By effectively observing the land surface and obtaining farmland conditions, satellite remote sensing has played an essential role in agricultural drought monitoring over past decades. Among all remote sensing techniques, optical and thermal remote sensing have the most extended history of being utilized in drought monitoring. The primary goal of this paper is to illustrate how optical and thermal remote sensing have been and will be applied in the monitoring, assessment, and prediction of agricultural drought. We group the methods into four categories: optical, thermal, optical and thermal, and multi-source. For each category, a concise explanation is given to show the inherent mechanisms. We pay special attention to solar-induced chlorophyll fluorescence, which has great potential in early drought detection. Finally, we look at the future directions of agricultural drought monitoring, including (1) early detection; (2) spatio-temporal resolution; (3) organic combination of multi-source data; and (4) smart prediction and assessment based on deep learning and cloud computing.

Agricultural drought is a frequent global phenomenon. Solar-induced chlorophyll fluorescence (SIF) is a by-product of photosynthesis that can be used to monitor vegetation growth and agricultural drought. The global 0.05° spatial resolution data set has been obtained using the data-driven algorithm method. However, the broken farmland is not conducive to regional agricultural drought monitoring. Hence, 0.05° SIF products should be downscaled. On this basis, a convolutional neural network (CNN) downscaled work was conducted in this article to obtain 0.008° spatial resolution SIF results. The downscaled SIF and land surface temperature (LST) data were used to establish the temperature fluorescence dryness index (TFDI). The new TFDI was subsequently used for monitoring agricultural drought in Henan province (China) during the corn-growing season (from June to October 2013-2017). Results showed that the downscaled SIF data exhibit a good correlation with gross primary productivity (GPP) from the Moderate Resolution Imaging Spectroradiometer (MODIS) than 0.05° SIF products. During the study period, the soil moisture fluctuation corresponded well with precipitation, and the value of TFDI had an opposite fluctuation with soil moisture. Meanwhile, the annual averaged TFDI had a high correlation with summer corn yield (R = -0.84). In conclusion, the SIF results through the CNN-based downscaled method were reliable, and the new TFDI was suitable for region agricultural drought monitoring.

Soil moisture (SM) downscaling has been extensively investigated in recent years to improve coarse resolution of SM products. However, available methods for downscaling are generally based on pixel-to-pixel strategy, which ignores the information among pixels. Hence, a new downscaling method based on a convolutional neural network (CNN) is proposed to solve the problem. Furthermore, a weight layer is designed for the input, and residual SM is treated as the output of the CNN to improve the accuracy. This method is applied to downscale Soil Moisture Active Passive (SMAP) SM products (i.e., 36-km L 3 _ SM _ P and 9-km L 3 _ SM _ P _ E ) from January 1, 2018 to December 30, 2018. Compared with 9-km L 3 _ SM _ P _ E , the 9-km downscaling result is satisfactory with obtained correlation coefficient (R), root mean square error (RMSE), and unbiased RMSE (ubRMSE) values of 95.81%, 2.77%, and 2.67%, respectively. Moreover, SMAP SM products (36 and 9 km) and downscaling SM (3 and 1 km) are validated by the in situ data, which are collected by the 109 stations of the Oklahoma Mesonet SM monitoring network. Mean R, RMSE, and ubRMSE values are 67.92%, 7.94%, and 4.87% for 36-km L 3 _ SM _ P ; 67.78%, 8.35%, and 4.95% for 9-km L 3 _ SM _ P _ E ; 67.28%, 8.34%, and 4.97% for 3-km downscaling SM; 65.90%, 8.40%, and 5.18% for 1-km downscaling SM, respectively. The 3-km downscaling SM generated by this method can improve the coarse resolution of 9-km L 3 _ SM _ P _ E while preserving its accuracy. However, error will remarkably increase in the 1-km downscaling SM. Therefore, the proposed method provides a new strategy for SM downscaling and obtains satisfactory results in practice. Additional studies can be conducted in the future.

