K. R. Manjunath

Evaluation of RADARSAT Standard Beam data for identification of potato and rice crops in India

Abstract The Canadian satellite RADARSAT launched in November 1995 acquires C-band HH polarisation Synthetic Aperture Radar (SAR) data in various incident angles and spatial resolutions. In this study, the Standard Beam S7 SAR data with 45°–49° incidence angle has been used to discriminate rice and potato crops grown in the Gangetic plains of West Bengal state. Four-date data acquired in the 24-day repeat cycle between January 2 and March 15, 1997 was used to study the temporal backscatter characteristics of these crops in relation to the growth stages. Two, three and four-date data were used to classify the crops. The results show that the backscatter was the lowest during puddling of rice fields and increased as the crop growth progressed. The backscatter during this period changed from −18 dB to −8 dB. This temporal behaviour was similar to that observed in case of ERS-SAR data. The classification accuracy of rice areas was 94% using four-date data. Two-date data, one corresponding to pre-field preparation and the other corresponding to transplantation stage, resulted in 92% accuracy. The last observation is of particular interest as one may estimate the crop area as early as within 20–30 days of transplantation. Such an early estimate is not feasible using optical remote sensing data or ERS-SAR data. The backscatter of potato crop varied from −9 dB to −6 dB during the growth phase and showed large variations during early vegetative stage. Two-date data, one acquired during 40–45 days of planting and another at maturing stage, resulted in 93% classification accuracy for potato. All other combinations of two-date data resulted in less than 90% classification accuracy for potato.

Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation

There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.

Hyperspectral leaf signature as an added dimension for species discrimination: case study of four tropical mangroves

Hyperspectral leaf reflectance of a plant provides unique information that is characteristic of that plant. The present investigation is a preliminary attempt to assess whether spectra of leaves of mangrove species recorded under field conditions contain adequate spectral information for discerning mangroves at species rank. The paper highlights the hyperspectral characteristics of leaf surfaces of four prominent tropical mangrove species, viz., Avicennia alba, Avicennia marina, Rhizophora mucronata and Sonneratia caseolaris, found in the tidal forests of India. Hyperspectral observations were recorded using a field spectroradiometer, and pre-processed and averaged reflectance values of samples for three types of arrangements, viz., (1) randomly arranged leaves, (2) dorsal leaf surfaces and (3) ventral leaf surfaces of the species were statistically tested using one-way ANOVA to see whether the values significantly differed at every spectral location. All the four species were statistically different at all the spectral locations with majority of the bands exhibiting 99% confidence level. Finally, discriminant analysis was performed to identify the bands for maximum separability for the three types of arrangement of the leaves of the species taken separately and in different combinations. The optimal Wilks’ Lambda (L) were achieved with: six, three, eleven, six, five, thirteen and eleven wavelengths for discriminating random leaves of the four species, dorsal and ventral surfaces of A. alba, A. marina, R. mucronata, S. caseolaris, upper leaf surfaces of all the species, lower leaf surfaces, respectively. Factor analysis was used as an added tool to identify the wavelengths that were uncorrelated and contained maximum information in the combination of selected wavelengths. The results confirmed the unique spectral signatures of the four species, which could be explained in terms of leaf properties unique to the species. Cellular structure and pigmentation of the isolateral leaves of S. caseolaris are very different from the dorsiventral ones of the other three, which significantly changed the reflectance characteristics of the species.

Field hyperspectral data analysis for discriminating spectral behavior of tea plantations under various management practices

Abstract The quality and yield of tea depends upon management of tea plantations, which takes into account the factors like type, age of plantation, growth stage, pruning status, light conditions, and disease incidence. Recognizing the importance of hyperspectral data in detecting minute spectral variations in vegetation, the present study was conducted to explore applicability of such data in evaluating these factors. Also stepwise discriminant analysis and principal component analysis were conducted to identify the appropriate bands for accessing the above mentioned factors. The Green region followed by NIR region was found as most appropriate best band for discriminating different types of tea plants, and the tea in sunlit and shade condition. For discriminating age of plantation, growth stage of tea, and diseased and healthy bush, Blue region was most appropriate. The Red and NIR regions were best bands to discriminate pruned and unpruned tea. The study concluded that field hyperspectral data can be efficiently used to know the plantation that need special care and may be an indicator of tea productivity. The spectral signature of these characteristics of tea plantations may also be used to classify the hyperspectral satellite data to derive these parameters at regional scale.

Mapping of rice-cropping pattern and cultural type using remote-sensing and ancillary data: a case study for South and Southeast Asian countries

A geospatial database on the spatial distribution of rice areas and rice cultural types of major rice-producing countries of South and Southeast Asia has been developed in this study using remote-sensing and ancillary data sets. Multitemporal SPOT VGT normalized difference vegetation index (NDVI) data for the period 2009–2010 were used for the analysis. The classification was performed adopting ISODATA clustering to build a non-agricultural area mask followed by rice area mapping. The derived rice area was stratified by logical modelling of ancillary data sets into five rice cultural types: irrigated wet, upland, flood-prone, drought-prone, and deep-water. The uniqueness of this study is a synergistic approach based solely on single-source, high-temporal remote-sensing data coupled with ancillary data, which demonstrate the application of SPOT VGT NDVI data in building a geospatial database for rice crops over a wide spatial extent. This approach was adopted for cost effectivity as the study extent was vast and thus lacking ground truth information. Comparison of the derived rice area against the reported literature values for validation yielded a good correlation (linear coefficient of determination, R2 = 0.95–0.99). The high-temporal resolution NDVI data enabled effective characterization of vegetation phenology. The derived spatial outputs can be used in various studies associated with the assessment of greenhouse gas emissions from paddy fields, change detection, and inputs to crop simulation models, which are significantly related to different rice cultural types.

