The increasing number of Chinese sensor types used for terrestrial remote sensing has necessitated an additional effort to evaluate and standardize the data they acquire. In this study, we assessed the potential use of GF-1 WFV (Wild Field Camera), ZY-3 MUX (Multi-spectral camera), and HJ-1 CCD (Charge Coupled Device) sensor data for grassland monitoring by comparing spectral field measurements, vegetation coverage, and the leaf area index (LAI) of grassland stands with reflectance in the red and near-infrared bands and the Normalized Difference Vegetation Index (NDVI). Based on spectral field measurements, the characteristic differences of spectral response functions of the sensors were analyzed. Based on simulations using the SAIL bidirectional canopy reflectance model coupled with the PROSPECT leaf optical properties model (PROSAIL), we investigated the effects of changes in the sensors’ zenith angle caused by side sway. The following conclusions were drawn. (1) Differences in the adjusted coefficients of determination (R²) exist when comparing correlations between the reflectances from the three sensor types in different bands. The values of R² are 0.556–0.893 and 0.819–0.850 for the infrared and red bands, respectively, and these data show a better correlation for the red band than for the infrared band. Fitted slope equations revealed inconsistencies in the data between the different sensor types. In the red band, GF-1 WFV and HJ-1 CCD data are the most consistent, but in the near-infrared band, GF-1 WFV and ZY-3 MUX data are the most consistent; (2) The correlation of NDVIs obtained from the different sensor types is high (R² between 0.758 and 0.852); however, the consistency is low in that the NDVI based on GF-1 WFV data is significantly higher than that based on ZY-3 MUX and HJ-1 CCD data. In contrast, the mean difference is small between the NDVIs based on ZY-3 MUX and HJ-1 CCD; (3) Correlation analysis between ground grass-coverage and measured LAI data shows that the three sensor types are better at estimating coverage than the LAI, and that the GF-1 WFV sensor gave the best performance; (4) Changes in the sensors’ zenith angle caused by side sway were proven to have greater impact on reflectance and NDVI than the spectral response function; (5) For LAI values of 0–3, the NDVI changes significantly with increasing LAI, and differences between the three sensor types are obvious. For LAI > 3.5, the NDVI appears to experience a saturated tendency, which greatly reduces the differences between the sensors.

Land-cover classification provides the crucial data component for related Earth-science research. Currently, although multiscale remote-sensing images are the main source of data for classifying land-cover, the response of multi-resolution images to land-cover classification remains highly uncertain. In addition, because of the scarcity of time-synchronous multiscale satellite images of certain regions, up-scaling algorithms are generally used to generate and apply multiscale images. However, when using resolution-resampling images, it remains uncertain to what extent spectral loss or information distortion is responsible for the underlying differences in the accuracy of land-cover classification of various landscapes. To clarify this situation, we study the Hetian basin of Changting County in Fujian Province, south-east China by using quasi-synchronous multiple-resolution satellite observations (seven spatial resolution levels: 1 m, 2 m, 4 m, 8 m, 16 m, 30 m, and 50 m) to investigate possible correlations between spatial resolution and the land-cover classification. The classification is obtained by applying a support vector machine spectral classifier to random recordings made in 1875 sample plots. We also explore the effect of using lower-resolution images by comparing the classification results obtained by using several common up-scaling algorithms, such as nearest neighbour (NN), bilinear (BI), cubic convolution (CC), and pixel aggregation (PA). The results indicate that classification accuracy is significantly influenced by the spatial resolution of images (p < 0.05), with the accuracy increasing as the spatial resolution goes from 1 m to 4 m, then decreasing as the spatial resolution decreases beyond 4 m. In addition, for a resolution of 1 m to 30 m, almost all the up-scaled images provide a classification accuracy that differs from that obtained by using the native remote-sensing images of each resolution (p < 0.05), and the difference increases as the spatial-resolution ratio or up-scaling amplification factor increases. According to an analysis of the spatial scale of images using, e.g., multiband spectral reflectance and vegetation index, the up-scaling algorithms are less sensitive to spatial resolution and represent poorly the actual image characteristics. This result is attributed to the strong dependence of the spectral information in up-scaled images on the original images, which leads to discrepancies with respect to actual observations at the given scale. These results indicate that the effects of resolution cannot be ignored and that resampling data may not be adequate for multi-spatial-scale classification compared with the native satellite images. It is thus urgent to obtain an effective up-scaling algorithm that sharply reduces the problems caused by spatial heterogeneity.

Accurate phenological information is essential for monitoring crop development, predicting crop yield, and enhancing resilience to cope with climate change. This study employed a curve-change-based dynamic threshold approach on NDVI (Normalized Differential Vegetation Index) time series to detect the planting and harvesting dates for corn and soybean in Kentucky, a typical climatic transition zone, from 2000 to 2018. We compared satellite-based estimates with ground observations and performed trend analyses of crop phenological stages over the study period to analyze their relationships with climate change and crop yields. Our results showed that corn and soybean planting dates were delayed by 0.01 and 0.07 days/year, respectively. Corn harvesting dates were also delayed at a rate of 0.67 days/year, while advanced soybean harvesting occurred at a rate of 0.05 days/year. The growing season length has increased considerably at a rate of 0.66 days/year for corn and was shortened by 0.12 days/year for soybean. Sensitivity analysis showed that planting dates were more sensitive to the early season temperature, while harvesting dates were significantly correlated with temperature over the entire growing season. In terms of the changing climatic factors, only the increased summer precipitation was statistically related to the delayed corn harvesting dates in Kentucky. Further analysis showed that the increased corn yield was significantly correlated with the delayed harvesting dates (1.37 Bu/acre per day) and extended growing season length (1.67 Bu/acre per day). Our results suggested that seasonal climate change (e.g., summer precipitation) was the main factor influencing crop phenological trends, particularly corn harvesting in Kentucky over the study period. We also highlighted the critical role of changing crop phenology in constraining crop production, which needs further efforts for optimizing crop management practices.

Most natural forests are mixed forests, a mixed broadleaf-conifer forest is essentially a heterogeneously mixed pixel in remote sensing images. Satellite missions rely on modeling to acquire regional or global vegetation parameter products. However, these retrieval models often assume homogeneous conditions at the pixel level, resulting in a decrease in the inversion accuracy, which is an issue for heterogeneous forests. Therefore, information on the canopy composition of a mixed forest is the basis for accurately retrieving vegetation parameters using remote sensing. Medium and high spatial resolution multispectral time-series data are important sources for canopy conifer-broadleaf ratio estimation because these data have a high frequency and wide coverage. This paper highlights a successful method for estimating the conifer-broadleaf ratio in a mixed forest with diverse tree species and complex canopy structures. Experiments were conducted in the Purple Mountain, Nanjing, Jiangsu Province of China, where we collected leaf area index (LAI) time-series and forest sample plot inventory data. Based on the Invertible Forest Reflectance Model (INFORM), we simulated the normalized difference vegetation index (NDVI) time-series of different conifer-broadleaf ratios. A time-series similarity analysis was performed to determine the typical separable conifer-broadleaf ratios. Fifteen Gaofen-1 (GF-1) satellite images of 2015 were acquired. The conifer-broadleaf ratio estimation was based on the GF-1 NDVI time-series and semi-supervised k-means cluster method, which yielded a high overall accuracy of 83.75%. This study demonstrates the feasibility of accurately estimating separable conifer-broadleaf ratios using field measurement data and GF-1 time series in mixed broadleaf-conifer forests.