Use spatial- temporal Fusion Algorithm to extract vegetation indices in rice growth stages Abstract

Document Type : scientific research article

Authors

1 Ph.D. Student of Water Engineering, Irrigation and Drainage, Dept. of Water Engineering, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University (SANRU), Sari, Iran

2 Corresponding Author, Associate Prof., Dept. of Water Engineering, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University (SANRU), Sari, Iran

3 Professor, Dept. of Agronomy, Faculty of Crops Science, Sari Agricultural Sciences and Natural Resources University (SANRU), Sari, Iran.

4 Associate Prof., Dept. of Water Engineering, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University (SANRU), Sari, Iran.

Abstract

Use spatial- temporal Fusion Algorithm to extract vegetation indices in rice growth stages
Abstract

Background and objectives: Earth monitoring satellites and vegetation indices are very useful to study the plant greenness at different stages of growth. Widely paddy fields in the north of the Iran has provided the opportunity for research related to rice crops by new technologies, but cloudy sky in plant growth periods leads to remote sensing information in humid areas is less welcomed. The existence of spatial-temporal fusion algorithms has provided the opportunity and rebuilding satellite images in cloudy periods to use remote sensing data in the period of plant growth. In this study, spatial-temporal fusion algorithm were used to rebuilding the images of Landsat 8 and Sentinel 2 satellites during the growing season of rice to estimate the leaf area index as a representative of vegetation health and development at different stages of growth.
Materials and methods: To achieve the objectives of this study, images of Landsat 8 and Sentinel 2 satellites were used. In the cloud period, with the help of STARFM algorithm, two satellite images were rebuilt and used to extract Normalized Difference Vegetation Index (NDVI), Rice Growth Vegetation Index (RGVI) and Soil Adjustment Vegetation Index (SAVI).To estimate leaf area index by satellite images, a suitable relationship was obtained between vegetation indices and rice leaf area index at different stages of growth.
Results: Due to the cloudiness in July and the peak of greenery of the rice plant, using the STARFM algorithm to rebuild the images was very efficient. With the help of 15 images in the whole rice growth period (90 days), four linear relationships were established between Normalized Difference Vegetation Index (NDVI), Rice Growth Vegetation Index (RGVI) and Soil Adjustment Vegetation Index (SAVI) for four stages of growth with leaf area index. The highest and lowest correlation coefficients were observed between vegetation indices and leaf area index of 0.96 for NDVI index in transplanting and maturing stage and 0.75 for RGVI index in transplanting stage, respectively. Also, the map of changes in leaf area index for both satellites during the crop growth period showed well the changes in the greenness of rice cover.
Conclusion: In general, it seems that by using satellite data and image rebuilding on cloudy days, it is possible to achieve the leaf area index with high accuracy and extract various information such as age and growth stage for the rice plant

