Search Results (2,561)

Rice is a primary food crop, and rice production ensures food security and maintains social stability with great significance. Flooding paddy rice fields as an important step in rice production affects the entire growth process of rice. The selection of flooding time is highly correlated with paddy rice yield and water resource utilization. In the background of global warming, early flooding in high-latitude paddy rice planting areas can ensure that rice has sufficient growing time to increase yield. However, overly early flooding may cause waste of water resources due to insufficient heat. Currently, research on flooding timing is relatively lacking, and monitoring of temperature during flooding is particularly deficient. To respond to climate change, it is necessary to explore whether the current flooding schedule meets the actual needs. Based on MODIS surface reflectivity data, we identified the First Flooding Day (FFD) and Peak Flooding Day (PFD) in the Sanjiang Plain. Using MODIS Land Surface Temperature (LST) data and meteorological station-provided air temperature data, we analyzed the corresponding LST and air temperature for PFD from 2008 to 2024. The main conclusions are as follows: (1) both FFD and PFD in the Sanjiang Plain have a trend of advancing year by year, with PFD showing stronger advancement than FFD; (2) the LST and air temperature during flooding in the Sanjiang Plain show a downward trend year by year; and (3) by 2024, the flooding temperature of paddy rice fields in the Sanjiang Plain has generally met the needs for the next step of production. This study first attempts to use high-temporal-resolution remote sensing images to identify the flooding time of paddy fields and achieve timely monitoring of flooding and changes in flooding temperature. Full article

High-spatiotemporal-resolution and accurate soil moisture (SM) data are crucial for investigating climate, hydrology, and agriculture. Existing SM products do not yet meet the demands for high spatiotemporal resolution. The objective is to develop and evaluate a retrieval framework to derive SM estimates with high spatial (100 m) and temporal (<3 days) resolution that can be used on a national scale in China. Therefore, this study integrates multi-source data, including optical remote sensing (RS) data from Sentinel-2 and Landsat-7/8/9, synthetic aperture radar (SAR) data from Sentinel-1, and auxiliary data. Four machine learning and deep learning algorithms are applied, including Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) networks, and Ensemble Learning (EL). The integrated framework (IF) considers three feature scenarios (SC1: optical RS + auxiliary data, SC2: SAR + auxiliary data, SC3: optical RS + SAR + auxiliary data), encompassing a total of 33 features. The results are as follows: (1) The correlation coefficients (r) between auxiliary data (such as sand fraction, r = −0.48; silt fraction, r = 0.47; and evapotranspiration, r = −0.42), SAR features (such as the backscatter coefficients for VV-pol (${\sigma }_{vv}^0$), r = 0.47), and optical RS features (such as Shortwave Infrared Band 2 (SWIR2) reflectance data from Sentinel-2 and Landsat-7/8/9, r = −0.39) with observed SM are significant. This indicates that multi-source data can provide complementary information for SM monitoring. (2) Compared to XGBoost and LSTM, RFR and EL demonstrate superior overall performance and are the preferred models for SM prediction. Their R² for the training and test sets exceed 0.969 and 0.743, respectively, and their ubRMSE are below 0.022 and 0.063 m³/m³, respectively. (3) The SM prediction accuracy is highest for the scenario of optical + SAR + auxiliary data, followed by SAR + auxiliary data, and finally optical + auxiliary data. (4) With an increasing Normalized Difference Vegetation Index (NDVI) and SM values, the trained models exhibit a general decrease in prediction performance and accuracy. (5) In 2021 and 2022, without considering cloud cover, the IF theoretically achieved an SM revisit time of 1–3 days across 95.01% and 96.53% of China’s area, respectively. However, SC1 was able to achieve a revisit time of 1–3 days over 60.73% of China’s area in 2021 and 69.36% in 2022, while the area covered by SC2 and SC3 at this revisit time accounted for less than 1% of China’s total area. This study validates the effectiveness of combining multi-source RS data with auxiliary data in large-scale SM monitoring and provides new methods for improving SM retrieval accuracy and spatiotemporal coverage. Full article

