Hidden Markov Models for Real-Time Estimation of Corn Progress Stages Using MODIS and Meteorological Data
<p>Illustration of study area and selected meteorological stations. The study area covers three states of the United States: Iowa, Illinois and Nebraska. Stations are marked as circle dots, and colors are labeled for different states. The number of meteorological stations of Iowa (blue dots), Illinois (green dots), and Nebraska (red dots) is 23, 33, and 37, respectively.</p> ">
<p>Distributions of normalized mean Normalized Difference Vegetation Index (NDVI), fractal dimension (fd), and AGDDs along the corn life cycle (Iowa, 2007).</p> ">
<p>Basic principle of proposed Hidden Markov Model (HMM).</p> ">
<p>NASS’s CPRs Normalization, Iowa (2011). PS = pre-season. PL = planted, EM = emerged, SI = silking, DO = dough, DE = dent, MA = mature, and HA = harvested. (<b>a</b>) original corn progress percentages; (<b>b</b>) normalized corn progress percentages.</p> ">
<p>Illustration of corn progress stage transition along a life cycle.</p> ">
<p>RMSE of corn progress percentage estimates. (<b>a</b>) Iowa; (<b>b</b>) Illinois; (<b>c</b>) Nebraska.</p> ">
Abstract
:1. Introduction
2. Study Area and Data Sets
- (1)
- Daily NDVI time series, which is derived from the atmospherically corrected MODIS MOD09GQ (MODIS Surface Reflectance Daily L2G Global 250 m) dataset with 250 m spatial resolution. This data set is publicly available through the “Vegetation Condition Explorer” ( http://dss.csiss.gmu.edu/NDVIDownload/), maintained by the Center for Spatial Information Science and Systems (CSISS), George Mason University.
- (2)
- NASS’s Cropland Data Layer (CDL), which is a raster, geo-referenced crop-specific land use data layer. The spatial resolution of years 2006–2009 is 56 m, and the rest is 30 m. The data set is publicly available via “CropScape” ( http://nassgeodata.gmu.edu/), produced operationally by USDA/NASS.
- (3)
- NASS’s CPRs, which record the percent complete (area ratio) of crop fields that has either reached or completed a specific progress stage over a specific administrative unit. It is publicly available via NASS’s “Quick Stats 2.0” service ( http://www.nass.usda.gov/Quick_Stats/). More details of the first three data sets can refer to [19].
- (4)
- Daily minimum and maximum temperatures, which are derived from the United States Historical Climatology Network (USHCN) [27]. USHCN is a high-quality network of US Cooperative Observer Network stations, specially selected for analyzing long-term variability and change in the whole contiguous United States [27]. In this study, 23, 33, and 37 meteorological stations are chosen for the states of Iowa, Illinois, and Nebraska, respectively (Figure 1 and Appendix: Table A1). The meteorological stations were selected with a number of criteria including length of period of record, and spatial coverage.
3. Feature Extraction
3.1. Mean NDVI
3.2. Fractal Dimension
3.3. AGDDs
4. Corn Progress Percentages Estimation
4.1. Specifying an HMM
4.2. Mixture Model in HMMs
4.3. NASS’s CPRs Normalization
4.4. HMM Parameters Determination
4.4.1. Initial Probability Distribution
4.4.2. Stage Transition Probability Matrix
4.4.3. Observation Probability Matrix
4.5. Progress Percents Estimation
5. Results and Discussions
5.1. RMSE Results
5.2. Accuracy Comparison
5.3. Performance and Analysis
- (1)
- The accuracy of NASS’s CPRs. The NASS’s CPRs are surveyed data, and mainly depended on the subjective assessment of investigators. Thus, a bias error is inevitably introduced in the NASS’s CPRs data [41];
- (2)
- The quality of MODIS NDVI. Noise has inevitably disturbed the daily MODIS-NDVI images, e.g., cloud cover, missing data, mixed pixels, or some of the systematic errors that reduce the index value of daily MODIS-NDVI images;
- (3)
- The reliability of meteorological data, regarding to the observation data of weather stations, data missing, instrumentation, or observation station location change may affect the data homogeneity and spatial coverage;
- (4)
- Irregularities in raining and temperature pattern in different years, e.g., extensive drought occurs in a particular year, can significantly affect the stability of results. It would specially impacted on HMM parameters training, e.g., the stage transition probability matrix.
- (5)
- The insufficiency of temporal resolution. The temporal resolution of data is an important factor that affects the accuracy of corn progress stages estimation. As shown in Figure 4, the emerged stage just 9.5 days delays to planted stage, and dent stage approximate 15.4 days delays to dough stage. Accurate distinction between these growth stages requires a higher temporal resolution. It is really intractable that we have to trade off temporal resolution and data quality.
