Discrimination Of Crop Types Research Articles

A robust and unified land surface phenology algorithm for diverse biomes and growth cycles in China by using harmonized Landsat and Sentinel-2 imagery

Land surface phenology (LSP) is beneficial to understand ecosystem response to climate change, vegetation and crop type discrimination, and ecological modeling. However, the existing efforts based on coarse resolution data (≥500 m) cannot perform well in regions with higher spatial heterogeneity and multi-cropping system, such as China. Given that the majority of 10 m/30 m-based phenological research has focused on North America and Europe, developing spatiotemporally explicit LSP data in China is imperative. More importantly, the existing 30 m LSP products are mainly suitable for vegetation types with a single vegetation cycle, but cannot work well for biomes with complex seasonality (e.g., multiple growth cycles). Here we first harmonized three vegetation indices, i.e., the normalized difference vegetation index (NDVI), two-band enhanced vegetation index (EVI2), and land surface water index (LSWI) from Landsat-7/8 and Sentinel-2 imagery on the Google Earth Engine (GEE) platform. We then developed a new 30 m LSP algorithm that unified different phenological cycle-seeking processes per vegetation type and improved the existing algorithm. Furthermore, we used the algorithm to estimate the LSP product (LSP30CHN) for 2016–2019 across China, suitable for all vegetation types. The validation results showed a reasonably high accuracy (R2 > 0.6, RMSE < 15 days, mostly) of the LSP30CHN data against multi-sources in-situ observational (e.g., PhenoCam) and satellite-retrieved vegetation phenology data. Moreover, LSP30CHN data showed a consistent pattern but finer spatial details with the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) phenology product (MCD12Q2) at the homogenous area. We also found that phenological differences between LSP30CHN and MCD12Q2 increased with surface fragmentation, suggesting the potential of LSP30CHN to delineate phenological information on more fragmented landscapes. In contrast, the 500 m LSP data cannot provide such details in the regions with mixed cropping structures (e.g., corn, rice, and soybean) and multiple cropping index (e.g., single- and double-cropping systems). This study offers high accuracy of the LSP map for China, valuable for finer phenology-based services such as field-level crop management and agricultural phenology monitoring. It opens up new insights about exploring large-scale refined agricultural management and ecological assessment for other regions with complicated, fragmented landscapes and vegetation seasonality.

A Spatial and Temporal Evaluation of Broad-Scale Yield Predictions Created from Yield Mapping Technology and Landsat Satellite Imagery in the Australian Mediterranean Dryland Cropping Region

Contemplation of potential strategies to adapt to a changing and variable climate in agricultural cropping areas depends on the availability of geo-information that is at a sufficient resolution, scale and temporal length to inform these decisions. We evaluated the efficacy of creating high-resolution, broad-scale indicators of yield from simple models that combine yield mapping data, a precision agriculture tool, with the normalised difference vegetation index (NDVI) from Landsat 5 and 7 ETM+ imagery. These models were then generalised to test its potential operationalisation across a large agricultural region (&gt;1/2 million hectares) and the state of South Australia (&gt;8 million hectares). Annual models were the best predictors of yield across both areas. Moderate discrimination accuracy in the regional analysis meant that models could be extrapolated with reasonable spatial precision, whereas the accuracy across the state-wide analysis was poor. Generalisation of these models to further operationalise the methodology by removing the need for crop type discrimination and the continual access to annual yield data showed some benefit. The application of this approach with past and contemporary datasets can create a long-term archive that fills an information void, providing a powerful evidence base to inform current management decisions and future on-farm land use in cropping regions elsewhere.

Crop type discrimination using Geo-Stat Endmember extraction and machine learning algorithms

