The availability of Landsat 8 and Sentinel-2 has led to a steady increase in both temporal and spatial resolution of satellite data, offering new opportunities for large-scale crop condition monitoring and crop yield mapping. This study investigated the potential of using Landsat 8 and Sentinel-2 data from the harmonized Landsat 8 and Sentinel-2 (HLS) products for crop biomass estimation for six crops in Manitoba, Canada. Crop biomass was estimated using remotely sensed leaf area index (LAI) to reparametrize a simple crop growth model. The results showed that the LAI of six different crops can be estimated using a generic relationship between LAI and red-edge based vegetation indices (VIs, e.g., modified simple ratio red-edge (MSR_RE) and red-edge normalized difference VI (NDVI_RE)) for the Multispectral Instrument (MSI) of Sentinel-2. For the Operational Land Imager of Landsat 8 without the red-edge band, LAI can be best estimated using a VI derived from Near-infrared (NIR) and short-wave infrared (SWIR) bands (Normalized Difference Water Index, NDWI1). Above-ground dry biomass of these six crops was more accurately estimated from the assimilation of LAI derived from both satellites (R² (the coefficient of determination) = 0.81, RMSE (the root-mean-square-error) = 135.4 g/m², nRMSE (the normalized RMSE) = 37.9%, RPD (the ratio of percent deviation) = 2.26) than that of LAI derived from MSI-data (R² = 0.80, RMSE = 136.7 g/m², nRMSE = 38.3%, RPD = 2.23) or that from LAI derived from OLI-data (R² = 0.68, RMSE = 191.0 g/m², nRMSE = 53.5%, RPD = 1.16). Further analysis showed that these three assimilation cases (MSI and OLI; MSI alone; OLI alone) with a different number of LAI observations resulted in differences in parameter optimization, particularly the parameters relevant to crop phenology and biomass partitioning. Both crop growth stage (e.g., the emergence date for crop growth) and leaf dry biomass estimated from the assimilation of LAI derived from MSI and OLI, or MSI alone, produced the most accurate estimates. These results are likely attributed to the improved temporal coverage associated with Sentinel-2 and the availability of a red-edge band on this sensor.

Landsat 8和Sentinel-2的可用性导致卫星数据的时间和空间分辨率稳步提高，为大规模作物状况监测和作物产量制图提供了新的机会。这项研究调查了使用来自协调一致的Landsat 8和Sentinel-2（HLS）产品的Landsat 8和Sentinel-2数据来估算加拿大曼尼托巴省六种作物的作物生物量的潜力。使用遥感叶面积指数（LAI）估算作物生物量，以重新设定简单的作物生长模型。结果表明，可以使用LAI与基于红边的植被指数（VI，例如改良的简单比率红边（MSR _RE）和红边归一化差异VI（NDVI））之间的一般关系来估算六种不同作物的LAI_回覆））用于Sentinel-2的多光谱仪器（MSI）。对于没有红边波段的Landsat 8的可操作陆地成像仪，可以使用从近红外（NIR）和短波红外（SWIR）波段（归一化差水指数，NDWI1）得出的VI来最佳估计LAI。通过对两颗卫星的LAI进行同化，可以更准确地估算这六种作物的地上干生物量（R ²（测定系数）= 0.81，RMSE（均方根误差）= 135.4 g / m）²，nRMSE（归一化RMSE）= 37.9％，RPD（偏差百分比）= 2.26）比MSI数据得出的LAI（R ²  = 0.80，RMSE = 136.7 g / m ²，nRMSE = 38.3％ ，RPD = 2.23）或源自OLI-data的LAI（R ^2） ＝ 0.68，RMSE ＝191.0g / m ²，nRMSE ＝ 53.5％，RPD ＝ 1.16）。进一步的分析表明，这三种同化情况（MSI和OLI；仅MSI；仅OLI）具有不同的LAI观测值，导致参数优化（尤其是与作物物候和生物量分配相关的参数）的差异。从MSI和OLI或单独的MSI衍生的LAI的同化估计的作物生长阶段（例如，作物生长的出苗日期）和叶片干生物量均产生了最准确的估计。这些结果可能归因于与Sentinel-2相关的改进的时间覆盖范围以及此传感器上的红边带的可用性。