Search

Cross-domain Ad Hoc network is a network that self-organizes nodes on different media and adapts to network topology. In cross-domain communication networks, direct measurement technology helps obtain accurate end-to-end network traffic information. However, the low computational power and low storage characteristics of some nodes in the cross-domain network hinder all nodes from running the network traffic measurement process. To address this issue, a network traffic measurement optimization method based on meta-learning and proximal policy optimization(PPO) was proposed. This method determined the set of nodes that performed network traffic measurement in the next time slot according to the network operating environment of the previous time slot, so as to perform the measurement process on as few nodes as possible to obtain as much network traffic information as possible. Three network datasets were used to verify the proposed method. The experimental results show that the traffic measurement optimization algorithm for cross-domain Ad Hoc networks based on meta-learning and reinforcement learning can effectively select the nodes with large traffic flow, with faster convergence speed and higher measurement efficiency.

Abstract Remote sensing technology has the potential to enhance the lake’s large-scale and long-term dynamic monitoring capabilities significantly. High-quality in-situ datasets are essential for improving the accuracy and reliability of remote sensing retrieval of lake ecosystems. This dataset provides satellite-ground synchronized in-situ data on water multi-parameters for typical lakes in China spanning the period between 2020 and 2023. It includes quality-checked water optical parameters (remote sensing reflectance (Rrs), chlorophyll-a (Chl-a), total suspended matter (TSM) and Secchi disk depth (SDD)), and water surface temperature (WST) data. It encompasses 586 sampling points across 18 lakes. The dataset exhibits two significant highlights: Firstly, synchronous observations from multiple satellites are coordinated during the data collection effectively supporting the retrieval and validation of water remote sensing products. Secondly, it encompasses diverse data types, collecting synchronous measurements of Rrs and various parameters. This dataset will continuously update, substantially enhancing regional and global lake monitoring capabilities through satellite remote sensing data.

Accurate coastline extraction is crucial for the scientific management and protection of coastal zones. Due to the diversity of ground object details and the complexity of terrain in remote sensing images, the segmentation of sea and land faces challenges such as unclear segmentation boundaries and discontinuous coastline contours. To address these issues, this study improve the accuracy and efficiency of coastline extraction by improving the DeepLabv3+ model. Specifically, this study constructs a sea-land segmentation network, DeepSA-Net, based on strip pooling and coordinate attention mechanisms. By introducing dynamic feature connections and strip pooling, the connection between different branches is enhanced, capturing a broader context. The introduction of coordinate attention allows the model to integrate coordinate information during feature extraction, thereby allowing the model to capture longer-distance spatial dependencies. Experimental results has shown that the model can achieves a land-sea segmentation mean intersection over union (mIoU) ration and Recall of over 99% on all datasets. Visual assessment results show more complete edge details of sea-land segmentation, confirming the model’s effectiveness in complex coastal environments. Finally, using remote sensing data from a coastal area in China as an application instance, coastline extraction and dynamic change analysis were implemented, providing new methods for the scientific management and protection of coastal zones.

Abstract Background Unlike in lower vertebrates, Müller glia (MG) in adult mammalian retinas lack the ability to reprogram into neurons after retinal injury or degeneration and exhibit reactive gliosis instead. Whether a transition in MG cell fate from gliosis to reprogramming would help preserve photoreceptors is still under exploration. Methods A mouse model of retinitis pigmentosa (RP) was established using MG cell lineage tracing mice by intraperitoneal injection of sodium iodate (SI). The critical time point for the fate determination of MG gliosis was determined through immunohistochemical staining methods. Then, bulk-RNA and single-cell RNA seq techniques were used to elucidate the changes in RNA transcription of the retina and MG at that time point, and new genes that may determine the fate transition of MG were screened. Finally, the selected gene was specifically overexpressed in MG cells through adeno-associated viruses (AAV) in the mouse RP model. Bulk-RNA seq technique, immunohistochemical staining methods, and visual function testing were used to elucidate and validate the mechanism of new genes function on MG cell fate transition and retinal function. Results Here, we found the critical time point for MG gliosis fate determination was 3 days post SI injection. Hmga2 was screened out as a candidate regulator for the cell fate transition of MG. After retinal injury caused by SI, the Hmga2 protein is temporarily and lowly expressed in MG cells. Overexpression of Hmga2 in MG down-regulated glial cell related genes and up-regulated photoreceptor related genes. Besides, overexpressing Hmga2 exclusively to MG reduced MG gliosis, made MG obtain cone’s marker, and retained visual function in mice with acute retinal injury. Conclusion Our results suggested the unique reprogramming properties of Hmga2 in regulating the fate transition of MG and neuroprotective effects on the retina with acute injury. This work uncovers the reprogramming ability of epigenetic factors in MG.

Fractional vegetation cover (FVC) is an essential parameter of vegetation canopy. Understanding its dynamics is vital for agricultural monitoring and climate change response. The physically based method for retrieving FVC from remote sensing data has great potential due to the theoretical basis of the radiative transfer model (RTM). However, the method is limited when applied to satellite imagery due to its uncertainty in simulating canopy reflectance. This article proposes a method that combines three-dimensional (3-D) RTM and convolutional neural network based transfer learning (CNN–TL) to address the inconsistency between simulated and satellite reflectance, improving maize FVC retrieval accuracy. First, 3-D RTM was employed to generate canopy reflectance datasets of maize at various growth stages. Second, CNN–TL is used to eliminate the discrepancy between 3-D RTM simulated reflectance and satellite reflectance, and the retrieval accuracy of CNN–TL is compared with random forest regression (RF) and CNN. Finally, the feasibility of the method was validated using time-series of measured data from multiple samples covering different growth stages of maize from 2021 to 2023. The results showed that, when retrieving maize FVC on GF-1, HJ-2, and Sentinel-2, CNN–TL performed the best (RMSE = 0.117, 0.063, and 0.081) compared to RF (RMSE = 0.186, 0.226, and 0.184) and CNN (RMSE = 0.133, 0.117, and 0.098). The spatial distribution of FVC maps remains highly consistent across all three satellites, indicating the exceptional performance of CNN–TL. These results contribute to the development of physically based methods for FVC retrieval and serve as a reference for multisource satellite studies.

