## 1 Introduction  
Walnuts are rich in nutrients such as proteins and fats and have a high economic value, making them a major part of the economy in southern Xinjiang, where they are a characteristic forest fruit of the region [1]. Located at the north-west edge of the Taklamakan Desert, Ganquan Town has rich light and heat resources, and is mainly planted with walnuts. Its planting area accounts for 97% of the walnut planting area in the Alar City of the First Agricultural Division; moreover, the town has become a pillar of the industry for poverty alleviation in the area. It is important to promote the distribution and growth of walnuts in Ganquan Town in order to ensure their production. Field surveys, as a traditional research method, are usually used to extract the crop's planting area, and they have poor timeliness, lack accurate spatial information, and can be easily interfered with. With characteristics such as high timeliness, low costs, and wide coverage, remote sensing technology makes it possible to extract and monitor large areas, and it also provides crop information over a long-term period. It has become the main method for monitoring the distribution and growth of crops [2; 3; 4]. The most commonly used methods for extracting crop planting areas nationally and internationally today are detailed below. (1) The threshold method sets thresholds for different crops via sequential variations of multiple vegetation indexes, such as EVI, VDVI, NDVI, etc., in combination with supervised classification, to extract the spatial distribution information of different crops [5]. Based on a MODIS-EVI image and phenological characteristics of maize, [PERSON] et al. [6] extracted the planting area of maize in north-east China, adopting the threshold method. The overall accuracy was 79%, which is higher than that of statistical data, with an R\({}^{2}\) of 0.82 and RMSE of 283.98. (2) The twi-difference algorithm is used to classify and extract different crops by identifying crop pixels with obvious crests [7]. [PERSON] et al. [8] proposed identifying and extracting the winter wheat planting area in the Heilongjiang region from 2014 to 2017. Their work was based on MODIS remote sensing imagery, a technique based on the normalized difference vegetation index (NDVI) and time series coefficient of variation (NDVI-CV) combined with the NDVI curve features of different objects on the ground and second-order differencing (3) The supervised classification method: in this method, after classifying different ground objects in remote sensing images, the spatial distribution information of different crops is extracted with the use of decision trees [9], support vector machines [10; 11], random forests [12; 13; 14], etc. Among the above-mentioned supervised classification algorithms, the random forest algorithm (RF) has a high degree of automation and low computational overhead, making it easy to implement [15], and it is widely used in remote sensing image classification and other research. [PERSON] et al. [16] constructed the MODIS-NDVI data set, smoothed it with a harmonic analysis algorithm, and extracted and analyzed the planting area of winter wheat and summer corn within the study area from 2004 to 2016 using the random forest algorithm. It was verified that user accuracy was 94.5%, which was good and consistent with the Statistical Yearbook database. The Google Earth Engine (GEE) cloud platform, developed by Google, integrates a large amount of satellite remote sensing data, and the parallel computing mechanism improves the efficiency of data processing [17]. It is also widely used in vegetation cover changes, the extraction of crop planting areas and classification, and growth monitoring. [PERSON] et al.