A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data

Wheat is one of the key cereal crops grown worldwide, providing the primary caloric and nutritional source for millions of people around the world. In order to ensure food security and sound, actionable mitigation strategies and policies for management of food shortages, timely and accurate estimates of global crop production are essential. This study combines a new BRDF-corrected, daily surface reflectance dataset developed from NASA's Moderate resolution Imaging Spectro-radiometer (MODIS) with detailed official crop statistics to develop an empirical, generalized approach to forecast wheat yields. The first step of this study was to develop and evaluate a regression-based model for forecasting winter wheat production in Kansas. This regression-based model was then directly applied to forecast winter wheat production in Ukraine. The forecasts of production in Kansas closely matched the USDA/NASS reported numbers with a 7% error. The same regression model forecast winter wheat production in Ukraine within 10% of the official reported production numbers six weeks prior to harvest. Using new data from MODIS, this method is simple, has limited data requirements, and can provide an indication of winter wheat production shortfalls and surplus prior to harvest in regions where minimal ground data is available.     

Yield forecasting for wheat and corn in Hungary by satellite remote sensing

We have developed an advanced version of our yield estimation method [Ferencz et al., 2004 Cs, Ferencz, Bognár, P., Lichtenberger, J., Hamar, D., Gy, Tarcsai, Timár, G., Molnár, G., Sz, Pásztor, Steinbach, P., Székely, B., Ferencz, O.E. and Ferencz-Árkos, I. 2004. Crop yield estimation by satellite remote sensing. International Journal of Remote Sensing, 25: 4113–4149.  [Google Scholar], Crop yield estimation by satellite remote sensing. International Journal of Remote Sensing, 25, pp. 4113–4149], that is able to provide reliable forecasts for corn and wheat, several weeks before the harvest. The forecasting method is based on the data of the Advanced Very High Resolution Radiometer (AVHRR) instruments of the National Oceanic and Atmospheric Administration's (NOAA) Polar Orbiting Environmental Satellites (POES). The method was applied to Hungary between the years 1996 and 2000. The forecasted yield values are all within 5% reliability with respect to the actual yield data produced by classic (non-satellite based) methods and provided by the Hungarian Statistical Office, with the exception of 1997, where the absolute error is about 8%.     

A big data framework for stock price forecasting using fuzzy time series

Multimedia Tools and Applications - Stock price forecasting is the most difficult field owing to irregularities. Therefore, the stock price forecasting and recommendation is an extremely...     

Impact of nutrients on peatland GPP estimations using MODIS time series data

Time series of satellite sensor-derived data can be used in the light use efficiency (LUE) model for gross primary productivity (GPP). The LUE model and a closely related linear regression model were studied at an ombrotrophic peatland in southern Sweden. Eddy covariance and chamber GPP, incoming and reflected photosynthetic photon flux density (PPFD), field-measured spectral reflectance, and data from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used in this study. The chamber and spectral reflectance measurements were made on four experimental treatments: unfertilized control (Ctrl), nitrogen fertilized (N), phosphorus fertilized (P), and nitrogen plus phosphorus fertilized (NP). For Ctrl, a strong linear relationship was found between GPP and the photosynthetically active radiation absorbed by vegetation (APAR) (R² = 0.90). The slope coefficient (ε_s, where s stands for “slope”) for the linear relationship between seasonal time series of GPP and the product of the normalized difference vegetation index (NDVI) and PPFD was used as a proxy for the light use efficiency factor (ε). There were differences in ε_s depending on the treatments with a significant effect for N compared to Ctrl (ANOVA: p = 0.042, Tukey's: p ≤ 0.05). Also, ε_s was linearly related to the cover degree of vascular plants (R² = 0.66). As a sensitivity test, the regression coefficients (ε_s and intercept) for each treatment were used to model time series of 16-day GPP from the product of MODIS NDVI and PPFD. Seasonal averages of GPP were calculated for 2005, 2006, and 2007, which resulted in up to 19% higher average GPP for the fertilization treatments compared to Ctrl. The main conclusion is that the LUE model and the regression model can be applied in peatlands but also that temporal and spatial changes in ε or the regression coefficients should be considered.     