The occurrence of drought is a complex process and is caused by the interaction of multiple drought-causing factors. The construction of traditional drought models and indexes seldom considers multiple drought-causing factors. This study integrated the precipitation, soil water and heat balance, and crop growth during drought. From the beginning of the process of agricultural drought, the atmosphere, soil, and crops that characterize drought are considered, through the principal component analysis method to construct a comprehensive drought monitoring index (CDMI). This index was verified by using the areas covered by drought, areas affected by drought, relative soil moisture, and crop yield. The annual average CDMI had negative correlations with areas covered and affected by drought. The correlation coefficients were –0.68 and –0.73. Moreover, the CDMI value had positive correlations with relative soil moisture and crop yield. The maximum correlation coefficient between CDMI and relative soil moisture was 0.91, and the correlation coefficient with maize yield was 0.52. Subsequently, the CDMI was applied to long-term drought monitoring in agricultural areas during the summer maize growing season (June to September) in Henan Province. Results showed that the most severe years of agricultural drought in Henan Province were 2000, 2001, 2004, 2006, 2008, and 2014. The most severe agricultural drought occurred in July and August 2014. Statistics found that Henan Province had high frequencies of severe drought. This study proved that CDMI calculated by multisource remote sensing data is a reliable and effective indicator for monitoring and assessing agricultural drought.

Soil moisture content is an important parameter in hydrological, meteorological, and agricultural applications. Balenzano et al. proposed the alpha approximation method in 2011 for solving some complex issues during the retrieval of soil moisture over agricultural crops with synthetic aperture radar data. However, determining the constraints and solving the underdetermined system of equations in this method add new challenges. Considering the questions of constraints and underdetermined system of equations, the alpha approximation method is used to augment the measured data, and can avoid solving the underdetermined system of equations with constraints directly. Then, these data are applied in a support vector regression machine for soil moisture estimation. It is found that when an optimal model is determined, the method proposed in this article is superior to the direct use of the alpha approximation method, and the root-mean-squared error (RMSE) decreased from 0.0775 to 0.0339 and R^2 increased from 0.0467 to 0.6491. In addition, the method obtained a good result from a data set collected that included a different growing period of crops by changing the standardized method from StandardScaler to Scale, where the RMSE is 0.0501 and R^2 is 0.3204. This indicates the good generalization capability of this method. In conclusion, the proposed method solves the two questions effectively and provides a potential way for long-time or large-scale soil moisture monitoring with much less in situ measurements.

With burgeoning economic development, a surging influx of greenhouse gases, notably carbon dioxide (CO₂), has precipitated global warming, thus accentuating the critical imperatives of monitoring and predicting carbon emissions. Conventional approaches employed in the examination of carbon emissions predominantly rely on energy statistics procured from the National Bureau of Statistics and local statistical bureaus. However, these conventional data sources, often encapsulated in statistical yearbooks, exclusively furnish insights into energy consumption at the national and provincial levels, so the assessment at a more granular scale, such as the municipal and county levels, poses a formidable challenge. This study, using nighttime light data and statistics records spanning from 2000 to 2019, undertook a comparative analysis, scrutinizing various modeling methodologies, encompassing linear, exponential, and logarithmic models, with the aim of assessing carbon emissions across diverse spatial scales. A multifaceted analysis unfolded, delving into the key attributes of China’s carbon emissions, spanning total carbon emissions, per capita carbon emissions, and carbon emission intensity. Spatial considerations were also paramount, encompassing an examination of carbon emissions across provincial, municipal, and county scales, as well as an intricate exploration of spatial patterns, including the displacement of the center of gravity and the application of trend analyses. These multifaceted analyses collectively contributed to the endeavor of predicting China’s future carbon emission trajectory. The findings of the study revealed that at the national scale, total carbon emissions exhibited an annual increment throughout the period spanning 2000 to 2019. Secondly, upon an in-depth evaluation of model fitting, it was evident that the logarithmic model emerged as the most adept in terms of fitting, presenting a mean R² value of 0.83. Thirdly, the gravity center of carbon emissions in China was situated within Henan Province, and there was a discernible overall shift towards the southwest. In 2025 and 2030, it is anticipated that the average quantum of China’s carbon emissions will reach 7.82 × 10² million and 25.61 × 10² million metric tons, with Shandong Province emerging as the foremost contributor. In summary, this research serves as a robust factual underpinning and an indispensable reference point for advancing the scientific underpinnings of China’s transition to a low-carbon economy and the judicious formulation of policies governing carbon emissions.