The identification of crop diversity in today's world is very crucial to ensure adaptation of the crop with changing climate for better productivity as well as food security. Towards this, Hyperspectral Remote Sensing (HRS) is an efficient technique that offers the opportunity to discriminate crop types based on morphological as well as physiological features due to availability of contiguous spectral bands. The current work utilized the benefits of Airborne Visible Infrared Imaging Spectrometer- New Generation (AVIRIS-NG) data and explored the techniques for classification and identification of crop types. The endmembers were identified using the Geo-Stat Endmember Extraction (GSEE) algorithm for pure pixels identification and to generate the spectral library of the different crop types. Spectral feature comparison was done among AVIRIS-NG, Analytical Spectral Device (ASD)-Spectroradiometer and Continuum Removed (CR) spectra. The best-fit spectra obtained with the Reference ASD-Spectroradiometer and Pure Pixel spectral library were then used for crop discrimination using the ten supervised classifiers namely Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), Support Vector Machine (SVM), Minimum Distance Classifier (MDC), Binary Encoding, deep learning-based Convolution Neural Network (CNN) and different algorithms of Ensemble learning such as Tree Bag, AdaBoost (Adaptive Boosting), Discriminant and RUSBoost (Random Under Sampling). In total, nine crop types were identified, namely, wheat, maize, tobacco, sorghum, linseed, castor, pigeon pea, fennel and chickpea. The performance evaluation of the classifiers was made using various metrics like Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score. The classifier 2D-CNN was found to be the best with Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score values of 89.065 %, 0.871, 87.565%, 89.541% and 88.678% respectively. The output of this work can be utilized for large scale mapping of crop types at the species level in a short interval of time with high accuracy.

Mapping Crop Types of Germany by Combining Temporal Statistical Metrics of Sentinel-1 and Sentinel-2 Time Series with LPIS Data

Nationwide and consistent information on agricultural land use forms an important basis for sustainable land management maintaining food security, (agro)biodiversity, and soil fertility, especially as German agriculture has shown high vulnerability to climate change. Sentinel-1 and Sentinel-2 satellite data of the Copernicus program offer time series with temporal, spatial, radiometric, and spectral characteristics that have great potential for mapping and monitoring agricultural crops. This paper presents an approach which synergistically uses these multispectral and Synthetic Aperture Radar (SAR) time series for the classification of 17 crop classes at 10 m spatial resolution for Germany in the year 2018. Input data for the Random Forest (RF) classification are monthly statistics of Sentinel-1 and Sentinel-2 time series. This approach reduces the amount of input data and pre-processing steps while retaining phenological information, which is crucial for crop type discrimination. For training and validation, Land Parcel Identification System (LPIS) data were available covering 15 of the 16 German Federal States. An overall map accuracy of 75.5% was achieved, with class-specific F1-scores above 80% for winter wheat, maize, sugar beet, and rapeseed. By combining optical and SAR data, overall accuracies could be increased by 6% and 9%, respectively, compared to single sensor approaches. While no increase in overall accuracy could be achieved by stratifying the classification in natural landscape regions, the class-wise accuracies for all but the cereal classes could be improved, on average, by 7%. In comparison to census data, the crop areas could be approximated well with, on average, only 1% of deviation in class-specific acreages. Using this streamlined approach, similar accuracies for the most widespread crop types as well as for smaller permanent crop classes were reached as in other Germany-wide crop type studies, indicating its potential for repeated nationwide crop type mapping.

Sugarcane Crop Type Discrimination and Area Mapping at Field Scale Using Sentinel Images and Machine Learning Methods

Sugarcane Crop Type Discrimination and Area Mapping at Field Scale Using Sentinel Images and Machine Learning Methods

Crop Discrimination using Non-Imaging Hyperspectral Data

Crop type discrimination is still very challenging task for researchers using non-imaging hyperspectral data. It is because of spectral reflectance similarity between crops. In this research work we have discriminated between four crops wheat, jowar, bajara and maize. We have tried to overcome the problems which have been faced my researchers. Initially by visual analysis we have selected 22 reflectance band which shows the absorption property of particular molecules and classification technique is applied, but it has given us very poor result of classification. We observed only 24% classification accuracy. So we considered nine vegetation indices along with spectral bands and achieved better classification accuracy. ASD FieldSpec 4 Spectroradiometer device is used for capturing spectral reflectance data. We calculated nine different vegetation indices and some selective reflectance bands are used for crop classification. We have used Support Vector Machine (SVM) for classification.

