Satellite data for studying surface dynamics in heterogeneous landscapes are missing due to frequent cloud contamination, low temporal resolution, and technological difficulties in developing satellites. A modified spatiotemporal fusion algorithm for predicting the reflectance of paddy rice is presented in this paper. The algorithm uses phenological information extracted from a moderate-resolution imaging spectroradiometer enhanced vegetation index time series to improve the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). The algorithm is tested with satellite data on Yueyang City, China. The main contribution of the modified algorithm is the selection of similar neighborhood pixels by using phenological information to improve accuracy. Results show that the modified algorithm performs better than ESTARFM in visual inspection and quantitative metrics, especially for paddy rice. This modified algorithm provides not only new ideas for the improvement of spatiotemporal data fusion method, but also technical support for the generation of remote sensing data with high spatial and temporal resolution.

High-accuracy retrieval of the crop leaf area index (LAI) in farmlands via remote sensing is the premise of reflecting the true growth condition of the crop. This paper aimed at scaling correction of LAI retrieval and developed an LAI scaling transfer model for farmland landscape pattern with multitype spatial heterogeneities according to the multiple types of farmland underlying surfaces in China. The interclass heterogeneity (caused by the alternate distribution of different cover types) and intraclass heterogeneity (caused by the difference in growth conditions within the same crop) both exist in the farmland landscape. The contextural parameters (fractions of components) and fractal dimension of the up-scaling pixel were used to quantitatively describe and correct the scaling effect caused by the two types of spatial heterogeneity, respectively. A scaling transfer model of inversed LAI was built by comprehensively considering intraclass and interclass heterogeneities. Results indicated that the LAI scaling bias of the up-scaling mixed pixel was mainly caused by the interclass heterogeneity even when the areal proportion of the noncrop component was low. The scaling transfer model corrected the scaling effect of LAI, with the root-mean-square error and mean absolute percentage error decreasing from 0.599 and 10.00% to 0.077 and 1.11%, respectively. The developed method based on fractal theory and contextural parameters effectively weakened the influence of the scaling effect on the accuracy of LAI retrieval.

Crops under various types of stresses, such as stress caused by heavy metals, drought and pest/disease exhibit similar changes in physiological-biochemical parameters (e.g., leaf area index [LAI] and chlorophyll). Thus, differentiating between heavy metal stress and nonheavy metal stress presents a great challenge. However, different stressors in crops do cause variations in spatiotemporal characteristics. This study aims to develop a spatiotemporal index based on LAI time series to identify heavy metal stress under complex stressors on a regional scale. The experimental area is located in Zhuzhou City, Hunan Province. The situ measured data and Sentinel-2A images from 2017 and 2018 were collected. First, a series of LAI in rice growth stages was simulated based on the WOrld FOod STudies (WOFOST) model incorporated with Sentinel 2 images. Second, the local Moran’s I and dynamic time warping (DTW) of LAI were calculated. Third, a stress index based on spatial and temporal features (SIST) was established to assess heavy metal stress levels according to the spatial autocorrelation and temporal dissimilarity of LAI. Results revealed the following: (1) The DTW of LAI is a good indicator for distinguishing stress levels. Specifically, rice subjected to high stress levels exhibits high DTW values. (2) Rice under heavy metal stress is well correlated with high-high SIST clusters. (3) Rice plants subjected to high pollution are observed in the northwest of the study regions and rice under low heavy metal stress is found in the south. The results suggest that SIST based on a sensitive indicator of rice biochemical impairment can be used to accurately detect regional heavy metal stress in rice. Combining spatial-temporal features and spectral information appears to be a highly promising method for discriminating heavy metal stress from complex stressors.