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IntroductionCortinariaceae, which belongs to the Agaricales order, is a globally recognized family, known for its high species diversity.MethodsEight internal transcribed spacer (ITS) and nuclear large ribosomal subunit (LSU) sequences were newly generated, and phylogenetic analyses were performed by combining ITS and LSU datasets. Four species were identified as forming four independent lineages with robust support in phylogenies based on both datasets.ResultsThese new species in the taxa, Cortinarius gansuensis, Cortinarius tricholomoidus, Cortinarius vinoso-griseum, and Phlegmacium subcalyptratum from Northwestern China are described and illustrated based on morphological and molecular evidence. Cortinarius gansuensis is characterized by a convex and brownish vinaceous pileus, generative hyphae with clamp connections, and ellipsoid basidiospores (8.5–10.6 μm × 5.4–6.8 μm); Cortinarius tricholomoidus is characterized by a broadly umbonate and snuff brown pileus, generative hyphae with clamp connections, and broadly ellipsoid to subglobose basidiospores (7.4–8.5 μm × 6.2–7.3 μm); Cortinarius vinoso-griseum is characterized by a violaceous gray pileus, generative hyphae with clamp connections, and smaller basidiospores (7.5–9.7 μm × 5.6–7.8 μm); and Phlegmacium subcalyptratum is characterized by a small and apricot-orange pileus, generative hyphae with clamp connections, and fusiform basidiospores (10.0–12.7 μm × 5.6–6.8 μm).DiscussionFull descriptions, illustrations, and results of phylogenetic analyses of the four species along with discussions on related species are provided.

Abstract The unique physical tumor microenvironment (TME) and aberrant immune metabolic status are two obstacles that must be overcome in cancer immunotherapy to improve clinical outcomes. Here, an in situ mechano‐immunometabolic therapy involving the injection of a biomimetic hydrogel is presented with sequential release of the anti‐fibrotic agent pirfenidone, which softens the stiff extracellular matrix, and small interfering RNA IDO1, which disrupts kynurenine‐mediated immunosuppressive metabolic pathways, together with the multi‐kinase inhibitor sorafenib, which induces immunogenic cell death. This combination synergistically augmented tumor immunogenicity and induced anti‐tumor immunity. In mouse models of clear cell renal cell carcinoma, a single‐dose peritumoral injection of a biomimetic hydrogel facilitated the perioperative TME toward a more immunostimulatory landscape, which prevented tumor relapse post‐surgery and prolonged mouse survival. Additionally, the systemic anti‐tumor surveillance effect induced by local treatment decreased lung metastasis by inhibiting epithelial‐mesenchymal transition conversion. The versatile localized mechano‐immunometabolic therapy can serve as a universal strategy for conferring efficient tumoricidal immunity in “cold” tumor postoperative interventions.

Cut blasting is the key technology for roadway blasting excavation. High confining pressure has obvious inhibition effect on cut blasting effect, while empty hole is of great significance to improve cut blasting efficiency, but the relevant research is not sufficient so far. To study the crushing effect of the cut blasting with different empty hole diameters under the confining pressure, cubic cement mortar test blocks whose side length was 600 mm were made with sand cement ratio of 2:1. The test blocks were subjected to equal bi-axial confining pressure through an electro-hydraulic servo loading system, and the parallel cut blasting cement mortar physical model test was carried out. After the blasting fragmentation was screened and statistically analyzed, the fragmentation distribution and average particle size of model fragments under different empty hole diameters were analyzed and investigated. With fractal features of fragments characterized by fractal dimension, the fragmentation degree was quantitatively analyzed. ANSYS/LS–DYNA software was used to establish numerical models based on the parameters of the test model, and the stress contour at different times with different hole diameters was analyzed. The research result shows that, when two empty holes are charged with different charge of 2 g and 3 g, respectively, the maximum fractal dimension of the model fragment can be attained with empty holes in diameter of 24 mm and 16 mm, respectively, with the particle size of the fragment mainly concentrated in the middle segment between 19 to 75 mm, and the largest total mass of the fragment is 3.128 kg and 3.615 kg, respectively; When the sizes of empty holes are different, the stress concentration effect and the reflected tensile wave produced near the empty hole are also different, which brings the difference of cut blasting fragmentation effect and can effectively benefit from the empty hole effect. The peak value and action range of stress wave near the empty hole are bigger, with a long action time, suggesting a better crushing effect; the diameter of the empty hole is too small, the stress around the empty hole is small. An oversized empty hole will lead to the overflow of the stress wave from the hole. The compressive stress is small after the superposition of stress concentration, and the action area of tensile stress is also small. The proportion of large rock pieces will be high with extremely large or small empty hole. When charged with 3 g, the fractal dimension difference between empty hole model fragments of 32 mm and 16 mm is greater than the difference of model fragments between 16 mm and without empty hole, indicating that the influence on the fragmentation of rock is greater when the empty hole diameter is too large. When the empty hole diameter is the same as 16 mm, the average particle size of the fragments in the model charged with 3 g is less than that in the model charged with 2 g, with the greater fractal dimension and total mass of the fragments than that in the test model charged with 2 g. This shows that an increased charge can contribute to rock fragmentation and the volume of the cut cavity with the same diameter of empty holes.

