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As one of the earliest remote sensing indices, the Normalized Difference Vegetation Index (NDVI) has been employed extensively for vegetation research. However, despite an abundance of NDVI review articles, these studies are predominantly limited to either one subject area or one area, with systematic NDVI reviews being relatively rare. Bibliometrics is a useful method of analyzing scientific literature that has been widely used in many disciplines; however, it has not yet been applied to comprehensively analyze NDVI research. Therefore, we used bibliometrics and scientific mapping methods to analyze citation data retrieved from the Web of Science during 1985–2021 with NDVI as the topic. According to the analysis results, the amount of NDVI research increased exponentially during the study period, and the related research fields became increasingly varied. Moreover, a greater number of satellite and aerial remote sensing platforms resulted in more diverse NDVI data sources. In future, machine learning methods and cloud computing platforms led by Google Earth Engine will substantially improve the accuracy and production efficiency of NDVI data products for more effective global research.

Soil moisture is a crucial component of land–atmosphere interaction systems. It has a decisive effect on evapotranspiration and photosynthesis, which then notably impacts the land surface water cycle, energy transfer, and material exchange. Thus, soil moisture is usually treated as an indispensable parameter in studies that focus on drought monitoring, climate change, hydrology, and ecology. After consistent efforts for approximately half a century, great advances in soil moisture retrieval from in situ measurements, remote sensing, and reanalysis approaches have been achieved. The quality of soil moisture estimates, including spatial coverage, temporal span, spatial resolution, time resolution, time latency, and data precision, has been remarkably and steadily improved. This review outlines the recently developed techniques and algorithms used to estimate and improve the quality of soil moisture estimates. Moreover, the characteristics of each estimation approach and the main application fields of soil moisture are summarized. The future prospects of soil moisture estimation trends are highlighted to address research directions in the context of increasingly comprehensive application requirements.

Precipitation is an important component of the hydrological cycle and has significant impact on ecological environment and social development, especially in arid areas where water resources are scarce. As a typical arid and semi-arid region, the Mongolian Plateau is ecologically fragile and highly sensitive to climate change. Reliable global precipitation data is urgently needed for the sustainable development over this gauge-deficient region. With high-quality estimates, fine spatiotemporal resolutions, and wide coverage, the state-of-the-art Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and European Center for Medium-range Weather Forecasts Reanalysis 5 (ERA5) have great potential for regional climatic, hydrological, and ecological applications. However, how they perform has not been well investigated on the Mongolian Plateau. Therefore, this study evaluated the performance of three IMERG V06 datasets (ER, LR and FR), two ERA5 products (ERA5-HRES and ERA5-Land), and their predecessors (TMPA-3B42 and ERA-Interim) over the region across 2001–2018. The results showed that all products broadly characterized seasonal precipitation cycles and spatial patterns, but only the three reanalysis products, IMERG FR and TMPA-3B42 could capture interannual and decadal variability. When describing daily precipitation, dataset performances ranked ERA5-Land > ERA5-HRES > ERA-Interim > IMERG FR > IMERG LR > IMERG ER > TMPA-3B42. All products showed deficiencies in overestimating weak precipitation and underestimating high-intensity precipitation. Besides, products performed best in agricultural lands and forests along the northern and south-eastern edges, followed by urban areas and grasslands closer to the center, and worst in the sparse vegetation and bare areas of the south-west. Due to a negative effect of topographic complexity, IMERG showed poor detection capabilities in forests. Accordingly, this research currently supports the applicability of reanalysis ERA5 data over the arid, topographically complex Mongolian Plateau, which can inform regional applications with different requirements.

