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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Publications at this Location » Publication #402585

Research Project: Improving Crop Performance and Precision Irrigation Management in Semi-Arid Regions through Data-Driven Research, AI, and Integrated Models

Location: Water Management and Systems Research

Title: Spatiotemporal modeling of maize light extinction coefficient using Sentinel-2 multispectral data

item COSTA-FILHO, EDSON - Colorado State University
item CHÁVEZ, JOSÉ - Colorado State University
item Zhang, Huihui
item ANDALES, ALLAN - Colorado State University
item BROWN, ANSLEY - Colorado State University

Submitted to: Annual American Geophysical Union Hydrology Days
Publication Type: Abstract Only
Publication Acceptance Date: 3/13/2023
Publication Date: N/A
Citation: N/A

Interpretive Summary: N/A

Technical Abstract: Adequate vegetation growth of crops relies on plants effectively absorbing solar energy as photosynthetically active radiation (PAR), which directly impacts canopy ecological processes such as plant transpiration and soil carbon sequestration. The light extinction coefficient (kp), a critical term for vegetation growth, ecosystem flux, and evapotranspiration modeling, is an important parameter to characterize canopy architecture. Classic modeling techniques for kp do not account for spatial variability in canopy architecture, a complex feature in cropland fields across the globe due to factors such as soil texture differences and inadequate irrigation water management practices. This study proposed a modeling approach for maize kp using Sentinel-2 multispectral data (10-m spatial resolution). The proposed maize kp model has the Normalized Difference Vegetation Index (NDVI), green vegetation fractional cover (fc), and leaf area index (LAI) as inputs. Two maize fields under different irrigation methods provided on-site measurements of canopy architecture as fc and LAI in Northern Colorado, USA. From July to August 2021, data collected from a surface (furrow) irrigated field were used to fit the kp model. Data from a subsurface drip maize field (July to August 2020) were used to evaluate the accuracy of the predicted kp values independently. The evaluation compared the estimated to observed kp values from LAI and above and below canopy PAR measurements. Further kp modeling performance evaluation was done when comparing the accuracy of the proposed kp approach with the classical kp model based on plant leaf geometry and solar angle. Preliminary results indicate that the overall maize kp estimation error was -0.01 (-3%) ± 0.06 (11%). Among the kp modeling input variables, LAI was estimated with somewhat large errors, 0.30 m2/m2 (9%) ± 0.85 m2/m2 (25%). The proposed kp model outperformed the classical kp approach by 39%. Thus, the proposed maize kp model shows two significant advantages compared to the classic kp modeling technique: It is more accurate and predicts spatiotemporal values of kp. Further research is needed to develop a model for other remote sensing sensors (platforms) and to evaluate the proposed kp approach for different crop types under different climate regimes.