Wenjin wu

• Chinese Academy of Sciences

In the Antarctic, Arctic, and Tibetan Plateau—recognized as the Earth’s three poles characterized by extremely harsh environments—lichens prevail in the ecosystem and play crucial roles as pioneer species. Despite their importance, studies investigating the spatial distribution patterns of lichen attributes are scarce due to a lack of appropriate d...

Adjustments in foliar photoprotective pigments are crucial for plant adaptation to harsh environments, serving as indicators of environmental stress. However, understanding when and where these adjustments occur across diverse biomes remains unclear due to challenges in large-scale observation. Here, we propose a novel approach to assess dynamics i...

Changes in water content dynamics can serve as predictive indicators for catastrophic transitions in vegetation tipping elements. Vegetation water content is a rapidly changing parameter with sub-daily fluctuations, and current space-borne sensors fall short of providing adequate spatiotemporal coverage for effective analysis. The Moon-based Earth...

Antarctica's response to climate change varies greatly both spatially and temporally. Surface melting impacts mass balance and also lowers surface albedo. We use a 43-year record (from 1978 to 2020) of Antarctic snow melt seasons from spaceborne microwave radiometers with a machine learning algorithm to show that both the onset and the end of the m...

Currently, numerous studies have reported that the invasion of Cassytha filiformis has affected both above and below ground communities, resulting in difficulties in the growth of original vegetation. Meanwhile, Cassytha filiformis was observed on the Xisha Islands in recent years which brings up the importance of monitoring its invasion to protect...

Moon-based synthetic aperture radar (SAR) offers unprecedented temporal and spatial coverage. Its repeat-pass interferometry is expected to play a substantial role in earth science because of its large-scale, long-term, near 24.8 h revisit period, and stable earth observation ability. However, it faces a greater challenge when the signal passes thr...

Gross primary production (GPP) estimation usually involves a priori assumptions about biome-specific rules or climate controls, which hampers an objective analysis of driving mechanisms. Observation-based methods that are biome-invariant and globally uniform are thus highly desirable. To facilitate this, a reflectance index representing the ratio o...

Tropical forests are of vital importance for maintaining biodiversity, regulating climate and material cycles while facing deforestation, agricultural reclamation, and managing various pressures. Remote sensing (RS) can support effective monitoring and mapping approaches for tropical forests, and to facilitate this we propose a deep neural network...

Drought is one of the most damaging environmental hazards and a naturally occurring phenomenon in Central Asia that is accompanied by crucial consequences for the agriculture sector. This research aimed at understanding the nature and extent of drought over the cropland regions of Central Asia with the help of spatiotemporal information from the re...

Spatiotemporal patterns of global forest net primary productivity (NPP) are pivotal for us to understand the interaction between the climate and the terrestrial carbon cycle. In this study, we use Google Earth Engine (GEE), which is a powerful cloud platform, to study the dynamics of the global forest NPP with remote sensing and climate datasets. I...

In this article, we propose a universal data-driven model to acquire FLUXNET-consistent annual forest gross primary productivity and net ecosystem exchange globally. The model is developed based on a deep-learning network with a time series of seven ecological and climatic parameters as inputs. To avoid tedious data downloading for large-area studi...

As a new potential platform for Earth observation, the Moon which is a natural satellite unique to the Earth has been paid more and more attention for its consistent and continuous observation capability of global-scale and macroscopic geoscience phenomena on Earth. Because of the effect of Earth curvature and the Earth–Moon geometric relationship,...

Suffering from speckle noise and complex scattering phenomena, classification results of SAR images are usually noisy and shattered, which makes them difficult to use in practical applications. Deep-learning-based semantic segmentation realizes segmentation and categorization at the same time, and thus can obtain smooth and fine-grained classificat...

Numerous studies have shown that intact tropical forests account for half of the total terrestrial sink for anthropogenic carbon dioxide. Here, we analyzed and compared changes in three main tropical forest regions from 2000 to 2014, based on time-series analysis and landscape metrics derived from moderate-resolution imaging spectroradiometer data....

How to jointly use spatial and polarimetric information in PolSAR analysis has long been an open question. Benefiting from advanced architectures and large visual databases, deep convolutional neural networks or deep convnets (DCNNs) can generate high-level spatial features and achieve state-of-the-art performance in image analyses. However, becaus...

Aiming at the problems and prospects in millimeter-wave ultrahigh resolution synthetic aperture radar applications, we have developed a method with a new feature set for sophisticated classification of large images. It includes innovative parameters derived from different kinds of spectral and characteristic signatures, such as the correlation sign...

Synthetic aperture radar (SAR) tomography (TomoSAR) estimates scene reflectivity along elevation coordinates, based on multi-baseline measurements. Common TomoSAR approaches are based on every single range-azimuth cell or the cell’s neighborhood. By using an additional synthetic aperture for elevation, these techniques have higher resolution power...

As an important advanced technique in the field of Earth observations, Synthetic Aperture Radar (SAR) plays a key role in the study of global environmental change, resources exploration, disaster mitigation, urban environments, and even lunar exploration. However, studies on imaging, image processing, and Earth factor inversions have often been con...

Information containing in the complex data is seldom considered by researchers when dealing with single synthetic aperture radar (SAR) image processing. In 2015, the statistical noncircularity, which indicates the distribution consistency between the real and imaginary parts, has been found to be surprisingly effective when analyzing the ultrahigh-...

The rapid development of synthetic aperture radar (SAR) sensors results in the acquisition of substantial ultrahigh-resolution SAR images. In this paper, we, for the first time, present three scenes of single-polarization SAR images with decimeter resolution obtained by a millimeter-wave (MMW) Chinese airborne SAR system. An innovative framework ba...

This paper proposed an innovative framework to almost automatically extract man-made target from a high-resolution (HR) polarimetric SAR (PolSAR) image of an urban area. The core part of this framework is a new PolSAR image feature extraction method, which is developed by combining the spherically invariant random vector (SIRV) product model with t...

Urban area man-made target detection based on SAR images has been a challenging field for years due to the complicated scattering mechanisms of dense buildings and the poor visual quality of SAR images caused by speckle noises. To overcome the effect of speckle noise, a substantial portion of SAR image processing methods are based on statistical ch...

Urban areas are the primary living environments of human beings, and a frequent focus in Earth observation based analyses. Chances for advancement in this field especially exist in the detection of man-made targets in urban area using synthetic aperture radar (SAR) images. Especially stationarity is a useful parameter in SAR image information extra...

A method based on Rician distribution is proposed in this article to detect nonstationary targets in urban area from Synthetic aperture radar (SAR) image. Rician distribution is an adaptive model which could better fit the statistical characters of urban area SAR image than Wishart distribution, and then improve the detection results. The method ha...

Remote sensing technique has become one of the most important means of red tide detection. Now, there have been a number of successful applications of red tide detections using remote sensing technology in the world. The present detecting technology basically based on the true color images of the ocean, chlorophyll a (CHL-a) maps and sea surface te...