Hybrid phenology matching model for robust crop phenological retrieval
Hybrid phenology matching model for robust crop phenological retrieval
复制标题
DOI：
10.1016/j.isprsjprs.2021.09.011
复制
发表时间：
2021-11
影响因子：
12.7
通讯作者：
C. Diao;Zi-Yan Yang;F. Gao;Xiaoyang Zhang;Zhengwei Yang
中科院分区：
文献类型：
--
作者：
C. Diao;Zi-Yan Yang;F. Gao;Xiaoyang Zhang;Zhengwei Yang
作物物候学调节季节性农业生态系统碳、水和能量交换，是基于经验和基于过程的作物模型的关键组成部分，用于模拟农田的生物地球化学循环、评估总初级生产和净初级生产以及预测作物产量。物候匹配模型的进步为利用遥感观测监测作物物候进展提供了一种可行的方法，并提供了参考形状和参考物候转变日期的先验信息。然而，模型的基本几何尺度假设以及定义物候参考的挑战阻碍了物候匹配在作物物候研究中的适用性。本研究的目的是开发一种新型混合物候匹配模型，以利用卫星时间序列稳健地检索多种作物物候阶段。设计的混合模型利用了表观提取方法和物候匹配模型的互补优势。它放宽了几何尺度假设，可以表征作物周期的关键物候阶段，范围从农业实践相关阶段（例如种植和收获）到作物发育阶段（例如出苗和成熟）。为了系统地评估物候参考对物候匹配的影响，利用公开的物候信息，进一步设计了不同时间和空间物候校准水平下的四种具有代表性的物候参考情景。结果表明，混合物候匹配模型可以实现伊利诺伊州玉米和大豆物候生长阶段的高精度估计，特别是在经过年份和地区调整的物候参考（大多数物候阶段的 R 平方高于 0.9，RMSE 小于 5 天）的情况下。混合模型所表征的年际和区域物候模式与美国农业部国家农业统计局（NASS）的作物进度报告（CPR）中的情况非常吻合。与基准物候匹配模型相比，混合模型对于物候参考校准水平的下降具有更强的鲁棒性，并且在物候参考信息有限的情况下，在反演作物早期物候阶段（例如种植和出苗阶段）时特别有优势。这种创新的混合物候匹配模型与支持 CPR 的物候参考校准相结合，在揭示广泛地理区域作物物候的时空模式方面具有广阔的前景。
Crop phenology regulates seasonal agroecosystem carbon, water, and energy exchanges, and is a key component in empirical and process-based crop models for simulating biogeochemical cycles of farmlands, assessing gross and net primary production, and forecasting the crop yield. The advances in phenology matching models provide a feasible means to monitor crop phenological progress using remote sensing observations, with a priori information of reference shapes and reference phenological transition dates. Yet the underlying geometrical scaling assumption of models, together with the challenge in defining phenological references, hinders the applicability of phenology matching in crop phenological studies. The objective of this study is to develop a novel hybrid phenology matching model to robustly retrieve a diverse spectrum of crop phenological stages using satellite time series. The devised hybrid model leverages the complementary strengths of phenometric extraction methods and phenology matching models. It relaxes the geometrical scaling assumption and can characterize key phenological stages of crop cycles, ranging from farming practice-relevant stages (e.g., planted and harvested) to crop development stages (e.g., emerged and mature). To systematically evaluate the influence of phenological references on phenology matching, four representative phenological reference scenarios under varying levels of phenological calibrations in terms of time and space are further designed with publicly accessible phenological information. The results indicate that the hybrid phenology matching model can achieve high accuracies for estimating corn and soybean phenological growth stages in Illinois, particularly with the year- and region-adjusted phenological reference (R-squared higher than 0.9 and RMSE less than 5 days for most phenological stages). The inter-annual and regional phenological patterns characterized by the hybrid model correspond well with those in the crop progress reports (CPRs) from the USDA National Agricultural Statistics Service (NASS). Compared to the benchmark phenology matching model, the hybrid model is more robust to the decreasing levels of phenological reference calibrations, and is particularly advantageous in retrieving crop early phenological stages (e.g., planted and emerged stages) when the phenological reference information is limited. This innovative hybrid phenology matching model, together with CPR-enabled phenological reference calibrations, holds considerable promise in revealing spatio-temporal patterns of crop phenology over extended geographical regions.