Amin Eatidal, Verrelst Jochem, Rivera-Caicedo Juan Pablo, Pipia Luca, Ruiz-Verdú Antonio, Moreno José
Image Processing Laboratory (IPL), Parc Científic, Universitat de Valencia, 46980 Patema, Valencia, Spain.
CONACYT-UAN, Secretaria de Investigation y Posgrado, Universidad Autónoma de Nayarit, Tepic 63155, Nayarit, Mexico.
Remote Sens Environ. 2020 Nov 21;255. doi: 10.1016/j.rse.2020.112168. eCollection 2021 Mar 15.
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 ) next to green LAI (LAI ). By using LAI ground measurements from multiple campaigns associated with airborne or satellite spectra, Gaussian processes regression (GPR) models were developed for both LAI and LAI , 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 : R = 0.7, RMSE = 0.67 m/m; LAI : R = 0.62, RMSE = 0.43 m/m). 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 and LAI can be achieved. To demonstrate the capability of LAI 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 product permits the detection of harvest (i.e., sudden drop in LAI ) and the determination of crop residues (i.e., remaining LAI ), 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 and LAI 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.
对于农业应用而言，识别非光合地上植被具有重要意义，因为它有助于评估收获作业、检测作物残茬或干旱事件，以及更好地预测碳、水和养分的吸收情况。虽然绿叶面积指数（LAI）的测绘已较为成熟，但当前的业务反演模型并未针对衰老棕色植被的LAI估算进行校准。这不仅会导致作物成熟时LAI被低估，也是一个错失的监测机会。哨兵 - 2（S2）卫星星座的高空间和时间分辨率提供了在估算绿色LAI（LAI₉）的同时估算棕色LAI（LAI₈）的可能性。通过使用与机载或卫星光谱相关的多次测量活动中的LAI地面测量数据，针对LAI₉和LAI₈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，这使得能够屏蔽掉不确定性较高的不可靠LAI反演结果。实施了一个处理链，将这两个模型应用于S2图像，生成了空间分辨率为20米的多波段LAI产品。这些模型通过来自欧洲各个研究站点的实地数据得到了充分验证（LAI₉：R = 0.7，RMSE = 0.67 m²/m²；LAI₈：R = 0.62，RMSE = 0.43 m²/m²）。得益于S2在红边波段（B5：705纳米和B6：740纳米）以及短波红外波段（B12：2190纳米）的波段设置，可以区分LAI₉和LAI₈。为了证明LAI₈在识别作物何时开始衰老方面的能力，对多个欧洲研究站点的S2时间序列进行了处理，并制作了季节地图，这些地图显示了绿色植被峰值之后作物衰老的开始。特别是，LAI₈产品能够检测到收获（即LAI₉的突然下降）并确定作物残茬（即剩余的LAI₈），不过若能在短波红外波段有更好的光谱采样，将有助于从土壤变异性及其干扰效应中区分出棕色LAI。最后，通过合并LAI₉和LAI₈的估计值创建了一个单一总得LAI产品，然后将其与从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，相对于仅考虑光合活性绿色植被的标准SNAP LAI产品，所提出的描述符有了改进。