During the process of urbanization, a large number of impervious land surfaces are replacing the biologically active surface. Land surface temperature is a key factor reflecting the urban thermal environment and a crucial factor affecting city livability and resident comfort. Therefore, the accurate measurement of land surface temperature is of great significance. Thermal infrared remote sensing is widely applied to study the urban thermal environment due to its distinctive advantages of high sensitivity, wide coverage, high resolution, and continuous measurement. Low-altitude remote sensing, performed using thermal infrared sensors carried by unmanned aerial vehicles (UAVs), is a common method of land surface observation. However, thermal infrared sensors may experience varying degrees of sway due to wind, affecting the quality of the data. It is still uncertain as to what degree angle changes affect thermal infrared data in urban environments. To investigate this effect, a near-ground remote sensing experiment was conducted to observe three common urban land surfaces, namely, marble tiles, cement tiles and grasses, at observation angles of 15°, 30°, 45°, and 60° using a thermal infrared imager. This is accompanied by synchronous ground temperature measurements conducted by iButton digital thermometers. Our results suggest that the temperature differences between the remote sensing data of the land surface and the corresponding ground truth data increase as a function of the increasing observation angle of the three land surfaces. Furthermore, the differences are minor when the observation angle changes are not more than 15° and the changes are not the same for different land surfaces. Our findings increase the current understanding of the effects of different angles on thermal infrared remote sensing in urban land surface temperature monitoring. Full article

After the launch of a high-resolution remote sensing satellite, representative spatial quality estimators (RER, FWHM, MTF50, MTFA) are measured from images taken of ground Edge targets. In this work, the best spatial quality estimator is proposed by quantitatively comparing and analyzing the precision between the Relative Edge Response (RER), the Full Width at Half Maximum (FWHM), the MTF value at the Nyquist frequency (MTF50), and the MTF Area between 0 and the Nyquist frequency (MTFA). While the basic method for the measurement of spatial quality estimators on Edge targets is already well established, this work summarizes and explains the uncertain factors and problems in the measurement procedure that affect the accuracy and precision of spatial quality estimators. It also considers how to improve the precision of spatial quality estimators during the measurement procedure. The contents and results of this work were discussed by various satellite development organizations in the Geo-Spatial Working Group within CEOS WGCV IVOS from 2012 to 2019, and the Edge target Spatial quality Measurement Python code (ESMP) was developed in 2019 to reflect the findings of this workshop. Using 483 Edge targets from worldwide images taken by KOMPSAT-3A, which has been in operation since 2017, the results obtained via ESMP show that the precision levels of RER, FWHM, and MTFA are approximately three to four times higher than that of MTF50 when comparing the Coefficient of Variance (CV) statistics. This is the first statistical comparison of spatial quality estimators using 7 years of ground Edge target imagery from a single satellite of KOMPSAT-3A. Full article

A nondestructive approach for accurate crop yield prediction at the field scale is vital for precision agriculture. Considerable progress has been made in the use of the spectral index (SI) derived from unmanned aerial vehicle (UAV) hyperspectral images to predict crop yields before harvest. However, few studies have explored the most sensitive wavelengths and SIs for crop yield prediction, especially for different nitrogen fertilization levels and soil types. This study aimed to investigate the appropriate wavelengths and their combinations to explore the ability of new SIs derived from UAV hyperspectral images to predict yields during the growing season of spring maize. In this study, the hyperspectral canopy reflectance measurement method, a field-based high-throughput method, was evaluated in three field experiments (Wang-Jia-Qiao (WJQ), San-Ke-Shu (SKS), and Fu-Jia-Jie (FJJ)) since 2009 with different soil types (alluvial soil, black soil, and aeolian sandy soil) and various nitrogen (N) fertilization levels (0, 168, 240, 270, and 312 kg/ha) in Lishu County, Northeast China. The measurements of canopy spectral reflectance and maize yield were conducted at critical growth stages of spring maize, including the jointing, silking, and maturity stages, in 2019 and 2020. The best wavelengths and new SIs, including the difference spectral index, ratio spectral index, and normalized difference spectral index forms, were obtained from the contour maps constructed by the coefficient of determination (R²) from the linear regression models between the yield and all possible SIs screened from the 450 to 950 nm wavelengths. The new SIs and eight selected published SIs were subsequently used to predict maize yield via linear regression models. The results showed that (1) the most sensitive wavelengths were 640–714 nm at WJQ, 450–650 nm and 750–950 nm at SKS, and 450–700 nm and 750–950 nm at FJJ; (2) the new SIs established here were different across the three experimental fields, and their performance in maize yield prediction was generally better than that of the published SIs; and (3) the new SIs presented different responses to various N fertilization levels. This study demonstrates the potential of exploring new spectral characteristics from remote sensing technology for predicting the field-scale crop yield in spring maize cropping systems before harvest. Full article