Vegetation phenology in spring has received much attention for its importance to terrestrial ecosystem carbon exchange and climate-biosphere interactions studies. Through control on surface water and heat balance, snow cover largely impacts on spring vegetation phenology. However, under the background of global warming and rapid reduction of spring snow cover extent across the Northern Hemisphere (NH), the responses of spring vegetation phenology have not been well documented. Using two satellite-derived land cover dynamic datasets and 420 in situ vegetation phenology observations from five filed datasets, this study evaluated the accuracy of satellite-derived vegetation phenology datasets and explored the changes of start of the growing season (SOS) across the NH snow-covered landmass for the period 2001-2014. Compared with MEaSUREs VIPPHEN, the MODIS SOS maps displayed higher accuracy in capture the real SOS climatology by validating with in situ observations (R2 = 0.67, bias = −3.99 d). Moreover, evidences from MODIS SOS maps pointed out that the SOS advanced by approximately 2.36 d in NH middle to high latitudes (43.5°N-70.0°N), but delayed by about 0.53 d in lower latitudes (33.0°N-43.5°N) from 2001 to 2014. The contrast SOS anomalies across the NH snow-covered landmass were further proved by changes in spring NDVI derived from GIMMS in the corresponding period. In addition, the observed changes in SOS were consistent with the spatiotemporal pattern of spring snow end date found in previous studies, indicating vegetation phenology changes should be taken into account in estimating the impacts of snow in climate-biosphere interactions studies.

Precipitation is an important controlling parameter for land surface processes, and is crucial to ecological, environmental, and hydrological modeling. In this study, we propose a spatial downscaling approach based on precipitation–land surface characteristics. Land surface temperature features were introduced as new variables in addition to the Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM) to improve the spatial downscaling algorithm. Two machine learning algorithms, Random Forests (RF) and support vector machine (SVM), were implemented to downscale the yearly Tropical Rainfall Measuring Mission 3B43 V7 (TRMM 3B43 V7) precipitation data from 25 km to 1 km over the Tibetan Plateau area, and the downscaled results were validated on the basis of observations from meteorological stations and comparisons with previous downscaling algorithms. According to the validation results, the RF and SVM-based models produced higher accuracy than the exponential regression (ER) model and multiple linear regression (MLR) model. The downscaled results also had higher accuracy than the original TRMM 3B43 V7 dataset. Moreover, models including land surface temperature variables (LSTs) performed better than those without LSTs, indicating the significance of considering precipitation–land surface temperature when downscaling TRMM 3B43 V7 precipitation data. The RF model with only NDVI and DEM produced much worse accuracy than the SVM model with the same variables. This indicates that the Random Forests algorithm is more sensitive to LSTs than the SVM when downscaling yearly TRMM 3B43 V7 precipitation data over Tibetan Plateau. Moreover, the precipitation–LSTs relationship is more instantaneous, making it more likely to downscale precipitation at a monthly or weekly temporal scale.

Monitoring land-use and land-cover change (LUCC) is extremely important in the sustainable development and management of terrestrial ecosystems. Taking the Heilongjiang (Amur) River Basin as the study area, this study aimed to identify the spatiotemporal characteristics of land cover at the entire basin and at the country levels of the three countries using the land-use change index, frequency statistics and land-cover change degree. The results showed that: (1) The LULC types were mainly forest land and grassland (accounting for nearly 83% in total) from 2001 to 2019. The main land-cover types in China, Russia and Mongolia were forest land, forest land and grassland, respectively. (2) The area of urban land, cropland and wetland increased significantly from 2001 to 2019, while the area of forest land and bare land decreased during this time. In general, the ecological environment has greatly improved over the last 19 years, although the relevant ecological restoration still needs to be further implemented. The findings can provide a scientific basis for ecological protection and sustainable development in the Heilongjiang (Amur) River Basin. The approaches here are also applicable to land-cover change research in other similar regions.

BiVO4 as a promising semiconductor absorber is widely investigated as photoanode in photoelectrochemical water splitting. Herein, the rational design of 3D hierarchical ternary SnO2/TiO2/BiVO4 arrays is reported as photoanode for photoelectrochemical application, in which the SnO2 hierarchically hollow microspheres core/nanosheets shell arrays act as conductive skeletons, while the sandwiched TiO2 and surface BiVO4 are working as hole blocking layer and light absorber, respectively. Arising to the hierarchically ordered structure and synergistic effect between each component in the composite, the ternary SnO2/TiO2/BiVO4 photoanode enables high light harvesting efficiency as well as enhanced charge transport and separation efficiency, yielding a maximum photocurrent density of ≈5.03 mA cm−2 for sulfite oxidation and ≈3.1 mA cm−2 for water oxidation, respectively, measured at 1.23 V versus reversible hydrogen electrode under simulated air mass (AM) 1.5 solar light illumination. The results reveal that electrode design and interface engineering play important roles on the overall PEC performance. A novel 3D hierarchical ternary SnO2/TiO2/BiVO4 arrays photoanode is designed with excellent photoelectrochemical performance.

