The leaf area index (LAI) is a crucial parameter that governs the physical and biophysical processes of plant canopies and acts as an input variable in land surface and soil moisture modeling. The ScatSat-1 is the latest microwave Ku-band scatterometer mission of Indian Space Research Organization (ISRO), provides data at a higher temporal and spatial resolution for various applications. Due to its all-weather operational capability, it could be used as an alternative to the optical/IR sensors for the LAI estimation. In the technical literature domain, no testing has been done to estimate the LAI using ScatSat-1 scatterometer data. Therefore, the objective of this study is to retrieve the LAI using the ScatSat-1 backscattering by modifications of two different models viz. water cloud model (WCM) and the recently developed Oveisgharan et al. model and compared against the PROBA-V, MODIS, and ground-based LAI products. To assess the performance of these models, coefficient of determination ( $R^{2}$ ), root-mean-squared error (RMSE) and bias are computed. For Oveisgharan et al., the values of $R^{2}$ , RMSE and bias were obtained as 0.87, 0.57 m²m⁻², and 0.05 m²m⁻² respectively, whereas for WCM model, the values were found as 0.82, 0.67 m²m⁻², and 0.32 m²m⁻² respectively. This investigation showed that the modifications in Oveisgharan et al. model provide marginally better results in the retrieval of LAI using ScatSat-1 data than the WCM model. The models’ limitation may be less serious for crop management studies because the majority of crops attains its maturity at LAI values less than 6 m²/m².

中文翻译：

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ScatSat-1 叶面积指数产品：农田模型比较、开发和验证

叶面积指数 (LAI) 是控制植物冠层物理和生物物理过程的关键参数，并在地表和土壤水分建模中充当输入变量。ScatSat-1 是印度空间研究组织 (ISRO) 最新的微波 Ku 波段散射计任务，可为各种应用提供更高时空分辨率的数据。由于其全天候操作能力，它可以用作 LAI 估计的光学/红外传感器的替代品。在技​​术文献领域，尚未进行使用 ScatSat-1 散射计数据估计 LAI 的测试。因此，本研究的目的是通过修改两个不同的模型，使用 ScatSat-1 反向散射来检索 LAI。水云模型 (WCM) 和最近开发的 Oveisgharan等。模型并与 PROBA-V、MODIS 和地面 LAI 产品进行比较。为了评估这些模型的性能，决定系数（ $R^{2}$ )，计算均方根误差 (RMSE) 和偏差。对于 Oveisgharan等。, 的值 $R^{2}$ 、RMSE 和偏差分别为 0.87、0.57 m ² m ^-2和 0.05 m ² m ^-2，而对于 WCM 模型，发现值为 0.82、0.67 m ² m ^-2和 0.32 m ² m ^{- 2}分别。这项调查表明，Oveisgharan 中的修改等。模型在使用 ScatSat-1 数据检索 LAI 时提供的结果略好于 WCM 模型。对于作物管理研究，模型的局限性可能不那么严重，因为大多数作物在 LAI 值小于 6 m ² /m ^{2 时}达到成熟。