Timely and accurate monitoring of large-scale crop harvest progress (CHP) is crucial for optimizing agricultural management. Remote sensing shows great potential, but most studies focus on small-scale analysis with limited features and single data sources, lacking large-area mapping. To address this, we developed a regional-scale CHP monitoring framework combining optical and radar data. We analyzed the response of features to corn harvest at Youyi Farm. We developed new indices and algorithms, achieving large-scale CHP mapping. Our findings: First, all 15 features respond to harvest, with shortwave infrared reflectance and the vegetation tillage index (VTI) being the most sensitive. Second, the current binary classification algorithm for mature corn (MC) and crushed straw (CS) is unsuitable for regions with plowed soil (PS). It often misclassifies PS as MC due to similar optical characteristics, causing inaccurate corn harvest monitoring. The new VTI and simple VTI solve this by better distinguishing MC, CS, and PS. Third, corn harvest progress monitoring shows high accuracy, with an overall accuracy of 0.99 for optical data and 0.95 for radar data. Fourth, combining Sentinel-1 and Sentinel-2 data increased the monitoring frequency from 6–8 days to an average of 2–3 days. Fifth, a time-series misclassification correction strategy was developed and applied, correcting an average area of 3.3% of the study area. Sixth, an earlier harvest start time in 2021, aligned with drought occurrences, confirms the feasibility of the CHP mapping framework. This research provides an effective tool for monitoring CHP, which can support agricultural management.

An in-depth exploration of fractional vegetation coverage (FVC) changes and driving mechanisms in the Yangtze River Delta (YRD) is crucial for maintaining regional ecological health and achieving sustainable development. We therefore calculate FVC in the YRD from 2012 to 2021 based on MODIS NDVI data, and analyze its spatiotemporal evolution. Multiple regression residual analysis and geographic detector model were used along with various auxiliary data to further explore its driving mechanisms in a hierarchical manner. Finally, the Hurst index was used to forecast future trends in FVC. The results show that: 1) Significant changes in FVC occurred around 2016, rapidly declining at a rate of 8.9×10-3a-1 from 2012 to 2016, followed by fluctuating growth at a rate of 5.2×10-3a-1 thereafter. 2) Although the overall development of FVC in the YRD tends toward stability, the fluctuation was pronounced in the Taihu Lake Basin. Improvement areas were mainly concentrated on both sides of the Yangtze River in the central part of the YRD and in the southern mountainous region. Degraded areas were concentrated in cities north of the Huai River. 3) Dynamic changes in FVC were primarily driven by a combination of climate and human activities, whereas spatial heterogeneity was mainly driven by factors such as elevation, slope, and landform type. The improvement in the explanatory power of anthropogenic factors for spatial heterogeneity when combined with natural factors was significant.

Soil surface roughness (SSR) is an important factor affecting soil erosion and soil nutrient transport. Human tillage leads to increased instability in SSR, and the characteristics of SSR caused by different tillage practices await further study. This research utilizes terrestrial laser scanning (TLS) to measure the SSR of six farmland plots (25 m × 25 m) and analyzes the characteristics of SSR under different tillage practices (plowing, harrowing, ridging, crusting, etc.). The study results show: 1) Different agricultural tillage practices lead to significant differences in SSR. The plowed and harrowed plot corresponds to the maximum (2.49 cm) and minimum (1.5 cm) root mean square height (RMSH), respectively. Correlation length (CL) is more affected by different tillage practices than RMSH. The difference in CL between the ridged and harrowed plot is 2.6 times. 2) Ridging and crusting caused significant directional variation in SSR. The SSR anisotropy of the harrowed plot can be disregarded. 3) Under the condition of measuring soil profile in 12 directions and randomly sampling 70 times in each direction, the profile length must be at least 3 m to ensure that the measurement error of SSR is better than 5% compared to the “true” value. TLS can measure two-dimensional SSR. Therefore, it is only necessary to ensure that the measurement range is at least 3 m × 3 m. The study results provide a reference for the high-precision measurement of SSR (RMSH and CL) under different agricultural tillage practices.

Herein, in situ water reflectance and TSM data obtained from several experiments on the Nanyi Lake from 2018 to 2022 and the Sentinel-3 Ocean and Land Colour Instrument (OLCI) satellite synchronization data were used to compare four atmospheric correction methods (FLAASH, 6S, ACOLITE, and C2RCC) and construct an empirical model for TSM inversion in the Nanyi Lake to analyze the spatial and temporal changes in the water quality in the Nanyi Lake from 2018 to 2023. On the Sentinel-3 OLCI data, the C2RCC algorithm showed the highest accuracy and overall performance stability (RMSE: 0.0014–0.0051 Sr−1, MAPE: 18.44%–68.47%, and BIAS: from −3.68% to 23.63%). The highest correlation was observed between the three-band ratio (B9 + B18)/B10 and the in situ TSM; the TSM inversion model constructed based on this inversion factor showed the best accuracy for the Nanyi Lake (R2: 0.76, RMSE: 5.01 mg/L, and MAPE: 28.46%). The spatial and temporal changes in TSM in the Nanyi Lake exhibited significant regularity. Specifically, the TSM was higher in 2018–2019, significantly decreased in 2020, and stabilized in 2021–2023. Owing to the effects of human activities, precipitation, and illumination, seasonal variation in the TSM in the Nanyi Lake was detected, with TSM decreasing in the following order: summer > autumn > spring > winter. Concerning spatial variations, high TSM was observed in the northwest, northeast, and southeast of the Nanyi Lake reclamation area and its surrounding lake area. River confluence and human activities affected the area, leading to significant fluctuations in TSM in 2018–2023.

