Phenology is an indicator of crop growth conditions, and is correlated with crop yields. In this study, a phenological approach based on a remote sensing vegetation index was explored to predict the yield in 314 counties within the US Corn Belt, divided into semi-arid and non-semi-arid regions. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product MOD09Q1 was used to calculate the normalized difference vegetation index (NDVI) time series. According to the NDVI time series, we divided the corn growing season into four growth phases, calculated phenological information metrics (duration and rate) for each growth phase, and obtained the maximum correlation NDVI (Max-R²). Duration and rate represent crop growth days and rate, respectively. Max-R² is the NDVI value with the most significant correlation with corn yield in the NDVI time series. We built three groups of yield regression models, including univariate models using phenological metrics and Max-R², and multivariate models using phenological metrics, and multivariate models using phenological metrics combined with Max-R² in the whole, semi-arid, and non-semi-arid regions, respectively, and compared the performance of these models. The results show that most phenological metrics had a statistically significant (p < 0.05) relationship with corn yield (maximum R² = 0.44). Models established with phenological metrics realized yield prediction before harvest in the three regions with R² = 0.64, 0.67, and 0.72. Compared with the univariate Max-R² models, the accuracy of models built with Max-R² and phenology metrics improved. Thus, the phenology metrics obtained from MODIS-NDVI accurately reflect the corn characteristics and can be used for large-scale yield prediction. Overall, this study showed that phenology metrics derived from remote sensing vegetation indexes could be used as crop yield prediction variables and provide a reference for data organization and yield prediction with physical crop significance.

The reliable prediction of corn yield for the United States of America is essential for effective food and energy management of the world. Three satellite-derived variables were selected, namely enhanced vegetation index (EVI), leaf area index (LAI) and land surface temperature (LST). The least absolute shrinkage and selection operator (LASSO) was used for regression, while random forest (RF), support vector regression (SVR) and long short-term memory (LSTM) methods were selected for machine learning. The three variables serve as inputs to these methods, and their efficacy in predicting corn yield was assessed in relation to evapotranspiration (ET). The results confirmed that a high level of performance can be achieved for yield prediction (mean predicted R² = 0.63) by combining EVI + LAI + LST with the four methods. Among them, the best results were obtained by using LSTM (mean predicted R² = 0.67). EVI and LST provided extra and unique information in peak and early growth stages for corn yield, respectively, and the usefulness of including LAI was not readily apparent across the whole season, which was consistent with the field growing conditions affecting the ET of corn. The satellite-derived data and the methods used in this study could be used for predicting the yields of other crops in different regions.

Surface water is a crucial resource and environmental element for human survival and ecosystem stability; therefore, accurate information on the distribution of surface water bodies is essential. Extracting this information on a large scale is commonly implemented using moderate- and low-resolution satellite images. However, the detection and analysis of more detailed surface water structures and small water bodies necessitate the use of very high-resolution (VHR) satellite images. The large-scale application of VHR images for water extraction requires convenient and accurate methods. In this paper, a method combining a pixel-level water index and image object detection is proposed. The method was tested using 2018/2019 multispectral 4-m resolution images obtained from the Chinese satellite Gaofen-2 across Beijing, China. Results show that the automatic extraction of water body information over large areas using the proposed method and VHR images is feasible. Kappa coefficient and overall accuracy of 0.96 and 99.8% after post-classification improvement were obtained for testing images inside the Beijing area. The Beijing water body dataset obtained included a total of 489.53 km² of surface water in 2018/2019, 108.01 km² of which were ponds with an area smaller than 2 km². This study can be applied for water body extraction and mapping in other large regions and provides a reference for other methods for using VHR images to extract water body information on a large scale.