Rain-Fed Wheat Area Mapping Using MODIS and Landsat Images (Case Study: Ahar City)

Document Type : Original Article

Authors

Abstract

Application of the remote sensing methods in crop area mapping on a large spatiotemporal scale serves is as an alternative to costly time-consuming field data gathering methods. So far, some methods have been developed for wheat and rice area mapping using the images from optical and radar sensors. Some of these methods are appropriate for humid climates with several cloudy days, while others use complex processes in terms of combining both optics and radar images. Meanwhile, methods based on the unique variation of the vegetation index time series belongs to each crop are relatively simple methods that can be used for crop area mapping. The objective of this study is to improve one of the proposed methods for rain-fed wheat area mapping, in which a step-by-step elimination algorithm of non-wheat pixels was applied to MODIS images. The Improved algorithm took advantage of both MODIS and Landsat Images in terms of their high temporal and high spatial resolutions, respectively. The mentioned process could detect rain-fed wheat areas from the pastures and heterogeneous areas with higher accuracy in comparison with the previous algorithm. The overall accuracy, Kapa index, and F1 score for the final rain-fed wheat map was 92.5%, 0.67, and 0.71 respectively.

Keywords

References

خداکرمی، ل.، سفیانیان، ع.، 1391، کاربرد سنجش از دور چندزمانی در تعیین سطح زیر کشت‎ علوم و فنون کشاورزی و منابع طبیعی، علوم آب‌وخاک، دورة 16، شمارة 59، صص. 231-215.
ذوقی، ف.، 1394، نتایج تفصیلی سرشماری کشاورزی سال 1393 استان آذربایجان‌شرقی، سازمان مدیریت و برنامه‌ریزی استان آذربایجان‌شرقی.
سالنامة آماری استان آذربایجان‌شرقی، 1391، کشاورزی، جنگلداری و شیلات، سازمان مدیریت و برنامه‌ریزی استان آذربایجان‌شرقی، ج. 4، صص. 255-189.
صدرالدینی، ع.ا.، سبزچی دهخوارقانی، ح.، ناظمی، ا.ح.، مجنونی هریس، الف.، 1399، کاربرد الگوریتم سبال در تعیین حداکثر سهم روزانة گندم دیم از منابع آب سبز با استفاده از تصاویر سنجندة مودیس (مطالعة موردی: شهرستان اهر)، سنجش از دور و سامانة اطلاعات جغرافیایی در منابع طبیعی، دورة 11، شمارة 1، صص. 2-1.
Busetto, L., Meroni, M. & Colombo, R., 2008, Combining Medium and Coarse Spatial Resolution Satellite Data to Improve the Estimation of Sub-Pixel NDVI Time Series, Remote Sensing of Environment, 112(1), PP. 118-131.
Chen, C.F., Chen, C.R. & Son, N.T., 2012, Investigating Rice Cropping Practices and Growing Areas from MODIS Data Using Empirical Mode Decomposition and Support Vector Machines,GIScience & Remote Sensing, 49(1), PP. 117-138.
ـــــــــــــــــــــــــــــــــــــــــــــــــــــــــــ
[1]. Pan
 
