Retrieving Corn Canopy Leaf Area Index from Multitemporal Landsat Imagery and Terrestrial LiDAR Data

Abstract

Leaf angle is a critical structural parameter for retrieving canopy leaf area index (LAI) using the PROSAIL model. However, the traditional method using default leaf angle distribution in the PROSAIL model does not capture the phenological dynamics of canopy growth. This study presents a LAI retrieval method for corn canopies using PROSAIL model with leaf angle distribution functions referred from terrestrial laser scanning points at four phenological stages during the growing season. Specifically, four inferred maximum-probability leaf angles were used in the Campbell ellipsoid leaf angle distribution function of PROSAIL. A Lookup table (LUT) is generated by running the PROSAIL model with inferred leaf angles, and the cost function is minimized to retrieve LAI. The results show that the leaf angle distribution functions are different for the corn plants at different phenological growing stages, and the incorporation of derived specific corn leaf angle distribution functions distribute the improvement of LAI retrieval using the PROSAIL model. This validation is done using in-situ LAI measurements and MODIS LAI in Baoding City, Hebei Province, China, and compared with the LAI retrieved using default leaf angle distribution function at the same time. The root-mean-square error (RMSE) between the retrieved LAI on 4 September 2014, using the modified PROSAIL model and the in-situ measured LAI was 0.31 m2/m2, with a strong and significant correlation (R2 = 0.82, residual range = 0 to 0.6 m2/m2, p < 0.001). Comparatively, the accuracy of LAI retrieved results using default leaf angle distribution is lower, the RMSE of which is 0.56 with R2 = 0.76 and residual range = 0 to 1.0 m2/m2, p < 0.001. This validation reveals that the introduction of inferred leaf angle distributions from TLS data points can improve the LAI retrieval accuracy using the PROSAIL model. Moreover, the comparations of LAI retrieval results on 10 July, 26 July, 19 August and 4 September with default and inferred corn leaf angle distribution functions are all compared with MODIS LAI products in the whole study area. This validation reveals that improvement exists in a wide spatial range and temporal range. All the comparisons demonstrate the potential of the modified PROSAIL model for retrieving corn canopy LAI from Landsat imagery by inferring leaf orientation from terrestrial laser scanning data.

Monitoring the Damage of Armyworm in Summer Corn by Unmanned Aerial Vehicle Imaging

“…Second, in this study, four machine learning classifiers, RF, SVM, MLPC and NB, were used to extract the regions in which armyworm outbreaks occurred in the field, with OA of more than 95%, with the OA of RF reaching 99.57%, indicating the advantages of applying machine learning classifiers to agricultural applications. Agricultural remote sensing also includes many crop growth simulation models (e.g., WOFOST, DSSAT) and radiation transfer models (e.g., PROSAIL, DART), which can accurately describe material and energy exchanges between crops and the external environment [55,[59][60][61]. However, these models are often complex and cannot directly retrieve crop growth parameters.…”

Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

“…The rainy season is in July and August, and it is also the key period of maize growth, which is closely related to the final yield. The lack of clear sky observations from optical satellite due to weather factors such as clouds and smog is widespread in other agricultural regions of China [60]. Because of the shortcomings of temporal resolution or spatial resolution, single sensor remote sensing satellite cannot satisfy the requirement of long-term monitoring of crop accurately.…”

Joint Retrieval of Growing Season Corn Canopy LAI and Leaf Chlorophyll Content by Fusing Sentinel-2 and MODIS Images

“…A weighted RMSE was used in this study, which is expressed as follows: After the restriction and optimization of the LAI and chlorophyll content values, the LUT was generated, and the cost function was used to find the best matching values between the simulated and observed reflectances in multiple bands. There are four kinds of inputs of the PROSAIL model for retrieving: leaf optical properties, canopy structure, background soil reflectance, and sun-view geometry [48]. Considering the sensitivity of PROSAIL inputs, the LAI and C ab inputs were set using the former restriction conditions in Table 1 and Figure 5, and the other inputs were set in line with the setting in our previous work [48].…”

“…There are four kinds of inputs of the PROSAIL model for retrieving: leaf optical properties, canopy structure, background soil reflectance, and sun-view geometry [48]. Considering the sensitivity of PROSAIL inputs, the LAI and C ab inputs were set using the former restriction conditions in Table 1 and Figure 5, and the other inputs were set in line with the setting in our previous work [48].…”