*2.3. Remote Sensing Data*

For the subsequent spatial-scale research in Changchun, the Landsat 8 images (30 m) and MOD11A1 data (1 km) in 2017 were downloaded from the website of USGS (https:// earthexplorer.usgs.gov/ accessed on 21 June 2021) with the aim of acquiring the LST data. The available remote sensing images can be seen in Table S1. Daily LST data were derived from the MOD11A1 product (1 km). Due to the cloudiness, there were only four clear Landsat 8 images in 2017 relevant to maize growth that would be used for mapping maize in the research area and verified by the field monitoring data, cropland map from the GlobeLand30 platform, and statistics data.

To obtain the maize distribution in Changchun, some calculations and preprocessing needed to be finished. Firstly, the normalized difference vegetation index (NDVI) and land surface water index (LSWI) were calculated by Equations (1) and (2), which were also followed for four Landsat 8 images (Operational Land Imager, OLI) [41,42]. The Landsat 8 image on 25 September, which is the best one just in the maize growth stage, was chosen as a sample to classify the region of interest (ROI). The classification thresholds could be determined by combining the features of vegetation in different stages and the changes in NDVI and LSWI of ROI samples. The process contained the following four steps (Figure S1): (1) to distinguish the vegetation and the others according to the NDVI values on 25 September (Julian day 268), which belonged to the optimum discrimination period of vegetation in four images; (2) to obtain the thresholds by analyzing the NDVI values of forest and crop samples on 5 June (Julian day 156), when the crops were in the early growth stage, and the NDVI of the forest should be obviously higher; (3) to employ the LSWI of rice samples in ROI on 5 June (Julian day 156) to determine the corresponding thresholds considering the rice was irrigated during this stage, and the LSWI values of rice should be higher; and (4) to determine the relative thresholds between the maize and the other vegetations by analyzing the LSWI ranges of them in ROI on 5 June (Julian day 156) and 25 September (Julian day 268). Based on these thresholds, the distribution of maize + rice could be obtained according to the classification rules of decision tree classification.

$$\text{NDVI} = (\mathfrak{p}\_{NIR} - \mathfrak{p}\_{RED}) / (\mathfrak{p}\_{NIR} + \mathfrak{p}\_{RED}) \tag{1}$$

$$\text{LSWI} = (\mathfrak{p}\_{NIR} - \mathfrak{p}\_{SWIR}) / (\mathfrak{p}\_{NIR} + \mathfrak{p}\_{SWIR}) \tag{2}$$

where ᐩ*NIR* (band5), ᐩ*RED* (band4), and ᐩ*SW IR* (band6) are the reflectivity of near-infrared, red, and shortwave infrared band, respectively.

Based on the decision tree classification mentioned above, the combination pattern for maize + rice in Changchun was obtained (Figure 3a). As the dominant field crops are maize and rice here, it was assumed that these results are mostly cropland. In order to evaluate the classification precision, the land-cover data with a spatial resolution of 30 m in 2020 (Figure 3b) were downloaded from the platform of GlobeLand30 (http://www.globallandcover.com/ home.html accessed on 3 October 2021), published by the Ministry of Natural Resources of China. Meanwhile, the statistical data of crop areas were obtained from the Statistic Bureau of Jilin Province (http://tjj.jl.gov.cn/tjsj/ accessed on 3 October 2021) for further evaluating the classification precision.

**Figure 3.** Crop patterns and cropland in Changchun area: (**a**) maize and rice obtained by decision tree classification; (**b**) cropland data in 2020 from the GlobeLand30 platform; (**c**) maize mapping by decision tree classification.

Using the confusion matrix, the value of the producer's accuracy (ratio of the number of estimated correct pixels to reference pixels) about cropland was 82.18%, determined through comparing Figure 3a,b. This value proved that the classification method established above was available and appropriate. It should be emphasized that the validation method used here was restricted by the accuracy of the GlobeLand30 product. Meanwhile, this product is not offered for every year, which will affect the accuracy evaluation of crop mapping. The maize mapping could be obtained by the same method (Figure 3c), which took up 87% of cropland in Figure 3b. The ratio was consistent with the value from statistics data (84%).

For the Jiefangzha sub-irrigation district, the spatial distribution of maize was derived from the report from Bai et al. [39], as shown in Figure 1c. Fortunately, the images from Landsat 8 (30 m) and MOD11A1 (1 km) can support the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) algorithm [43] to improve the spatial accuracy of LST. Therefore, the fused LST was used in this region. Details of the extraction process can be found in research by Huang et al. [40].
