Recently, solar-induced chlorophyll fluorescence (SIF) from satellites has shown potential for evaluating vegetation status and stress responses. Fluorescence quantum yield (ΦF) is essentially linked to vegetation stress. However, the complex physiological and structural responses of SIF and ΦFto drought need further study. This study normalized SIF as SIFnto account for angular variations and fluctuations in photosynthetically active radiation (PAR), aiming for more accurate drought monitoring. SIFnanomalies were compared to historical baselines (2019–2021 averages) of vegetation indices (VIs), raw SIF, and ΦFduring a 2019 drought in the North China Plain (NCP). The results show SIFnprovides an effective method for drought monitoring, showing the earliest decline compared to raw SIF, VIs, and ΦF. In the first two weeks of drought, SIFndecreased by 8.2%, 7.0%, 12.5%, and 8.2% across the four NCP subdivisions. SIFnoutperformed other indicators, proving sensitive to early drought detection. SIFnwas also examined for tracking drought alleviation by rainfall. The uncertainty under different viewing geometries was quantified. SIFnanomalies showed a strong correlation with rainfall anomalies (R: 0.45 ~ 0.52) and meteorological factors like PAR (R: 0.80 ~ 0.84) and relative humidity (R:0.52 ~ 0.54). The correlation of near-infrared reflectance (NIRv) and ΦFanomalies with SIF was weak during drought onset (R: 0.16 ~ 0.32) but strong at the end (R: 0.83 ~ 0.87). These suggest both canopy structure (mainly characterized by NIRv) and vegetation chlorophyll (ΦF) are impacted by drought and influence SIF at different stages.

Solar-induced chlorophyll fluorescence (SIF) shows potential in exploring plant responses to environmental changes caused by extreme climatic factors. However, how to accurately assess climate stresses (especially the low-temperature stress) suffered on crops at the regional scale in a systematic approach has not been extensively explored. In this study, we developed a climate vegetation stress index (CVSI) to assess and quantify the impacts of climate stress on crops at large scales by combining TROPOspheric Monitoring Instrument (TROPOMI) SIF and land surface temperature (LST) data through an easy-to-operate approach. This index was employed to identify low-temperature stress conditions in Henan Province’s winter wheat in 2018. Results indicate that, influenced by climate characteristics, crops in the northern part of Henan Province experienced more severe low-temperature stress than those in the southern part. The daily average SIF values experienced reductions of 0.74, 0.45, 0.61, and 0.86 mW$\cdot ~\text{m}^{-2}~\cdot $sr$^{-1}~\cdot $nm−1 during the four cooling episodes within the two phenological periods, respectively. As low-temperature stress intensified, winter wheat growth was hindered, reducing grain yield. Indeed, the CVSI provides an accurate depiction of crop stress levels and patterns. In areas with high-CVSI values, yield losses are particularly severe. In addition, the significant positive correlation between the CVSI and net primary productivity (NPP), along with the similar spatial intensity pattern, shows the effectiveness of CVSI in monitoring low-temperature stress. CVSI provides a new approach to understand the impacts of climate change on overwintering crops and offers a practical reference for climate stress effects monitoring at the regional scale.

Data assimilation has been demonstrated as the potential crop yield estimation approach. Accurate quantification of model and observation errors is the key to determining the success of a data assimilation system. However, the crop growth model error is not fully taken into account in most of the previous studies. The objective of this study is to better quantify the model uncertainty in the data assimilation system. Firstly, we calibrated a crop growth model and inferred its posterior uncertainty based on the Global LAnd Surface Satellite (GLASS) 250-m LAI product, regional statistical data, station observations, and field measurements with a Markov chain Monte Carlo (MCMC) method. Secondly, the model posterior uncertainty was used in the Ensemble Kalman Filter (EnKF) algorithm to better characterize the ensemble distribution of model errors. Our results indicated the proposed Bayesian posterior-based EnKF can improve the accuracy of winter wheat yield estimation at both the point scale (the coefficient of determination R2value increasing from 0.06 to 0.41, the mean absolute percentage error MAPE value decreasing from 12.65% to 7.82%, and the root mean square error RMSE value decreasing from 987 to 688 kg∙ha-1) and the regional scale (R2value from 0.30 to 0.57, MAPE value from 19.67% to 10.13%, and RMSE value from 1275 to 695 kg∙ha-1) compared with the open-loop estimation. Our analysis also indicated that the Bayesian posterior-based EnKF can perform better compared to the standard Gaussian perturbation-based EnKF. The proposed framework provides an important reference for crop yield estimation at the regional scale in similar agricultural landscapes worldwide.