Fuzzy based trend mapping and forecasting for time series data

The study demonstrates the superiority of fuzzy based methods for non-stationary, non-linear time series. Study is based on unequal length fuzzy sets and uses IF-THEN based fuzzy rules to capture the trend prevailing in the series. The proposed model not only predicts the value but can also identify the transition points where the series may change its shape and is ready to include subject expert’s opinion to forecast. The series is tested on three different types of data: enrolment for Alabama university, sales volume of a chemical company and Gross domestic capital of India: the growth curve. The model is tested on both kind of series: with and without outliers. The proposed model provides an improved prediction with lesser MAPE (mean average percentage error) for all the series tested.     

Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data

The remote sensing of Earth surface changes is an active research field aimed at the development of methods and data products needed by scientists, resource managers, and policymakers. Fire is a major cause of surface change and occurs in most vegetation zones across the world. The identification and delineation of fire-affected areas, also known as burned areas or fire scars, may be considered a change detection problem. Remote sensing algorithms developed to map fire-affected areas are difficult to implement reliably over large areas because of variations in both the surface state and those imposed by the sensing system. The availability of robustly calibrated, atmospherically corrected, cloud-screened, geolocated data provided by the latest generation of moderate resolution remote sensing systems allows for major advances in satellite mapping of fire-affected area. This paper describes an algorithm developed to map fire-affected areas at a global scale using Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance time series data. The algorithm is developed from the recently published Bi-Directional Reflectance Model-Based Expectation change detection approach and maps at 500 m the location and approximate day of burning. Improvements made to the algorithm for systematic global implementation are presented and the algorithm performance is demonstrated for southern African, Australian, South American, and Boreal fire regimes. The algorithm does not use training data but rather applies a wavelength independent threshold and spectral constraints defined by the noise characteristics of the reflectance data and knowledge of the spectral behavior of burned vegetation and spectrally confusing changes that are not associated with burning. Temporal constraints are applied capitalizing on the spectral persistence of fire-affected areas. Differences between mapped fire-affected areas and cumulative MODIS active fire detections are illustrated and discussed for each fire regime. The results reveal a coherent spatio-temporal mapping of fire-affected area and indicate that the algorithm shows potential for global application.     

An operational method to estimate evapotranspiration using MODIS data during winter wheat growing season

The Penman–Monteith (P-M) model has been widely used to estimate actual evapotranspiration (ET). However, its application is mainly constrained to within field scales because the surface resistance under water stress at large scales is difficult to define. The Normalized Difference Water Index (NDWI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data was shown to be sensitive to the crop water content and water deficit, and used to estimate the surface resistance in the P-M model. The modelled latent heat fluxes matched well with the eddy correlation observations, and the spatial distributions also showed a similar pattern as the results from the one-layer model in an irrigated area at the downstream of Yellow River. To reduce the influence of cloud and other atmospheric disturbances, the daily surface resistance was retrieved from 8-day temporal composite MODIS NDWI. The modelled daily ET showed consistent temporal changes with the observations during the wheat growing season. This method showed advantages over the other remote sensing models, for example, the one-layer model, which required daily radiative temperature inputs and cannot be implemented under cloudy conditions.     

Estimating floodwater from MODIS time series and SRTM DEM data

Real-time flood mapping with an automatic flood-detection technique is important in emergency response efforts. However, current mapping technology still has limitations in accurately expressing information on flood areas such as inundation depth and extent. For this reason, the authors attempt to improve a floodwater detection method with a simple algorithm for a better discrimination capacity to discern flood areas from turbid floodwater, mixed vegetation areas, snow, and clouds. The purpose of this study was to estimate a flood area based on the spatial distribution of a nationwide flood from the Moderate Resolution Imaging Spectroradiometer (MODIS) time series images (8-day composites, MOD09A1, 500-m resolution) and a digital elevation model (DEM). The results showed the superiority of the developed method in providing instant, accurate flood mapping by using two algorithms, which modified land surface water index from MODIS image and eight-direction tracking algorithm based on DEM data.     