Keywords


1.Jafari Sayadi, F. 2016. Application of remote sensing for estimating rice cultivation and water consumption. M.Sc. Thesis, Sari Agricultural Sciences and Natural Resources University (SANRU). (In Persian)
2.Ahmadi, K., Abaszadeh, H., Hatami, F., Abdshah, H. and Kazemian, A. 2019. Agricultural statistics report, 2017-2018 for crop yields. Crops. Ministry of Jihad Agriculture, Deputy of Planning and Economy. Information and Communication Technology Center. 163p. (In Persian)
3.Behrang Manesh, M., Khosravi, H., Azarnivand, H. and Senatore, A. 2019. Quantifying the trend of vegetation changes using remote sensing (Case study: Fars Province). J. Plant Ecosyst. Conservation. 7: 15. 295-318. (In Persian)
4.Pordel, F., Ebrahimi, A.A. and Azizi,Z. 2017. Evaluating spatio-temporal phytomass changes using vegetation index derived from Landsat 8 (Case study: Mrajan rangeland, Boroujen). J. Rangeland. 2: 166-178. (In Persian)
5.Sanaeinejad, H., Nassiri Mahallati, M., Zare, H., Salehnia, N. and Ghaemi, M. 2014. Wheat yield estimation using Landsat images and field observation: A case study in Mashhad. J. Plant Prod.20: 4. 45-63. (In Persian)
6.Rouse, J.W., Haas, R.H., Schell, J.A.and Deering, D.W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. P 309-317, In: S. C. Freden (eds), 3rd Earth Resource Technology Satellite (ERTS), Symposium. Washington. D.C. USA.
7.Huete, A.R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sens. of Environ. J. 25: 295-309.
8.Nuarsa, I.W., Nishio, F. and Hongo, C. 2011. Spectral characteristics and mapping of rice plants using multi-temporal Landsat data. Agric. Sci. J.3: 54-67.
9.Jafari Sayadi, F., Gholami Sefidkhohi, M.A. and Ziyaeetabar Ahmadi, M.K. 2018. Leaf area index and crop coefficient estimation from operational land imager (OLI) sensor data. J. Water Res. Agric. 32: 395-404. (In Persian)
10.Geo, F., Anderson, M.C., Kustas, W.P. and Wang, Y. 2012. Simple method for retrieving leaf area index from Landsat using MODIS leaf area index products as reference. App. Remote Sens. J.6: 1-15.
11.Ihlen, V. and Zanter, K. 2019. Landsat 8 (L8) data handbook. Department of the Interior U.S. Geological Survey (USGS). South Dakota, USA. 96p.
12.Hoersch, B. 2015. SENTINEL-2 user handbook. European Space Agency (ESA).Europe. 64p.
13.Attarchi, S. and Poorakbar, N. 2020. Preliminary comparative assessment of Sentinel 2 and Landsat 8 (MSI and OLI sensors) images. Sepehr J. 29: 114. 67-78. (In Persian)
14.Rakhsh Mahpour, A. 2016. Evaluating Spatial-Temporal image fusion algorithms for MODIS and Landsat data in the land cover application. M.Sc. Thesis, Ferdowsi University of Mashhad. (In Persian)
15.Fu, D., Chen, B., Wang, J., Zhu, X. and Hilker, T. 2013. An improved image fusion approach based on enhanced spatial and temporal the adaptive reflectance fusion model. Remote Sens. J. 5: 6346-6360.
16.Walker, J.J., De Beurs, K.M., Wynne, R.H. and Gao, F. 2012. Evaluation of Landsat and MODIS data fusion products for analysis of dry landforest phenology. Rem. Sens. Environ.117: 381-393.
17.Wu, M., Wu, C., Huang, W., Niu, Z., Wang, C., Li, W. and Hao, P. 2016. An improved high spatial and temporal data fusion approach for combining Landsat and MODIS data to generate daily synthetic Landsat imagery. Info Fusion, J. 31: 14-25.
18.Mokhtari, A., Noory, H., Vazifedoust, M., Palouj, M., Bakhtiari, A., Barikani, E., Zabihi Afrooz, R.A., Fereydooni, F., Sadeghi Naeni, A., Pourshakouri, F. Badiehneshin, A.R. and Afrasiabian, Y. 2019. Evaluation of single crop coefficient curves derived from Landsat satellite image for major crops in Iran. Agric. Water Manag. J. 218: 234-249.
19.Moreno-Martίnez, Á., Izquierdo-Verdiguier, E., Maneta, M.P., Camps-Valls, G., Rpbinson, N., Muñoz-Marί, J., Sedano, F., Clinton, N. and Running, S. W. 2020. Multispectral high resolution sensor fusion for smoothing andgap-filling in the cloud. Remote Sens. Environ. J. 247: 1-19.