Compared with discrete emotion space, image emotion analysis based on dimensional emotion space can more accurately represent fine-grained emotion. Meanwhile, this high-precision representation of emotion requires dimensional emotion prediction methods to sense and capture emotional information in images as accurately and richly as possible. However, the existing methods mainly focus on emotion recognition by extracting the emotional regions where salient objects are located while ignoring the joint influence of objects and background on emotion. Furthermore, in the existing literature, when fusing multi-level features, no consideration has been given to the varying contributions of features from different levels to emotional analysis, which makes it difficult to distinguish valuable and useless features and cannot improve the utilization of effective features. This paper proposes an image emotion prediction network named ARMNet. In ARMNet, a unified affective region extraction method that integrates eye fixation detection and attention detection is proposed to enhance the combined influence of objects and backgrounds. Additionally, the multi-level features are fused with the consideration of their different contributions through an improved channel attention mechanism. In comparison to the existing methods, experiments conducted on the CGnA10766 dataset demonstrate that the performance of valence and arousal, as measured by Mean Squared Error (MSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²), has improved by 4.74%, 3.53%, 3.62%, 1.93%, 6.29%, and 7.23%, respectively. Furthermore, the interpretability of the network is enhanced through the visualization of attention weights corresponding to emotional regions within the images.

Abstract High-quality ground observation networks are an important basis for scientific research. Here, an automatic soil observation network for high-resolution satellite applications in China (SONTE-China) was established to measure both pixel- and multilayer-based soil moisture and temperature. SONTE-China is distributed across 17 field observation stations with a variety of ecosystems, covering both dry and wet zones. In this paper, the average root mean squared error (RMSE) of station-based soil moisture for well-characterized SONTE-China sites is 0.027 m3/m3 (0.014~0.057 m3/m3) following calibration for specific soil properties. The temporal and spatial characteristics of the observed soil moisture and temperature in SONTE-China conform to the geographical location, seasonality and rainfall of each station. The time series Sentinel-1 C-band radar signal and soil moisture show strong correlations, and the RMSE of the estimated soil moisture from radar data was lower than 0.05 m3/m3 for the Guyuan and Minqin stations. SONTE-China is a soil moisture retrieval algorithm that can validate soil moisture products and provide basic data for weather forecasting, flood forecasting, agricultural drought monitoring and water resource management.

Excessive light exposure can damage photoreceptors and lead to blindness. Oxidative stress serves a key role in photo-induced retinal damage. Free radical scavengers have been proven to protect against photo-damaged retinal degeneration. Fullerol, a potent antioxidant, has the potential to protect against ultraviolet-B (UVB)-induced cornea injury by activating the endogenous stem cells. However, its effects on cell fate determination of Müller glia (MG) between gliosis and de-differentiation remain unclear. Therefore, we established a MG lineage-tracing mouse model of light-induced retinal damage to examine the therapeutic effects of fullerol. Fullerol exhibited superior protection against light-induced retinal injury compared to glutathione (GSH) and reduced oxidative stress levels, inhibited gliosis by suppressing the TGF-β pathway, and enhanced the de-differentiation of MG cells. RNA sequencing revealed that transcription candidate pathways, including Nrf2 and Wnt10a pathways, were involved in fullerol-induced neuroprotection. Fullerol-mediated transcriptional changes were validated by qPCR, Western blotting, and immunostaining using mouse retinas and human-derived Müller cell lines MIO-M1 cells, confirming that fullerol possibly modulated the Nrf2, Wnt10a, and TGF-β pathways in MG, which suppressed gliosis and promoted the de-differentiation of MG in light-induced retinal degeneration, indicating its potential in treating retinal diseases.

Moiré materials provide fertile ground for the correlated and topological quantum phenomena. Among them, the quantum anomalous Hall (QAH) effect, in which the Hall resistance is quantized even under zero magnetic field, is a direct manifestation of the intrinsic topological properties of a material and an appealing attribute for low-power electronics applications. The QAH effect has been observed in both graphene and transition metal dichalcogenide (TMD) moiré materials. It is thought to arise from the interaction-driven valley polarization of the narrow moiré bands. Here, we show that the newly discovered QAH state in AB-stacked MoTe_{2}/WSe_{2} moiré bilayers is not valley polarized but valley coherent. The layer- and helicity-resolved optical spectroscopy measurement reveals that the QAH ground state possesses spontaneous spin (valley) polarization aligned (antialigned) in two TMD layers. In addition, saturation of the out-of-plane spin polarization in both layers occurs only under high magnetic fields, supporting a canted spin texture. Our results call for a new mechanism for the QAH effect and highlight the potential of TMD moiré materials with strong electronic correlations and spin-orbit interactions for exotic topological states.

