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Declining cultivated land poses a serious threat to food security. However, existing Change Detection (CD) methods are insufficient for overcoming intra-class differences in cropland, and the accumulation of irrelevant features and loss of key features leads to poor detection results. To effectively identify changes in agricultural land, we propose a Difference-Directed Multi-scale Attention Mechanism Network (DDAM-Net). Specifically, we use a feature extraction module to effectively extract the cropland's multi-scale features from dual-temporal images, and we introduce a Difference Enhancement Fusion Module (DEFM) and a Cross-scale Aggregation Module (CAM) to pass and fuse the multi-scale and difference features layer by layer. In addition, we introduce the Attention Refinement Module (ARM) to optimize the edge and detail features of changing objects. In the experiments, we evaluated the applicability of DDAM-Net on the HN-CLCD dataset for cropland CD and non-agricultural identification, with F1 and precision of 79.27% and 80.70%, respectively. In addition, generalization experiments using the publicly accessible PX-CLCD and SET-CLCD datasets revealed F1 and precision values of 95.12% and 95.47%, and 72.40% and 77.59%, respectively. The relevant comparative and ablation experiments suggested that DDAM-Net has greater performance and reliability in detecting cropland changes. [ABSTRACT FROM AUTHOR]

The China Near-Earth Object Survey Telescope (CNEOST) conducted four photometric surveys from 2015 to 2018 using image processing and aperture photometry techniques to obtain extensive light curve data on asteroids. The second-order Fourier series method was selected for its efficiency in determining the rotation periods of the observed asteroids. Our study successfully derived rotation periods for 892 asteroids, with 648 of those matching values recorded in the LCDB (for asteroids with U > 2). To enhance the reliability of the derived spin parameters and shape models, we also amassed a comprehensive collection of published light curve data supplemented by additional photometric observations on a targeted subset of asteroids conducted using multiple telescopes between 2021 and 2022. Through the application of convex inversion techniques, we successfully derived spin parameters and shape models for seven main-belt asteroids (MBAs): (2233) Kuznetsov, (2294) Andronikov, (2253) Espinette, (4796) Lewis, (1563) Noel, (2912) Lapalma, and (5150) Fellini. Our thorough analysis identified two credible orientations for the rotational poles of these MBAs, shedding light on the prevalent issue of "ambiguity in pole direction" that often accompanies photometric inversion processes. CNEOST continues its observational endeavors, and future collected data combined with other independent photometric measurements will facilitate further inversion to better constrain the spin parameters and yield more refined shape models. [ABSTRACT FROM AUTHOR]

This study proposes a new method for integrating active and passive remote sensing data during critical reproductive periods in order to extract maize areas early and to address the problem of low accuracy in the classification of maize-growing areas affected by climate change. Focusing on Jiaozuo City, this study utilized active–passive remote sensing images to determine the optimal time for maize identification. The relative importance of features was assessed using a feature selection method combined with a machine learning algorithm, the impact of both single-source and multi-source features on accuracy was analyzed to generate the optimal feature subset, and the classification accuracies of different machine learning classification methods for maize at the tasseling stage were compared. Ultimately, this study identified the most effective remote sensing features and methods for maize detection during the optimal fertility period. The experimental results show that the feature set optimized for the tasseling stage significantly enhanced maize recognition accuracy. Specifically, the random forest (RF) method, when applied to the multi-source data fusion feature set, yielded the highest accuracy, improving classification accuracy by 24.6% and 4.86% over single-source features, and achieving an overall accuracy of 93.38% with a Kappa coefficient of 0.91. Data on the study area's maize area were also extracted for the years 2018–2022, with accuracy values of 93.83%, 98.77%, 97%, and 98.05%, respectively. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) serves as a proxy indicator for vegetation photosynthesis and can directly reflect the growth status of vegetation. Using SIF for drought monitoring offers greater potential compared to traditional vegetation indices. This study aims to develop and validate a novel approach, the improved Temperature Fluorescence Dryness Index (iTFDI), for more accurate drought monitoring in Henan Province, China. However, the low spatial resolution, data dispersion, and short temporal sequence of SIF data hinder its direct application in drought studies. To overcome these challenges, this study constructs a random forest SIF downscaling model based on the TROPOspheric Monitoring Instrument SIF (TROPOSIF) and the Moderate-resolution Imaging Spectroradiometer (MODIS) data. Assuming an unchanging spatial scale relationship, an improved SIF (iSIF) product with a temporal resolution of 500 m over the period March to September, 2010–2022 was obtained for Henan Province. Subsequently, using the retrieved iSIF and the surface temperature difference data, the iTFDI was proposed, based on the assumption that under the same vegetation cover conditions, lower soil moisture and a greater diurnal temperature range of the surface indicate more severe drought. Results showed that: (1) The accuracy of the TROPOSIF downscaling model achieved coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values of 0.847, 0.073 mW m−2 nm−1 sr−1, and 0.096 mW m−2 nm−1 sr−1, respectively. (2) The 2022 iTFDI drought monitoring results indicated favorable soil moisture in Henan Province during March, April, July, and August, while extensive droughts occurred in May, June, and September, accounting for 70.27%, 71.49%, and 43.61%, respectively. The monitored results were consistent with the regional water conditions measured at ground stations. (3) The correlation between the Standardized Precipitation Evapotranspiration Index (SPEI) and iTFDI at five stations was significantly stronger than the correlation with the Temperature Vegetation Dryness Index (TVDI), with the values −0.631, −0.565, −0.612, −0.653, and −0.453, respectively. (4) The annual Sen's slope and Mann–Kendall significance test revealed a significant decreasing trend in drought severity in the southern and western regions of Henan Province (6.74% of the total area), while the eastern region showed a significant increasing trend (4.69% of the total area). These results demonstrate that the iTFDI offers a significant advantage over traditional indices, providing a more accurate reflection of regional drought conditions. This enhances the ability to identify drought trends and supports the development of targeted drought management strategies. In conclusion, the iTFDI constructed using the downscaled iSIF data and surface temperature differential data shows great potential for drought monitoring. [ABSTRACT FROM AUTHOR]