Owing to advancements in satellite remote sensing technology, the acquisition of global land surface parameters, notably, the leaf area index (LAI), has become increasingly accessible. The Sentinel-2 (S2) satellite plays an important role in the monitoring of ecological environments and resource management. The prevalent use of the 20 m spatial resolution band in S2-based inversion models imposes significant limitations on the applicability of S2 data in applications requiring finer spatial resolution. Furthermore, although a substantial body of research on LAI retrieval using S2 data concentrates on agricultural landscapes, studies dedicated to forest ecosystems, although increasing, remain relatively less prevalent. This study aims to establish a viable methodology for retrieving 10 m resolution LAI data in forested regions. The empirical model of the soil adjusted vegetation index (SAVI), the backpack neural network based on simulated annealing (SA-BP) algorithm, and the variational heteroscedastic Gaussian process regression (VHGPR) model are established in this experiment based on the LAI data measured and the corresponding 10 m spatial resolution S2 satellite surface reflectance data in the Saihanba Forestry Center (SFC). The LAI retrieval performance of the three models is then validated using field data, and the error sources of the best performing VHGPR models (R2 of 0.8696 and RMSE of 0.5078) are further analyzed. Moreover, the VHGPR model stands out for its capacity to quantify the uncertainty in LAI estimation, presenting a notable advantage in assessing the significance of input data, eliminating redundant bands, and being well suited for uncertainty estimation. This feature is particularly valuable in generating accurate LAI products, especially in regions characterized by diverse forest compositions.

Three new wood-inhabiting fungal species, Cerioporus yunnanensis, Perenniporiopsis sinensis, and Sarcoporia yunnanensis, are proposed based on a combination of the morphological features and molecular evidence. Cerioporus yunnanensis is characterized by the pileate basidiomata having a fawn brown to black pileal surface, a dimitic hyphal system with clamped generative hyphae, and the presence of the fusoid cystidioles and cylindrical basidiospores (9–12.5 × 3.5–5 µm). Perenniporiopsis sinensis is distinct from the osseous pileus with verrucose, an orange-yellow to dark reddish-brown pileal surface with a cream margin, a trimitic hyphal system with clamped generative hyphae, and the presence of the fusiform cystidioles and ellipsoid basidiospores (9–11 × 5.5–6.5 µm). Sarcoporia yunnanensis is typical of the pileate basidiomata with a salmon to reddish-brown pileal surface, a monomitic hyphal system with clamped generative hyphae, and the presence of the ellipsoid basidiospores (4–5.5 × 2.5–4 µm). Sequences of ITS + nLSU + mt-SSU + TEF1 + RPB1 + RPB2 genes were used for the phylogenetic analyses using maximum likelihood, maximum parsimony, and Bayesian inference methods. The multiple genes with six loci analysis showed that the three new species nested within the order Polyporales, in which C. yunnanensis and P. sinensis nested into the family Polyporaceae, and S. yunnanensis grouped into the family Sarcoporiaceae.

In this present study, three new wood-inhabiting fungal taxa, Hyphoderma niveomarginatum, H. sordidum and H. weishanense, are proposed. Hyphoderma niveomarginatum is characterized by the ceraceous basidiomata having a smooth, cracking hymenial surface and the presence of the moniliform cystidia and ellipsoid basidiospores (7–9 × 3.5–5 µm). Hyphoderma sordidum is characterized by its resupinate basidiomata with a smooth hymenial surface with the fimbriate margin, the presence of the tubular cystidia and ellipsoid basidiospores (3–4.5 × 2–3 µm). Hyphoderma weishanense differs in its membranous basidiomata with a slightly buff to buff hymenial surface and the presence of broadly ellipsoid basidiospores (4.5–8.5 × 4–7 µm). Sequences of ITS+nLSU+mt-SSU+RPB1+RPB2 genes were used for the phylogenetic analyses using three methods. The ITS+nLSU+mt-SSU+RPB1+RPB2 analysis of the genus Hyphoderma indicated that the 3 new species of Hyphoderma were nested into genus Hyphoderma, in which H. niveomarginatum formed a single group and then grouped with H. membranaceum and H. sinense; H. sordidum was a sister to H. nudicephalum; and H. weishanense closely grouped with H. crystallinum.