Comparing satellite retrieval with high-precision ground observations is an essential component for the validation of CO2 satellite products. The initial stage of assessing the bias in retrieval products from satellite and ground sources involves establishing a geographical connection between observations that are temporally and spatially proximate. The primary aim of this paper is to evaluate the influence of variations in neighborhood definitions and colocation methods on the assessment of satellite products and provide quantitative references. To achieve this, a series of experiments were conducted involving the Global Total Column Carbon Observation Network (TCCON) and the OCO-2 satellite. Various spatial-temporal neighborhoods and colocation methods were considered in these experiments. The results indicate that spatial neighborhoods exert a more substantial influence on bias compared to temporal neighborhoods. In the mid-latitudes of the Northern Hemisphere, there is an observed linear increase trend between the difference of OCO-2 and TCCON observations and the spatial neighborhood, with an average increase of 0.32 ppm as the neighborhood size changes from 1° to 10°. Regarding colocation methods, the simple spatiotemporal geographic constraints tend to overlook changes in the atmospheric state to a certain extent. The target geographic constraint method reduces the bias by 2% to 5% by increasing the proportion of OCO-2 observations targeting TCCON while the method of introducing T700 potential temperature reduces by 2% to 13% by screening the gradient of CO2 concentration change. Moreover, an evident correlation exists between the bias and their corresponding latitudes, with a 0.20 ppm increase in bias observed for every 10° increment in latitudes in the Northern Hemisphere. The bias of TCCON and OCO-2 shows a pronounced seasonal regularity, with the highest in summer. The study also discusses the selection of spatiotemporal matching with low satellite coverage, the bias distribution, and the attribution of bias to the natural wind field.

The validation of satellite remote sensing surface reflectance products is aimed at comparing the reflectance pixel values of products with ground measurement values at the pixel scale. Due to the existence of surface heterogeneity, we cannot obtain the satellite pixel scale truth value through ground sampling, and only the satellite relative pixel scale truth value that closely approximates it can be acquired. The process of converting the point-scale spectrum of ground sampling into a pixel-scale spectrum will produce certain errors, known as point-to-pixel-scale conversion uncertainty, which is closely related to the type of sample area and the ground sampling method. In this study, we conducted research on the uncertainty of point-to-pixel-scale conversion generated via different ground sampling methods in the upscaling process. We utilized unmanned aerial vehicle (UAV) hyperspectral images to invert the surface reflectance spectral curves of wheat, corn, bare soil, and soybeans at the pixel scale, and simulate the ground measurement spectra and satellite pixel scale ground truth of different sampling methods, so as to realize the quantitative calculation of the uncertainty of the ground truth at the satellite pixel scale. On this basis, we analyzed in depth the effects of the sampling method, measurement height, and number of spectra on the scale conversion uncertainty. The research results show that airborne hyperspectral images can accurately simulate the spectra of ground measurements, and can be used as an effective means of ground spectral sampling and uncertainty analysis. When using the systematic sampling method, the more the sampling points, the smaller the uncertainty. However, the uncertainty of scale conversion tends to stabilize when the number of sampling points is increased to a certain quantity. As the height of ground measurement increases, the number of spectra within the elementary sampling unit (ESU) increases, leading to smaller scale conversion uncertainties. The research results of this study will provide support for the subsequent optimization of ground sampling methods and the improvement of measurement efficiency and measurement accuracy.

The vegetation cover of forests and grasslands in mountain regions plays a crucial role in regulating climate at both regional and global scales. Thus, it is necessary to develop accurate methods for estimating and monitoring fractional vegetation cover (FVC) in mountain areas. However, the complex topographic and climate factors pose significant challenges to accurately estimating the FVC of mountain forests and grassland. Existing remote sensing products, FVC retrieval methods, and FVC samples may fail to meet the required accuracy standards. In this study, we propose a method based on spatio-temporal transfer learning for the retrieval of FVC in mountain forests and grasslands, using the mountain region of Huzhu County, Qinghai Province, as the study area. The method combines simulated FVC samples, Sentinel-2 images, and mountain topographic factor data to pre-train LSTM and 1DCNN models and subsequently transfer the models to HJ-2A/B remote sensing images. The results of the study indicated the following: (1) The FVC samples generated by the proposed method (R2 = 0.7536, RMSE = 0.0596) are more accurate than those generated by the dichotomy method (R2 = 0.4997, RMSE = 0.1060) based on validation with ground truth data. (2) The LSTM model performed better than the 1DCNN model: the average R2 of the two models was 0.9275 and 0.8955; the average RMSE was 0.0653 and 0.0735. (3) Topographic features have a significant impact on FVC retrieval results, particularly in relatively high-altitude mountain regions (DEM > 3000 m) or non-growing seasons (May and October). Therefore, the proposed method has better potential in FVC fine spatio-temporal retrieval of high-resolution mountainous remote sensing images.

Total Suspended Matter is the core parameter of water color remote sensing and the important indicator for water quality evaluation of lakes. Rapid and high-precision monitoring of TSM is an important guarantee for water quality remote-sensing applications. China has launched many broad-bandwidth remote sensing satellites, all of which have similar bandwidth. The coordinated observation of multiple satellites can effectively meet the large-scale and high-frequency dynamic monitoring requirements of TSM concentration in lakes. This study proposed a machine-learning model to retrieve the TSM concentration from broad bandwidth satellites. The reliability and accuracy of various retrieve models (i.e., linear regression model, support vector regression model, random forest model, and back propagation neural networks model) were evaluated through the in-situ datasets of TSM concentration in lakes. The RF model was selected as the retrieved model of TSM concentration using broad bandwidth satellites. The results showed that 1) Compared with four machine learning models, the RF model can provide better performance (R2=0.88, Mean Absolute Percentage Error (MAPE) = 22.5%). Similarly, compared with the documented six TSM retrieve model, the RF retrieve model also has substantial advantages. 2) the Forel-Ule Index (FUI) can effectively enhance the precision and accuracy of the TSM retrieve model. 3) The RF model has good generalization ability and accuracy in the validation datasets (Lake Chagan: MAPE = 3.7%, Lake Changdang: MAPE = 4.3%). 4) The RF model was applied to the broad bandwidth satellites retrieve of TSM concentrations in Lake Bosten, Lake Chagan, and Lake Changdang, and the MAPEs were 5.3%, 8.1%, and 12.1%, respectively. This study showed that the RF model could effectively improve the retrieve performance and generalization ability of the broad bandwidth satellite’s TSM concentration, which meets the accuracy requirements of high-frequency dynamic monitoring of TSM concentration.