Modeling MODIS LAI time series using three statistical methods

Leaf Area Index (LAI) is one of the most important variables characterizing land surface vegetation and dynamics. Many satellite data, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), have been used to generate LAI products. It is important to characterize their spatial and temporal variations by developing mathematical models from these products. In this study, we aim to model MODIS LAI time series and further predict its future values by decomposing the LAI time series of each pixel into several components: trend, intra-annual variations, seasonal cycle, and stochastic stationary or irregular parts. Three such models that can characterize the non-stationary time series data and predict the future values are explored, including Dynamic Harmonics Regression (DHR), STL (Seasonal-Trend Decomposition Procedure based on Loess), and Seasonal ARIMA (AutoRegressive Intergrated Moving Average) (SARIMA). The preliminary results using six years (2001-2006) of the MODIS LAI product indicate that all these methods are effective to model LAI time series and predict 2007 LAI values reasonably well. The SARIMA model gives the best prediction, DHR produces the smoothest curve, and STL is more sensitive to noise in the data. These methods work best for land cover types with pronounced seasonal variations.     

Real-time retrieval of Leaf Area Index from MODIS time series data

Real-time/near real-time inversion of land surface biogeophysical variables from satellite observations is required to monitor rapid land surface changes, and provide the necessary input for numerical weather forecasting models and decision support systems. This paper develops a new inversion method for the real-time estimation of the Leaf Area Index (LAI) of land surfaces from MODIS time series reflectance data (MOD09A1). It consists of a series of procedures, including time series data smoothing, data quality control and real-time estimation of LAI. After the historical LAI time series is smoothed by a multi-step Savitzky-Golay filter to determine the upper LAI envelope, a Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model is used to derive the LAI climatology. Based on the climatology from the SARIMA model to evolve LAI in time, a dynamic model is then constructed and used to provide the short-range forecast of LAI. Predictions from this model are used with Ensemble Kalman Filter (EnKF) techniques to recursively update biophysical variables as new observations arrive. The validation results produced using MODIS surface reflectance data and field-measured LAI data at eight BELMANIP sites show that the real-time inversion method is able to efficiently produce a relatively smooth LAI product. In addition, the accuracy is significantly improved over the MODIS LAI product.     

Integrating big data driven sentiments polarity and ABC-optimized LSTM for time series forecasting

Stock market is a dynamic and volatile market that is considered as time series data. The growth of financial data exposed the computational efficiency of the conventional systems. This paper proposed a hybrid deep learning model based on Long Short- Term Memory (LSTM) and Artificial Bee Colony (ABC) algorithm. ABC is best fit for hyper parameter selection for deep LSTM models and maintains the equilibrium of exploitation and exploration issues. Handling a large volume of multidimensional reviews from social media is a major challenge. This paper evolves the multiple aspects of market sentiments and uses the reliable Big data platform Hadoop ecosystem and its services to compute sentiment polarity index. The ABC-LSTM hybrid model is validated with other core and hybrid models with evolutionary algorithms as Differential Evolution (DE) and Genetic Algorithm (GA). For the experiments, 10 years of historical datasets and social media reviews of IT sector funds Apple Inc. (AAPL), Microsoft corporation (MSFT) and Intel corporation (INTL) from NASDAQ GS, an American stock exchange are considered to validate hybrid forecasting models. Proposed algorithm ABC-LSTM is used to tune the hyperparameters (window size, LSTM units, dropout probability, epochs, batch size and learning rate) and evaluated through Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) as loss function. Performance analysis proves that with sentiment polarity, ABC optimized LSTM obtains improved forecasting accuracy over its counterpart models.