6. Conclusion
Acknowledgments
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Appendix
No | ID | SA | Name | Lat (°N) | Lon (°W) | Elev (m) | No | ID | SA | Name | Lat (°N) | Lon (°W) | Elev (m) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 130112 | IA | ALBIA 3 NNE | 41.07 | 92.79 | 268.2 | 48 | 116579 | IL | PANA 3E | 39.37 | 89.02 | 213.4 |
2 | 130133 | IA | ALGONA 3 W | 43.07 | 94.31 | 377.6 | 49 | 116610 | IL | PARIS WTR WKS | 39.64 | 87.69 | 207.3 |
3 | 130600 | IA | BELLE PLAINE | 41.88 | 92.28 | 246.9 | 50 | 116910 | IL | PONTIAC | 40.89 | 88.64 | 198.1 |
4 | 131402 | IA | CHARLES CITY | 43.08 | 92.67 | 309.1 | 51 | 117551 | IL | RUSHVILLE | 40.12 | 90.56 | 201.2 |
5 | 131533 | IA | CLARINDA | 40.72 | 95.02 | 298.7 | 52 | 118147 | IL | SPARTA 1 W | 38.12 | 89.72 | 163.1 |
6 | 131635 | IA | CLINTON #1 | 41.79 | 90.26 | 178.3 | 53 | 118740 | IL | URBANA | 40.08 | 88.24 | 219.8 |
7 | 132724 | IA | ESTHERVILLE 2 N | 43.43 | 94.82 | 396.8 | 54 | 118916 | IL | WALNUT | 41.55 | 89.6 | 210.3 |
8 | 132789 | IA | FAIRFIELD | 41.02 | 91.96 | 225.6 | 55 | 119241 | IL | WHITE HALL 1 E | 39.44 | 90.38 | 176.8 |
9 | 132864 | IA | FAYETTE | 42.85 | 91.82 | 344.4 | 56 | 119354 | IL | WINDSOR | 39.44 | 88.6 | 210.3 |
10 | 132977 | IA | FOREST CITY 2 NNE | 43.28 | 93.63 | 396.2 | 57 | 250130 | NE | ALLIANCE 1WNW | 42.11 | 102.9 | 1,217.4 |
11 | 132999 | IA | FORT DODGE 5NNW | 42.58 | 94.2 | 347.5 | 58 | 250375 | NE | ASHLAND NO 2 | 41.04 | 96.38 | 326.1 |
12 | 134063 | IA | INDIANOLA 2W | 41.37 | 93.65 | 287.1 | 59 | 250435 | NE | AUBURN 5 ESE | 40.37 | 95.75 | 283.5 |
13 | 134142 | IA | IOWA FALLS | 42.52 | 93.25 | 344.4 | 60 | 250640 | NE | BEAVER CITY | 40.13 | 99.83 | 658.4 |
14 | 134735 | IA | LE MARS | 42.78 | 96.15 | 364.2 | 61 | 251145 | NE | BRIDGEPORT | 41.67 | 103.1 | 1,117.4 |
15 | 134894 | IA | LOGAN | 41.64 | 95.79 | 301.8 | 62 | 251200 | NE | BROKEN BOW 2 W | 41.41 | 99.68 | 762 |
16 | 135769 | IA | MT AYR | 40.71 | 94.24 | 359.7 | 63 | 252020 | NE | CRETE | 40.62 | 96.95 | 437.4 |
17 | 135796 | IA | MT PLEASANT 1 SSW | 40.95 | 91.56 | 222.5 | 64 | 252100 | NE | CURTIS 3NNE | 40.67 | 100.49 | 829.4 |
18 | 135952 | IA | NEW HAMPTON | 43.05 | 92.31 | 349.9 | 65 | 252205 | NE | DAVID CITY | 41.25 | 97.13 | 490.7 |
19 | 137147 | IA | ROCK RAPIDS | 43.43 | 96.17 | 411.5 | 66 | 252820 | NE | FAIRBURY 5S | 40.07 | 97.17 | 411.5 |
20 | 137161 | IA | ROCKWELL CITY | 42.4 | 94.63 | 364.2 | 67 | 252840 | NE | FAIRMONT | 40.64 | 97.59 | 499.9 |
21 | 137979 | IA | STORM LAKE 2 E | 42.63 | 95.17 | 434.3 | 68 | 253175 | NE | GENEVA | 40.53 | 97.6 | 496.8 |
22 | 138296 | IA | TOLEDO 3N | 42.04 | 92.58 | 289.3 | 69 | 253185 | NE | GENOA 2 W | 41.45 | 97.76 | 484.6 |
23 | 138688 | IA | WASHINGTON | 41.28 | 91.71 | 210.3 | 70 | 253365 | NE | GOTHENBURG | 40.94 | 100.15 | 787.9 |
24 | 110072 | IL | ALEDO | 41.2 | 90.75 | 219.5 | 71 | 253615 | NE | HARRISON | 42.69 | 103.88 | 1,478.3 |
25 | 110187 | IL | ANNA 2 NNE | 37.48 | 89.23 | 195.1 | 72 | 253630 | NE | HARTINGTON | 42.62 | 97.26 | 417.6 |