This study aims to assess the spatiotemporal changes in ecological environment quality (EEQ) in arid regions, using Xinjiang as a case study, from 2000 to 2023, with an improved remote sensing ecological index (IRSEI). Due to the complex ecology of arid regions, the traditional remote sensing ecological index (RSEI) has limitations in capturing ecological dynamics. To address this, we propose an enhanced IRSEI model that replaces normalization with standardization, improving robustness against outliers. Additionally, the kernel normalized difference vegetation index (kNDVI) and normalized difference salinity index (NDSI) are integrated to assess saline areas more effectively. The methodology includes time series analysis, spatial distribution analysis, and statistical evaluations using the difference method, coefficient of variation, and the Hurst index. Results show that the IRSEI more accurately reflects ecological dynamics than the RSEI. Temporal analysis reveals stable overall EEQ, with some areas improving. Spatially, the environment is generally better in the north and in mountainous regions than in the south and plains. Statistical evaluations suggest a positive trend in ecological changes, with improved areas surpassing degraded ones. This study contributes to the monitoring, protection, and management of arid region ecosystems, emphasizing the need for high-resolution data and further analysis. Full article

The spectral reflectance measured in situ is often regarded as the “truth”. However, its limited coverage and large spatial heterogeneity often make the ground-based reflectance unable to represent the remote sensing images. Since the spatial scale mismatch between ground-based, airborne, and spaceborne measurements, the applications of geological exploration, metallogenic prognosis and mine monitoring are facing severe challenges. In order to explore the influence of spatial scale effect on rock spectra, spectral reflectance with uncertainty caused by differences in illumination view geometry and spatial heterogeneity is introduced into the Bayesian Maximum Entropy (BME) method. Then, the rock spectra are upscaled from the point-scale to meter-scale and to 10 m-scale, respectively. Finally, the influence of spatial scale effect is evaluated based on the reflectance value, spectral shape, and spectral characteristic parameters. The results indicate that the BME model shows better upscaling accuracy and stability than Ordinary Kriging and Ordinary Least Squares model. The maximum Euclidean Distance of rock spectra caused by spatial resolution change is 6.271, and the Spectral Angle Mapper can reach 0.370. The spectral absorption position, absorption depth, and spectral absorption index are less affected by scale effect. For the area with similar spatial heterogeneity to the Huangshan Copper–Nickel Ore District, when the spatial resolution of the image is greater than 10 m, the rock’s spectrum is less influenced by the change in spatial resolution. Otherwise, the influence of spatial scale effect should be considered in applications. In addition, this work puts forward a set of processes to evaluate the influence of spatial scale effect in the study area and carry out the upscaling. Full article

Traditional gross ecosystem product (GEP) accounting methods often operate at macro scales, failing to reflect the localized and nuanced values of wetland ecosystems. This study addresses these challenges by introducing a fine-grained classification system based on a localized adaptation of international standards. The framework integrates high-precision national land surveys and remote sensing quantitative analysis while incorporating fisheries resource models, climate regulation beneficiary mapping, and visitor interpolation to address data scarcity related to human activities. This approach refines the spatial calculation methods for functional quantity accounting at fine scales. The results demonstrate that the refined classification maintains consistency with traditional methods in total value while adapting to multi-scale accounting, filling gaps at small and medium scales and providing a more accurate representation of localized wetland characteristics. Additionally, the study highlights the dominance of cultural services in GEP, emphasizing the need to balance cultural and regulatory services to ensure fairness in decision-making. Finally, a village-scale decision-support model is proposed, offering actionable guidance for wetland management and sustainable development planning. Full article

Remote sensing is a valuable tool in precision agriculture due to its spatial and temporal coverage, non-destructive method of data collection, and cost-effectiveness. In this study, we measured the canopy reflectance of potato (Solanum tuberosum L.) crops on a plant-by-plant basis with a handheld spectrometer instrument. Our study pursues two primary objectives: (1) determining the optimal temporal aggregation for measuring canopy signals related to potato yield and (2) identifying the best spectral bands in the 350–2500 nm domain and vegetation indices. The study was conducted over two consecutive years (2020 and 2021) with 60 plants per plot, encompassing six potato varieties and three replicates annually throughout the growth season. Employing correlation analysis and dimensionality reduction, we identified 23 independent features significantly correlated with tuber yield. We used multiple linear regression analysis to model the relationship between the selected features and yield and to compare their influence in the fitted model. We used the Leave-One-Out Cross-Validation (LOOCV) method to assess the validity of the model (RMSE = 702 g and %RMSE = 29.2%). The most significant features included the Gitelson2 and Vogelmann indices. The optimal time period for measurements was determined to be from 56 to 100 days after planting. These findings may contribute to the advancement of precision farming by proposing tailored sensor applications, paving the way for improved agricultural practices and enhanced food security. Full article