Ultraviolet-visible spectroscopy is an effective tool for reagent-free qualitative analysis and quantitative detection of water parameters. Suspended particles in water cause turbidity that interferes with the ultraviolet-visible spectrum and ultimately affects the accuracy of water parameter calculations. This paper proposes a deep learning method to compensate for turbidity interference and obtain water parameters using a partial least squares regression approach. Compared with orthogonal signal correction and extended multiplicative signal correction methods, the deep learning method specifically utilizes an accurate one-dimensional U-shape neural network (1D U-Net) and represents the first method enabling turbidity compensation in sampling real river water of agricultural catchments. After turbidity compensation, the R2 between the predicted and true values increased from 0.918 to 0.965, and the RMSE (Root Mean Square Error) value decreased from 0.526 to 0.343 mg. Experimental analyses showed that the 1D U-Net is suitable for turbidity compensation and provides accurate results.

To obtain more abundant target information for vegetation remote sensing research, the wide field of view (WFV) of the GaoFen-6 (GF-6) satellite utilizes eight bands, including four new bands. Unfortunately, it finds the unavailable reference bands from well-calibrated sensors to implement radiometric cross-calibration of the WFV sensor for these new bands, which severely affects the radiometric quality estimation and the radiometric degradation detection of WFV. Therefore, taking the Dunhuang radiometric calibration site (DRCS) in China as the radiometric transfer platform and treating the moderate-resolution imaging spectroradiometer as a reference sensor, a novel radiometric cross-calibration method for WFV is proposed. The validation results show that compared with the official calibration coefficients (OCCs) published by the China centre for resources satellite data and application (CRESDA), the proposed method can obtain reliable radiometric cross-calibration results for each band even without available corresponding reference bands. Moreover, the influences of the interpolation method and the spectral band adjustment factor (SBAF) correction on the calibration results are discussed. The total radiometric cross-calibration uncertainty is less than 4.33%.

GaoFen-4 (GF-4) infrared spectrum (IRS) camera has a special spectral band located in the mid-wave infrared spectrum with the wavelength ranging from 3.50 to 4.10 μm. It is very important for forest fire-detection and other quantitative remote sensing applications at a high spatial resolution of 400 m. Due to the similarity of the spectral characteristics, the thermal emissive band 20 (3.660–3.840 μm) and 22 (3.929–3.989 μm) of medium resolution imaging spectrometer (MODIS) are taken as the reference bands for the radiometric cross-calibration of the GF-4/IRS camera. The time difference between selected GF-4/IRS and MODIS images at the Qinghai Lake and Se lincuo Lake test sites is less than one hour. After spatial matching, single-band and dual-band spectral matching methods are proposed in this article. Then, the DN value of GF-4 image is linearly fitted to the radiance of the corresponding MODIS image to obtain the radiometric calibration coefficients of GF-4/IRS camera. The validation results show that the cross-calibration coefficients calculated by integrating the measurement of two MODIS bands with the mean value spectral matching approach has the highest radiometric calibration consistency with the official calibration coefficients.

Although biochar has potential benefits in environmental and agronomic aspects, short-term application of biochar to rice does not increase grain yield. The objective of this study was to identify the crop growth characteristics responsible for why applying biochar over the short-term does not increase grain yield in rice, thus providing information for how to increase grain yield in rice following short-term application of biochar. Field experiments were conducted to investigate the effects of biochar application (20 t ha–1) on yield attributes and crop growth characteristics in rice grown under two N rates in two years. Results showed that biochar application had no significant effects on grain yield or yield components. Biochar application increased radiation use efficiency and biomass production during post-heading and total biomass production by 7–16%. Pre-heading biomass translocation and harvest index were decreased by 4–11% with biochar application. These results indicate that the lack of increased yield in rice following short-term application of biochar is attributable to compensation between increased biomass production resulting from improved post-heading crop growth and decreased harvest index resulting from reduced pre-heading biomass translocation. The finding of this study suggests that establishing a compatible relationship between post-heading crop growth and pre-heading biomass translocation is critical for increasing grain yield in rice following application of biochar over the short-term.

Recently, remote sensing based on the unmanned aerial vehicle (UAV) has become a novel remote sensing technology for researching the characteristics of the earth and the properties of the objects near the earth's surface. Accurate reflectance products are fundamental for optical remote sensing applications. Although the application of remote sensing based on UAV is widely applied, the quality, standards, strategies, and procedures of remote sensing products are faced challenges. To obtain convincing reflectance products, the methods, processes, and strategies obtaining the reflectance products for UAV-remote sensing are validated, compared, and improved. Meanwhile, three methods (ELM, MIR, and ARTM) are exploited to obtain spectral reflectance for hyperspectral images based on UAV in this paper. The comparison with the spectral reflectance of verified targets measured in the field indicates that the spectral reflectance of MIR has higher accuracy than others, with the average spectral absolute error (ASAE) of ±2.5%. By contrast, the ASAE of ELM and ARTM is ±7.0% and ±9.6%, respectively. Besides, based on the principle of radiative transfer, the radiation factors including incident radiation, background radiance, and environmental radiance are analyzed for improving the performance of the three methods. By recalibrating the hyperspectral sensor, the average absolute error of MIR is reduced to ±1.8%. By optimizing the parameters, the performance of ELM and ARTM is improved significantly, and the average absolute error of the two methods is reduced to ±1.7% and ±4.4%, respectively.