26 | 110338 | IL | AURORA | 41.78 | 88.31 | 201.2 | 73 | 253660 | NE | HASTINGS 4N | 40.65 | 98.38 | 591.3 |
27 | 111280 | IL | CARLINVILLE | 39.29 | 89.87 | 189.3 | 74 | 253735 | NE | HEBRON | 40.18 | 97.59 | 451.1 |
28 | 111436 | IL | CHARLESTON | 39.48 | 88.17 | 198.1 | 75 | 253910 | NE | HOLDREGE | 40.45 | 99.38 | 707.1 |
29 | 112140 | IL | DANVILLE | 40.14 | 87.65 | 170.1 | 76 | 254110 | NE | IMPERIAL | 40.52 | 101.66 | 999.7 |
30 | 112193 | IL | DECATUR WTP | 39.83 | 88.95 | 189 | 77 | 254440 | NE | KIMBALL 2NE | 41.25 | 103.63 | 1,435 |
31 | 112483 | IL | DU QUOIN 4 SE | 37.99 | 89.19 | 128 | 78 | 254900 | NE | LODGEPOLE | 41.15 | 102.64 | 1,168 |
32 | 113335 | IL | GALVA | 41.17 | 90.04 | 246.9 | 79 | 254985 | NE | LOUP CITY | 41.28 | 98.97 | 627.3 |
33 | 113879 | IL | HARRISBURG | 37.74 | 88.52 | 111.3 | 80 | 255080 | NE | MADISON | 41.83 | 97.45 | 481.6 |
34 | 114108 | IL | HILLSBORO | 39.15 | 89.48 | 192 | 81 | 255310 | NE | MC COOK | 40.22 | 100.62 | 796.1 |
35 | 114198 | IL | HOOPESTON 1 NE | 40.47 | 87.66 | 216.4 | 82 | 255470 | NE | MERRIMAN | 42.92 | 101.71 | 986 |
36 | 114442 | IL | JACKSONVILLE 2E | 39.73 | 90.2 | 185.9 | 83 | 255565 | NE | MINDEN | 40.52 | 98.95 | 658.4 |
37 | 114823 | IL | LA HARPE | 40.58 | 90.97 | 210.3 | 84 | 256135 | NE | OAKDALE | 42.07 | 97.97 | 521.2 |
38 | 115079 | IL | LINCOLN | 40.15 | 89.34 | 177.7 | 85 | 256570 | NE | PAWNEE CITY | 40.12 | 96.16 | 378 |
39 | 115326 | IL | MARENGO | 42.29 | 88.65 | 248.4 | 86 | 256970 | NE | PURDUM | 42.07 | 100.25 | 819.9 |
40 | 115712 | IL | MINONK | 40.91 | 89.03 | 228.6 | 87 | 257070 | NE | RED CLOUD | 40.1 | 98.52 | 524.3 |
41 | 115768 | IL | MONMOUTH | 40.92 | 90.64 | 227.1 | 88 | 257515 | NE | SAINT PAUL 4N | 41.27 | 98.47 | 541 |
42 | 115833 | IL | MORRISON | 41.80 | 89.97 | 183.8 | 89 | 257715 | NE | SEWARD | 40.9 | 97.09 | 438.9 |
43 | 115901 | IL | MT CARROLL | 42.1 | 89.98 | 195.1 | 90 | 258395 | NE | SYRACUSE | 40.68 | 96.19 | 335.3 |
44 | 115943 | IL | MT VERNON 3 NE | 38.35 | 88.85 | 149.4 | 91 | 258465 | NE | TECUMSEH 1S | 40.35 | 96.19 | 338.3 |
45 | 116446 | IL | OLNEY 2S | 38.7 | 88.08 | 146.3 | 92 | 258480 | NE | TEKAMAH | 41.79 | 96.23 | 338.3 |
46 | 116526 | IL | OTTAWA 5SW | 41.33 | 88.91 | 160 | 93 | 258915 | NE | WAKEFIELD | 42.27 | 96.86 | 423.7 |
47 | 116558 | IL | PALESTINE | 39 | 87.62 | 140.2 |
Share and Cite
Shen, Y.; Wu, L.; Di, L.; Yu, G.; Tang, H.; Yu, G.; Shao, Y. Hidden Markov Models for Real-Time Estimation of Corn Progress Stages Using MODIS and Meteorological Data. Remote Sens. 2013, 5, 1734-1753. https://doi.org/10.3390/rs5041734
Shen Y, Wu L, Di L, Yu G, Tang H, Yu G, Shao Y. Hidden Markov Models for Real-Time Estimation of Corn Progress Stages Using MODIS and Meteorological Data. Remote Sensing. 2013; 5(4):1734-1753. https://doi.org/10.3390/rs5041734
Chicago/Turabian StyleShen, Yonglin, Lixin Wu, Liping Di, Genong Yu, Hong Tang, Guoxian Yu, and Yuanzheng Shao. 2013. "Hidden Markov Models for Real-Time Estimation of Corn Progress Stages Using MODIS and Meteorological Data" Remote Sensing 5, no. 4: 1734-1753. https://doi.org/10.3390/rs5041734