Due to the significant threat to forest health posed by beetle infestations on pine trees, timely and accurate predictions are crucial for effective forest management. This study developed a pine tree stress probability prediction workflow based on monthly cloud-free Sentinel-2 composite images to address this challenge. First, representative pine tree stress samples were selected by combining long-term forest disturbance data using the Continuous Change Detection and Classification (CCDC) algorithm with high-resolution remote sensing imagery. Monthly cloud-free Sentinel-2 images were then composited using the Multifactor Weighting (MFW) method. Finally, a Random Forest (RF) algorithm was employed to build the pine tree stress probability model and analyze the importance of spectral, topographic, and meteorological features. The model achieved prediction precisions of 0.876, 0.900, and 0.883, and overall accuracies of 89.5%, 91.6%, and 90.2% for January, February, and March 2023, respectively. The results indicate that spectral features, such as band reflectance and vegetation indices, ranked among the top five in importance (i.e., SWIR2, SWIR1, Red band, NDVI, and NBR). They more effectively reflected changes in canopy pigments and leaf moisture content under stress compared with topographic and meteorological features. Additionally, combining long-term stress disturbance data with high-resolution imagery to select training samples improved their spatial and temporal representativeness, enhancing the model’s predictive capability. This approach provides valuable insights for improving forest health monitoring and uncovers opportunities to predict future beetle outbreaks and take preventive measures. Full article

Eliminating poverty, reducing inequality, and achieving balanced development are one of the United Nations Sustainable Development Goals. Objectively and accurately measuring regional economic vitality and development equilibrium is a pressing scientific issue that needs to be addressed in order to achieve common prosperity. Nighttime light (NTL) remote sensing data have been proven to be a good proxy variable for socio-economic development, and are widely used due to their advantages of convenient access and wide spatial coverage. Based on multi-source data, this study constructs an Economic Development Index (EDI) that comprehensively reflects regional economic vitality from two aspects, economic quality and development potential, combines the Nighttime Light Development Index (NLDI) as the evaluation indicators to measure the economic vitality and development equilibrium, analyzes the economic vitality and development equilibrium of 300 district and county units in China’s three major urban agglomerations from 2000 to 2020 and their temporal and spatial variation characteristics, and discusses the connotation of EDI and its availability. The results show the following: (1) From 2000 to 2020, the average growth rate of EDI in China’s three major urban agglomerations reached 36.32%, while the average decrease rate of NLDI reached 38.75%; both economic vitality and the development equilibrium have been continuously enhanced. Among them, the Yangtze River Delta (YRD) urban agglomeration experienced the fastest economic growth, while the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) exhibited the strongest economic strength. (2) Both economic vitality and the development equilibrium in these three urban agglomerations exhibited distinct spatial agglomeration characteristics, namely center-surrounding distribution, coastal–inland distribution, and radial belt–pole distribution, respectively. (3) Over the past two decades, the economic development of these three urban agglomerations has progressed towards the pattern of regional coordinated development, pole-driven development and urban–rural integrated development. The research results can provide new research perspectives and scientific support for promoting regional balanced development, achieving sustainable development goals, and reducing inequality. Full article

Forest health monitoring is crucial for sustainable management, especially with the challenges posed by climate warming. Remote sensing data provide vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), that are widely used in assessing forest health. However, studies considering the validation of these data with field assessments of tree vigor are still scarce. To address this issue, we explored the relationships in declining (D) and non-declining (N) silver fir (Abies alba Mill.) stands from the Spanish Pyrenees between changes in canopy (a proxy of vigor), vegetation indices (NDVI, EVI) and climate variables. We compared trends in the NDVI and EVI for the period of 1984–2023 for D and N stands showing high and low crown defoliation levels, respectively. The EVI values allowed for the separation of stands according to their vigor earlier and more clearly than NDVI values, which did not show clear patterns throughout the time series. Significant negative correlations were found between the EVI and stand defoliation (r = −0.57) or mean radial growth (r = 0.81). Late-spring drought reduced the EVI. The EVI series reflected similar spatial patterns in terms of stand defoliation and tree growth, offering complementary information, along with the strengths of remote sensing with respect to its spatial and temporal coverage, for the early detection of forest dieback. This study also contributes to a better understanding of remote sensing indices, which is useful for forest health monitoring. Full article

