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ABSTRACTDigital Elevation Models (DEMs) are critical datasets in the field of Earth sciences, essential for accurate measurement and analysis of the Earth's surface. We used ICESat-2 to quantitatively evaluate 5 DEMs (ALOS PALSAR, ASTER GDEM V3, COPERNICUS, NASADEM and TanDEM-X) on the Tibetan Plateau. The research findings indicate that the ALOS exhibits the highest level of accuracy, as evidenced by its root mean square error (RMSE) value of 5.05m. It is closely followed by NASA and COP, which have RMSE values of 6.23m and 8.10m, respectively. In contrast, the ASTER and TDX90 demonstrate comparatively lower levels of accuracy, as indicated by their respective RMSE values of 11.47m and 12.32m. It is worth mentioning that the accuracy of DEMs is significantly influenced by land cover, especially during the transition from areas with low vegetation to those with high vegetation. This transition often results in a decrease in accuracy. Furthermore, the accuracy of DEMs tends to decrease with increasing slope values. Aspect exhibits a notable spatial distribution pattern characterized by a "low in the Southwest direction, high in all other directions" phenomenon. This study offers significant contributions to the evaluation of DEMs accuracy and its suitability in the Qinghai-Tibet Plateau.

Spectral information plays a crucial role in fractional vegetation cover (FVC) estimation, and selecting the appropriate spectral information is essential for improving the accuracy of FVC estimation. Traditionally, spectral feature selection is primarily guided by physical mechanisms or empirical statistical models. This has led to the use of multispectral and hyperspectral images, which often result in missing or redundant information, thereby decreasing the efficiency and accuracy of FVC estimation. This study proposes a novel dual-attention network to select the feature bands of Sentinel-2 multispectral images for the accurate FVC estimation of winter wheat. In the first step, the importance of hyperspectral band reflectances was determined using simulated data from the PROSAIL model, by combining the dual-attention mechanism with the convolutional neural network (DAM-CNN). In the second step, the importance of Sentinel-2 multispectral bands was converted from the hyperspectral band importance identified in the previous stage, and subsequently ranked accordingly. Based on the feature ranking results, multispectral simulated data translated from hyperspectral simulated data were used for CNN training, and multispectral feature selection was conducted based on FVC accuracy. Finally, the selected features were assessed based on their performance in FVC estimation using a CNN model with real data. The experimental results indicate that during the key growth period of winter wheat, the combination of red, green, and red-edge bands significantly influences the FVC estimation accuracy. Band 3 (Green), band 4 (Red), band 5 (Red-edge 1), and band 6 (Red-edge 2) of Sentinel-2 satellite images contribute most significantly to winter wheat FVC estimation, achieving an accuracy comparable to that obtained using all bands, while reducing the training time by 19.1%, as confirmed by field survey data. [ABSTRACT FROM AUTHOR]

In the past, the use of remote sensing for winter wheat growth monitoring mainly relied on the relative growth assessment of a single vegetation index, such as normalized Vegetation index (NDVI). This study advanced the methodology by integrating field-measured data with Sentinel-2 data. In addition to NDVI, it innovatively incorporated two parameters, aboveground biomass (AGB) and leaf area index (LAI), for a more comprehensive relative growth assessment. Furthermore, the study employed the agricultural production systems simulator (APSIM) model to use LAI and AGB for absolute growth monitoring. The results showed that the simulated LAI and AGB closely match the field-measured values throughout the entire growth period of winter wheat under various conditions (R2 > 0.9). For relative growth monitoring, NDVI showed significant linear positive correlations (r > 0.74 and P< 0.05) with both LAI and AGB simulated by the APSIM model. Overall, this research shows that LAI and AGB obtained from the APSIM model provide a more detailed and accurate approach to monitoring of winter wheat growth. This improved monitoring capability can support effective land management arable and provide technical guidance to advance precision agriculture practices. [ABSTRACT FROM AUTHOR]

Accurately predicting winter wheat yield before harvest could greatly benefit decision-makers when making management decisions. In this study, we utilized weather forecast (WF) data combined with Sentinel-2 data to establish the deep-learning network and achieved an in-season county-scale wheat yield prediction in China's main wheat-producing areas. We tested a combination of short-term WF data from the China Meteorological Administration to predict in-season yield at different forecast lengths. The results showed that explicitly incorporating WF data can improve the accuracy in crop yield predictions [Root Mean Square Error (RMSE) = 0.517 t/ha] compared to using only remote sensing data (RMSE = 0.624 t/ha). After comparing a series of WF data with different time series lengths, we found that adding 25 days of WF data can achieve the highest yield prediction accuracy. Specifically, the highest accuracy (RMSE = 0.496 t/ha) is achieved when predictions are made on Day of The Year (DOY) 215 (40 days before harvest). Our study established a deep-learning model which can be used for early yield prediction at the county level, and we have proved that weather forecast data can also be applied in data-driven deep-learning yield prediction tasks. [ABSTRACT FROM AUTHOR]

Accurate monitoring of leaf phenology, from individual trees to entire ecosystems, is vital for understanding and modeling forest carbon and water cycles, as well as assessing climate change impact. However, the accuracy of many remote-sensing phenological products remains difficult to directly corroborate using ground-based monitoring, owing to variations in the observed indicators and the scales used. This limitation hampers the practical implementation of remote-sensing phenological metrics. In our study, the start of growing season (SOS) from 2016 to 2021 was estimated for the continental USA using Sentinel-2 images. The results were then matched with several ground-based spring vegetation phenology metrics obtained by the USA National Phenology Network (USA-NPN). In this study, we focused on the relationships between the leaf-unfolding degree (LUD), the SOS, and the factors that drive these measures. Our results revealed that: (1) the ground-based leaves and increasing leaf size stages were significantly correlated with the SOS; (2) with the closest match being observed for a leaf spread of 13%; (2) the relationship between the SOS and LUD varied according to the species and ecoregion, and the pre-season cumulative radiation was found to be the main factor affecting the degree of matching between the ground observations and the metrics derived from the Sentinel-2 data. Our investigations provide a ground-based spring phenology metric that can be used to verify or evaluate remote-sensing spring phenology products and will help to improve the accuracy of remote-sensing phenology metrics. [ABSTRACT FROM AUTHOR]

Global cropland expansion has been recognized as a key driver of food security. However, cropland-expansion-induced alterations in biophysical properties of the Earth's surface and greenhouse gas emissions may potentially impact the Earth's climate system. These changes could, in turn, affect cropland productivity and the potential distribution of croplands, although the underlying mechanisms remain relatively underexplored. In this study, a global climate model was employed to quantify the impact of global cropland expansion on cropping potential utilizing observed and derived cropland expansion data. Our findings reveal that since 10 000 BCE, a 28 % increase in cropland expansion has led to a 1.2 % enhancement in global cropping potential owing to more favorable precipitation and temperature conditions. This suggests that global cropland expansion yields dual benefits to crop production. However, in regions with low growth rates of cropping potential, cropland expansion proves to be an inefficient method for augmenting the yield of local crop potential. As croplands continue to expand worldwide, the capacity to support populations in different regions is altered, thereby reducing cropping potential inequality among nations. [ABSTRACT FROM AUTHOR]