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Crop growth stages are integral components of plant phenology and are of significant ecological and agricultural importance. While the use of remote sensing methods for phenology identification in cropland ecosystems has been extensively explored in previous studies, the focus has often been on land surface phenology, primarily related to the start and end of the growing season. In contrast, the monitoring of crop growth within an agronomic framework has been limited, particularly in the context of recently developed solar-induced chlorophyll fluorescence (SIF) data. Additionally, some critical growth stages have not received adequate attention or evaluation. This study aims to assess the utility of SIF data, collected from both ground and satellite measurements, for identifying critical crop growth stages within the realm of remote sensing phenological estimation. A comparative analysis was conducted using enhanced vegetation index (EVI) data at the Shangqiu site in the North China Plain from 2018 to 2022. Both SIF and EVI time-series data, obtained from ground and satellite sources, undergo a comprehensive phenological estimation framework encompassing pre-processing, modeling, and transition characterization. This approach involves reconciling time-series phenological patterns with crop growth stages, revealing the necessity of redefining the mapping relationship between these two fundamental concepts. After preprocessing the time-series data, the framework incorporates the phenological modeling process employing two double logistic models and a spline model for comparison. Additionally, it includes phenological transition characterization using four different methods. Consequently, each input dataset undergoes an assessment, resulting in 12 sets of estimations, which are compared to select the ideal estimation portfolio for identifying the growth stages of maize and winter wheat. Our findings highlight the efficacy of SIF data in accurately identifying the growth stages of maize and winter wheat, achieving remarkable results with an R-square exceeding 0.9 and an RMSE of less than 1 week for key growth stages (KGSs). Notably, SIF data demonstrate superior accuracy, robustness, and sensitivity to phenological events when compared to EVI data. This study establishes an estimation portfolio utilizing SIF data, involving the Gu model, a double logistic model, as the preferred phenological modelling method together with various compositing methods and transition characterization methods, suitable for most KGSs. These findings create opportunities for future research aimed at enhancing and standardizing crop growth stage identification using remote sensing data for a wide range of KGSs.

Solar-induced chlorophyll fluorescence (SIF) has provided novel methods for monitoring vegetation growth and the carbon cycle of terrestrial ecosystems. However, the effects of spatial heterogeneity on canopy SIF measurements remain unclear. The unmanned aerial vehicle (UAV) platform provides a unique opportunity to assess the impact of spatial heterogeneity on SIF variability due to its adjustable observational height at the intermediate canopy scale. In this work, we used a UAV-based SIF system to investigate the influences of fractional vegetation cover (FVC) on SIF measurements over (1) a homogeneous rice paddy field and (2) a heterogeneous planted forest characterized by unevenly distributed differing plant species. We first simultaneously conducted an experiment with UAV- and tower-based SIF systems in a homogeneous paddy field to test the reliability of SIF measurements from UAV-based system. The results showed that the SIF measured by UAV- and tower-based systems have a strong linear relationship (R2 = 0.98), demonstrating that UAV-based SIF system is capable of capturing the diurnal variations of canopy SIF. Then, we operated the UAV flying over the heterogeneous planted forest field in five flights, with each flight observing the same plant species across different heights to investigate the influences of FVC on the spatial variability of SIF. We found that FVC exerted substantial effects on the spatial variability of SIF, with the coefficient of variation (CV) of SIF observations at different flying heights increasing from 5% (high FVC) to 30% (low FVC), which was consistent with DART (Discrete Anisotropic Radiative Transfer) model simulations. Furthermore, our results indicated that the escaping probability of SIF and total emitted SIF showed nonlinear responses to FVC at individual observational heights. In particular, the escaping probability reached its lowest value when FVC was at an intermediate level (FVC = 0.6). These findings highlight the significant effect of spatial heterogeneity on canopy SIF measurements, especially at low FVC. Therefore, an exhaustive consideration of the SIF measurement footprint on different underlying surfaces (homogeneous or heterogeneous) is essential to advance SIF applications in terrestrial vegetation science.

Vegetation phenology plays a critical role in inter-annual changes of the terrestrial carbon cycle. Land surface phenology (LSP) has been widely used to monitor vegetation phenology from remotely-sensed data (RSD) across multiple spatial scales. However, it remains unclear how the temporal resolution of RSD influences the accuracy of LSP estimation. This study systematically analyzed the influences of temporal resolution, including the observation temporal resolution (OTR) and composite temporal resolution (CTR), of RSD on LSP estimation from continuous ground-based remote sensing observations. Specifically, this study quantitatively assessed the sensitivity of LSP estimation to temporal resolution from both structural indicators including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation (NIRV), and physiological indicators including photochemical reflectance index (PRI), gross primary productivity (GPP), and solar-induced chlorophyll fluorescence (SIF). The results showed that the effects of temporal resolution of RSD on LSP estimation can be divided into systematic error and random error. The systematic error of CTR was caused by the methods of LSP estimation and affected by data compositing methods, while the random error of both OTR and CTR was caused by data noise. The sensitivity of SIF and GPP to temporal resolution (both OTR and CTR) in LSP estimation was higher than that of vegetation indices (NDVI, EVI, NIRV, PRI) due to higher data noise. Furthermore, in LSP estimation, the selection of the required temporal resolution of RSD was directly related to the data quality. The results highlight the importance of the temporal resolution in LSP estimation from RSD and provide two possible insights to reduce the errors of LSP estimation. First, in the case of the appropriate data compositing strategies and very little data noise, temporal resolution (both OTR and CTR) can be considered to have little influence on LSP estimation. Second, most of the difference in sensitivity of different indicators to temporal resolution comes from external factors, including observation and model or algorithm errors, rather than the properties of indicators.

Timely monitoring of the changes in coverage and growth conditions of vegetation (forest, grass) is very important for preserving the regional and global ecological environment. Vegetation information is mainly reflected by its spectral characteristics, namely, differences and changes in green plant leaves and vegetation canopies in remote sensing domains. The normalized difference vegetation index (NDVI) is commonly used to describe the dynamic changes in vegetation, but the NDVI sequence is not long enough to support the exploration of dynamic changes due to many reasons, such as changes in remote sensing sensors. Thus, the NDVI from different sensors should be scientifically combined using logical methods. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI from the Advanced Very High Resolution Radiometer (AVHRR) and Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI are combined using the Savitzky–Golay (SG) method and then utilized to investigate the temporal and spatial changes in the vegetation of the Ruoergai wetland area (RWA). The dynamic spatial and temporal changes and trends of the NDVI sequence in the RWA are analyzed to evaluate and monitor the growth conditions of vegetation in this region. In regard to annual changes, the average annual NDVI shows an overall increasing trend in this region during the past three decades, with a linear trend coefficient of 0.013/10a, indicating that the vegetation coverage has been continuously improving. In regard to seasonal changes, the linear trend coefficients of NDVI are 0.020, 0.021, 0.004, and 0.004/10a for spring, summer, autumn, and winter, respectively. The linear regression coefficient between the gross domestic product (GDP) and NDVI is also calculated, and the coefficients are 0.0024, 0.0015, and 0.0020, with coefficients of determination (R2) of 0.453, 0.463, and 0.444 for Aba, Ruoergai, and Hongyuan, respectively. Thus, the positive correlation coefficients between the GDP and the growth of NDVI may indicate that increased societal development promotes vegetation in some respects by resulting in the planting of more trees or the promotion of tree protection activities. Through the analysis of the temporal and spatial NDVI, it can be assessed that the vegetation coverage is relatively large and the growth condition of vegetation in this region is good overall.