CropSow: An integrative remotely sensed crop modeling framework for field-level crop planting date estimation
CropSow: An integrative remotely sensed crop modeling framework for field-level crop planting date estimation
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DOI：
10.1016/j.isprsjprs.2023.06.012
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发表时间：
2023-08
影响因子：
12.7
通讯作者：
Yin Liu;C. Diao;Zi-Ling Yang
中科院分区：
文献类型：
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作者：
Yin Liu;C. Diao;Zi-Ling Yang
作物种植时间是调节作物生长的环境条件的关键，也是作物模拟模型中用于估计干物质积累和产量的重要参数。准确的播种日期信息是描述不同耕作方式下作物生长动态和促进农业适应气候变化的关键。目前，获取播种期的方法主要有实地调查法、气象法和遥感物候检测法。然而，由于缺乏合适的田间模型设计以及缺乏地面种植参考数据，在田间水平上有效地估计作物种植日期仍然具有挑战性。在我们的研究中，我们开发了一种新的CropSow建模框架，通过将遥感物候检测方法与作物生长模型相结合来估计田间种植日期。提出了一种从遥感时间序列中提取农田作物关键物候指标的遥感物候检测方法，并将其集成到考虑土壤-作物-大气连续体的作物生长模型中，用于大田播种期的估算。CropSow利用作物生长模型中嵌入的丰富生理知识，可在各种环境和管理条件下对卫星观测进行可扩展的解释，以进行田间种植日期检索。以美国伊利诺斯州的玉米为例，所开发的CropSow模型优于三个先进的基准模型（即，2016 - 2020年，遥感累积生长度日法、天气依赖法和形状模型3种方法在大田作物播种期估算中的R平方大于0.68，均方根误差（RMSE）小于10 d，平均偏差误差（MBE）在5 d左右。与基准模型相比，该模型具有更好的泛化性能，对异常天气条件的适应性更强，在农田播种日期估计方面具有更强的鲁棒性。CropSow在空间和时间上有很大的潜力，可以用来估计大规模单个农田的作物种植时间。
Crop planting timing is critical in regulating environmental conditions of crop growth throughout the season, and is an essential parameter in crop simulation models for estimating dry matter accumulation and yields. Accurate planting date information is key to characterizing crop growing dynamics under varying farming practices and facilitating agricultural adaptation to climate change. To date, the main methods to acquire planting dates include field survey methods, weather-dependent methods, and remote sensing phenological detecting methods. However, it is still challenging to effectively estimate the crop planting dates at field levels due to the lack of appropriate field-level modeling design as well as the dearth of ground planting reference data. In our study, we develop a novel CropSow modeling framework to estimate field-level planting dates by integrating the remote sensing phenological detecting method with the crop growth model. The remote sensing phenological detecting method is devised to retrieve the critical crop phenological metrics of farm fields from remote sensing time series, which are then integrated into the crop growth model for field planting date estimation in consideration of soil-crop-atmosphere continuum. CropSow leverages the rich physiological knowledge embedded in the crop growth model to scalably interpret satellite observations under a variety of environmental and management conditions for field-level planting date retrievals. With corn in Illinois, US as a case study, the developed CropSow outperforms three advanced benchmark models (i.e., the remote sensing accumulative growing degree day method, the weather-dependent method, and the shape model) in crop planting date estimation at the field level, with R square higher than 0.68, root mean square error (RMSE) lower than 10 days, and mean bias error (MBE) around 5 days from 2016 to 2020. It achieves better generalization performance than the benchmark models, as well as stronger adaptability to abnormal weather conditions with more robust performance in estimating the planting dates of farm fields. CropSow holds considerable promise to extrapolate over space and time for estimating the timing of crop planting of individual farm fields at large scales.