Terrestrial laser scanning (TLS) provides highly detailed 3D information of forest environments but is limited to small spatial scales, as data collection is time consuming compared to other remote sensing techniques. Furthermore, TLS data collection is heavily dependent on wind conditions, as the movement of trees negatively impacts the acquired data. Hardware advancements resulting in faster data acquisition times have the potential to be valuable in upscaling efforts but might impact overall data quality. In this study, we investigated the impact of the pulse repetition rate (PRR), or pulse frequency, which is the number of laser pulses emitted per second by the scanner. Increasing the PRR reduces the scan time required for a single scan but decreases the power (amplitude) of the emitted laser pulses commensurately. This trade-off could potentially impact the quality of the acquired data. We used a RIEGL VZ400i laser scanner to test the impact of different PRR settings on the point cloud quality and derived tree structural metrics from individual tree point clouds (diameter, tree height, crown projected area) as well as quantitative structure models (total branch length, tree volume). We investigated this impact across five field plots of different forest complexity and canopy density for three different PRR settings (300, 600 and 1200 kHz). The scan time for a single scan was 180, 90 and 45 s for 300, 600 and 1200 kHz, respectively. Differences among the raw acquired scans from different PRR replicates were largely removed by several necessary data processing steps, notably the removal of uncertain points with a low reflectance attribute. We found strong agreement between the individual tree structural metrics derived from each of the PRR replicates, independent of the forest complexity. This was the case for both point cloud-based metrics and those derived from quantitative structural models (QSMs). The results demonstrate that the PRR in high-end TLS instruments can be increased for data collection with negligible impact on a selection of derived structural metrics that are commonly used in the context of aboveground biomass estimation. Full article

Resource-based cities face numerous sustainability challenges, making the coupled and coordinated relationship between urbanization and the eco-environment critical for sustainable development strategies. The Loess Plateau is an essential energy base and ecologically fragile area in China, holding unique and significant research value. This research employed the Remote Sensing Ecological Index (RSEI) and the Compound Night Light Index (CNLI), based on MODIS and night light data, to investigate the socio-economic development and eco-environmental changes across 25 resource-based cities on the Loess Plateau (LP) in China over the past 20 years. The Coupling Coordination Degree Model (CCDM) and Multi-Scale Geographically Weighted Regression (MGWR) were utilized to assess the relationship between urbanization and ecological factors. The average RSEI values for these cities ranged from 0.4524 to 0.4892 over the 20 years, reflecting an upward trend with a growth rate of 8.13%. Simultaneously, the average CNLI values ranged from 1.5700 to 6.0864, with a change of 4.5164. Over the past two decades, all cities in the study area experienced rapid urbanization and ecological development. The correlation between urbanization and ecological factors strengthened, alongside an increasing spatial heterogeneity. While the coupling coordination relationship in most cities showed improvement, many remained within the low to middle grades. These findings enhance the understanding of the intricate relationships between urbanization and ecology, offering valuable insights for policy-making aimed at creating sustainable and livable resource-based cities. Full article

Constructing high-speed railways (HSRs) is critical for developing countries to stimulate economic growth and urbanization. This study focuses on the Lao section of the China–Laos Railway (CLR) and employs explicitly spatial remote sensing images to investigate the urban development surrounding HSR stations. Data-driven machine learning and causal inference approaches are integrated to quantify the spatial–temporal evolution and discover its driving factors. The results suggest that the CLR has had positive spatial spillover effects on the development of the surrounding urban space. These spillover effects have exhibited a distance attenuation pattern, reflecting obvious development in 2D rather than in 3D urban space. Meanwhile, the distance to stations and adjacent city centers as well as functional urban characteristics, such as land use patterns and industrialization level, have significantly influences the surrounding spatial development. Specifically, in industrial-dominated cities, the surrounding spatial changes have been most significant under the influence of the HSR. Change related to industrial and residential land use has shown significant land expansion patterns and increased utilization efficiency, reflecting that industrialization and urbanization have been the primary drivers of land demand surrounding the HSR. The findings offer valuable insights and references for developing nations to formulate and implement spatial management policies and initiatives related to HSR. Full article