     

Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data

Understory fires in Amazon forests alter forest structure, species composition, and the likelihood of future disturbance. The annual extent of fire-damaged forest in Amazonia remains uncertain due to difficulties in separating burning from other types of forest damage in satellite data. We developed a new approach, the Burn Damage and Recovery (BDR) algorithm, to identify fire-related canopy damages using spatial and spectral information from multi-year time series of satellite data. The BDR approach identifies understory fires in intact and logged Amazon forests based on the reduction and recovery of live canopy cover in the years following fire damages and the size and shape of individual understory burn scars. The BDR algorithm was applied to time series of Landsat (1997-2004) and MODIS (2000-2005) data covering one Landsat scene (path/row 226/068) in southern Amazonia and the results were compared to field observations, image-derived burn scars, and independent data on selective logging and deforestation. Landsat resolution was essential for detection of burn scars < 50 ha, yet these small burns contributed only 12% of all burned forest detected during 1997-2002. MODIS data were suitable for mapping medium (50-500 ha) and large (> 500 ha) burn scars that accounted for the majority of all fire-damaged forests in this study. Therefore, moderate resolution satellite data may be suitable to provide estimates of the extent of fire-damaged Amazon forest at a regional scale. In the study region, Landsat-based understory fire damages in 1999 (1508 km²) were an order of magnitude higher than during the 1997-1998 El Niño event (124 km² and 39 km², respectively), suggesting a different link between climate and understory fires than previously reported for other Amazon regions. The results in this study illustrate the potential to address critical questions concerning climate and fire risk in Amazon forests by applying the BDR algorithm over larger areas and longer image time series.     

Recursive prediction for long term time series forecasting using advanced models

There exists a wide range of paradigms, and a high number of different methodologies that are applied to the problem of time series prediction. Most of them are presented as a modified function approximation problem using input/output data, in which the input data are expanded using values of the series at previous steps. Thus, the model obtained normally predicts the value of the series at a time (t+h) using previous time steps (t-τ₁),(t-τ₂),…,(t-τ_n). Nevertheless, learning a model for long term time series prediction might be seen as a more complicated task, since it might use its own outputs as inputs for long term prediction (recursive prediction). This paper presents the utility of two different methodologies, the TaSe fuzzy TSK model and the least-squares SVMs, to solve the problem of long term time series prediction using recursive prediction. This work also introduces some techniques that upgrade the performance of those advanced one-step-ahead models (and in general of any one-step-ahead model), where they are used recursively for long term time series prediction.     

Long memory time series forecasting by using genetic programming

Real-world time series have certain properties, such as stationarity, seasonality, linearity, among others, which determine their underlying behaviour. There is a particular class of time series called long-memory processes, characterized by a persistent temporal dependence between distant observations, that is, the time series values depend not only on recent past values but also on observations of much prior time periods. The main purpose of this research is the development, application, and evaluation of a computational intelligence method specifically tailored for long memory time series forecasting, with emphasis on many-step-ahead prediction. The method proposed here is a hybrid combining genetic programming and the fractionally integrated (long-memory) component of autoregressive fractionally integrated moving average (ARFIMA) models. Another objective of this study is the discovery of useful comprehensible novel knowledge, represented as time series predictive models. In this respect, a new evolutionary multi-objective search method is proposed to limit complexity of evolved solutions and to improve predictive quality. Using these methods allows for obtaining lower complexity (and possibly more comprehensible) models with high predictive quality, keeping run time and memory requirements low, and avoiding bloat and over-fitting. The methods are assessed on five real-world long memory time series and their performance is compared to that of statistical models reported in the literature. Experimental results show the proposed methods’ advantages in long memory time series forecasting.     

