data from three SPOT images in 1995 and one SPOT image in 1996 were used to calculate the Optimised Soil Adjusted Vegetation Index (OSAVI), from which crop canopy cover was estimated using a relationship determined previously by canopy modelling. The crop cover values estimated by satellite were in good agreement with those measured on ground with the Parkinson radiometer. Radar data may be able to provide useful estimates of canopy cover for crop production modelling, especially in the case of loss of optical data due to cloud.     

Use of field reflectance data for crop mapping using airborne hyperspectral image

Recent developments in hyperspectral remote sensing technologies enable acquisition of image with high spectral resolution, which is typical to the laboratory or in situ reflectance measurements. There has been an increasing interest in the utilization of in situ reference reflectance spectra for rapid and repeated mapping of various surface features. Here we examined the prospect of classifying airborne hyperspectral image using field reflectance spectra as the training data for crop mapping. Canopy level field reflectance measurements of some important agricultural crops, i.e. alfalfa, winter barley, winter rape, winter rye, and winter wheat collected during four consecutive growing seasons are used for the classification of a HyMAP image acquired for a separate location by (1) mixture tuned matched filtering (MTMF), (2) spectral feature fitting (SFF), and (3) spectral angle mapper (SAM) methods. In order to answer a general research question “what is the prospect of using independent reference reflectance spectra for image classification”, while focussing on the crop classification, the results indicate distinct aspects. On the one hand, field reflectance spectra of winter rape and alfalfa demonstrate excellent crop discrimination and spectral matching with the image across the growing seasons. On the other hand, significant spectral confusion detected among the winter barley, winter rye, and winter wheat rule out the possibility of existence of a meaningful spectral matching between field reflectance spectra and image. While supporting the current notion of “non-existence of characteristic reflectance spectral signatures for vegetation”, results indicate that there exist some crops whose spectral signatures are similar to characteristic spectral signatures with possibility of using them in image classification.