Vegetation, especially forest ecosystems, plays an important role in the global energy flow and material cycle. The vegetation index (VI) is an important index reflecting the dynamic change in vegetation and directly reflects the response of ecosystem to global climate change. The Greater Khingan Mountains Forest region is located in the northeast of China. It is the largest primeval forest region in China, which is well preserved and less affected by human activities. It is of great significance to study the driving mechanism of forest vegetation change for future ecological prediction and management. In this study, GIMMS NDVI data were used to explore the characteristics of nonlinear temporal and spatial variation of NDVI in the Greater Khingan Mountains and its relationship with climatic factors. Firstly, the EEMD method was used to analyze the characteristics of vegetation change in the study area from 1982 to 2015. Secondly, the relationship between vegetation change and climate was discussed by using precipitation and temperature data. The results showed that the following: (1) from 1982 to 2015, the interannual change in vegetation in the Greater Khingan Mountains presented a trend of slow fluctuation and gradual decrease (SLOPE = −0.1645/10,000, p < 0.01). (2) The spatial distribution of vegetation change had obvious geographical differences, and in the central region, the overall distribution characteristics had an obvious browning trend, and in the northwest and southeast, the distribution characteristics had a green trend. (3) The correlation analysis results of vegetation change and climate factors showed that NDVI change was significantly positively correlated with temperature and precipitation; additionally, NDVI change was more correlated with temperature with a range of 0.8–1 than precipitation. (4) The results of vegetation attribution analysis in four typical areas of the study area showed that the following: the coniferous forest area has good cold tolerance and drought tolerance, the correlation between vegetation change and climate factors (temperature, precipitation) was not the strongest, which was 0.537 and 0.828, respectively. The ecological transition area and the broad-leaved forest area, which was located at the edge of the study area, have relatively fragile ecosystems, showed a strong correlation with precipitation, and the correlation coefficients reached 0.670 and 0.632, respectively. The surface water resources provide favorable conditions for the growth of vegetation, it showed a weak correlation with precipitation, and the correlation coefficient was 0.5349.

Land surface albedo (LSA) is one of the driving factors in the energy balance of surface radiation and the interaction between the earth and atmosphere. LSA is an important parameter that is widely used in surface energy balance, medium- and long-term weather forecasting, and global change studies. GF-1 wide field view (WFV) data provide a spatial resolution of 16 m and temporally intensive land surface observations, but efficient algorithm was still lacking for quantitatively land surface parameters estimation. It is essential to improve the data use ability by generating efficient land surface parameter retrieval algorithms. This study proposed an LSA retrieval algorithm by using GF-1 WFV data. Land surface bidirectional reflectance distribution function characteristic parameters were used to represent the non-Lambertian characteristic of land surface. The top of atmosphere (TOA) reflectance is simulated by the 6S radiative transfer model by considering non-Lambertian land surfaces. Linear regression is applied in the TOA reflectance, and LSA is simulated with the surface bidirectional reflectance characteristic parameters to build a lookup table. The proposed algorithm can estimate LSA with high accuracy according to the TOA reflectance without the complex multistep inversion process. The validation results of ground measurements in Northwest China for different land cover types show that the algorithm is effective, and the overall root mean square error was 0.036 when compared with field observation. The algorithm also shows great consistency with Landsat albedo data. The proposed algorithm is of great significance for improving GF data utilization.

Clear cell renal cell carcinoma (ccRCC) is a lethal urological malignancy. DNA methylation is involved in the regulation of ccRCC occurrence and progression. This study aimed to establish a prognostic model based on DNA methylation to predict the overall survival (OS) of patients with ccRCC. To create this model, we used the transcriptome and DNA methylation data of patients with ccRCC from The Cancer Genome Atlas (TCGA) database. We then used the MethylMix R package to identify methylation-driven genes, and LASSO regression and multivariate Cox regression analyses established the prognostic risk model, from which we derived risk scores. We incorporated these risk scores and clinical parameters to develop a prognostic nomogram to predict 3-, 5-, and 7-year overall survival, and its predictive power was validated using the ArrayExpress cohort. These analyses identified six methylation-driven genes (SAA1, FUT6, SPATA18, SHROOM3, AJAP1, and NPEPL1) that produced risk scores, which were sorted into high- and low-risk patient groups. These two groups differed in nomogram-predicted prognosis, the extent of immune cell infiltration, tumor mutational burden, and expected response to additional therapies. In conclusion, we established a nomogram based on six DNA methylation-driven genes with excellent accuracy for prognostic prediction in ccRCC patients. This nomogram model might provide novel insights into the epigenetic mechanism and individualized treatment of ccRCC.

BIM cost, owner's punishment intensity, liquidated damage, size and allocation ratio of incentive pool, Integrated Project Delivery (IPD) gain, loss caused by BIM data resource sharing and other variable parameters are introduced into tripartite evolutionary game model which is established based on evolutionary game theory. Tripartite behavioral strategies are analyzed by evolutionary game theory. The stability of system equilibrium and influence of variable parameters on the stability of evolutionary games are discussed. The tripartite dynamic evolutionary game law based on collaborative behavior under the premise of limited rationality is studied. Then, the tripartite game pathways are numerically simulated by using MATLAB software. The influence of initial intention and variable parameters on the process and results of system evolution are analyzed. The dynamic mechanism of owner's supervision on contractor's and designer's collaborative behavior strategies is clarified. It shows that key point of driving participants to choose collaborative behavior is on owner side.