Inbred rice has been grown more and more widely, while the planting area of hybrid rice has decreased by approximately 25% in China since 1995. This study aimed to assess the changes in grain yield and yield attributes due to cultivar development in indica (Oryza sativa ssp. indica) inbred rice in China. Field experiments were conducted in 2019 and 2020 to determine the performance of grain yield and yield attributes of an indica super inbred rice cultivar Jinnongsimiao (JNSM) released in 2010 by comparing it with an indica high-yielding inbred rice cultivar Guichao 2 (GC2) released in 1978 and an indica super hybrid rice cultivar Y-liangyou 900 (YLY900) released in 2016. Results showed that JNSM produced 18% higher grain yield than GC2 but 6% lower grain yield than YLY900. Compared with GC2, JNSM had higher spikelets per panicle, spikelet-filling percentage, and harvest index by 67%, 4%, and 11%, respectively. Compared with YLY900, JNSM had 14% lower grain weight and 19% lower biomass production during the pre-heading period. The difference in biomass production during the pre-heading period between JNSM and YLY900 was explained more by crop growth rate than growth duration. This study suggests that (1) the recently released indica super inbred rice cultivar JNSM outyields the old indica high-yielding inbred rice cultivar GC2 as a result of increasing panicle size, spikelet-filling percentage, and harvest index, and (2) further improvement in grain yield in indica inbred rice can be achieved by improving biomass production through promoting pre-heading crop growth.

Lake ecosystem eutrophication is a crucial water quality issue that can be efficiently monitored with remote sensing. GF-6 WFV with a high spatial and temporal resolution provides a comprehensive record of the dynamic changes in water quality parameters in a lake. In this study, based on GF-6 WFV images and the field sampling data of Xingkai Lake from 2020 to 2021, the accuracy of three machine learning models (RF: random forest; SVR: support vector regression; and BPNN: back propagation neural network) was compared by considering 11 combinations of surface reflectance in different wavebands as input variables for machine learning. We mapped the spatiotemporal variations of Chl-a concentrations in Xingkai Lake from 20192021 and integrated machine learning algorithms to demonstrate that RF obtained a better degree of derived-fitting (Calibration: N = 82, RMSE = 0.82 μg/L, MAE = 0.57 μg/L, slope = 0.94, and R2 = 0.98; Validation: N = 40, RMSE = 2.12 μg/L, MAE = 1.58 μg/L, slope = 0.91, R2 = 0.89, and RPD = 2.98). The interannual variation from 2019 to 2021 showed that the Chl-a concentration in Xingkai Lake was low from June to July, while maximum values were observed from October to November, thus showing significant seasonal differences. Spatial distribution showed that Chl-a concentrations were higher in Xiao Xingkai Lake than in Da Xingkai Lake. Nutrient inputs (N, P) and other environmental factors such as high temperature could have an impact on the spatial and temporal distribution characteristics of Chl-a, therefore, combining GF-6 WFV satellite images with RF could realize large-scale monitoring and be more effective. Our results showed that remote-sensing-based machine learning algorithms provided an effective method to monitor lake eutrophication as well as technical support and methodological reference for inland lake water quality parameter inversion.

Chlorophyll-a (Chl-a) is an important characterized parameter of lakes. Monitoring it accurately through remote sensing is thus of great significance for early warnings of water eutrophication. Sentinel Multispectral Imager (MSI) images from May to September between 2020 and 2021 were used along with in-situ measurements to estimate Chl-a in Lake Chagan, which is located in Jilin Province, Northeast China. In this study, the extreme gradient boosting (XGBoost) and Random Forest (RF) models, which had similar performances, were generated by six single bands and six band combinations. The RF model was then selected based on the assessments (R2 = 0.79, RMSE = 2.51 μg L−1, MAPE = 9.86%), since its learning of the input features in the model conformed to the bio-optical properties of Case 2 waters. The study considered Chl-a concentrations in Lake Chagan as a seasonal pattern according to the K-Nearest-Neighbors (KNN) classification. The RF model also showed relatively stable performance for three seasons (spring, summer and autumn) and it was applied to map Chl-a in the whole lake. The research presents a more reliable machine learning (ML) model with higher precision than previous empirical models, as shown by the effects of the input features linked with the biological mechanisms of Chl-a. Its robustness was revealed by the temporal and spatial distributions of Chl-a concentrations, which were consistent with in-situ measurements in the map. This research was capable of revealing the current ecological situation in Lake Chagan and can serve as a reference in remote sensing of inland lakes.

Abstract The timing of formation of the low‐gradient, internally drained landscape of the Tibetan Plateau is fundamental to understanding the evolution of the plateau as a whole. Well‐dated sedimentary records of internal drainage of rivers into lakes are used to reveal the timing of this evolution. Here we redate the youngest continental sedimentary successions of central Tibet in the Lunpola Basin and propose a new age range of ca. 35 to 9 Ma, significantly younger than previously thought. We demonstrate long‐standing internal drainage in central Tibet since the late Eocene and stable sedimentary environments, source regions, and low topographic relief since at least the early Miocene. We suggest that sediment aggradation of internal drainage and reduction of hillslope gradients by erosion dominate the formation of low‐relief landscapes and that the late Cenozoic drainage basins in central Tibet developed in response to flow in the lower crust and/or mantle lithosphere.

The development of low-nitrogen (N) tolerant varieties is an effective way to improve the use efficiency of N in rice and is crucial to the sustainable rice production in China. Recently, we observed that higher grain yields were obtained in the hybrid rice variety Deyou 4727 (DY4727) than in the hybrid rice variety Y-liangyou 900 (YLY900) under zero-N fertilization conditions, suggesting that DY4727 has higher low-N tolerance compared to YLY900. In this study, field experiments were performed in 2020 and 2021 to compare the responses of yield attributes to N fertilization (180 kg N ha–1) relativized to zero-N fertilization between DY4727 and YLY900. Results showed that the response of grain yield to N fertilization was 13–55% for two varieties in two years. DY4727 had lower response of grain yield to N fertilization than YLY900 by 47% in 2020 and by 70% in 2021. There were no significant or consistent differences in responses of spikelets per m2, spikelet filling percentage, aboveground biomass, intercepted radiation, radiation use efficiency, and aboveground N uptake to N fertilization between DY4727 and YLY900. DY4727 had 32% and 26% lower positive response of grain weight to N fertilization compared to YLY900 in 2020 and 2021, respectively. The response of harvest index to N fertilization was negative (−3% in 2020 and −9% in 2021) for DY4727 but positive (1% in 2020 and 12% in 2021) for YLY900. DY4727 had higher negative response of internal N-use efficiency to N fertilization (−41% in 2020 and −24% in 2021) than YLY900 (−11% in 2020 and −10% in 2021). This study suggests that grain weight, harvest index, and internal N-use efficiency are key traits determining the difference in response of grain yield to N fertilization between DY4727 and YLY900.

