Abstract

Dual-pol Shannon Entropy captures the dynamic crop growth parameters. It was used to evaluate biophysical parameters of important crops using Sentinel-1 data. Improved phenology information is vital input to crop-growth models. Mustard experienced increase as it advanced in phenology but for wheat, decreased due to absorption. A significant relationship at initial crop stages (height < 150 cm and biomass < 5 kg m⁻²) R² =0.65 between Shannon Entropy (SE) and crop parameters, though less strong (R²=0.33) for entire mustard cycle and wheat (plant height < 80 cm and biomass < 6 kg m⁻²) was observed. The SE appeared sensitive to low-medium biomass and useful in monitoring crop phenology in low biomass and initial phenophases. The dynamic crop profile is manifested conjunctively as Radar Vegetation Index (RVI). Random Forest Regression (RFR) and Support Vector Regression (SVR) were evaluated to predict the phenophases. Phenology responses till peak/beyond were found to perform close to ground observations.

摘要

Dual-pol Shannon Entropy 捕获动态作物生长参数。它用于使用 Sentinel-1 数据评估重要作物的生物物理参数。改进的物候信息是作物生长模型的重要输入。芥末随着物候的发展而增加，但对于小麦，由于吸收而减少。在作物的初始阶段（高度 < 150 cm 和生物量 < 5 kg m ^-2）R ² =0.65，香农熵 (SE) 和作物参数之间存在显着关系，尽管整个芥菜周期和小麦的关系较弱 (R ² =0.33) （株高 < 80 cm 且生物量 < 6 kg m ^-2） 被观测到。SE 似乎对中低生物量敏感，可用于监测低生物量和初始物候期的作物物候。动态作物剖面联合表现为雷达植被指数 (RVI)。评估随机森林回归 (RFR) 和支持向量回归 (SVR) 以预测物候期。发现直到峰值/超越的物候响应接近地面观测。