Phenological metrics extracted from satellite data (phenometrics) have been increasingly used to access timely, spatially explicit information on crop phenology, but have rarely been calibrated and validated with field observations. In this study, we developed a calibration procedure to make phenometrics more comparable to ground-based phenological stages by optimising the settings of Best Index Slope Extraction (BISE) and smoothing algorithms together with thresholds. We used a six-year daily Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series and 211 ground-observation records from four major crop species (winter wheat/barley, oilseed rape, and sugar beet) in central Germany. Results showed the superiority of the Savitzky-Golay algorithm in combination with BISE. The satellite-derived senescence dates matched ripeness stages of winter crops and the dates with maximum NDVI were closely related to the field-observed heading stage of winter cereals. We showed that the emergence of winter crops corresponded to the dates extracted with a threshold of 0.1, which translated into 8.89 days of root-mean-square error (RMSE) improvement compared to the standard threshold of 0.5. The method with optimised settings and thresholds can be easily transferred and applied to areas with similar growing conditions. Altogether, the results improve our understanding of how satellite-derived phenometrics can explain in situ phenological observations.

Detailed information on the temporal and spatial evolution of soil moisture patterns is of fundamental importance to improve runoff prediction, optimize irrigation management and to enhance crop forecasting. However, obtaining representative soil moisture measurements at the catchment scale is challenging because of the dynamic spatial and temporal behavior of soil moisture. High-resolution remote sensing data provide detailed spatial information about catchment characteristics (e.g., terrain and land use) that can be used as proxies to estimate soil moisture. We assessed the potential use of combined multitemporal multispectral remote sensing (RS) and terrain data for estimating spatial soil moisture patterns at the small catchment scale. The fuzzy c-means sampling and estimation approach (FCM SEA) was applied to conduct a sensor (proxy) directed (guided) sampling and to reconstruct multitemporal soil moisture patterns based on time domain reflectometry measurements. A comprehensive soil moisture database for the Schäfertal catchment, located in central Germany, was used to test, validate, and compare the FCM SEA performances of the combined remote sensing data with those of a benchmark approach driven solely by terrain data. Results from the study show that a FCM SEA model that integrates bi-temporal RS imagery and terrain data was more effective in estimating spatial soil moisture patterns relative to the benchmark model. It outperformed the benchmark model in 58% of the cases and was stable to explain about 50% of the total observed variance for a range of different catchment moisture conditions. This was achieved with only a small sample size (n = 30). The results of this study are promising because they highlight the importance of considering multitemporal RS and terrain data and demonstrate how in situ sensors can be optimally placed to enable cost-efficient monitoring and prediction of spatial soil moisture patterns at the small catchment scale.Abbreviations: DEM, digital elevation model; FCM SEA, fuzzy c-means sampling and estimation approach; LAI, leaf area index; NDVI, normalized difference vegetation index; PC, principal component; PCA, principal component analysis; reNDVI, red-edge-based normalized difference vegetation index; RS, multitemporal multispectral remote sensing; TDR, time domain reflectometry.