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Raw multi-source data include multi-channel multi-spectral images as well as single-channel panchromatic images. Raw multispectral images are rich in spectral detail but low in resolution, whereas raw panchromatic images have high resolution but lack spectral information. Pansharpening is the process of using raw panchromatic and multispectral images to obtain highly accurate remote sensing images. Compared with traditional fusion methods, deep learning-based pansharpening methods perform better. Therefore, this study uses an unsupervised fusion algorithm based on an improved generative adversarial network (GAN) and names it the multi-scale detail injection generative adversarial network (MSDI-GAN). This algorithm uses a network structure with two discriminators and a generator, and the spatial and spectral information of the original multi-source data is retained separately. Each convolutional layer of the generator is used to extract multi-scale feature information of the original data, and injects them into the corresponding discriminator layers of the spatial and spectral discriminator. This method can deepen the exchange of gradient information between the generator and discriminators, optimize the actual network training process and accelerate network convergence. Extensive experiments are conducted to evaluate the performance of our algorithm, and to compare with other algorithms. Based on a qualitative visual effects evaluation of the experimental results and a comparison of quantitative evaluation indicators, it is demonstrated that our method achieves superior results in the pansharpening of remote sensing images. [ABSTRACT FROM AUTHOR]

Abstract: Disturbances such as wildfire, insect outbreaks, and forest clearing, play an important role in regulating carbon, nitrogen, and hydrologic fluxes in terrestrial watersheds. Evaluating how watersheds respond to disturbance requires understanding mechanisms that interact over multiple spatial and temporal scales. Simulation modeling is a powerful tool for bridging these scales; however, model projections are limited by uncertainties in the initial state of plant carbon and nitrogen stores. Watershed models typically use one of two methods to initialize these stores: spin‐up to steady state or remote sensing with allometric relationships. Spin‐up involves running a model until vegetation reaches equilibrium based on climate. This approach assumes that vegetation across the watershed has reached maturity and is of uniform age, which fails to account for landscape heterogeneity and non‐steady‐state conditions. By contrast, remote sensing, can provide data for initializing such conditions. However, methods for assimilating remote sensing into model simulations can also be problematic. They often rely on empirical allometric relationships between a single vegetation variable and modeled carbon and nitrogen stores. Because allometric relationships are species‐ and region‐specific, they do not account for the effects of local resource limitation, which can influence carbon allocation (to leaves, stems, roots, etc.). To address this problem, we developed a new initialization approach using the catchment‐scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spin‐up with the spatial fidelity of remote sensing. It uses remote sensing to define spatially explicit targets for one or several vegetation state variables, such as leaf area index, across a watershed. The model then simulates the growth of carbon and nitrogen stores until the defined targets are met for all locations. We evaluated this approach in a mixed pine‐dominated watershed in central Idaho, and a chaparral‐dominated watershed in southern California. In the pine‐dominated watershed, model estimates of carbon, nitrogen, and water fluxes varied among methods, while the target‐driven method increased correspondence between observed and modeled streamflow. In the chaparral watershed, where vegetation was more homogeneously aged, there were no major differences among methods. Thus, in heterogeneous, disturbance‐prone watersheds, the target‐driven approach shows potential for improving biogeochemical projections. [ABSTRACT FROM AUTHOR]

Total suspended matter (TSM) directly determines the underwater light field distribution and thus affects the primary productivity in a water body. Estimation of TSM plays a vital role in monitoring, evaluating, and protecting water quality. Many empirical and semianalytical models have been established for clear open ocean waters or extremely turbid coasts/lakes. However, few are generally applicable to inland, optically complex, deep waters. Using data sets of observed in situ data for the slightly turbid water of the Xin’anjiang Reservoir, we developed a semianalytical algorithm to estimate the TSM concentration for slightly turbid waters (\textTSM < 25\;\textmg\;\textL^ - 1; \textTSM/Chla\ < 2.2 \times 10^3) [chlorophyll a (Chla)] using [R{\text{rs}}{{{(542)}}^{{{ - 1}}}}{{ - }}{R{\text{rs}}}{{{(600)}}^{{{ - 1}}}}{{]\ \times\ }}{R{\text{rs}}}\ {{(668)}}, (R{\textbf{rs}}: remote sensing reflectance), with a coefficient of determination (R^2) > 0.9 and a normalized root-mean-square error \text(NRMSE) < 12\% . The semianalytical model was then applied to 14 Landsat 8 OLI images from December 2013 to April 2015, with R^2 = 0.85 and \textNRMSE = 23\%, indicating the feasibility of the semianalytical model for estimating TSM. The TSM concentration estimated from Landsat 8 OLI data in the Xin’anjiang Reservoir exhibited a significant spatial and seasonal difference. The spatial heterogeneity, significantly higher in incoming rivers than the main body of the reservoir, was due to watershed inputs and anthropogenic dredging activity. The temporal heterogeneity of TSM, significantly higher in summer and autumn than in winter and spring, was mainly caused by the seasonal rainfall and seasonal growth of phytoplankton. Our study showed that the semianalytical model for Landsat 8 OLI images could be used to quantitatively monitor TSM in slightly turbid, inland waters. [ABSTRACT FROM PUBLISHER]