Agricultural crop discrimination in a heterogeneous low-mountain range region based on multi-temporal and multi-sensor satellite data

Crop type information such as its location and spatial distribution is relevant for agricultural planning and decision making about food sustainability and security. This information can be obtained through the analysis of images obtained through optical satellite remote sensing. Activities such as accurate discrimination of crops require dense time-series of satellite data which can capture the diverse crop phenology. However, given the presence of clouds at important periods of crops’ development, the required time-series is impossible to obtain from just one optical satellite sensor. The Harmonized Landsat and Sentinel-2 (HLS) project by NASA provides fused data from both Operational Land Imager and Multispectral Instrument optical sensors of Landsat and Sentinel systems respectively. The present study used a multi-temporal HLS data and a target-oriented cross-validation (TOV) modelling approach with random forest algorithm to discriminate 13 crop types. 15 phenological metrics derived from time-series HLS data, together with 48 spectral and 2 topographic information were used as predictors in the model. A forward feature selection (FFS) procedure of the TOV was used to improve the classification model. 16 predictors comprising of spectral, phenological and topographic information were selected as useful for the crop discrimination. An independent accuracy assessment of the final model based on the selected predictors by the FFS procedure resulted in an overall accuracy of 76%. While most of the crop classes, achieved higher producer’s and user’s accuracies (>80%), the discrimination accuracies of potato, summer oat and winter triticale were comparatively low (<60%). Based on the outcome of the FFS, three models consisting of different predictor combinations, were further developed to ascertain the contribution of the predictors. A comparison of the models showed that the addition of phenological metrics increased the discrimination performance of the model as a whole and for cereals in particular. However, the inclusion of the topographic information did not have any significant effect on the overall model performance. Therefore, future crop type discrimination research in areas with similar terrain characteristics like Northern Hesse should reconsider the use of topographic predictors. Overall, our findings depict that the TOV modelling approach based on multi-temporal and multi-sensor data is promising for effective, accurate and practical crop type discrimination.

Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery

Dense time series of Landsat 8 and Sentinel-2 imagery are creating exciting new opportunities to monitor, map, and characterize temporal dynamics in land surface properties with unprecedented spatial detail and quality. By combining imagery from the Landsat 8 Operational Land Imager and the MultiSpectral Instrument on-board Sentinel-2A and -2B, the remote sensing community now has access to moderate (10–30 m) spatial resolution imagery with repeat periods of ~3 days in the mid-latitudes. At the same time, the large combined data volume from Landsat 8 and Sentinel-2 introduce substantial new challenges for users. Land surface phenology (LSP) algorithms, which estimate the timing of phenophase transitions and quantify the nature and magnitude of seasonality in remotely sensed land surface conditions, provide an intuitive way to reduce data volumes and redundancy, while also furnishing data sets that are useful for a wide range of applications including monitoring ecosystem response to climate variability and extreme events, ecosystem modelling, crop-type discrimination, and land cover, land use, and land cover change mapping, among others. To support the need for operational LSP data sets, here we describe a continental-scale land surface phenology algorithm and data product based on harmonized Landsat 8 and Sentinel-2 (HLS) imagery. The algorithm creates high quality times series of vegetation indices from HLS imagery, which are then used to estimate the timing of vegetation phenophase transitions at 30 m spatial resolution. We present results from assessment efforts evaluating LSP retrievals, and provide examples illustrating the character and quality of information related to land cover and terrestrial ecosystem properties provided by the continental LSP dataset that we have developed. The algorithm is highly successful in ecosystems with strong seasonal variation in leaf area (e.g., deciduous forests). Conversely, results in evergreen systems are less interpretable and conclusive.

Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

Timely and accurate crop type mapping is a critical prerequisite for the estimation of water availability and environmental carrying capacity. This research proposed a method to integrate time series Sentinel-1 (S1) and Sentinel-2 (S2) data for crop type mapping over oasis agricultural areas through a case study in Northwest China. Previous studies using synthetic aperture radar (SAR) data alone often yield quite limited accuracy in crop type identification due to speckles. To improve the quality of SAR features, we adopted a statistically homogeneous pixel (SHP) distributed scatterer interferometry (DSI) algorithm, originally proposed in the interferometric SAR (InSAR) community for distributed scatters (DSs) extraction, to identify statistically homogeneous pixel subsets (SHPs). On the basis of this algorithm, the SAR backscatter intensity was de-speckled, and the bias of coherence was mitigated. In addition to backscatter intensity, several InSAR products were extracted for crop type classification, including the interferometric coherence, master versus slave intensity ratio, and amplitude dispersion derived from SAR data. To explore the role of red-edge wavelengths in oasis crop type discrimination, we derived 11 red-edge indices and three red-edge bands from Sentinel-2 images, together with the conventional optical features, to serve as input features for classification. To deal with the high dimension of combined SAR and optical features, an automated feature selection method, i.e., recursive feature increment, was developed to obtain the optimal combination of S1 and S2 features to achieve the highest mapping accuracy. Using a random forest classifier, a distribution map of five major crop types was produced with an overall accuracy of 83.22% and kappa coefficient of 0.77. The contribution of SAR and optical features were investigated. SAR intensity in VH polarization was proved to be most important for crop type identification among all the microwave and optical features employed in this study. Some of the InSAR products, i.e., the amplitude dispersion, master versus slave intensity ratio, and coherence, were found to be beneficial for oasis crop type mapping. It was proved the inclusion of red-edge wavelengths improved the overall accuracy (OA) of crop type mapping by 1.84% compared with only using conventional optical features. In comparison, it was demonstrated that the synergistic use of time series Sentinel-1 and Sentinel-2 data achieved the best performance in the oasis crop type discrimination.