Against rapid development in urban areas, timely urban land cover changes (ULCC) information is beneficial for understanding the urban environment and promoting sustainable development. To realize real-time urban land cover change detection, high-frequency remotely sensed data are urgently needed. In this study, we tested the detection capability of urban land cover changes using a new daily nighttime light image (Black Marble). Firstly, time series of VNP46V2 from 2012-2019 were collected and decoded into annual trend segments using the BFAST Monitor model. Then, we recognized the jump point in trend segments and defined the corresponding pixel as urban land cover change. We analyzed the Normalized Difference Vegetation Index (NDVI) time series from Landsat images spanning 2014-2019 and removed pixels without significant seasonal fluctuations from ULCC assembled. Finally, the magnitude and change time of ULCC pixels were quantified through BFAST Monitor. It was proved that Black Marble performed well in ULCC detection, achieving an overall accuracy of 87.75%, and detected change time was accurate to 81.12% under ± 1 year allowable error. The present Black Marble data have the potential for real-time urban land use detection and global mapping of ULCC, especially in areas without enough clear-sky observations.

Assessing crop lodging at the regional scale is an important requirement for breeding lodging-resistant varieties and harvest planning. Accurately and continuously estimating crop lodging area from remote sensing data remains challenging due to the high randomness scattering signal of SAR images and the insufficient number of applicable optical images. This study developed a new framework for estimating crop lodging area based on SAR data using the spatial aggregation approach of field units, overcoming the deficit of the traditional pixel-based approach susceptible to speckle noise and spatial heterogeneity. We aggregated the field’s pixel in SAR images using the spatial aggregation approach. The lodging area estimation models of dual-pol and quad-pol were established using a random forest (RF) algorithm. The Sobol approach evaluated the uncertainty and sensitivity at a regional scale. Finally, we analyzed the scattering mechanisms of the lodging field. Results indicate that the proposed method achieves the high performance of the lodging area estimates at the regional scale, in the testing set, with R2and RMSE of the GF-3 model being 0.57 and 18.63%, and the Sentinel-1 model is 0.49 and 20.59%. Besides, the uncertainties of models are below 10%, and are insensitive to the variation of parameters inter-correlation. The depolarization effect and scattering randomness gradually weaken with the increase of lodging percentages. In contrast, the surface scattering quickly increases and finally dominates the total scattering after lodging percentages greater than 80%. This proposed approach would help develop a real-time crop lodging monitoring system using SAR data.

Satellite-derived nighttime light (NTL) data from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) have been extensively used for monitoring human activities and urbanization processes. Differences of these two datasets in their spatial and radiometric properties make it difficult for a temporally consistent analysis using these two datasets together. In this article, we developed a new approach to integrate these two datasets and generated a temporally consistent NTL dataset from 1992 to 2018. First, we performed the pixel-level spatial resampling of VIIRS data using a kernel density method after preprocessing the raw VIIRS data. Second, we conducted a logarithmic transformation of the aggregated VIIRS data. Third, we proposed a sigmoid function between DMSP and processed VIIRS data to characterize their relationship. Using the proposed method, we generated a series of consistent DMSP NTL data in Southeast Asia from 1992 to 2018 and analyzed the dynamic of resulted NTL at different scales. The evaluations based on profile curves, spatial patterns, scatter correlations, and histograms, of NTLs, indicate that our approach can achieve a good agreement between DMSP and simulated DMSP data in the same year. Our approach offers the potential for generating a time series of global DMSP NTL data from 1992 to present, which can contribute a more continuous and consistent monitoring of human activities and a better understanding of the urbanization process.

