Monitoring tillage practices is important for explaining soil quality and yield trends, and their impact on environmental quality. However, a common problem in sustainable residue management is scarcity of accurate residue maps. Because predictive insights on soil quality dynamics across a spatial domain are vital, this entry explicates on a new remote sensing-based technique for assessing surface residue cover. Here, an empirical model for mapping surface residue cover was created by integrating line-transect % residue cover field measurements with information gleaned from ground spectroradiometers and Advanced Wide-Field Sensor (AWiFS) satellite imagery. This map was validated using non-photosynthetic vegetation (NPV) fractional component extracted by spectral mixture analysis (SMA). SMA extracts fractional components of sensed signals in imagery, which within agricultural fields are NPV, green vegetation, bare soil, and shade. A stepwise linear regression between residue estimates by line transect and map generated using satellite imagery had R 2 = 87%. Upon map categorization according to surface residue for a single AWiFS imagery encompassing an area of 836,868 ha, but focused on corn ( Zea mays ) fields within South Dakota, revealed that <4% of these corn fields had >15% surface residue cover left in the field by November 2009. Findings such as these may guide policy on soil quality, which is directly correlated with residue management. In the future, the spatial distribution of surface residues remaining after harvest in field planted with other crops and other seasons will be mapped. Besides, the efficacy of integrating hyperspectral sensor data to enhance accuracy will be investigated.

中文翻译：

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使用空间信息技术绘制耕作实践图

监测耕作方式对于解释土壤质量和产量趋势及其对环境质量的影响非常重要。然而，可持续残留管理中的一个常见问题是缺乏准确的残留地图。由于对跨空间域土壤质量动态的预测见解至关重要，因此本条目解释了一种基于遥感的新技术，用于评估地表残留物覆盖。在这里，通过将线-横断面 % 残留覆盖率场测量与从地面光谱仪和高级广域传感器 (AWiFS) 卫星图像收集的信息相结合，创建了一个用于绘制表面残留覆盖率的经验模型。该地图使用通过光谱混合分析 (SMA) 提取的非光合植被 (NPV) 部分成分进行了验证。SMA 提取图像中感测信号的分数分量，这些分量在农田中是 NPV、绿色植被、裸土和阴影。通过线断面估计的残留物与使用卫星图像生成的地图之间的逐步线性回归具有 R 2 = 87%。根据包含 836,868 公顷面积的单个 AWiFS 图像的表面残留物进行地图分类，但重点是南达科他州内的玉米（Zea mays）田，显示这些玉米田中 <4% 的地表残留物覆盖率大于 15%到 2009 年 11 月该领域。诸如此类的发现可以指导土壤质量政策，这与残留物管理直接相关。未来，将绘制其他作物和其他季节种植的田间收获后剩余地表残留物的空间分布图。除了，