Hidden Markov Models for Real-Time Estimation of Corn Progress Stages Using MODIS and Meteorological Data

Abstract

Real-time estimation of crop progress stages is critical to the US agricultural economy and decision making. In this paper, a Hidden Markov Model (HMM) based method combining multisource features has been presented. The multisource features include mean Normalized Difference Vegetation Index (NDVI), fractal dimension, and Accumulated Growing Degree Days (AGDDs). In our case, these features are global variable, and measured in the state-level. Moreover, global feature in each Day of Year (DOY) would be impacted by multiple progress stages. Therefore, a mixture model is employed to model the observation probability distribution with all possible stage components. Then, a filtering based algorithm is utilized to estimate the proportion of each progress stage in the real-time. Experiments are conducted in the states of Iowa, Illinois and Nebraska in the USA, and our results are assessed and validated by the Crop Progress Reports (CPRs) of the National Agricultural Statistics Service (NASS). Finally, a quantitative comparison and analysis between our method and spectral pixel-wise based methods is presented. The results demonstrate the feasibility of the proposed method for the estimation of corn progress stages. The proposed method could be used as a OPEN ACCESSRemote Sens. 2013, 5 1735 supplementary tool in aid of field survey. Moreover, it also can be used to establish the progress stage estimation model for different types of crops.

Predicting Spatial Distribution of Key Honeybee Pests in Kenya Using Remotely Sensed and Bioclimatic Variables: Key Honeybee Pests Distribution Models

“…Thereafter, a smooth model function is used to extract phenological variables for each growing season, which in turn reduces the influence of residual signal noise in the NDVI time series data [31,32] and data dimensionality [33,34]. Function-fitting parameters used in TIMESAT for this study were: a Savitzky-Golay filter procedure, 3-and 4-point window over 2 fitting steps, adaptation strength of 3.0, no spike or amplitude cutoffs, season cutoff of 0.0, and begin and end of season threshold of 20%.…”

A Hidden Markov Models Approach for Crop Classification: Linking Crop Phenology to Time Series of Multi-Sensor Remote Sensing Data

“…Previous work has tested the application of HMMs in Landsat time series to classify mountain vegetation in Norway [26] and arable land in Brazil [27], in MODIS-NDVI time series covering cultivated areas of the United States [28] and NDVI data derived from the Advanced Very High Resolution Radiometer (AVHRR) over the West African savanna [24]. In all the aforementioned studies, the low and medium resolution images have been reported to be adequate for the classification of large-sized agricultural holdings.…”

Using Hidden Markov Models for Land Surface Phenology: An Evaluation Across a Range of Land Cover Types in Southeast Spain

“…Despite the HMM being proposed very early as a tool to investigate vegetation dynamics from remote sensing observations [54], and although there have been a few examples of applications of HMM in phenology in recent years [47,49,55,56], to our knowledge there has been no previous published evaluation of the performance of HMM to define phenology metrics in comparison with other methods. In this work we have employed a very simple model of HMM and tested its ability to provide phenology metrics in comparison to ready applications of different smoothing methods, without trying to optimize any of the methods or adjusting them to particular study areas.…”

“…Cerqueira Leite et al [47] and Siachalou et al [49] used HMM modeling of crop phenology with the aim of classifying different types of crops from time series of remote sensed images. Shen et al [55] used HMM modeling of corn phenological phases to estimate corn progress from NDVI and meteorological data. García et al [56] discussed the application of HMM to extract LSP metrics from the time series of NDVI data.…”