Impervious surfaces are commonly acknowledged as major components of human settlements. The expansion of impervious surfaces could lead to a series of human−dominated environmental and ecological issues. Tracing impervious surface dynamics at a finer temporal−spatial scale is a critical way to better understand the increasingly human-dominated system of Earth. In this study, we put forward a new scheme to conduct long-term monitoring of impervious−relevant land disturbances using high frequency Landsat archives and the Google Earth Engine (GEE). First, the developed region was identified using a classification-based approach. Then, the GEE-version LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery) was used to detect land disturbances, characterizing the conversion from vegetation to impervious surfaces. Finally, the actual disturbance areas within the developed regions were derived and quantitatively evaluated. A case study was conducted to detect impervious surface dynamics in Nanjing, China, from 1988 to 2018. Results show that our scheme can efficiently monitor impervious surface dynamics at yearly intervals with good accuracy. The overall accuracy (OA) of the classification results for 1988 and 2018 are 95.86% and 94.14%. Based on temporal−spatial accuracy assessments of the final detection result, the temporal accuracy is 90.75%, and the average detection time deviation is −1.28 a. The OA, precision, and recall of the sampling inspection, respectively, are 84.34%, 85.43%, and 96.37%. This scheme provides new insights into capturing the expansion of impervious−relevant land disturbances with high frequency Landsat archives in an efficient way.

Relative radiometric normalization (RRN) is a critical preprocessing step that is widely applied to remote sensing data. Essential to RRN are the pseudo-invariant features (PIFs) – ground objects with invariant reflectance across the acquisition dates of multiple images. To meet the requirements of RRN for change detection in multi-temporal images, this paper proposed a novel automatic method for PIF extraction: first, we calculated spectral, spatial, and visual features for the images. Next, a set of change vectors were generated to quantify the features’ differences. Then, the initial PIF was obtained by synthesizing similar pixels from each change vector. Finally, an iterative linear regression was performed to generate the final PIFs. Furthermore, we employed the random sample consensus (RANSAC) algorithm to establish the RRN model for normalizing the target image. The methods and workflow of PIF extraction and the RRN model establishment were validated with two TripleSat-2 multi-spectral images with a spatial resolution of 3.2 metres. The applicability of the proposed PIF extraction method was further validated by Landsat-8 OLI, GF-1 WFV, Sentinel-2 MSI, and WorldView-2 images. The results showed that: (1) The proposed PIF extraction method can automatically obtain the PIFs with high precision. Based on the extracted PIFs, the RANSAC algorithm is capable of building the RRN model with a significant linear relationship. (2) The PIF extraction method is applicable to multi-spectral images with different spatial resolutions. (3) Compared with the multivariate alteration detection (MAD) and the iteratively reweighted MAD (IR-MAD), the proposed PIF extraction method achieved a better performance in selecting the pixels with smaller radiometric differences, which is helpful to reach a higher accuracy of the RRN model. (4) Differences in image brightness, spectral domain, and spatial domain are considered in the PIF extraction method with fewer parameters and high portability. (5) Spectral features are dominant to obtain ideal PIFs, and feature combinations can get the advantages to enhance the effectiveness of the PIFs.

Citrus orchard planting is a typical land-use change process that can impact terrestrial ecosystem services both locally and globally. Long-term monitoring of citrus orchard dynamics is critical for understanding its change patterns as well as the potential driving factors. Satellite remote-sensing imagery has been a primary data source for this purpose. However, most previous studies with multi-year intervals only captured some, but not all detailed information on citrus orchard expansion. In this study, we developed a framework for mapping annual citrus orchard extent and track its long-term dynamics in Xunwu County, China, using the historical Landsat repository from 1990 to 2016. The results suggested that the average overall accuracy of original annual mapping was 87.73%, and its performance was significantly improved after the temporal filtering approach (91.46%). Several features (e.g. elevation, slope, normalized difference vegetation index) played more important roles in citrus orchard identification. With the achieved annual mapping layers, we found a rapid citrus orchard expansion trend during the study interval (i.e. from 22.18 to 697.21 km²). Moreover, this expansion process was unevenly distributed in time. Spatially, emerging citrus orchards were primarily transformed from forests and croplands and mainly distributed in areas with elevations from 200 to 500 m and slopes range from 5° to 20°. This study demonstrated the potential of mapping citrus orchard dynamics at a higher temporal frequency with remote-sensing time-series, which can contribute to providing reference for sustainable land-use policy.

Leaf water content (LWC) is a key physiological parameter for assessing maize moisture status, with direct implications for crop growth and yield. Accurate LWC estimation is essential for water resource management and precision agriculture. This study introduces a high-precision method for estimating maize LWC utilizing UAV-based multispectral imagery combined with a Random Forest Regression (RFR) model. By extracting vegetation indices, image coverage, and texture features and integrating them with ground-truth data, the study examines the variation in LWC estimation accuracy across different growth stages. The results indicate that the RFR model performs optimally during the seedling stage, with a root relative mean square error (RRMSE) of 2.99%, whereas estimation errors are larger during the tasseling stage, with an RRMSE of 4.13%. Moreover, the RFR model consistently outperforms multiple linear regression (MLR) and ridge regression (RR) models throughout the growing season, demonstrating lower errors on both training and testing datasets. Notably, the RFR model exhibits significantly reduced errors in the training dataset compared to both MLR and RR models. Following particle swarm optimization (PSO), the prediction accuracy of the RFR model is notably enhanced, with the RRMSE on the training dataset decreasing from 1.46% to 1.19%. This study provides an effective approach for estimating maize LWC across different growth stages, supporting crop water management and precision agriculture, and offering valuable insights for the estimation of water content in other crops.