The selection of high-yielding, short-duration rice cultivars is essential for the double-rice cropping system. High hill density could be achieved with less labor under machine transplanted conditions. Therefore, dense planting is more practical for machine-transplanted rice. While few studies have been conducted to certify the feasibility of short-duration cultivar combined with dense planting in machine transplanted conditions. The current study was executed to determine the effects of dense planting on yield attributes and grain yield of short-duration cultivars under mechanically transplanted conditions. A field experiment comprises two treatments—i.e., the short-duration cultivar Lingliangyou104 cultivated at a high planting density (SDH) and the long-duration Taiyou390 cultivar cultivated at a low planting density (LDL)—were conducted in the 2018 and 2019 late seasons. The results showed that the SDH exhibited 17% and 19% higher panicle number in a unit, 26% and 24% higher spikelet filling, 8% and 8% higher grain weight, 21% and 11% higher harvest index, and consequently 12% and 4% higher grain yield and 13 and 15 days shortened growth duration compared to the LDL in the 2018 and 2019, respectively. The data revealed that the difference in grain yield between the SDH and LDL was mainly due to the higher harvest index and reasonable dry matter distribution of the SDH, which was conducive to improving yield components and increasing rice grain yield. As a result, short-duration rice cultivars combined with dense planting is a feasible strategy for improving the grain yield of mechanically transplanted late rice.

The spatial representativeness of the in-situ data is an important prerequisite for ensuring the reliability and accuracy of remote sensing product retrieval and verification. Limited by the collection cost and time window, it is essential to simultaneously collect multiple water parameter data in water tests. In the shipboard measurements, sampling design faces problems, such as heterogeneity of water quality multi-parameter spatial distribution and variability of sampling plan under multiple constraints. Aiming at these problems, a water multi-parameter sampling design method is proposed. This method constructs a regional multi-parameter weighted space based on the single-parameter sampling design and performs adaptive weighted fusion according to the spatial variation trend of each water parameter within it to obtain multi-parameter optimal sampling points. The in-situ datasets of three water parameters (chlorophyll a, total suspended matter, and Secchi-disk Depth) were used to test the spatial representativeness of the sampling method. The results showed that the sampling method could give the sampling points an excellent spatial representation in each water parameter. This method can provide a fast and efficient sampling design for in-situ data for water parameters, thereby reducing the uncertainty of inversion and the validation of water remote sensing products.

Abstract Biochar application can depress nitrification in paddy soils, but the microbiological mechanisms involved in this process are not clearly understood. This study was conducted to test the hypothesis that biochar may affect nitrification bacteria in paddy soils. Bacterial communities in a paddy soil with and without applications of biochar were compared in a field experiment. Results indicated that the total number of operational taxonomic units (OTUs) of bacteria was not significantly affected by biochar application (20 Mg rice husk biochar ha–1). However, the application of biochar significantly changed the structure of the bacterial community. In particular, the number of OTUs affiliated with Nitrospirae, a key bacterial phylum in nitrification, was significantly reduced, by 25%, by biochar application. The biochar‐induced reduction in Nitrospirae could be explained in part by interspecific competition with Firmicutes and Bacteroidetes phyla, which were significantly increased by application of biochar. The results of this study suggest that biochar may affect nitrification by changing levels of nitrification bacteria in paddy soils.

Validation is a crucial technique used to strengthen the application capabilities of earthobservation satellite data and solve the quality problems of remote-sensing products. Observing land-surface parameters in the field is one of the key steps of validation. Therefore, the demand for long-term stable validation stations has gradually increased. However, the current location-selection procedure of validation stations lacks a systematic and objective evaluation system. In this research, a data-driven selection of a land product validation station (DSS-LPV) based on Machine Learning is proposed. Firstly, we construct an evaluation indicator system in which all factors affecting the location of validation stations are divided into surface characteristics, atmospheric conditions and the social environment. Then, multi-scale evaluation grids are constructed and indicators are allocated for spatial evaluation. Finally, four Machine Learning (ML) methods are used to learn the established reliable stations, and different data-driven scoring models are constructed to explore the intrinsic relationship between evaluation indicators and station locations. In this article, the reliability of DSS-LPV is effectively validated by the example of China using the national-level land product validation station that has been established. After a comparison between the four ML models, the random forest (RF) with the highest accuracy was selected as the modeling method of DSS-LPV. The correlation between the regression value of test stations and the target value is 0.9133. The average score of test stations is 0.8304. The test stations are generally located within the calculated hot-spot area of the score density map, which means that it is highly consistent with the location of the built stations. Research results indicate that DSS-LPV is an effective method that can provide a reasonable geographical distribution of the stations. The location-selection results can provide scientific decision-making support for the construction of land product validation stations.

