Evaluating optical remote sensing methods for estimating leaf area index for corn and soybean

Authors: NANDAN, ROHIT - University Of Maryland; Bandaru, Varaprasad; HE, JIAYING - University Of Maryland; Daughtry, Craig; Gowda, Prasanna; SUYKER, ANDY - University Of Nebraska

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/20/2022
Publication Date: 10/23/2022
Citation: Nandan, R., Bandaru, V., He, J., Daughtry, C.S., Gowda, P.H., Suyker, A. 2022. Evaluating optical remote sensing methods for estimating leaf area index for corn and soybean. Remote Sensing. 14(21). Article 5301. https://doi.org/10.3390/rs14215301.
DOI: https://doi.org/10.3390/rs14215301

Interpretive Summary: The leaf area index (LAI) is an important crop variable influencing different crop growth processes (e.g., photosynthesis). Regional LAI estimates are required to develop and improve modeling tools that can help monitor crop condition and yields at various scales ranging from small regional to global scales. Many methods have been developed to estimate regional LAI using optical remote sensing data however, it is not clear which methods perform well irrespective of the regional differences. Therefore, we conducted this study to evaluate existing statistical and physical methods to estimate LAI of corn and soybeans cultivated at two climatically distant locations in the U.S (i.e., Mead, Nebraska and Bushland, Texas). We used existing statistical and physical methods developed based on parametric, non-parametric and radiative transfer model (RTM)-look-up-table based inversion. Further Landsat 5,7 and 8 satellite observations were used to estimate LAI. Our study showed that overall, parametric methods performed better than other methods irrespective of location and crops. But parametric methods showed higher inconsistency, meaning that one parametric method (e.g. RSR_CA_cross method) performed the best (ranked 1) for corn but it performed very poorly for soybean (ranked 38). When considering consistency, the physical based method developed using RTM-LUT inversion was found to perform better than other methods with reasonable accuracy. These results are of interest for remote sensing and modeling communities developing approaches to monitor crop condition and yields at regional scale.

Technical Abstract: The leaf area index (LAI) is a key crop biophysical variable influencing many vegetation processes. Spatial LAI estimates are essential to develop and improve spatial modeling tools to monitor vegetation conditions at large regional scales. Numerous optical remote sensing methods have been explored to retrieve crop-specific LAI at a regional scale using satellite observations. However, a major challenge is selecting a method that performance well under various conditions without local scale calibration. As such, we assessed the performance of existing statistical and physical approaches, developed based on parametric, non-parametric and radiative transfer model (RTM)-look-up-table based inversion, using field observations from two geographically distant locations and Landsat 5, 7, and 8 satellite observations. These methods were implemented for corn and soybeans cultivated at two locations in the U.S (i.e., Mead, Nebraska, and Bushland, Texas). The evaluation metrics (i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2)) were used to study the performance of each method, and then the methods were ranked based on these metrics. Our study showed that overall parametric methods outperformed other methods. The RMSE (MAE) for the top five methods was less than 1.3 (0.95) for corn and 1.0 (0.8) for soybeans, irrespective of location. Even though they outperformed, parametric methods exhibited inconsistency in their performance. For instance, the SR_CA_cross method ranked 1 for corn, however, it performed poorly for soybean (ranked 15). The non-parametric methods showed moderate accuracy partly due to the availability of a smaller number of observations for training. The RTM-LUT inversion physical-based approach was found to perform reasonably well RMSE (MAE) less than 1.5 (1.0) consistently irrespective of location and crop, implying that this approach is more suitable for regional-scale LAI estimation. The results of this study highlighted the drawbacks and advantages of available optical remote sensing approaches to estimate LAI for corn and soybean crops using Landsat imagery. These results are of interest for remote sensing and modeling communities developing spatial-scale approaches to model and monitor agricultural vegetation.