Amin Eatidal, Belda Santiago, Pipia Luca, Szantoi Zoltan, El Baroudy Ahmed, Moreno José, Verrelst Jochem
Image Processing Laboratory (IPL), University of Valencia, Catedrático Agustín Escardino 9, 46980 Valencia, Spain.
Department of Applied Mathematics, University of Alicante, 03690 Alicante, Spain.
Remote Sens (Basel). 2022 Apr 9;14(8):1812. doi: 10.3390/rs14081812.
Space-based cropland phenology monitoring substantially assists agricultural managing practices and plays an important role in crop yield predictions. Multitemporal satellite observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or by deriving biophysical variables. The Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant and multi-cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a workflow for cropland phenology characterization and mapping based on time series of green Leaf Area Index (LAI) generated from NASA's Harmonized Landsat 8 (L8) and Sentinel-2 (S2) surface reflectance dataset from 2016 to 2019. LAI time series were processed for each satellite dataset, which were used separately and combined to identify seasonal dynamics for a selection of crop types (wheat, clover, maize and rice). For the combination of L8 with S2 LAI products, we proposed two time series smoothing and fitting methods: (1) the Savitzky-Golay (SG) filter and (2) the Gaussian Processes Regression (GPR) fitting function. Single-sensor and L8-S2 combined LAI time series were used for the calculation of key crop Land Surface Phenology (LSP) metrics (start of season, end of season, length of season), whereby the detection of cropland growing seasons was based on two established threshold methods, i.e., a seasonal or a relative amplitude value. Overall, the developed phenology extraction scheme enabled identifying up to two successive crop cycles within a year, with a superior performance observed for the seasonal than for the relative threshold method, in terms of consistency and cropland season detection capability. Differences between the time series collections were analyzed by comparing the phenology metrics per crop type and year. Results suggest that L8-S2 combined LAI data streams with GPR led to a more precise detection of the start and end of growing seasons for most crop types, reaching an overall detection of 74% over the total planted crops versus 69% with S2 and 63% with L8 alone. Finally, the phenology mapping allowed us to evaluate the spatial and temporal evolution of the croplands over the agroecosystem in the Nile Delta.
基于太空的农田物候监测极大地有助于农业管理实践，并在作物产量预测中发挥重要作用。多期卫星观测使得通过使用植被指数或推导生物物理变量来分析大面积的植被季节动态成为可能。尼罗河三角洲约占埃及所有农业用地的一半。在该地区，集约化耕作系统占主导地位，多熟种植轮作方案不断增加，这就需要高时间和空间分辨率的监测来捕捉连续的作物生长周期。本研究提出了一种基于2016年至2019年美国国家航空航天局（NASA）的协调陆地卫星8号（L8）和哨兵2号（S2）地表反射率数据集生成的绿叶面积指数（LAI）时间序列进行农田物候特征描述和制图的工作流程。对每个卫星数据集的LAI时间序列进行了处理，这些数据集被单独使用并合并，以确定所选作物类型（小麦、三叶草、玉米和水稻）的季节动态。对于L8和S2的LAI产品组合，我们提出了两种时间序列平滑和拟合方法：（1）萨维茨基-戈拉伊（SG）滤波器和（2）高斯过程回归（GPR）拟合函数。单传感器和L8-S2组合的LAI时间序列用于计算关键作物的陆地表面物候（LSP）指标（季节开始、季节结束、季节长度），其中农田生长季节的检测基于两种既定的阈值方法，即季节阈值或相对振幅值。总体而言，所开发的物候提取方案能够识别一年内多达两个连续的作物周期，就一致性和农田季节检测能力而言，季节阈值方法的性能优于相对阈值方法。通过比较每种作物类型和年份的物候指标，分析了时间序列集合之间的差异。结果表明，L8-S2组合的LAI数据流与GPR相结合，能够更精确地检测大多数作物类型生长季节的开始和结束，在总种植作物中总体检测率达到74%，而S2单独使用时为69%，L8单独使用时为63%。最后，物候制图使我们能够评估尼罗河三角洲农业生态系统中农田的时空演变。