Crop Type Discrimination and Health Assessment using Hyperspectral Imaging

Crop Type Discrimination and Health Assessment using Hyperspectral Imaging

The use of geomatics techniques to evaluate crop water requirement in Mujib Basin

Geomatics was used in this paper to provide efficient tools to create maps and to perform all the necessary analysis about crops (computation of areas, discrimination of crop types etc.) in Mujib Basin (MB), in a timely and cost-effective manner which can’t be delivered using the conventional surveying technology. The use of these techniques allowed the identification and the delineation of agricultural parcels and then the computation of their areas in this basin. This leads to the production of a digital crop map. The obtained crop map beside FAO and climatic data permitted the evaluation of water requirements for crops during the intended period. This study showed the efficiency of geomatics techniques to reveal the illegal use of the underground water resources in comparing with the well production in this basin and to estimate the water requirement to irrigate all parcels during the year 2015. Significant difference between well production and water requirements for the parcels irrigated from underground water is found. This proves the use of illegal water resources for example by digging wells without permission or taking water from main pipelines before metring. This metring helps the control of the consumed water from underground reservoirs.

Assessing fruit-tree crop classification from Landsat-8 time series for the Maipo Valley, Chile

Abstract Satellite image time series (SITS) provide spectral–temporal features that describe phenological changes in vegetation over the growing season, which is expected to facilitate the classification of crop types. While most SITS-based crop type classifications were focused on NDVI (normalized difference vegetation index) temporal profiles, less attention has been paid to using the complete image spectral resolution of the time series. In this work we assessed different approaches to SITS-based classification of four major fruit-tree crops in the Maipo Valley, central Chile, during the 2013–14 growing season. We compared four feature sets from a time series comprised of eight cloud-free Landsat-8 images: the full-band SITS, the NDVI and NDWI (normalized difference water index) temporal profiles, and an image stack with all the feature sets combined. State-of-the-art classifiers (linear discriminant analysis, LDA; random forest; and support vector machine) were applied on each feature set at different training sample sizes ( N = 100, 200, 400, 800 and 2291 fields), and classification results were assessed by cross-validation of the misclassification error rate (MER). For all the feature sets overall results were good (MERs ≤ 0.21) although substantially improved classification accuracies were achieved when the full-band SITS was employed (MER 0.14–0.05). Classifications applied on the NDVI temporal profile consistently had the worst performance. For a sample size of 200 fields, LDA using the full-band SITS of image dates 1, 3, 6 and 8 produced the best tradeoff between the number of images and classification accuracy (MER = 0.06), being the green, red, blue and SWIR (short-wave infrared) bands of image date 1 (acquired at the early greenup stage) the most relevant for crop type discrimination. Our results show the importance of considering the complete image spectral resolution for SITS-based crop type classifications as the commonly used NDVI temporal profile and their red and near infrared bands were not found the most significant to discriminate the crop types of interest. Furthermore, in light of the good results obtained, the methodology used here might be transferred to similar agricultural lands cultivated with the same crop types, thus providing a reliable and relatively efficient methodology for creating and updating crop inventories.

