Multi-temporal monitoring of leaf area index of rice under different nitrogen treatments using UAV images

This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth estimation, based on a multi-temporal set of high-resolution digital surface models (DSMs) of snow-free and of snow-covered conditions, taken in a partially healthy to insect-induced Norway spruce forest and meadow coverage area within the Šumava National Park (Šumava NP) in the Czech Republic, was assessed over a winter season. The UAV-derived DSMs featured a resolution of 0.73–1.98 cm/pix. By subtracting the DSMs, the snow depth was determined and compared with manual snow probes taken at ground control point (GCP) positions, the root mean square error (RMSE) ranged between 0.08 m and 0.15 m. A comparative analysis of UAV-based snow depth with a denser network of arranged manual snow depth measurements yielded an RMSE between 0.16 m and 0.32 m. LAI assessment, crucial for correct interpretation of the snow depth distribution in forested areas, was based on downward-looking UAV images taken in the forest regime. To identify the canopy characteristics from downward-looking UAV images, the snow background was used instead of the sky fraction. Two conventional methods for the effective winter LAI retrieval, the LAI-2200 plant canopy analyzer, and digital hemispherical photography (DHP) were used as a reference. Apparent was the effect of canopy density and ground properties on the accuracy of DSMs assessment based on UAV imaging when compared to the field survey. The results of UAV-based LAI values provided estimates were comparable to values derived from the LAI-2200 plant canopy analyzer and DHP. Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring LAI was underestimated by using UAV photogrammetry method. Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics.

Download Full-text

Derivation of leaf area index for grassland within alpine upland using multi-temporal RapidEye data

International Journal of Remote Sensing ◽

10.1080/01431161.2013.845316 ◽

2013 ◽

Vol 34 (23) ◽

pp. 8628-8652 ◽

Cited By ~ 17

Author(s):

Sarah Asam ◽

Heiko Fabritius ◽

Doris Klein ◽

Christopher Conrad ◽

Stefan Dech

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Area Index ◽

Multi Temporal

Download Full-text

Retrieval of rapeseed leaf area index using the PROSAIL model with canopy coverage derived from UAV images as a correction parameter

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2021.102373 ◽

2021 ◽

Vol 102 ◽

pp. 102373

Author(s):

Bo Sun ◽

Chufeng Wang ◽

Chenghai Yang ◽

Baodong Xu ◽

Guangsheng Zhou ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Area Index ◽

Canopy Coverage ◽

Uav Images

Download Full-text

Assessment of Leaf Area Index of Rice for a Growing Cycle Using Multi-Temporal C-Band PolSAR Datasets

Remote Sensing ◽

10.3390/rs11222640 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2640 ◽

Cited By ~ 1

Author(s):

Ze He ◽

Shihua Li ◽

Yong Wang ◽

Yueming Hu ◽

Feixiang Chen

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Growth Period ◽

Estimation Accuracy ◽

Area Index ◽

Acceptable Error ◽

Lower Estimation ◽

Wide Availability ◽

Multi Temporal ◽

Phenological Cycle

C-band polarimetric synthetic aperture radar (PolSAR) data has been previously explored for estimating the leaf area index (LAI) of rice. Although the rice-growing cycle was partially covered in most of the studies, details for each phenological phase need to be further characterized. Additionally, the selection and exploration of polarimetric parameters are not comprehensive. This study evaluates the potential of a set of polarimetric parameters derived from multi-temporal RADARSAT-2 datasets for rice LAI estimation. The relationships of rice LAI with backscattering coefficients and polarimetric decomposition parameters have been examined in a complete phenological cycle. Most polarimetric parameters had weak relationships (R2 < 0.30) with LAI at the transplanting, reproductive, and maturity phase. Stronger relationships (R2 > 0.50) were observed at the vegetative phase. HV/VV and RVI FD had significant relationships (R2 > 0.80) with rice LAI for the whole growth period. They were utilized to develop empirical models. The best LAI inversion performance (RMSE = 0.81) was obtained when RVI FD was used. The acceptable error demonstrated the possibility to use the decomposition parameters for rice LAI estimation. The HV/VV-based model had a slightly lower estimation accuracy (RMSE = 1.29) but can be a practical alternative considering the wide availability of dual-polarized datasets.

Download Full-text