Remote sensing provides us with an approach for the rapid identification and monitoring of spatiotemporal changes in the urban ecological environment at different scales. This study aimed to construct a remote sensing assessment index for urban ecological livability with continuous fine spatiotemporal resolution data from Landsat and MODIS to overcome the dilemma of single image-based, single-factor analysis, due to the limitations of atmospheric conditions or the revisit period of satellite platforms. The proposed Ecological Livability Index (ELI) covers five primary ecological indicators – greenness, temperature, dryness, water-wetness, and atmospheric turbidity – which are geometrically aggregated by non-equal weights based on an entropy method. Considering multisource time-series data of each indicator, the ELI can quickly and comprehensively reflect the characteristics of the Ecological Livability Quality (ELQ) and is also comparable at different time scales. Based on the proposed ELI, the urban ecological livability in the central urban area of Wuhan, China, from 2002 to 2017, in the different seasons was analyzed every 5 years. The ELQ of Wuhan was found to be generally at the medium level (ELI ≈0.6) and showed an initial trend of degradation but then improved. Moreover, the ecological livability in spring and autumn and near rivers and lakes was found to be better, whereas urban expansion has led to the outward ecological degradation of Wuhan, but urban afforestation has enhanced the environment. In general, this paper demonstrates that the ELI has an exemplary embodiment in urban ecological research, which will support urban ecological protection planning and construction.

Knowledge of regional net primary productivity (NPP) is important for the systematic understanding of the global carbon cycle. In this study, multi-source data were employed to conduct a regional NPP study in southwest China, with a 33-year time span and a 1-km scale. A multi-sensor fusion framework was applied to obtain a new normalized difference vegetation index (NDVI) time series from 1982 to 2014, combining the advantages of different remote sensing datasets. As another key parameter for NPP modeling, the total solar radiation was calculated utilizing the improved Yang hybrid model (YHM), based on meteorological station data. The accuracy of the data processes is proved reliable by verification experiments. Moreover, NPP estimated by fused NDVI shows an obvious improved accuracy than that based on the original data. The spatio-temporal analysis results indicated that 67% of the study area showed an increasing NPP trend over the past three decades. The correlation between NPP and precipitation was significant heterogeneous at the monthly scale; specifically, the correlation is negative in the growing season and positive in the dry season. Meanwhile, the lagged positive correlation in the growing season and no lag in the dry season indicated the important impacts of precipitation on NPP. What is more, we found that there are three distinct stages during the variation of NPP, which were driven by different climatic factors. Significant climate warming led to a great increase of NPP from 1992 to 2002, while NPP clearly decreased during 1982–1992 and 2002–2014 due to the frequent droughts caused by the precipitation decrease.

Complementary information from multi-sensors can be combined to improve the availability and reliability of stand-alone data. Typically, multispectral (MS) images contain plentiful spectral information of the Earth’s surface that is beneficial for identifying land cover types, while synthetic aperture radar (SAR) images can provide abundant information on the texture and structure of target objects. Therefore, this paper presents a fusion framework to integrate the information from MS and SAR images based on the Laplacian pyramid (LP) and sparse representation (SR) theory. LP is performed to decompose both the multispectral and SAR images into high-frequency components and low-frequency components, so that different processing strategies can be applied to multi-scale information. Low-frequency components are merged based on SR theory, whereas high-frequency components are combined based on a certain activity-level measurement, identifying salient features. Finally, LP reconstruction is performed to obtain the integrated image. We conduct experiments on several datasets to verify the effectiveness of the proposed method. Both visual interpretation and statistical analyses demonstrate that the proposed method strikes a satisfactory balance between spectral information preservation and the enhancement of spatial and textual characteristics. In addition, a further discussion regarding the adjustability property of the proposed method shows its flexibility for further application scenarios.