Urban cellular automata (CA) models propagate and accumulate errors during the modeling process due to the model structure or stochastic processes involved. It is feasible to assimilate real-time observations into an urban CA model to reduce model uncertainties. However, the assimilation performance is sensitive to the spatio-temporal units in the assimilation algorithm, that is, spatial block size and window length (temporal interval). In this study, we coupled an assimilation model, an ensemble Kalman filter (EnKF) and a Logistic-CA model to simulate the urban dynamic in Beijing over a period of two decades. Our results indicate that the coupled EnKF-CA model outperforms the CA-alone counterpart by about 10% in terms of the figure of merit, which reflects the agreement of modeled pixels. We also find that the assimilation performance using a finer block (1 km) is better than that using a coarser block (5 km and 10 km) because of the better depiction of spatial heterogeneity using a finer block. Moreover, the improvement of intermediate outputs using the coupled EnKF-CA model is effective for a certain period (e.g. 5 years). This implies that a high-frequency assimilation may not significantly improve the model performance. The sensitivity analyses of spatio-temporal assimilation in the EnKF-CA model provide a better understanding of the assimilation mechanism that couples with land-use change models.

Transition rules are the core of urban cellular automata (CA) models. Although the logistic cellular automata (Logistic-CA) is commonly used for rules extraction, it cannot always achieve satisfactory performance because of the spatial heterogeneity and the inherent complexity of urban expansion. This article presents an ensemble-urban cellular automata (Ensemble-CA) model to achieve better transition rules. First, an uncertainty map that assesses the performance of transition rules spatially was achieved. Then, two auxiliary models (i.e. classification and regression tree, CART; and artificial neural network, ANN), both of which have been stabilized with a Bagging algorithm, were prepared for integration using a proposed self-adaptive -nearest neighbors (-NN) combination algorithm. Thereafter, those unconfident sites were replaced with the ensemble output. This model was applied to Guangzhou, China, for an urban growth simulation from 2003 to 2008. Static validation confirmed that this ensemble framework (i.e. without substitution of uncertain sites) can achieve better performance (0.87) in terms of receiver operating characteristic (ROC) statistics (area under the curve, AUC), and outperformed the best single model (ANN, 0.82) and other common strategies (e.g. weighted average, 0.83). After the substitution of unconfident sites, the AUC of Logistic-CA was elevated from 0.78 to 0.81. Subsequently, two urban growth mechanisms (i.e. pixel- and patch-based) were implemented separately based on the integrated transition rules. Experimental results revealed that the accuracy obtained from simulation of the Ensemble-CA increased considerably. The obtained kappa outperformed the single model, with improvements of 1.74% and 2.76% for pixel- and patch-based approaches, respectively. Correspondingly, landscape similarity index (LSI) improvements of these two mechanisms were 4.24% and 1.82%.

Cellular automata (CA) have emerged as a primary tool for urban growth modeling due to its simplicity, transparency, and ease of implementation. Sensitivity analysis is an important component in CA modeling for a better understanding of errors or uncertainties and their propagation. Most studies on sensitivity analyses in urban CA modeling focus on specific component such as neighborhood configuration or stochastic perturbation. However, sensitivity analysis of transition rules, which is one of the core components in CA models, has not been systematically done. This article proposes a systematic sensitivity analysis of major operational components in urban CA modeling using a stepwise comparison approach. After obtaining transition rules, three stages (i.e. static calibration of transition rules, dynamic evolution with varied time steps, and incorporation with stochastic perturbation) are designed to facilitate a comprehensive analysis. This scheme implemented with a case study in Guangzhou City (China) reveals that gaps in performance from static calibration with different transition rules can be reduced when dynamic evolution is considered. Moreover, the degree of stochastic perturbation is closely related to obtain urban morphology. However, a more realistic (i.e. fragmented) urban landscape is achieved at the cost of decreasing pixel-based accuracy in this study. Thus, a trade-off between pixel-based and pattern-based comparisons should be balanced in practical urban modeling. Finally, experimental results illustrate that models for transition rules extraction with good quality can do an assistance for urban modeling through reducing errors and uncertainty range. Additionally, ensemble methods can feasibly improve the performance of CA models when coupled with nonparametric models (i.e. classification and regression tree).