Extreme Learning Machine-based Crop Classification using ALOS/PALSAR Images

Classification maps are required for agricultural management and the estimation of agricultural disaster compensation. The extreme learning machine (ELM), a newly developed single hidden layer neural network is used as a supervised classifier for remote sensing classifications. In this study, the ELM was evaluated to examine its potential for multi-temporal ALOS/PALSAR images for the classification of crop type. In addition, the k-nearest neighbor algorithm (k-NN), one of the traditional classification methods, was also applied for comparison with the ELM. In the study area, beans, beets, grasses, maize, potato, and winter wheat were cultivated; and these crop types in each field were identified using a data set acquired in 2010. The result of ELM classification was superior to that of k-NN; and overall accuracy was 79.3%. This study highlights the advantages of ALOS/PALSAR images for agricultural field monitoring and indicates the usefulness of regular monitoring using the ALOS-2/PALSAR-2 system. Discipline: Agricultural engineering Additional key words: Hokkaido, machine learning, sigma naught *Corresponding author: reysnb@gmail.com Received 12 May 2014; accepted 4 February 2015. Introduction Land-cover classification is one of the most common applications of remote sensing. Crop-type classification maps are useful for yield estimation and agricultural disaster compensation, in addition to the management of agricultural fields. Optical remote sensing remains one of the most attractive options for the accumulation of biomass information and forest monitoring (Samreth et al. 2012, Sarker and Nichol 2011). In addition, while optical satellites such as ALOS/AVNIR-2 (Sonobe et al. 2014a), Landsat (Hartfield et al. 2013), MODIS (Sakamoto et al. 2009), and NOAA (Hirano and Batbileg 2013) have been employed in the identification of species and conditions of vegetation, cloud cover significantly limits the number of available optical images, radar is unaffected by cloud cover or low solar zenith angles (Bindlish and Barros 2001). And significant information about soil and vegetation parameters can also be obtained through microwave remote sensing (Sonobe et al. 2014b). These techniques are employed increasingly to manage land and water resources for agricultural applications (Sonobe et al. 2014c). The number of studies on rice and wheat monitoring and mapping using SAR data has increased, and some studies utilizing multi-temporal SAR data have reported high correlations between backscattering coefficients, and plant height and age (Chakraborty et al. 2005, Sonobe et al. 2014d, Waisurasingha et al. 2008). These examples highlight possible uses in the area of agricultural management, specifically for the identification of paddy fields. They indicate the potential use of SAR data for the discrimination of crop types. Furthermore, a number of examples have shown the practical usefulness of supervised learning for classification (Sonobe et al. 2014b). This paper reports a comparison of crop classification using PALSAR data performed by extreme learning machine (ELM) and k-nearest neighbor algorithm (k-NN).

Discrimination of crop types with TerraSAR-X-derived information

Although classification maps are required for management and for the estimation of agricultural disaster compensation, those techniques have yet to be established. This paper describes the comparison of three different classification algorithms for mapping crops in Hokkaido, Japan, using TerraSAR-X (including TanDEM-X) dual-polarimetric data. In the study area, beans, beets, grasslands, maize, potatoes and winter wheat were cultivated. In this study, classification using TerraSAR-X-derived information was performed. Coherence values, polarimetric parameters and gamma nought values were also obtained and evaluated regarding their usefulness in crop classification. Accurate classification may be possible with currently existing supervised learning models. A comparison between the classification and regression tree (CART), support vector machine (SVM) and random forests (RF) algorithms was performed. Even though J–M distances were lower than 1.0 on all TerraSAR-X acquisition days, good results were achieved (e.g., separability between winter wheat and grass) due to the characteristics of the machine learning algorithm. It was found that SVM performed best, achieving an overall accuracy of 95.0% based on the polarimetric parameters and gamma nought values for HH and VV polarizations. The misclassified fields were less than 100a in area and 79.5–96.3% were less than 200a with the exception of grassland. When some feature such as a road or windbreak forest is present in the TerraSAR-X data, the ratio of its extent to that of the field is relatively higher for the smaller fields, which leads to misclassifications.