20.Guo, Y., Wang, C., Lei, S., Yang, J. and Zhao, Y. 2020. A framework of spatio-temporal fusion algorithm selection for Landsat NDVI time series construction. Geo-Inf, J. 665: 1-21.
21.Kumar Ranjan, A. and Ranjan Parida, B. 2021. Predicting paddy yield at spatial scale using optical and Synthetic Aperture Radar (SAR) based satellite data in conjunction with field-based crop cutting experiment (CCE) data. Remote Sens. 42: 2046-2071.
22.Vermote, E.F., Roger, J.C. and Ray, J.P. (2015). MODIS surface reflectance user's guide (MOD 09). MODIS land surface reflectance science computing facility. 35p.
23.Samadzadegan, F., Tabib Mahmoudi, F. and Bigdeli, B. 2014. Data fusion in remote sensing concepts and techniques. Tehran Univ. Press. 2th Edition. 275p. (In Persian)
24.Bazrgar Bojestani, A. and Akhoondzadeh Hanzaii, M. 2018. ESTARFM model for fusion of LST products of MODIS and ASTER sensors to retrieve the high resolution land surface temperature map. J. Geo_Sci & Tec (jgst). 7: 4. 147-161. (In Persian)
25.Palaniswany, K.M. and Gomez, K.A. 1974. Length-width method for estimating leaf area of rice. Agron. J.66: 430-433.
26.Yoshida, S. 1981. Fundamentals of rice crop science. The international rice research institute (IRRI). Philippines. 269p.
27.Wang, X., Mosley, C.T., Frankenberger, J.R. and Kladivko, E.J. 2006. Subsurface drain flow and crop yield predictions
for different drain spacings using DRAINMOD. Agric. Water. Manag.J. 79: 113-136.
28.Castro, A.I., Six, J., Plant, R.E. and Peǹa, J.M. 2018. Mapping crop calendar events and phenology-related metrics at the parcel level by object-based image analysis OBIA of MODIS-NDVI time-series (A case study in central California). Remote Sens. J. 10: 1-21.
29.Wang, J. Huang, J.F., Wang, X.Z., Jin, M.T., Zhou, Z., Guo, Q.Y., Zhao, Z.W., Huang, W.J., Zhang, Y. and Song, X.D. 2015. Estimation of rice phenology date using interated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images. Zhejiang Univ. J. Biomed. Biotechnol. 16: 832-844.
30.Bakhshandeh, A., Hoseyni, M., Farzin, N. and Pirdashti, H. 2016. Introducing a simple and fast method for estimating rice leaf area. P 1-4, In: H, Pirdashti (eds), 17th National Rice Conference, Sari Agricultural Sciences and Natural Resources University (SANRU), Sari. (In Persian)
31.Campos-Taberner, M., Garcia-Haro, F.J., Busetto, L., Ranghetti, L., Martinez, B., Amoaro Gilabert, M., Camps-Valls, G., Camacho, F. and Boschetti, M. 2018. A critical comparison of remote sensing leaf area index estimates over rice-cultivated areas: from Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT polar system. Remote Sens. J. 10: 1-23.
32.Wei, C., Chen, J., Chen, J. M., Yu, J.C., Cheng, C., Lai, Y.J., Chiang, P. N., Hong, C.Y., Tsai, M.J. and Wang, N. 2020. Evaluating relationships of standing stock, LAI and NDVI at a subtropical reforestation site in southern Taiwan using field and satellite data. Forest Res. J. 31: 1-10.
33.Rees, W.G., Golubeva, E.I., Tutubalina, O.V., Zimin, M.V. and Derkacheva, A.A. 2020. Relationship between leaf area index and NDVI for subarctic deciduous vegetation. Int. Remote Sens. J. pp. 22-41.
34.Xie, D., Zhang, J., Zhu, X., Pan, Y., Liu, H., Yuan, Z. and Yun, Y. 2016. An improved STARFM with help an unmixing-based method to generate high spatial and temporal resolution remote sensing data in complex heterogeneous regions. Sensors, J. 16: 1-19.
35.Mokhtari, S., Pirmoradian, N., Vazifehdoost, M. and Davatgar, N. 2013. Increasing accuracy of regional rice yield estimation by improvement of spatial resolution of leaf area index maps in VSM vegetative model. Guilan, J. Cereal Res. 2: 209-221. (In Persian)