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.

Grassland aboveground biomass is crucial for evaluating grassland desertification, degradation, and grassland and livestock balance. Given the lack of understanding of mechanical processes and limited simulation accuracy for grassland aboveground biomass estimation, especially at the regional scale, this study investigates a new method combining remote sensing data assimilation technology and a grassland process-based model to estimate regional grassland biomass, focusing on improving the simulation accuracy by modeling and revealing the mechanism interpretability of grassland growth processes. Xilinhot City of Inner Mongolia was used as the study area. The ModVege model was selected as the grass dynamic simulation model. A likelihood function was constructed composed of the LAI, grassland aboveground biomass, and daily measurements wherein the accumulated temperature reached ST₂ (the temperature sum defining the end of reproductive growth). Then, the Markov chain Monte Carlo (MCMC) methodology was adapted to calibrate the ModVege model by maximizing the likelihood function. The time-series LAI from MOD15A3H was assimilated into the ModVege model, and the model parameters ST₂ and BM_GV0 (initial biomass and green vegetative tissues, respectively) were optimized at a 500 m pixel scale based on the four-dimensional variational method (4DVar) method. Compared with August 15th, the RMSE and MAPE of aboveground biomass were 242 kg/ha and 10%, respectively, after calibration. Data assimilation improved this accuracy, with the RMSE decreasing to 214 kg/ha. Overall, the aboveground grassland biomass of Xilinhot City shows spatial distribution patterns of high value in the northeast and low value in the central and southeast areas. Generally, the method implemented in this study provides an important reference for the aboveground biomass estimation of regional grassland.

Grasslands, as an important part of terrestrial ecosystems, are facing serious threats of land degradation. Therefore, the remote monitoring of grasslands is an important tool to control degradation and protect grasslands. However, the existing methods are often disturbed by clouds and fog, which makes it difficult to achieve all-weather and all-time grassland remote sensing monitoring. Synthetic aperture radar (SAR) data can penetrate clouds, which is helpful for solving this problem. In this study, we verified the advantages of the fusion of multi-spectral (MS) and SAR data for improving classification accuracy, especially for cloud-covered areas. We also proposed an adaptive feature fusion method (the SK-like method) based on an attention mechanism, and tested two types of patch construction strategies, single-size and multi-size patches. Experiments have shown that the proposed SK-like method with single-size patches obtains the best results, with 93.12% accuracy and a 0.91 average f1-score, which is a 1.02% accuracy improvement and a 0.01 average f1-score improvement compared with the commonly used feature concatenation method. Our results show that the all-weather, all-time remote sensing monitoring of grassland is possible through the fusion of MS and SAR data with suitable feature fusion methods, which will effectively enhance the regulatory capability of grassland resources.