Global Gridded Crop Production Dataset at 10 km Resolution from 2010 to 2020.

The global gridded crop production dataset at 10 km resolution from 2010 to 2020 (GGCP10) for maize, wheat, rice, and soybean was developed to address limitations of existing datasets characterized by coarse resolution and discontinuous time spans. GGCP10 was generated using a series of adaptively trained data-driven crop production spatial estimation models integrating multiple data sources, including statistical data, gridded production data, agroclimatic indicator data, agronomic indicator data, global land surface satellite products, and ground data. These models were trained based on agroecological zones to accurately estimate crop production in different agricultural regions.

Marsh decrease was much faster than the water increase among the Yellow River Source wetlands during 1986-2022.

Wetlands are valuable and sensitive ecosystems that make them imperative to tracking the dynamics in their extent for sustainable management under global warming. Here we focused on the Yellow River Source (YRS) wetlands, which is renowned for hosting one of the world's largest plateau peat bog, unfortunately, it had experienced sharp degradation, threatening the safety of water supply for approximately 110 million people of the lower Yellow River basin. However, the lack of long-term, dense time-series data makes it challenging to assess its evolution trends and driving factors.

Challenges and opportunities in remote sensing-based crop monitoring: a review.

Building a more resilient food system for sustainable development and reducing uncertainty in global food markets both require concurrent and near-real-time and reliable crop information for decision making. Satellite-driven crop monitoring has become a main method to derive crop information at local, regional, and global scales by revealing the spatial and temporal dimensions of crop growth status and production. However, there is a lack of quantitative, objective, and robust methods to ensure the reliability of crop information, which reduces the applicability of crop monitoring and leads to uncertain and undesirable consequences.

A framework for separating natural and anthropogenic contributions to evapotranspiration of human-managed land covers in watersheds based on machine learning.

Actual EvapoTranspiration (ET) represents the water consumption in watersheds; distinguishing between natural and anthropogenic contributions to ET is essential for water conservation and ecological sustainability. This study proposed a framework to separate the contribution of natural and anthropogenic factors to ET of human-managed land cover types using the Random Forest Regressor (RFR). The steps include: (1) classify land cover into natural and human-managed land covers and then divide ET, meteorological, topographical, and geographical data into two parts corresponding to natural and human-managed land cover types; (2) construct a natural ET (ET) prediction model using natural land cover types of ET, and the corresponding meteorological, topographical and geographical factors; (3) the constructed ET prediction model is used to predict the ET of human-managed land cover types using the corresponding meteorological, topographical and geographical data as inputs, and (4) derive the anthropogenic ET (ET) by subtracting the natural ET from the total ET (ET) for human-managed land cover types.

Identification of Crop Type in Crowdsourced Road View Photos with Deep Convolutional Neural Network.

In situ ground truth data are an important requirement for producing accurate cropland type map, and this is precisely what is lacking at vast scales. Although volunteered geographic information (VGI) has been proven as a possible solution for in situ data acquisition, processing and extracting valuable information from millions of pictures remains challenging. This paper targets the detection of specific crop types from crowdsourced road view photos.

Wind estimation around the shipwreck of Oriental Star based on field damage surveys and radar observations.

Based on observational analyses and on-site ground and aerial damage surveys, this work aims to reveal the weather phenomena-especially the wind situation-when Oriental Star capsized in the Yangtze River on June 1, 2015. Results demonstrate that the cruise ship capsized when it encountered strong winds at speeds of at least 31 m s near the apex of a bow echo embedded in a squall line. As suggested by the fallen trees within a 2-km radius around the wreck location, such strong winds were likely caused by microburst straight-line wind and/or embedded small vortices, rather than tornadoes.