Timely and accurate information on crop productivity is essential for characterizing crop growing status and guiding adaptive management practices to ensure food security. Terrestrial biosphere models forced by satellite observations (satellite-TBMs) are viewed as robust tools for understanding large-scale agricultural productivity, with distinct advantages of generalized input data requirement and comprehensive representation of carbon–water-energy exchange mechanisms. However, it remains unclear whether these models can maintain consistent accuracy at field scale and provide useful information for farmers to make site-specific management decisions. This study aims to investigate the capability of a satellite-TBM to estimate crop productivity at the granularity of individual fields using harmonized Sentinel-2 and Landsat-8 time series. Emphasis was placed on evaluating the model performance in: (i) representing crop response to the spatially and temporally varying field management practices, and (ii) capturing the variation in crop growth, biomass and yield under complex interactions among crop genotypes, environment, and management conditions. To achieve the first objective, we conducted on-farm experiments with controlled nitrogen (N) fertilization and irrigation treatments to assess the efficacy of using satellite-retrieved leaf area index (LAI) to reflect the effect of management practices in the TBM. For the second objective, we integrated a yield formation module into the satellite-TBM and compared it with the semi-empirical harvest index (HI) method. The model performance was then evaluated under varying conditions using an extensive dataset consisting of observations from four crop species (i.e., soybean, wheat, rice and maize), 42 cultivars and 58 field-years. Results demonstrated that satellite-retrieved LAI effectively captured the effects of N and water supply on crop growth, showing high sensitivity to both the timing and quantity of these inputs. This allowed for a spatiotemporal representation of management impacts, even without prior knowledge of the specific management schedules. The TBM forced by satellite LAI produced consistent biomass dynamics with ground measurements, showing an overall correlation coefficient (R) of 0.93 and a relative root mean square error (RRMSE) of 31.4 %. However, model performance declined from biomass to yield estimation, with the HI-based method (R = 0.80, RRMSE = 23.7 %) outperforming mechanistic modeling of grain filling (R = 0.43, RRMSE = 43.4 %). Model accuracy for winter wheat was lower than that for summer crops such as rice, maize and soybean, suggesting potential underrepresentation of the overwintering processes. This study illustrates the utility of satellite-TBMs in crop productivity estimation at the field level, and identifies existing uncertainties and limitations for future model developments.

中文翻译：

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基于卫星的陆地生物圈模型的田间规模评估，用于估计作物对管理实践和生产力的反应


及时准确的作物生产力信息对于描述作物生长状况和指导适应性管理实践以确保粮食安全至关重要。由卫星观测 （satellite-TBM） 强制的陆地生物圈模型被视为理解大规模农业生产力的强大工具，具有广义输入数据要求和碳-水-能交换机制的综合表示的明显优势。然而，目前尚不清楚这些模型是否能够在田间规模上保持一致的准确性，并为农民做出特定地点的管理决策提供有用的信息。本研究旨在调查卫星 TBM 使用协调的 Sentinel-2 和 Landsat-8 时间序列在单个田地的粒度上估计作物生产力的能力。重点放在评估模型性能上：（i） 代表作物对空间和时间变化的田间管理实践的响应，以及 （ii） 捕捉作物基因型、环境和管理条件之间复杂相互作用下作物生长、生物量和产量的变化。为了实现第一个目标，我们进行了受控氮 （N） 施肥和灌溉处理的农场实验，以评估使用卫星检索的叶面积指数 （LAI） 来反映 TBM 管理实践效果的有效性。对于第二个目标，我们将产量形成模块集成到卫星 TBM 中，并将其与半经验收获指数 （HI） 方法进行比较。然后使用一个广泛的数据集评估模型在不同条件下的性能，该数据集包括来自四种作物物种（即大豆、小麦、水稻和玉米）、42 个栽培品种和 58 个田年的观察结果。 结果表明，卫星检索的 LAI 有效地捕捉了氮和水供应对作物生长的影响，对这些输入的时间和数量都表现出高度敏感性。这允许对管理影响进行时空表示，即使事先不了解具体的管理时间表。由卫星 LAI 强制的 TBM 产生了与地面测量一致的生物量动力学，显示总体相关系数 （R） 为 0.93，相对均方根误差 （RRMSE） 为 31.4 %。然而，从生物量到产量估计的模型性能下降，基于 HI 的方法 （R = 0.80， RRMSE = 23.7 %） 优于谷物填充的机理建模 （R = 0.43， RRMSE = 43.4 %）。冬小麦的模型准确性低于水稻、玉米和大豆等夏季作物的模型准确性，这表明越冬过程可能代表性不足。本研究说明了卫星 TBM 在田间作物生产力估计中的效用，并确定了未来模型开发存在的不确定性和局限性。