The cutting blasting plays a key role in rock excavation construction, which determines the blasting effect and efficiency of the entire blasting project. In the cutting blasting, parallel holes are often used as the auxiliary free surface and the compensation space of blasting rock, and the empty holes have a great influence on the blasting effect. In this paper, Ansys/Ls-Dyna finite element analysis software is carried out to simulate four models with different number of empty holes. The simulation results show that the stronger the guiding effect of the empty holes on the crack propagation, the more obvious the inhibition effect on the crack in the remaining direction. The initial crack near the empty hole is generated by the continuous action of the stress wave, and the empty hole promotes the propagation of the explosion stress wave. The inconsistent guiding directions of adjacent empty holes are one of the reasons for the unsatisfactory blasting effect of multiple small diameter empty holes. The closer the empty hole is to the blasthole, the larger the maximum principal stress. By comparing the results of calculation with the numerical simulation, it is verified that the maximum principal stress near the empty hole is similar and the change rule is consistent. The above research has reference meaning to the location of the hollow hole in the actual blasting construction and the density of the empty hole.

Background: Ferroptosis is a potential target for cancer therapy, and lncRNAs can also affect ferroptosis by regulating related genes. The pathogenesis of clear cell renal cell carcinoma (ccRCC) regarding the regulation of ferroptosis by lncRNAs is still unknown.Methods: We constructed a risk model based on data in ccRCC patients obtained from the TCGA database and validated the diagnostic and prognostic value of the model. In addition, immune function and immune checkpoint variability analysis validated the association of ferroptosis with ccRCC tumor immunity.Results: The characteristics of ferroptosis-related lncRNAs (FRLs) were significantly correlated with the prognosis of ccRCC patients. The prognostic characteristics of FRLs were independent prognostic factors in ccRCC patients. Gene function in the high-risk group was associated with oxygen metabolic processes and immune pathways. Immune checkpoint variability analysis showed that HAVCR2, NRP1, and HHLA2 were upregulated in the low-risk group, while CD44, TNFRSF18, TNFSF14, TNFRSF8, CD276, and TNFRSF25 were upregulated in the high-risk group.Conclusions: The prognostic characteristics of FRLs can effectively predict the prognosis of ccRCC patients and provide a new direction for clinical treatment.

BackgroundThis study aimed to investigate risk factors and prognostic factors in patients with clear cell renal cell carcinoma (ccRCC) with bone metastasis (BM) and establish nomograms to provide a quantitative prediction of the risk of BM and survival probability.MethodsThe clinicopathological characteristics of patients with ccRCC between January 2010 and December 2015 were obtained from the Surveillance, Epidemiology and End Results (SEER) database. Independent factors for BM in ccRCC patients were identified using univariate and multivariate logistic regression analyses. Prognostic factors for predicting cancer-specific death were evaluated using univariate and multivariate analyses based on a competing risk regression model. We then constructed a diagnostic nomogram and a prognostic nomogram. The two nomograms were evaluated using calibration curves, receiver operating characteristic curves, and decision curve analysis.ResultsOur study included 34,659 patients diagnosed with ccRCC in the SEER database, with 1,415 patients who presented with bone metastasis. Risk factors for BM in patients with ccRCC included age, stage T, stage N, brain metastasis, liver metastasis, lung metastasis, tumor size, and laterality. Independent prognostic factors for patients with ccRCC patients with BM were Fuhrman grade, tumor size, T stage, N stage, brain metastases, lung metastasis, and surgery. For the diagnostic nomogram, the area under the curve values in the training and testing cohorts were 0.863 (95% CI, 0.851–0.875) and 0.859 (95% CI, 0.839–0.878), respectively. In the prognostic cohort, the area under the curve values for 1-, 2-, and 3-year cancer-specific survival rates in the training cohort were 0.747, 0.774, and 0.780, respectively, and 0.671, 0.706, and 0.696, respectively, in the testing cohort. Through calibration curves and decision curve analyses, the nomograms displayed excellent performance.ConclusionsSeveral factors related to the development and prognosis of BM in patients with ccRCC were identified. The nomograms constructed in this study are expected to become effective and precise tools for clinicians to improve cancer management.

IntroductionUrinary stones cause hydronephrosis, which leads to kidney function impairment. The serum creatinine level is frequently used as a marker of kidney function. However, in some patients with hydronephrosis, it does not reflect the kidney function changes in the early stages of kidney stone disease. Neutrophil gelatinase-associated lipocalin (NGAL) is a novel indicator of the kidney function. Previous NGAL-related research has focused on its role in acute kidney injury. This study aimed to determine the usefulness of NGAL as an early marker of the kidney function in patients with urinary stones and hydronephrosis.MethodsEighty-five patients with urinary stones who were admitted to the Shanghai Tenth People's Hospital (USP group) and 65 healthy volunteers (NC group) were recruited. Blood and urine samples collected from the study participants were evaluated using an enzyme-linked immunosorbent assay to determine the NGAL concentration. Data on the height, weight, age, medical history, and blood and urine findings were collected. Computed tomography data were collected from the USP group.ResultsCompared to in the NC group, NGAL levels were significantly elevated in the USP group (P < 0.001). However, no significant differences in the NGAL levels were observed among the USP group members with different degrees of hydronephrosis. Furthermore, no significant between-group differences in the creatinine level or the estimated glomerular filtration rate were observed. The areas under the receiver operating characteristic curves for the serum and urinary NGAL levels with hydronephrosis were 92.03 and 99.54%, respectively. The areas under the receiver operating characteristic curves for the serum and urinary NGAL levels with kidney stones were 85.05 and 91.89%, respectively.ConclusionNGAL is a sensitive indicator of hydronephrosis secondary to urinary stones.

