Producing accurate crop maps during the current growing season is essential for effective agricultural monitoring. Substantial efforts have been made to study regional crop distribution from year to year, but less attention is paid to the dynamics of composition and spatial extent of crops within a season. Understanding how crops are distributed at the early developing stages allows for the timely adjustment of crop planting structure as well as agricultural decision making and management. To address this knowledge gap, this study presents an approach integrating object-based image analysis with random forest (RF) for mapping in-season crop types based on multi-temporal GaoFen satellite data with a spatial resolution of 16 meters. A multiresolution local variance strategy was used to create crop objects, and then object-based spectral/textural features and vegetation indices were extracted from those objects. The RF classifier was employed to identify different crop types at four crop growth seasons by integrating available features. The crop classification performance of different seasons was assessed by calculating F-score values. Results show that crop maps derived using seasonal features achieved an overall accuracy of more than 87%. Compared to the use of spectral features, a feature combination of in-season textures and multi-temporal spectral and vegetation indices performs best when classifying crop types. Spectral and temporal information is more important than texture features for crop mapping. However, texture can be essential information when there is insufficient spectral and temporal information (e.g., crop identification in the early spring). These results indicate that an object-based image analysis combined with random forest has considerable potential for in-season crop mapping using high spatial resolution imagery.

Soil organic carbon (SOC) is the most critical component of global carbon cycle in grassland ecosystems. There has been growing interest in understanding SOC dynamics and driving forces of grassland biomes at various temporal and spatial scales. Up to now, estimates of long‐term and large‐scale changes in SOC of grassland biomes have been mostly based on modeling approaches and manipulative experiments, rather than direct measurements. During 2007–2011, we repeated 141 soil profiles of the sampling in 1963–1964 (up to 1‐m depth) to quantify the long‐term changes of SOC storage in the major grassland types of Inner Mongolia in order to tease apart the relative contributions of climate change and grazing. We found that SOC decreased in all soil types, except in the eolian sandy soils, from 1963 to 2007, with an average reduction rate of 1.8 kg C m ⁻² (~22.9% or 0.52% year ⁻¹ ) in the grassland biome of Inner Mongolia. We quantitatively clustered the soils into four groups using principal component analysis (PCA) and detected clear spatial dependency of the changes on climate and grazing. The climate change was responsible for 15.3–34.9% of the total SOC variations, whereas grazing intensity accounted for <9.5% of the changes. Our findings indicated that climate change, rather than grazing, was the primary forcing for the changes in SOC of Inner Mongolia grasslands. We presume that other driving forces, such as changes in nongrazing‐resultant wind erosion and atmospheric nitrogen deposition, might have played a role albeit their effects need to be further examined.