###### Abstract  
This paper attempts to establish the accurate and timely forecasting model for the total output value of agriculture, forestry, animal husbandry, and fishery (TOVAFAF) in various provinces of China using NPP-VIIRS nighttime light (NTL) remote sensing data and machine learning algorithms. It can provide important data references for timely assessment of agricultural economic development level and policy adjustment. Firstly, multiple NTL indices for provincial-level administrative regions of China were constructed based on NTL images from 2013 to 2023 and various statistics. The results of correlation analysis and significance test show that the constructed total nighttime light index (TNLI), luminous pixel quantity index (LPQI), luminous pixel ratio index (LPRI), and nighttime light squared deviation sum index (NLSDSI) are highly correlated with the TOVAFAF. Subsequently, using the relevant data from 2013 to 2020 as the training set, the four NTL indices were separately taken as single independent variable to establish the linear model, exponential model, logarithmic model, power exponential model, and polynomial model. And all the four NTL indices were taken as the input features together to establish the multiple linear regression (MLR), extreme learning machine (ELM), and particle swarm optimization-ELM (PSO-ELM) models. The relevant data from 2021 to 2022 were taken as the validation set for the adjustment and optimization of the model weight parameters and the preliminary evaluation of the modeling effect. Finally, the established models were employed to forecast the TOVAFAF in 2023. The experimental results show that the ELM and PSO-ELM models can better explore and characterize the potential nonlinear relationship between NTL data and the TOVAFAF than all the models established based on single NTL index and the MLR model, and the PSO-ELM model achieves the best forecasting effect in 2023 with the MRE value for 32.20% and the R2 values of the linear relationship between the actual values and the forecasting values for 0.6460.  
n 2024 2  
farmers' income, and improving the urban and rural ecological environment [4]. Therefore, it is of great significance for grasping the trend of agricultural economic development and adjusting policies to accurately and timely obtain the TOVAFAF [3]. TOVAFAF, as the important basis for calculating the gross domestic product (GDP), mainly comes from surveys conducted by the National Bureau of Statistics or other administrative departments. Although the data obtained in this way are very reliable and authoritative, this method consumes a lot of time, manpower, and financial resources [5; 6]. In recent years, nighttime light (NTL) remote sensing images have been widely used to quantify human activities and assess socio-economic development, including the evaluation and prediction of population [7; 8], carbon dioxide emission [9; 10], urbanization process [11; 12], electricity consumption [13; 14], GDP [6; 15], poverty level [16; 17], and other socio-economic indicators. NTL remote sensing technology can detect and record weak visible and near-infrared radiation emitted from residential areas, buildings, streets, traffic, and other surface areas [18; 19]. And the radiation intensity of these lights reflects the intensity of human activities and the level of socio-economic development on the whole [20]. Furthermore, NTL remote sensing data possess the advantages of wide coverage, short periodicity, and availability [21]. Since numerous studies have demonstrated that there is a close correlation between NTL data and socio-economic indicators, some scholars directly utilized NTL data to characterize the level and differentiation of socio-economic development [18; 19; 22; 23; 24]. For example, on the basis of revealing the correlation between county-level statistical economic data and NTL data, [PERSON] et al. [22] analyzed the temporal and spatial evolution characteristics of county-level economic development using NTL data.