Modeling temporal growth profile of vegetation index from Indian geostationary satellite for assessing in-season progress of crop area

Highlights In-season agricultural area tracking at regular interval from geostationary satellite. Modelling of temporal profile of vegetation index spread across two consecutive agriculture seasons to track crop area. The crop area estimates and their frequent updates in an agricultural growing season are essential to formulate policies of country’s food security. A new methodology has been developed with high temporal vegetation index data at 1000 m spatial resolution from Indian geostationary satellite (INSAT 3A) to track progress of country-scale rabi (post-rainy) crop area in six agriculturally dominant states of India. The 10-day (dekad) maximum normalized difference vegetation index (NDVI) composite products at 0700 GMT (Greenwich Mean Time) were generated and used in the study. A cubic function was fitted to NDVI temporal profile on the training data-sets of 2009–2010. Model parameters were standardized over 40 agroclimatic subzones, which were used to estimate rabi crop area at 10-day interval in the next two seasons. Uncertainties in the model, in terms of days, were found to be less than (3–8 days) compositing period. The INSAT-based estimates showed –18.1% to 14.6% deviations from reported rabi crop area. Subpixel heterogeneity was found to be the major cause for the delay in crop area tracking in study region. The interseasonal variability in the estimate was consistent with the reported statistics with a correlation coefficient of 0.89. A comparative study showed that INSAT estimated rabi area had 16.36% deviation from high spatial resolution AWiFS (Advanced Wide-Field Sensor)-estimated area at 2 km × 2 km grid over ground observation points. It is recommended that high temporal NDVI product with finer spatial resolution satellite would, by offsetting the impact of subpixel heterogeneity, enable improved country-scale crop area monitoring.

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.

Derivation of rice crop calendar and evaluation of crop phenometrics and latitudinal relationship for major south and south-east Asian countries

A single source derived, un-extrapolated spatial rice crop calendar.Demonstrates extraction of crop phenological parameters using spatial inputs.Spatial rice crop serves as input for GHG emissions assessment from paddy fields. The crop calendar varies considerably with climatic and socio-economic factors as well as farming practices of the region. In present paper we demonstrate the application of remote sensing data to derive a geospatial database for rice crop calendar for major south and south-east Asian countries. The cultural type-wise variability of rice crop calendar and crop phenometrices-latitudinal relationship was also studied.A crop growth profile equation was used to simplify the parameterization necessary for identification of rice crop phenological matrices. A curve fitting approach was adapted for fitting the spectral Normalized Difference Vegetation Index (NDVI) growth profiles for rice derived from multi-date, multi-temporal SPOT VGT NDVI data and phenometrics viz. sowing/transplantation day, crop maturity/harvest day and total duration of rice crop were derived. The global distribution of the rice along the different latitudes is due to the adaptability of the rice to the regional conditions, which should reflect in the crop calendar. As latitude is one of the controlling factors of the climate, here we investigate the existence of relation between rice crop calendar and latitude. The strength of correlation between the rice crop phenometrics and the latitude was determined by two-tailed Pearson correlation coefficient analysis and Spearmans rank correlation across a latitudinal gradient which indicates an inverse relationship, with the (P<0.01) level of significance for Pearson linear correlation and (rho≤0) for Spearmans rank correlation.The high temporal NDVI data enabled characterizing the rice crop phenology effectively. The crop calendar derived in this study solely relies on the remote sensing data and can be used for of methane emission assessment from different rice cultural types.

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.

Retrieval of regional LAI over agricultural land from an Indian geostationary satellite and its application for crop yield estimation

Abstract Agriculture productivity at spatio-temporal scales can be modelled through quantification of biophysical parameters like LAI (leaf area index) and radiation parameters from satellites. The Indian geostationary satellite INSAT 3A CCD was used to retrieve agricultural LAI at regular temporal intervals from the ProSail 1-D (Dimensional) canopy radiative transfer (CRT) model. The ProSail model was customized to simulate reflectance for three CCD spectral bands. The model was run in forward mode and then inverted by using reflectances from the CCD and the generated LUT by applying the least square distance approach to retrieve LAI for agricultural crops. Daily CCD data from January 1 to March 30, 2010 at 0700 GMT were used to retrieve agricultural crop LAI data. The validation of daily retrieved LAI was done with available in situ measurements over wheat crops in Punjab, Haryana and Madhya Pradesh states. The overall root mean square error (RMSE) of 0.84 with correlation of 0.8 was observed for 20 in situ measured LAI at different phenological phases of wheat crops. Retrieved INSAT CCD LAI has been compared with LAI retrieved from high-resolution IRS P6 AWiFS using an empirical approach for wheat crop. The CCD-derived wheat crop LAI showed a RMSE of 0.45 (n = 55, 14.2 percent from mean) with mean absolute error (MAE) of 0.34. It was also compared with the 8-day MODIS TERRA global LAI product from January to March 2010. The LAI profiles extracted for different regions of India representing different crops using CCD data and MODIS products were compared and an overall RMSE of 2.25 (n = 156, 73 percent from mean) with MAE of 2.85 was observed. INSAT CCD-retrieved LAI was further used for wheat yield estimation over Madhya Pradesh state. At district level, yield showed a RMSE of 516.6 kg ha−1 with 29.4 percent deviation from the mean. Our demonstrative case studies recommended coupled use of satellite observations from multiple EO missions and radiative transfer simulation to make efficiency-based approaches operationally viable for regional crop yield estimation in near real time.