For agricultural applications, identification of non-photosynthetic above-ground vegetation is of great interest as it contributes to assess harvest practices, detecting crop residues or drought events, as well as to better predict the carbon, water and nutrients uptake. While the mapping of green Leaf Area Index (LAI) is well established, current operational retrieval models are not calibrated for LAI estimation over senescent, brown vegetation. This not only leads to an underestimation of LAI when crops are ripening, but is also a missed monitoring opportunity. The high spatial and temporal resolution of Sentinel-2 (S2) satellites constellation offers the possibility to estimate brown LAI (LAI_B) next to green LAI (LAI_G). By using LAI ground measurements from multiple campaigns associated with airborne or satellite spectra, Gaussian processes regression (GPR) models were developed for both LAI_G and LAI_B, providing alongside associated uncertainty estimates, which allows to mask out unreliable LAI retrievals with higher uncertainties. A processing chain was implemented to apply both models to S2 images, generating a multiband LAI product at 20 m spatial resolution. The models were adequately validated with in-situ data from various European study sites (LAI_G: R² = 0.7, RMSE = 0.67 m²/m²; LAI_B: R² = 0.62, RMSE = 0.43 m²/m²). Thanks to the S2 bands in the red edge (B5: 705 nm and B6: 740 nm) and in the shortwave infrared (B12: 2190 nm) a distinction between LAI_G and LAI_B can be achieved. To demonstrate the capability of LAI_B to identify when crops start senescing, S2 time series were processed over multiple European study sites and seasonal maps were produced, which show the onset of crop senescence after the green vegetation peak. Particularly, the LAI_B product permits the detection of harvest (i.e., sudden drop in LAI_B) and the determination of crop residues (i.e., remaining LAI_B), although a better spectral sampling in the shortwave infrared would have been desirable to disentangle brown LAI from soil variability and its perturbing effects. Finally, a single total LAI product was created by merging LAI_G and LAI_B estimates, and then compared to the LAI derived from S2 L2B biophysical processor integrated in SNAP. The spatiotemporal analysis results confirmed the improvement of the proposed descriptors with respect to the standard SNAP LAI product accounting only for photosynthetically active green vegetation.

对于农业应用，非光合作用地上植被的识别非常有意义，因为它有助于评估收获实践、检测作物残留物或干旱事件，以及更好地预测碳、水和养分的吸收。虽然绿色叶面积指数 (LAI) 的绘图已经很成熟，但当前的操作检索模型尚未针对衰老、棕色植被的 LAI 估计进行校准。这不仅导致作物成熟时 LAI 的低估，而且也错失了监测机会。 Sentinel-2 (S2) 卫星星座的高空间和时间分辨率提供了估计棕色 LAI (LAI _B ) 和绿色 LAI (LAI _G ) 的可能性。通过使用与机载或卫星光谱相关的多个活动的 LAI 地面测量结果，为 LAI _G和 LAI _B开发了高斯过程回归 (GPR) 模型，并提供了相关的不确定性估计，从而可以掩盖具有较高不确定性的不可靠的 LAI 检索。实施处理链以将这两个模型应用于 S2 图像，生成 20 m 空间分辨率的多波段 LAI 产品。这些模型经过欧洲各个研究地点的现场数据充分验证（LAI _G ：R ² = 0.7，RMSE = 0.67 m ² /m ² ；LAI _B ：R ² = 0.62，RMSE = 0.43 m ² /m ² ） 。 由于红边的 S2 波段（B5：705 nm 和 B6：740 nm）和短波红外波段（B12：2190 nm），可以区分 LAI _G和 LAI _B。为了证明 LAI _B识别作物何时开始衰老的能力，对多个欧洲研究地点的 S2 时间序列进行了处理，并生成了季节性地图，显示了绿色植被高峰后作物衰老的开始。特别是，LAI _B产品允许检测收获（即 LAI _B突然下降）和测定农作物残留物（即剩余的 LAI _B ），尽管需要在短波红外中进行更好的光谱采样以解开棕色。土壤变异性的 LAI 及其扰动效应。最后，通过合并 LAI _G和 LAI _B估计值创建单个总 LAI 产品，然后与 SNAP 中集成的 S2 L2B 生物物理处理器导出的 LAI 进行比较。时空分析结果证实了所提出的描述符相对于仅考虑光合活跃绿色植被的标准 SNAP LAI 产品的改进。