Assimilation of hyperspectral and laser scanning data: Extension and transfer of a regional crop growth model to Northeast China
Final Report Abstract
In the project we focused on two major objectives, (i) agro-ecosystem modelling on regional scale in the intensively used agricultural region of the Sanjiang Plain, Heilongjiang Province, Northeast China, and - within this context – (ii) to improve remote sensing data analysis of optical hyperspectral sensors and laser scanners to derive crop traits. In the context of this project, we proofed for the first time that UAV-based remote sensing data and terrestrial laser scanning (TLS) can be utilized for monitoring rice growth and height which is an estimator for rice biomass. The latter is important for estimating N uptake. We also investigated the potential of hyperspectral field data for crop trait estimation as well as for groundtruthing purposes of purchased satellite data. Furthermore, we linked satellite remote sensing data analysis with the DNDC model and improved satellite-based monitoring of rice nitrogen status. Originally, it was planned to extend the DANUBIA crop growth model for modelling rice growth in the study area. However, it was not possible to develop needed cultivar-specific parameters for the studied cultivars. During the project we faced several challenges, it was very difficult to use a terrestrial laser scanner in China. Due to data policy restriction, we could not use precise GPS equipment. The latter was also a problem for the UAV and fieldspectroradiometer data collection. We could not set up a proper proximal and remote sensing groundtruthing. However, thanks to the support of the Chinese project partners, it was a very productive and successful cooperation project.
Publications
- (2013): Precise plant height monitoring and biomass estimation with Terrestrial Laser Scanning in paddy rice, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5/W2, 295-300
Tilly, N., Hoffmeister, D., Cao, Q., Lenz-Wiedemann, V., Miao, Y., and Bareth, G.
(See online at https://doi.org/10.5194/isprsannals-II-5-W2-295-2013) - (2013): Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain. ISPRS Journal, Vol. 78, pp.102-115
Yu, K., Li, F., Gnyp, M.L., Miao, Y., Bareth, G., and Chen, X.
(See online at https://doi.org/10.1016/j.isprsjprs.2013.01.008) - (2013): Rice monitoring with multi-temporal and dual-polarimetric TerraSAR-X data. Intern. J. Appl. Earth Oserv. Geoinf., Vol. 21, pp. 568-576
Koppe, W., Gnyp, M.L., Hütt, C., Yao, Y., Miao, Y., Chen, X., and Bareth, G.
(See online at https://doi.org/10.1016/j.jag.2012.07.016) - (2014): Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages. Field Crops Research, Vol.155, pp. 42-55
Gnyp, M.L., Miao, Y., Yuan, F., Ustin, S.L., Yu, K., Yao, Y., Huang, S. and Bareth, G.
(See online at https://doi.org/10.1016/j.fcr.2013.09.023) - (2014): In-season estimation of rice nitrogen status with an active crop canopy sensor. Journal of Selected Topics in Applied Earth Observation and Remote Sensing
Yao, Y., Miao, Y., Cao, Q., Wang, H., Gnyp, M.L., Bareth, G., Khoshla, R., Yang, W. and Liu, C.
(See online at https://doi.org/10.1109/JSTARS.2014.2322659) - (2014): Multi-temporal Crop Surface Models: Accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice. J. Applied Remote Sensing 8(1), 083671
Tilly, N., Hoffmeister, D., Cao, Q., Huang, S., Miao, Y., Lenz-Wiedemann, V., Bareth, G.
(See online at https://doi.org/10.1117/1.JRS.8.083671) - (2015): Georeferencing Multi-source Geospatial Data Using Multi-temporal TerraSAR-X Imagery: a Case Study in Qixing Farm, Northeast China. PFG 2/2015, 173–185
Zhao, Q., Hütt, C., Lenz-Wiedemann, V.I.S., Miao, Y., Yuan, F., Zhang, F., and Bareth, G.
(See online at https://doi.org/10.1127/pfg/2015/0262) - (2015): Investigating within-field variability of rice from high resolution satellite imagery in Qixing Farm County, Northeast China. ISPRS International Journal of Geo-Information 4(1), 236-261
Zhao, Q., Lenz-Wiedemann, V.I.S., Yuan, F., Jiang, R., Miao, Y., Zhang, F., and Bareth, G.
(See online at https://doi.org/10.3390/ijgi4010236) - (2015): Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China. Remote Sensing 7(8), pp. 10646-10667
Huang, S., Miao, Y., Zhao, G., Yuan, F., Ma, X., Tan, C., Yu, W., Gnyp, M.L., Lenz- Wiedemann, V.I.S, Rascher, U., and Bareth, G.
(See online at https://doi.org/10.3390/rs70810646) - (2015): Transferability of Models for Estimating Paddy Rice Biomass from Spatial Plant Height Data. Agriculture 5(3), pp. 538-560
Tilly, N., Hoffmeister, D., Cao, Q., Lenz-Wiedemann, V., Miao, Y. and Bareth, G.
(See online at https://doi.org/10.3390/agriculture5030538) - (2016): Non-destructive monitoring of rice by hyperspectral in-field spectrometry and UAV-based remote sensing: Case study of field-grown rice in North Rhine-Westphalia, Germany. In:Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 1071-1077
Willkomm, M., Bolten, A., and Bareth, G.
(See online at https://doi.org/10.5194/isprs-archives-XLI-B1-1071-2016) - (2017): Detecting spatial variability of paddy rice yield by combining the DNDC model with high resolution satellite images. Agricultural Systems, Vol. 152, pp. 47-57
Zhao, Q., Brocks, S., Lenz-Wiedemann, V., Miao, Y., Thang, F., and Bareth, G.
(See online at https://doi.org/10.1016/j.agsy.2016.11.011) - (2017): Potential of RapidEye and WorldView-2 Satellite Data for Improving Rice Nitrogen Status Monitoring at Different Growth Stages. Remote Sensing, Vol. 9(3): 227
Huang, S., Miao, Y., Yuan, F., Gnyp, M.L., Yao, Y., Cao, Q., Wang, H., Lenz- Wiedemann, V., and Bareth, G.
(See online at https://doi.org/10.3390/rs9030227) - (2018): A new critical nitrogen dilution curve for rice nitrogen status diagnosis in Northeast China. Pedosphere 28 (5): 814-822
Huang, S., Miao, Y., Cao, Q., Yao, Y., Zhao, G., Yu, W., Shen, J., Yu, K., Bareth, G.
(See online at https://doi.org/10.1016/S1002-0160(17)60392-8) - (2019): In-Season Diagnosis of Rice Nitrogen Status Using Proximal Fluorescence Canopy Sensor at Different Growth Stages. Remote Sensing 11(16), 1847
Huang, S., Miao, Y., Yuan, F., Cao, Q., Ye, H., Lenz-Wiedemann, V.I.S., Bareth, G.
(See online at https://doi.org/10.3390/rs11161847)