Christian Schaaf, Manuel Gonzalez-Garay and colleagues report the identification of four individuals with truncating mutations on the paternal allele of MAGEL2 , a gene within the imprinted domain linked to Prader-Willi syndrome (PWS). The four individuals have PWS or PWS-related phenotypes, and all have autism. Prader-Willi syndrome (PWS) is caused by the absence of paternally expressed, maternally silenced genes at 15q11-q13. We report four individuals with truncating mutations on the paternal allele of MAGEL2 , a gene within the PWS domain. The first subject was ascertained by whole-genome sequencing analysis for PWS features. Three additional subjects were identified by reviewing the results of exome sequencing of 1,248 cases in a clinical laboratory. All four subjects had autism spectrum disorder (ASD), intellectual disability and a varying degree of clinical and behavioral features of PWS. These findings suggest that MAGEL2 is a new gene causing complex ASD and that MAGEL2 loss of function can contribute to several aspects of the PWS phenotype.

In the later-line setting or for patients with PD-L1-negative tumors, immunotherapy-based regimens remain ineffective against advanced triple-negative breast cancer (TNBC). In this multicentered phase II trial (NCT04303741), 46 patients with pretreated advanced TNBC were enrolled to receive camrelizumab 200 mg (day 1), and apatinib 250 mg daily, plus eribulin 1.4 mg/m 2 (day 1 and 8) on a 21-day cycle until progression, or unacceptable toxicity. Primary endpoint was objective response rate (ORR) according to RECIST 1.1. Secondary endpoints included toxicities, disease control rate (DCR), clinical benefit rate, progression-free survival (PFS), and 1-year overall survival. With a median of 3 lines of prior chemotherapy in the advanced setting, 17.4% had received PD-1/PD-L1 blockade plus chemotherapy for advanced disease. The ORR was 37.0% (17/46, 95% CI 23.2–52.5). The DCR was 87.0% (40/46, 95% CI 73.7–95.1). Median PFS was 8.1 (95% CI 4.6–10.3) months. Tertiary lymphoid structure was associated with higher ORR. Patients with lower tumor PML or PLOD3 expression had favorable ORR and PFS. PD-L1 status was not associated with ORR/PFS. Grade 3/4 treatment-related adverse events occurred in 19 (41.3%) of 46 patients. Camrelizumab plus apatinib and eribulin shows promising efficacy with a measurable safety profile in patients with heavily pretreated advanced TNBC. Therapeutic options for patients with triple-negative breast cancer (TNBC) in later-line setting are limited. Here the authors report the results of a phase 2 clinical trial to evaluate efficacy and safety of the combination of camrelizumab (anti-PD1), apatinib (VEGFR2 inhibitor), and eribulin in patients with heavily pre-treated advanced TNBC.

Environmental monitoring of Earth from space has provided invaluable information for understanding land–atmosphere water and energy exchanges. However, the use of satellite-based precipitation observations in hydrologic and environmental applications is often limited by their coarse spatial resolutions. In this study, we propose a downscaling approach based on precipitation–land surface characteristics. Daytime land surface temperature, nighttime land surface temperature, and day–night land surface temperature differences were introduced as variables in addition to the Normalized Difference Vegetation Index (NDVI), the Digital Elevation Model (DEM), and geolocation (longitude, latitude). Four machine learning regression algorithms, the classification and regression tree (CART), the k-nearest neighbors (k-NN), the support vector machine (SVM), and random forests (RF), were implemented to downscale monthly TRMM 3B43 V7 precipitation data from 25 km to 1 km over North China for the purpose of comparison of algorithm performance. The downscaled results were validated based on observations from meteorological stations and were also compared to a previous downscaling algorithm. According to the validation results, the RF-based model produced the results with the highest accuracy. It was followed by SVM, CART, and k-NN, but the accuracy of the downscaled results using SVM relied greatly on residual correction. The downscaled results were well correlated with the observations during the year, but the accuracies were relatively lower in July to September. Downscaling errors increase as monthly total precipitation increases, but the RF model was less affected by this proportional effect between errors and observation compared with the other algorithms. The variable importances of the land surface temperature (LST) feature variables were higher than those of NDVI, which indicates the significance of considering the precipitation–land surface temperature relationship when downscaling TRMM 3B43 V7 precipitation data.