Remote sensing (RS)-derived vegetation indices (VIs) with medium and high spatial resolution have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring at local or global scales. Before they can be used as reliable inputs for other research, conducting in situ measurements for validation is very critical. However, the spatial heterogeneity due to the diversity of land cover and its spatial organization in the landscape increases the uncertainty of validation, so design of optimal sampling is an important basis for the reliability of the validation. In this paper, we propose an integrative stratified sampling strategy (INTEG-STRAT) based on normalized difference vegetation index (NDVI) data as prior knowledge. The basic idea is to realize a sampling optimization by determining the optimal combination of the spatial sampling method (e.g., simple random sampling (SRS), spatial system sampling (SYS), stratified sampling, generalized random tessellation stratified (GRTS), balanced acceptance sampling (BAS)) and spatial stratification scheme with an objective rule. The objective rule in this paper is to minimize the root mean square error (RMSE) of 10-fold cross validation between estimated values (sample are not included) and the corresponding values on prior knowledge. Relative precision, correlation coefficient, and RMSE are used to compare the effectiveness of the proposed sampling strategy with each sampling method without considering sampling optimization. After comparing, we find that the INTEG-STRAT requires fewer samples to become stable and has higher accuracy. At site 1, when the correlation coefficient between NDVI image and the simulated NDVI surface reached 80%, INTEG-STRAT needed only 70 sampling points while other methods require more sampling points. At the same time, INTEG-STRAT strategy has a smaller RMSE between the estimated values and the corresponding values on prior knowledge image. In general, INTEG-STRAT is an effective method in the selection of representative samples to support the validation of vegetation indices products with medium and high spatial resolution.

Translocation of biomass produced during pre-heading to grains is a determinant of grain yield, but also plays an important role in adaptation to unfavorable environments during post-heading in rice. In this study, field experiments were conducted to determine the critical factors that regulate biomass translocation in rice. Biomass translocation and production characteristics of two rice hybrids (Guiliangyou 2 and Y-liangyou 1) were compared between two site-year environments (Naning-2014 and Yongan-2018). Results showed that biomass translocation parameters (biomass translocation amount and rate and contribution of biomass translocation to filled grain weight) and ratio of biomass production during pre-heading to post-heading (BPpre/BPpost ratio) decreased in Guiliangyou 2 but increased in Y-liangyou 1 with the environment change from Nanning-2014 to Yongan-2018. The decreased BPpre/BPpost in Guiliangyou 2 was attributable to increased biomass production during post-heading (BPpost), while the increased BPpre/BPpost ratio in Y-liangyou 1 was due to increased biomass production during pre-heading (BPpre). Higher cumulative incident solar radiation and larger diurnal temperature variation were responsible for the increased BPpost in Guiliangyou 2 and the increased BPpre in Y-liangyou 1 grown in Yongan in 2018 compared to in Nanning in 2014. The results of this study indicate that changes in biomass translocation and production with environment (climate) in rice are dependent on genotype and that the BPpre/BPpost ratio is an important factor regulating biomass translocation in rice.

Glutelin and prolamin are the two major proteins in rice grains. Grain content of glutelin is considerably higher than that of prolamin in rice, but there is limited information on the factors determining the different grain contents of glutelin and prolamin. To address this knowledge gap, the present study compared final weight per grain and accumulation characteristics of glutelin and prolamin in four rice cultivars. Results showed that final glutelin weight per grain was 3.24-3.95 times higher than final prolamin weight per grain. Glutelin and prolamin accumulation processes were well fitted by the logistic equation. The initial, maximum, and mean accumulation rates of glutelin were 1.69-4.67 times higher than those of prolamin. The active accumulation duration of glutelin was 2.9-5.1 d longer than that of prolamin. These results indicate that both higher accumulation rate and longer active accumulation duration are responsible for the higher final weight per grain of glutelin compared to prolamin in rice.

Validation is an essential process to evaluate the quality of waterbody remote sensing products, and the reliability and effective application of the in situ data of waterbody parameters are an important part of validation. Based on the in situ data of chlorophyll-a (Chl-a), total suspended solids (TSS) and other environmental variables (EVs) measured at the fixed station in Taihu Lake, we attempt to develop a prediction model to determine whether the in situ measurement has enough representativeness for validating waterbody remote sensing products. Key EVs that affect the changes of Chl-a and TSS are firstly identified by using correlation analysis, which participate in modeling as variables. In addition, three multi-parameter modeling approaches are selected to simulate the daily changes of Chl-a and TSS under different EVs configurations. The results indicate that the highest prediction accuracy can be achieved through the generalized regression neural network (GRNN) based model. In the all-valid dataset, the testing absolute average relative errors (AEs) of GRNN-based Chl-a and TSS prediction model are 11.4% and 11.3%, respectively, and in the sunny-day dataset, the testing AEs are 8.6% and 8.2%, respectively. Meanwhile, the application example proves that the prediction model in this paper can be effectively used to screen the in situ data and determine the time window for satellite-ground data matching.

Fumonisin B1 (FB1) is the most prevalent and toxic form among fumonisin homologues which are produced by fusarium species and it contaminates various types of food products, posing serious health hazards for humans and animals. In this work, a colorimetric assay for the detection of FB1 has been developed based on competitive horseradish peroxidase (HRP)-linked aptamer and dual biotin-streptavidin interaction. In short, a biotinylated aptamer of FB1 was immobilized on the microplate by biotin-streptavidin binding; the complementary strand (csDNA) of the aptamer was ligated with HRP by biotin-streptavidin binding again to form a csDNA-HRP sensing probe, competing with FB1 to bind to the aptamer. The color change can be observed after the addition of chromogenic and stop solution, thereby realizing the visual detection of FB1. Under optimal conditions, good linearity was observed within the concentration range of 0.5 to 300 ng/mL, with a detection of limit of 0.3 ng/mL. This assay is further validated by spike recovery tests towards beer and corn samples, it provides a simple, sensitive and reliable method for the screening of FB1 in food samples and may be potentially used as an alternative to conventional assays.