Linguistic time series forecasting using fuzzy recurrent neural network

It is known that one of the most spread forecasting methods is the time series analysis. A weakness of traditional crisp time series forecasting methods is that they process only measurement based numerical information and cannot deal with the perception-based historical data represented by linguistic values. Application of a new class of time series, a fuzzy time series whose values are linguistic values, can overcome the mentioned weakness of traditional forecasting methods. In this paper we propose a fuzzy recurrent neural network (FRNN) based time series forecasting method for solving forecasting problems in which the data can be presented as perceptions and described by fuzzy numbers. The FRNN allows effectively handle fuzzy time series to apply human expertise throughout the forecasting procedure and demonstrates more adequate forecasting results. Recurrent links in FRNN also allow for simplification of the overall network structure (size) and forecasting procedure. Genetic algorithm-based procedure is used for training the FRNN. The effectiveness of the proposed fuzzy time series forecasting method is tested on the benchmark examples.     

Use of Landsat TM and EOS MODIS imaging technologies for estimation of winter wheat yield in the North China Plain

This study focuses on the methodologies of winter wheat yield prediction based on Land Satellite Thematic Map (TM) and Earth Observation System Moderate Resolution Imaging Spectroradiometer (MODIS) imaging technologies in the North China Plain. Routine field measurements were initiated during the periods when the Landsat satellite passed over the study region. Five Landsat TM images were acquired. Wheat yields of the experimental sites were recorded after harvest. Spectral vegetation indices were calculated from TM and MODIS images. The correlation analysis among wheat yield and spectral parameters revealed that TM renormalized difference water index (RDWI) and MODIS near-infrared reflectance had the highest correlation with yield at grain-filling stages. The models from the best-fitting method were used to estimate wheat yield based on TM and MODIS data. The average relative error of the root mean square error (RMSE) of the predicted yield was smaller from TM than from MODIS.     

Fast correlation coefficient estimation algorithm for HBase-based massive time series data

In recent years, the rapid development of Internet of Things and sensor networks makes the time series data experiencing explosive growth. OpenTSDB and other emerging systems begin to use Hadoop, HBase to store massive time series data, and how to use these platforms to query and mine time series data has become a current research hotspot. As a typical time series distance measurementmethod, correlation coefficient is widely used in various applications. However, it requires a large amount of I/O and network transmission to compute the correlation coefficient of long time sequence on HBase in real time, and therefore cannot be applied to interactive query. To address this problem, in this paper, we present two methods to estimate the correlation coefficients of two sequences on HBase. We first propose a fast estimation algorithm for the upper and lower bounds of correlation coefficient, named as DCE. In order to further reduce the cost of I/O, we extend the DCE algorithm, and propose the ADCE algorithm, which can estimate the correlation coefficient quickly with an iterative manner. Experiments show that the algorithms proposed in this paper can quickly calculate the correlation coefficient of the long time series.     

Use of remote sensing data for estimation of winter wheat yield in the United States

This paper shows the application of remote sensing data for estimating winter wheat yield in Kansas. An algorithm uses the Vegetation Health (VH) Indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI)) computed for each week over a period of 23 years (1982–2004) from Advance Very High Resolution Radiometer (AVHRR) data. The weekly indices were correlated with the end of the season winter wheat (WW) yield. A strong correlation was found between winter wheat yield and VCI (characterizing moisture conditions) during the critical period of winter wheat development and productivity that occurs during April to May (weeks 16 to 23). Following the results of correlation analysis, the principal components regression (PCR) method was used to construct a model to predict yield as a function of the VCI computed for this period. The simulated results were compared with official agricultural statistics showing that the errors of the estimates of winter wheat yield are less than 8%. Remote sensing, therefore, is a valuable tool for estimating crop yields well in advance of harvest, and at a low cost.     

对时间序列查询的最优小波误差估计

使用小波变换缩减维度是解决高维时间序列查询的一个有效方法。传统的算法均使用变换后小波序列的前k个系数作为原始时间序列的一个近似估计。但是由于选择前k个系数不一定能很好地近似原始序列集合。给出相关定理，说明选择小波系数集合的列平方和最大的k列，可以更好近似原始序列集合。实验结果表明，相对于传统算法，该方法可以更好地缩小相对误差。