ObjectivesTo evaluate the in-hospital and later outcomes of thoracic endovascular aortic repair (TEVAR) for type B intramural hematoma (TBIMH) combined with an aberrant subclavian artery (aSCA).MethodsIn the period from January 2014 to December 2020, 12 patients diagnosed with TBIMH combined with aSCA and treated by TEVAR were enrolled in this retrospective cohort study, including 11 patients with the aberrant right subclavian artery (ARSA) and 1 with an aberrant left subclavian artery (ALSA). A handmade fenestrated stent-graft or chimney stent or hybrid repair was performed when the proximal landing zone was not enough.ResultsThe mean age of all the patients was 59.2 ± 7.6 years, and 66.7% of patients were men. There were 4 patients with Kommerell's diverticulum (KD). The procedures in all 12 patients were technically successful. There was one case each of postoperative delirium, renal impairment, and type IV endoleak after TEVAR. During follow-up, 1 patient died of acute pancreatitis 7 months after TEVAR. The overall survival at 1, 3, and 5 years for the patients was 90.9, 90.9, and 90.9%, respectively. KD was excluded in 2 patients, and the handmade fenestrated stent-graft was applied in the other 2 patients to preserve the blood flow of the aSCA. No neurological complications developed and no progression of KD was observed during the follow-up.ConclusionThoracic endovascular aortic repair for patients with aSCA and TBIMH is promising. When KD was combined, we could exclude KD or preserve the blood flow of aSCA with regular follow-up for the diverticulum according to the size of the KD.

The land surface albedo (LSA) represents the ability of the land surface to reflect solar radiation. It is one of the driving factors in the energy balance of land surface radiation and in land–air interactions. In this paper, we estimated the land surface albedo based on GF-1 WFV satellite data that have a high spatial and temporal resolution and cross-validated the albedo estimation results. The albedo estimations and validations were performed in the Ganzhou District, Zhangye City, China, and the Sindh Province, Pakistan. We used the direct estimation method which used a radiative transfer simulation to establish the relationship between the narrow band top of the atmosphere bidirectional reflectance and the land broadband albedo to estimate the albedo data. The results were validated with ground data, Landsat data, MODIS products, and GLASS products. The results show that the method can produce highly accurate albedo estimation results on different land cover types (RMSE: 0.026, R2: 0.835) and has a good consistency with the existing albedo products. This study makes a significant contribution to improving the utilization of GF data and contributes to the understanding of land–air interactions.

With the decrease and depletion of shallow coal resources, the depth of mining is increasing. The mechanism of high crustal stress blasting is not clear, and the effect of crustal stress on blasting effect is obvious. The law of the differential detonation is similar without crustal stress. The crustal stress of rock masses increases linearly with the increase in excavation depth, and the influence of crustal stress on blasting effect is great. In order to study the rock-breaking process of complex differential blasting under deep high crustal stress, the instantaneous detonation of each model and the priority detonation of the central hole are numerically simulated. The evolution law of the blasting crack and the method of optimizing blasting effect by differential blasting and increasing the aperture of the priority detonation hole under high crustal stress are put forward. The authors proposed a study on the 2D optimization simulation of complex five-hole cutting blasting under different lateral pressure coefficients of 400 m and 800 m with software ANSYS/LS-DYNA and analyzed the evolution rules of blasting cracks and lateral pressure coefficients. The results show that setting delayed detonation and hole diameter and optimizing blasthole spacing can optimize the rock-breaking effect under high crustal stress and different lateral pressure coefficients.

This report is related to the research article entitled “B cell phenotypes in baboons with pig artery patch grafts receiving conventional immunosuppressive therapy” (Yamamoto et al., in press). Herein we provide the data regarding pig artery patch xenotransplantation into the baboon׳s aorta, trough levels of tacrolimus and rapamycin in the blood after transplantation, analysis of B cell phenotype on the basis of IgD and CD27 expression in the blood, and analysis of T cell phenotype on the basis of CD28 and CD95 expression in the blood.