The Tibetan Plateau (TP) is an important component of the global environmental system, on which the snow cover greatly affects the regional climate and ecology. Moderate resolution imaging spectroradiometer (MODIS) snow cover products have been demonstrated to be appropriate for investigating the snow cover over the TP. However, they are subject to cloud obscuration, and the TP’s extremely complex terrain makes the snow monitoring difficult. Therefore, in this paper, we propose a two-stage spatio–temporal fusion framework for the cloud removal of MODIS C6 snow products, including an adjusted Terra and Aqua combination (TAC) and a spatio–temporal fusion based on Gaussian kernel function and error correction (STF-GKF-EC). To the best of our knowledge, this is the first time that a spatio–temporally continuous daily 500-m MODIS normalized difference snow index (NDSI) product has been generated for the TP, which greatly improves the spatial and temporal resolutions of the current snow cover products. The main stage, STF-GKF-EC, adaptively weights the spatial and temporal correlations by the Gaussian kernel function, and further takes the rapid changes of snow cover into consideration through the error correction. The experiments indicated that STF-GKF-EC removes clouds completely, achieving an overall accuracy (OA) and mean absolute error (MAE) of 91.48% and 3.88, respectively. Based on the cloud-removed results, during 2001–2017, as far as the intra-annual variation is concerned, a large proportion of the snow cover appears between October and May, with a peak in February/March, and the variation is mainly controlled by temperature. For the inter-annual variation, an obvious increasing trend of 0.68/year for NDSI is observed before 2005, followed by a slight decreasing trend of 0.16/year, in which precipitation is a better explanation factor than temperature.

Recently, classification using multiple classifier system (MCS) has been reported as an effective method to improve remote sensing (RS) image classification. Such systems provide a complementary mechanism to use multiple classifiers, which have shallow architecture to solve the same classification problem; however, the system exhibits shortcomings due to complex ensemble strategy. Deep learning (DL) has been proven to be an advanced method for complex data classification; however, how to use its advantages to overcome the shortcomings of MCS in ensemble strategy for classification accuracy improvement is worthy of study. Thus, with the multiple classifier mechanism and DL architecture, we propose a novel RS image classification framework, namely, deep–shallow learning (DSL), to improve classification accuracy. The DSL framework consists of a shallow learning (SL) layer and a DL layer. The SL layer contains various classifiers with shallow architecture, which can output different classification results for a certain input, whereas the DL layer is formed by DL networks, which can continue learning from the outputs of the SL layer. DSL simulates a human thinking model that continuously learns from the existing learnings to improve learning efficiency. In our experiment, three shallow classification algorithms, i.e., C4.5, k-nearest neighbor, and naive Bayesian, are used to train base classifiers in the SL layer, whereas a deep belief network (DBN) is used to train the DL layer. The experiment results on three different datasets indicate that DSL outperforms other methods in terms of classification accuracy by using backpropagation neural network, bagging, AdaBoost, random forest, multilayer perceptron, and DBN.

Solar-induced chlorophyll fluorescence (SIF) is an intuitive and accurate way to measure vegetation photosynthesis. Orbiting Carbon Observatory-2 (OCO-2)-retrieved SIF has shown great potential in estimating terrestrial gross primary production (GPP), but the discontinuous spatial coverage limits its application. Although some researchers have reconstructed OCO-2 SIF data, few have considered the uneven spatial and temporal distribution of the swath-distributed data, which can induce large uncertainties. In this article, we propose a spatiotemporal constrained light gradient boosting machine model (ST-LGBM) to reconstruct a contiguous OCO-2 SIF product (eight days, 0.05°), considering the data distribution characteristics. Two spatial and temporal constraining factors are introduced to utilize the relationships between the swath-distributed OCO-2 samples, combining the geographical regularity and vegetation phenological characteristics. The results indicate that the ST-LGBM method can improve the reconstruction accuracy in the missing data areas ( $R^{2}= 0.79$ ), with an increment of 0.05 in $R^{2}$ . The declined accuracy of the traditional light gradient boosting machine (LightGBM) method in the missing data areas is well alleviated in our results. The real-data comparison with TROPOspheric Monitoring Instrument (TROPOMI) SIF observations also shows that the results of the ST-LGBM method can achieve a much better consistency, in both spatial distribution and temporal variation. The sensitivity analysis also shows that the ST-LGBM can support stable results when using various input combinations or different machine learning models. This approach represents an innovative way to reconstruct a more accurate globally continuous OCO-2 SIF product and also provides references to reconstruct other data with a similar distribution.