First Results of Monitoring Nature Conservation Sites in Alpine Region by Using Very High Resolution (VHR) X-Band SAR Data

The exploitation of X-band imagery is currently being used in the field of agriculture for the discrimination of crop types and is also recently addressed for challenging tasks such as surface roughness and soil moisture retrieval. We investigated the potential of multi-temporal COSMO-SkyMed data for the monitoring of Natura 2000 habitats in alpine region. Short revisit time of currently available VHR synthetic aperture radar instruments (like TerraSAR-X, COSMO-SkyMed etc.) and their all-weather (day/night) image acquisition capability provides an additional advantage of continuous monitoring of nature conservation sites in particular which are small in size. Preliminary analysis of VV and VH signals indicates a predominant effect of attenuation with respect to the volume contribution as expected in case of X-band for the reduced penetration capabilities in the canopy. Significant changes in the VV signals are mainly ascribed to grazing/mowing event which reduces the canopy layer and then the signal attenuation. The final results are in agreement with the ancillary data (Normalized Vegetation Index (NDVI)) and management rule record for Natura 2000 habitats present in the alpine region.

Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion/EO-1 Data

The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy ~70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350-2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using ~20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was ~ 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or “the curse of high dimensionality”) in hyperspectral data for a particular application (e.g., biophysical characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI.

View angle effects on the discrimination of soybean varieties and on the relationships between vegetation indices and yield using off-nadir Hyperion data

Imaging spectrometry has the potential to provide improved discrimination of crop types and better estimates of crop yield. Here we investigate the potential of Hyperion to discriminate three Brazilian soybean varieties and to evaluate the relationship between grain yield and 17 narrow-band vegetation indices. Hyperion analysis focused on two datasets acquired from opposite off-nadir viewing directions but similar solar geometry: one acquired on 08 February 2005 (forward scattering) and the other on 14 January 2006 (back scattering). In 2005, the soybean canopies were observed by Hyperion at later reproductive stages than in 2006. Additional Hyperion datasets were not available due to cloud cover. To further examine the impact of viewing geometry within the same season, Hyperion data were complemented by 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) images (bands 1 and 2) acquired in consecutive days (05–06 February 2005) with opposite viewing geometries (− 42° and + 44°, respectively). MODIS data analysis was used to keep reproductive stage as a constant factor while isolating the impact of viewing geometry. For discrimination purposes, multiple discriminant analysis (MDA) was applied over each dataset using surface reflectance values as input variables and a stepwise procedure for band selection. All possible Hyperion band ratios and the 17 narrow-band vegetation indices with soybean grain yield were evaluated across years through Pearson's correlation coefficients and linear regression. MODIS-derived Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR) were evaluated within the same growing season. Results showed that: (1) the three soybean varieties were discriminated with highest accuracy in the back scattering direction, as deduced from MDA classification results from Hyperion and MODIS data; (2) the highest correlation between Hyperion vegetation indices and soybean yield was observed for the Normalized Difference Water Index (NDWI) ( r = + 0.74) in the back scattering direction and this result was consistent with band ratio analysis; (3) higher Hyperion correlation results were observed in the back scattering direction when compared to the forward scattering image. For the same reproductive stage, stronger shadowing effects were observed over the MODIS red band in the forward scattering direction producing lower and lesser variable reflectance for the sensor. As a result, the relationship between MODIS-derived NDVI and soybean yield improved from the forward ( r of + 0.21) to the back scattering view ( r of + 0.60). The same trend was observed for SR that increased from + 0.22 to + 0.58.

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.

Spectral Variability of LANDSAT-4 Thematic Mapper And Multispectral Scanner Data for Selected Crop And Forest Cover Types

The performance of both the Landsat-4 TM and MSS sensors is evaluated through the analysis of image and digital data simultaneously acquired over agricultural and forestry study sites in California. Spectral statistics extracted for selected cover types include band means, variances, coefficients of variation, range values, skewness, kurtosis, and covariance and correlation matrices. Spectral characteristics are evaluated through analysis of these statistics and interpretation of image products. Image products are used to visually represent significant spectral variations between the TM bands. Significant results include: 1) the overall spectral, spatial, and radiometric quality of the TM data are excellent; 2) discrimination of crop types on single-date image data is significantly improved by the addition of the first short-wave infrared band (TM5); 3) the thermal infrared data (TM6) allows the discrimination of agricultural and forestry cover types based on differences in their radiant temperature responses; and 4) the higher TM spatial resolution (28.5 m versus 57 m) provides the ability to discriminate small agricultural fields and boundaries, forest stand boundary conditions, road and stream networks in rough terrain, and small clearings resulting from various forest management practices.