Land surface albedo (LSA) is an important parameter that affects surface–air interactions and controls the surface radiation energy budget. The spatial and temporal variation characteristics of LSA reflect land surface changes and further influence the local climate. Ganzhou District, which belongs to the middle of the Hexi Corridor, is a typical irrigated agricultural and desert area in Northwest China. The study of the interaction of LSA and the land surface is of great significance for understanding the land surface energy budget and for ground measurements. In this study, high spatial and temporal resolution GF-1 wide field view (WFV) data were used to explore the spatial and temporal variation characteristics of LSA in Ganzhou District. First, the surface albedo of Ganzhou District was estimated by the GF-1 WFV. Then, the estimated results were verified by the surface measured data, and the temporal and spatial variation characteristics of surface albedo from 2014 to 2018 were analyzed. The interaction between albedo and precipitation or temperature was analyzed based on precipitation and temperature data. The results show that the estimation of surface albedo based on GF-1 WFV data was of high accuracy, which can meet the accuracy requirements of spatial and temporal variation characteristic analysis of albedo. There are obvious geographic differences in the spatial distribution of surface albedo in Ganzhou, with the overall distribution characteristics being high in the north and low in the middle. The interannual variation in annual average surface albedo in Ganzhou shows a trend of slow fluctuations and gradual increases. The variation in annual albedo is characterized by “double peaks and a single valley”, with the peaks occurring from December to February at the end and beginning of the year, and the valley occurring from June to August. Surface albedo was negatively correlated with precipitation and temperature in most areas of Ganzhou.

The leaf area index (LAI) is a critical parameter for characterizing the structure and function of vegetation in ecosystems. Currently, operational LAI products always have coarse spatial resolution, and fine-resolution LAI maps are urgently needed for ecological environment assessment and the precise monitoring of cropland growth. LAI downscaling methods are efficient at improving the spatial resolution of LAI products but often ignore the scaling effect of the model. In this study, a novel model-downscaling method is proposed for fine-resolution LAI estimation. It uses scaling equations of model parameters (SEMPs) to describe the scaling relations of models at different spatial resolutions and construct a downscaled model from a coarse-resolution model. Landsat Normalized Difference Vegetation Index (NDVI) at 30 m and Global LAnd Surface Satellite (GLASS) LAI at 1 km spatial resolutions are used because they are readily available. The downscaled model is evaluated by a fine-resolution model directly constructed with fine-resolution data. The fine-resolution LAI values estimated by this model-downscaling method are evaluated with field LAI measurements. The validation results show that the proposed method can generate highly accurate LAIs, with an RMSE of 0.821 at the Pshenichne cropland site in Ukraine and an RMSE of 0.515 at the Camerons forest site in Australia when compared with field LAI measurements. The results are also better than those of Ovakoglou’s downscaling method. These results demonstrate that the model-downscaling method for fine-resolution LAI estimation is viable and referable for related studies.

The leaf area index (LAI) is an important parameter for vegetation monitoring and land surface ecosystem research. Although a variety of LAI products have been generated, the moderate to coarse spatial resolution and low temporal resolution of these products are insufficient for regional-scale analysis. In this study, a modified ensemble Kalman filter model (MEnKF) was proposed to generate spatio-temporal complete 30 m LAI data. High-quality, filtered historical Moderate-resolution Imaging Spectroradiometer (MODIS) LAI data were used to obtain the LAI background, and an LAI temporal dynamic model was constructed based on it. An improved back-propagation (BP) neural network based on a simulated annealing algorithm (SA-BP) was constructed with paired Landsat surface reflectance data and field LAI data to generate a 30 m LAI. The MEnKF was used to estimate the spatio-temporal complete LAI beginning from the LAI peak value position where Landsat observations were available. The spatio-temporal 30 m LAI was estimated in farmland (Pshenichne), grassland (Zhangbei), and woodland (Genhe) sites. The results indicate that the MEnKF-estimated LAI is consistent with the field measurements for all sites (the coefficient of determination ( R 2 ) = 0.70; root mean squared error (RMSE) = 0.40) and is better than that of the conventional sequence data assimilation algorithm ( R 2 = 0.40; RMSE = 0.78). The regional LAI captures the vegetation growth pattern and is consistent with the Landsat LAI, with an R 2 larger than 0.65 and an RMSE less than 0.51. The proposed MEnKF algorithm, which effectively avoids error accumulation in the data assimilation scheme, is an efficient method for spatio-temporal complete 30 m LAI estimation.