Chen, J.; Pan, D.; Mao, Z.; Chen, N.; Zhao, J., and Liu, M., 2014. Land-cover reconstruction and change analysis using multisource remotely sensed imageries in Zhoushan Islands since 1970. Islands are hotspot areas with intensive interactions between land and ocean, and they are also the most vulnerable places to human activities and environmental change. As the frontier zone of oceanic economic development, coastal regions of China have undergone enhanced changes in land-cover change during recent decades. This study was conducted to investigate how land cover in Zhoushan Island and its surrounding islands, which are typical islands of China, has changed since the economic reform by using multisource remotely sensed imageries. The earliest land cover in 1970, 1976, and 1980 was interpreted and digitalized from CORONA and KH-9 photographs, respectively. For the period of 1986−2000, TM (Thematic Mapper) and ETM (Enhanced Thematic Mapper) imageries were classified to build land-cover maps with the supervised classification method. The most recent land-cover data in 2006 and 2011 were generated by inventory land-use vector map and SPOT5 imageries. The reconstructed land-cover time series indicate that Zhoushan Islands have involved substantial land-cover change since 1970. The arable land has changed into built-up types, mainly, and rate of change reached its peak in 2000, while the spatial distribution of transition was not uniform. Nevertheless, water bodies and woodlands have been well preserved during the past 40 years. During the urbanization process, the tidal zone of these islands shrank sharply, and some of them turned to built-up land-cover directly. [ABSTRACT FROM AUTHOR]

Abstract: There are large discrepancies among estimates of the cropland area in China due to the lack of reliable data. In this study, we used Landsat TM/ETM data at a spatial resolution of 30 m to reconstruct spatial and temporal patterns of cropland across China for the time period of 1990–2000. Our estimate has indicated that total cropland area in China in 2000 was 141.1 million hectares (ha), including 35.6 million ha paddy land and 105.5 million ha dry farming land. The distribution of cropland is uneven across the regions of China. The North-East region of China shows more cropland area per capita than the South-East and North regions of China. During 1990–2000, cropland increased by 2.79 million ha, including 0.25 million ha of paddy land and 2.53 million ha of dry farming land. The North-East and North-West regions of China gained cropland area, while the North and South-East regions showed a loss of cropland area. Urbanization accounted for more than half of the transformation from cropland to other land uses, and the increase in cropland was primarily due to reclamation of grassland and deforestation. Most of the lost cropland had good quality with high productivity, but most gained cropland was poor quality land with less suitability for crop production. The globalization as well as changing environment in China is affecting land-use change. Coordinating the conflict between environmental conservation and land demands for food will continue to be a primary challenge for China in the future. [Copyright &y& Elsevier]

Based on characteristics analysis about remote sensing reflectance, the Secchi Disk Depth (SDD) in the Qiandao Lake was predicted from the Landsat8/OLI data, and its changing rates on a pixel-by-pixel scale were obtained from satellite remote sensing for the first time. Using 114 matchups data pairs during 2013–2019, the SDD satellite algorithms suitable for the Qiandao Lake were obtained through both the linear regression and machine learning (Support Vector Machine) methods, with remote sensing reflectance (Rrs) at different OLI bands and the ratio of Rrs (Band3) to Rrs (Band2) as model input parameters. Compared with field observations, the mean absolute relative difference and root mean squared error of satellite-derived SDD were within 20% and 1.3 m, respectively. Satellite-derived results revealed that SDD in the Qiandao Lake was high in boreal spring and winter, and reached the lowest in boreal summer, with the annual mean value of about 5 m. Spatially, high SDD was mainly concentrated in the southeast lake area (up to 13 m) close to the dam. The edge and runoff area of the lake were less transparent, with an SDD of less than 4 m. In the past decade (2013–2020), 5.32% of Qiandao Lake witnessed significant (p < 0.05) transparency change: 4.42% raised with a rate of about 0.11 m/year and 0.9% varied with a rate of about −0.09 m/year. Besides, the findings presented here suggested that heavy rainfall would have a continuous impact on the Qiandao Lake SDD. Our research could promote the applications of land observation satellites (such as the Landsat series) in water environment monitoring in inland reservoirs. [ABSTRACT FROM AUTHOR]

The trophic state index (TSI) is a vital parameter for aquatic ecosystem assessment. Thus, information on the spatial and temporal distribution of TSI is critical for supporting scientifically sound water resource management decisions. We proposed a semi-analytical approach to remotely estimate TSI based on Landsat 8 OLI data for inland waters. The approach has two major steps: deriving the total absorption coefficient of optically active constituents (OACs) and building the relationship between the total absorption coefficient and TSI. First, version 6.0 of the Quasi-Analytical Algorithm (QAA_V6, developed by Zhongping Lee) was implemented with Landsat 8 OLI data to derive the total absorption coefficients of the OACs. Second, we modeled TSI using the total absorption coefficients of OACs at 440 nm based on a large in situ measurement dataset. The total absorption coefficient of OACs at 440 nm gave satisfactory validation results for modeling TSI with a mean absolute percent error of 6% and a root-mean-square error of 5.77. Then, we performed this approach in three inland waters with various eutrophic statuses to validate its results, and the approach demonstrated a robust and satisfactory performance. Finally, an application of the approach was demonstrated in Lake Qiandaohu. Our semi-analytical approach has a sound optical mechanism and extensive application for different trophic inland waters. • The relationships between TSI and absorption of OACs were built. • A semi-analytical approach for remotely estimating TSI was established. • Trophic status in Lake Qiandaohu was assessed by the approach with Landsat 8 data. [ABSTRACT FROM AUTHOR]