 
Congalton, R.G., 1991, A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data, Remote Sensing of Environment, 37(1), PP. 35-46.
Forkuor, G., Conrad, C., Thiel, M., Ullmann, T. & Zoungrana, E., 2014, Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa, Remote Sensing, 6(7), PP. 6472-6499.
Gomez, C., White, J.C. & Wulder, M.A., 2016, Optical Remotely Sensed Time Series Data for Land Cover Classification: A Review, ISPRS Journal of Photogrammetry and Remote Sensing, 116, PP. 55-72.
Guan, X., Huang, C., Liu, G., Meng, X. & Liu, Q., 2016, Mapping Rice Cropping Systems in Vietnam Using an NDVI-Based Time-Series Similarity Measurement Based on DTW Distance, Remote Sensing, 8(1), P. 19.
Huang, J., Tian, L., Liang, S., Ma, H., Becker-Reshef, I., Huang, Y., Su, W., Zhang, X., Zhu, D. & Wu, W., 2015, Improving Winter Wheat Yield Estimation by Assimilation of the Leaf Area Index from Landsat TM and MODIS Data into the WOFOST Model, Agricultural and Forest Meteorology, 204, PP. 106-121.
Inglada, J., Vincent, A., Arias, M. & Marais-Sicre, C., 2016, Improved Early Crop Type Identification by Joint Use of High Temporal Resolution SAR and Optical Image Time Series, Remote Sensing, 8(5), P. 362.
Jin, N., Tao, B., Ren, W., Feng, M., Sun, R., He, L., Zhuang, W. & Yu, Q., 2016, Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data, Remote Sensing, 8(3), P. 207.
Khan, A., Hansen, M.C., Potapov, P.V., Adusei, B., Pickens, A., Krylov, A. & Stehman, S.V., 2018, Evaluating Landsat and Rapideye Data for Winter Wheat Mapping and Area Estimation in Punjab, Pakistan, Remote Sensing, 10(4), P. 489.
Khan, A., Hansen, M.C., Potapov, P., Stehman, S.V. & Chatta, A.A., 2016, Landsat-based wheat mapping in the heterogeneous cropping system of Punjab, Pakistan, International Journal of Remote Sensing, 37(6), PP. 1391-1410.
Matsushita, B., Yang, W., Chen, J., Onda, Y. & Qiu, G., 2007, Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-Density Cypress Forest, Sensors, 7(11), PP. 2636-2651.
Mosleh, M., Hassan, Q. & Chowdhury, E., 2015, Application of Remote Sensors in Mapping Rice Area and Forecasting Its Production: A Review, Sensors, 15(1), PP. 769-791.
Ozdogan, M., 2010, The Spatial Distribution of Crop Types from MODIS Data: Temporal Unmixing Using Independent Component Analysis, Remote Sensing of Environment, 114(6), PP. 1190-1204.
Pan, Y., Li, L., Zhang, J., Liang, S., Zhu, X. & Sulla-Menashe, D., 2012, Winter Wheat Area Estimation from MODIS-EVI Time Series Data Using the Crop Proportion Phenology Index, Remote Sensing of Environment, 119, PP. 232-242.
Skakun, S., Vermote, E., Roger, J.C. & Franch, B., 2017, Combined Use of Landsat-8 and Sentinel-2A Images for Winter Crop Mapping and Winter Wheat Yield Assessment at Regional Scale, AIMS geosciences, 3(2), P. 163.
Son, N.T., Chen, C.F., Chen, C.R., Minh, V.Q. & Trung, N.H., 2014, A Comparative Analysis of Multitemporal MODIS EVI and NDVI Data for Large-Scale Rice Yield Estimation, Agricultural and Forest Meteorology, 197, PP. 52-64.
Song, Y. & Wang, J., 2019, Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series, Remote Sensing, 11(4), P. 449.
Sonobe, R., Tani, H., Wang, X., Kobayashi, N. & Shimamura, H., 2014, Random Forest Classification of Crop Type Using Multi-Temporal TerraSAR-X Dual-Polarimetric Data, Remote Sensing Letters, 5(2), PP. 157-164.
Tan, C.P., Koay, J.Y., Lim, K.S., Ewe, H.T. & Chuah, H.T., 2007, Classification of Multi-Temporal SAR Images for Rice Crops Using Combined Entropy Decomposition and Support Vector Machine Technique, Progress In Electromagnetics Research, 71, PP. 19-39.
Tao, J.B., Wu, W.B., Zhou, Y., Wang, Y. & Jiang, Y., 2017, Mapping Winter Wheat Using Phenological Feature of Peak before Winter on the North China Plain Based on Time-Series MODIS Data, J. Integr. Agric, 16, PP. 348-359.
Tokay, A., D’Adderio, L.P., Porcù, F., Wolff, D.B. & Petersen, W.A., 2017, A Field Study of Footprint-Scale Variability of Raindrop Size Distribution, Journal of Hydrometeorology, 18(12), PP. 3165-3179.
Villa, P., Stroppiana, D., Fontanelli, G., Azar, R. & Brivio, P., 2015, In-Season Mapping of Crop Type with Optical and X-band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features, Remote Sensing, 7(10), PP. 12859-12886.
Wang, J., Xiao, X., Qin, Y., Dong, J., Zhang, G., Kou, W., Jin, C., Zhou, Y. & Zhang, Y., 2015, Mapping Paddy Rice Planting Area in Wheat-Rice Double-Cropped Areas through Integration of Landsat-8 OLI, MODIS, and PALSAR Images, Scientific Reports, 5, P. 10088.
Wei, S., Zhang, H., Wang, C., Wang, Y. & Xu, L., 2019, Multi-Temporal SAR Data Large-Scale Crop Mapping Based on U-Net Model, Remote Sensing, 11(1), P. 68.
Zhou, T., Pan, J., Zhang, P., Wei, S. & Han, T., 2017, Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region, Sensors, 17(6), P. 1210.

Files

Share

How to cite

Statistics