As an important economic resource, rubber has rapidly grown in Xishuangbanna of Yunnan Province, China, since the 1990s. Tropical rainforests have been replaced by extensive rubber plantations, which has resulted in ecological problems such as the loss of biodiversity and local water shortages. It is vitally important to accurately map the rubber plantations in this region. Although several rubber mapping methods have been proposed, few studies have investigated methods based on optical remote sensing time series data with high spatio-temporal resolution due to the cloudy and foggy weather conditions in this area. This study presented a rubber plantation identification method that used spatio-temporal optical remote sensing data fusion technology to obtain vegetation index data at high spatio-temporal resolution within the optical remote sensing window in Xishuangbanna. The analysis of the proposed method shows that (1) fused optical remote sensing data with high spatio-temporal resolution could map the rubber distribution with high accuracy (overall accuracy of up to 89.51% and kappa of 0.86). (2) Fused indices have high R2 (R2 greater than 0.8, where R is the correlation coefficient) with the indices that were derived from the Landsat observed data, which indicates that fusion results are dependable. However, the fusion accuracy is affected by terrain factors including elevation, slope, and slope aspects. These factors have obvious negative effects on the fusion accuracy of high spatio-temporal resolution optical remote sensing data: the highest fusion accuracy occurred in areas with elevations between 1201 and 1400 m.a.s.l., and the lowest accuracy occurred in areas with elevations less than 600 m.a.s.l. For the 5 fused time series indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and tasseled cap angle (TCA)), the fusion accuracy decreased with increasing slope, and increasing slope had the least impact on the EVI, but the greatest negative impact on the NDVI; the slope aspect had a limited influence on the fusion accuracies of the 5 time series indices, but fusion accuracy was lowest on the northwest slope. (3) EVI had the highest accuracy of rubber plantation classification among the 5 time series indices, and the overall classification accuracies of the time series EVI for the four different years (2000, 2005, 2010, and 2015) reached 87.20% (kappa 0.82), 86.91% (kappa 0.81), 88.85% (kappa 0.84), and 89.51% (kappa 0.86), respectively. The results indicate that the method is a promising approach for rubber plantation mapping and the detection of changes in rubber plantations in this tropical area.

Continuous, long-term sequence, land surface albedo data have crucial significance for climate simulations and land surface process research. Sensors such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer (VIIRS) provide global albedo product data sets with a spatial resolution of 500 m over long time periods. There is demand for new high-resolution albedo data for regional applications. High-resolution observations are often unavailable due to cloud contamination, which makes it difficult to obtain time series albedo estimations. This paper proposes an “amalgamation albedo„ approach to generate daily land surface shortwave albedo with 30 m spatial resolution using Landsat data and the MODIS Bidirectional Reflectance Distribution Functions (BRDF)/Albedo product MCD43A3 (V006). Historical MODIS land surface albedo products were averaged to obtain an albedo estimation background, which was used to construct the albedo dynamic model. The Thematic Mapper (TM) albedo derived via direct estimation approach was then introduced to generate high spatial-temporal resolution albedo data based on the Ensemble Kalman Filter algorithm (EnKF). Estimation results were compared to field observations for cropland, deciduous broadleaf forest, evergreen needleleaf forest, grassland, and evergreen broadleaf forest domains. The results indicated that for all land cover types, the estimated albedos coincided with ground measurements at a root mean squared error (RMSE) of 0.0085–0.0152. The proposed algorithm was then applied to regional time series albedo estimation; the results indicated that it captured spatial and temporal variation patterns for each site. Taken together, our results suggest that the amalgamation albedo approach is a feasible solution to generate albedo data sets with high spatio-temporal resolution.

Land surface albedo is a key parameter in regulating surface radiation budgets. The gridded remote sensing albedo product often represents information concerning an area larger than the nominal spatial resolution because of the large viewing angles of the observations. It is essential to quantify the spatial representativeness of remote sensing products to better guide the sampling strategy in field experiments and match products from different sources. This study quantifies the spatial representativeness of the MODerate Resolution Image Spectroradiometer (MODIS) (collection V006) 500 m daily albedo product (MCD43A3) using the high-resolution product as intermediate data for different land cover types. A total of 1820 paired high-resolution Landsat Thematic Mapper (TM) and coarse-resolution (MODIS) albedo data from five land cover types were used. The TM albedo data was used as the spatial-complete high resolution data to evaluate the spatial representativeness of the MODIS albedo product. Semivarioagrams were estimated from 30 m Landsat data at different spatial scales. Surface heterogeneity was evaluated with sill value and relative coefficient of variation. The 30 m Landsat albedo data was aggregated to 450 m–1800 m using two different methods and compared with MODIS albedo product. The spatial representativeness of MODIS albedo product was determined according to the surface heterogeneity and the consistency of MODIS data and the aggregated TM value. Results indicated that for evergreen broadleaf forests, deciduous broadleaf forests, open shrub lands, woody savannas and grasslands, the MODIS 500 m daily albedo product represents a spatial scale of approximately 630 m. For mixed forests and croplands, the representative spatial scale was approximately 690 m. The difference obtained was primarily because of the complexity of the landscape structure. For mixed forests and croplands, the structure of the landscape was relatively complex due to the presence of different forest and plant types in the pixel area, whereas the other landscape structures were considerably simpler.

Leaf area index (LAI) remote sensing data products with a high resolution (HR) and long time series are in demand in a wide variety of applications. Compared with long time series LAI products with 1 km resolution, LAI products with high spatial resolution are difficult to acquire because of the lack of remote sensing observations in long-term sequences and the lack of estimation methods applicable to highly variable land-cover types. To address these problems, we proposed a recursive update model to estimate 30 m resolution LAI based on the updated Nonlinear Auto-Regressive with Exogenous Inputs (NARX) neural network and MODIS time series. First, we used a variety of HR satellite remote sensing observations to produce HR datasets for recent years. Historical low spatial resolution MODIS products were employed as background information and used to calculate the initial parameters of the NARX neural network for each pixel. Subsequently, one year’s reflectance from the HR dataset was used as the new observation that was input into the NARX model to estimate the HR LAI of that year, and the background and HR data were then used for remodeling to update the NARX model parameters. This procedure was recursively repeated year by year until both MODIS background data and all HR data were involved in the modeling. Finally, we obtained an LAI time series with 30 m resolution. In the cropland study area in Hebei Province, China, the results were compared with LAI measurements from ground sites in 2013 and 2014. A high degree of similarity existed between the results for the two study years (RMSE2013=0.288 and RMSE2014=0.296). The HR LAI estimates showed favorable spatiotemporal continuity and were in good agreement with the multisample ground survey LAI measurements. The results indicated that for data with a rapid revisit cycle and high spatial resolution, the recursive update model based on the NARX neural network has excellent LAI estimation performance and fairly strong fault-tolerance capability.

Land surface shortwave broadband albedo is a key parameter in general circulation models and surface energy budget models. Multispectral satellite data are typically used to generate broadband albedo products in a three-step process: atmospheric correction, for converting the top-of-atmosphere observations to surface directional reflectance; angular modeling, for converting the surface directional reflectance to spectral albedo of each individual band; and finally, narrowband-to-broadband conversion, for transforming the spectral albedos to broadband albedos. Spectroradiometers can be used for validating surface directional reflectance products and pyranometers or broadband albedometers, for validating broadband albedo products, but spectral albedo products are rarely validated using ground measurements. In this study, we designed a new type of albedometer that can measure spectral albedos. It consists of multiple interference filters and a silicon detector, for measuring irradiance from 400–1100 nm. The linearity of the sensors is 99%, and the designed albedometer exhibits consistency up to 0.993, with a widely-used commercial instrument. A field experiment for measuring spectral albedo of grassland using this new albedometer was conducted in Yudaokou, China and the measurements are used for validating the MODerate Resolution Imaging Spectroradiometer (MODIS) spectral albedos. The results show that the biases of the MODIS spectral albedos of the first four bands are −0.0094, 0.0065, 0.0159, and −0.0001, respectively. This new instrument provides an effective technique for validating spectral albedos of any satellite sensor in this spectral range, which is critical for improving satellite broadband albedo products.

Large-scale forest disturbance often leads to changes in forest cover and structure, which imposes a great uncertainty in the estimation of the forest carbon cycle and biomass and affects other applications. In northeastern China, the Daxinganling region has abundant forest resources, where the forest coverage is about 30%. The Global LAnd Surface Satellite (GLASS) leaf area index (LAI) time series data provide important information to monitor the possible change of forests. In this study, we developed a new method to detect forest disturbances using GLASS LAI data over the Daxinganling region of Northeast China. As a dynamic model, the season-trend model has a higher sensitivity toward a seasonal change in LAI. Based on the accumulation of multi-year GLASS LAI products from 1997 to 2002, the dynamic model of LAI time series for each pixel is established first. The time-stepping modeling (TSM) process was designed by using the season-trend method, and sequential tests for detecting disturbances from a time series of pixels. Significant changes in the model parameters were captured as disturbance signals. Then, the near-infrared and shortwave-infrared bands of Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance are used as auxiliary information to distinguish the types of forest disturbances. Here, the algorithm led to the detection of two different types of disturbances: fire and other (e.g., insect, drought, deforestation). In this study, we took the forest region as the study area, used the 8-day composite GLASS LAI data at 1000-m spatial resolution to identify each pixel as a fire disturbance, other disturbance, or non-disturbance. Validation was performed using reference burned area data derived from Landsat 30 m imagery. Results were also compared with the MCD64 product. The validation results were based on confusion matrices showing the overall accuracy (OA) exceeded 92% for our method and the MCD64 product. Statistical tests identified that TSM’s product accuracy is higher than that of MCD64. This study demonstrated that the TSM algorithm using a season-trend model provides a simple and automated approach to identify and map forest disturbance.

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE) comprises a network of remote sensing experiments designed to enhance the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Two types of experimental campaigns were established under the framework of COMPLICATE. The first was designed for continuous and elaborate experiments. The experimental strategy helps enhance our understanding of the radiative and scattering mechanisms of soil and vegetation and modeling of remotely sensed information for complex land surfaces. To validate the methodologies and models for dynamic analyses of remote sensing for complex land surfaces, the second campaign consisted of simultaneous satellite-borne, airborne, and ground-based experiments. During field campaigns, several continuous and intensive observations were obtained. Measurements were undertaken to answer key scientific issues, as follows: 1) Determine the characteristics of spatial heterogeneity and the radiative and scattering mechanisms of remote sensing on complex land surfaces. 2) Determine the mechanisms of spatial and temporal scale extensions for remote sensing on complex land surfaces. 3) Determine synergist inversion mechanisms for soil and vegetation parameters using multi-mode remote sensing on complex land surfaces. Here, we introduce the background, the objectives, the experimental designs, the observations and measurements, and the overall advances of COMPLICATE. As a result of the implementation of COMLICATE and for the next several years, we expect to contribute to quantitative remote sensing science and Earth observation techniques.