Abstract Remote sensing assessment of crop residue cover (fR) and tillage intensity can improve predictions of the environmental impact of agricultural practices and promote sustainable management. Spectral indices for estimating fR are sensitive to soil and crop residue water contents, therefore the uncertainty of fR estimates increases when moisture conditions vary. Our goals were to evaluate the robustness of spectral residue indices based on the shortwave infrared region (SWIR) for estimating fR and to mitigate the uncertainty caused by variable moisture conditions on fR estimates. Ten fields with center pivot irrigation systems (eight partially irrigated and two uniformly dry fields) were identified in Worldview-3 satellite imagery acquired for a study site in Maryland (USA). The fields were mid-irrigation at the time of imagery acquisition, allowing comparison of residue cover under dry and wet conditions. Fields were subdivided into approximately equal-size wedges within the dry and wet portions of each field, and the SWIR bands were extracted for each pixel. Two crop residue indices (Normalized Difference Tillage Index (NDTI); Shortwave Infrared Normalized Difference Residue Index (SINDRI) and a water index (WI) were calculated. Reflectance in each band was moisture-adjusted based on the WI difference between wet and dry wedges, and updated NDTI and SINDRI were calculated. Finally, the probability density distributions of fR estimated from the residue indices were calculated for each field. SINDRI was more robust than NDTI for estimating fR. Moisture corrections of spectral bands reduced the root mean square error of NDTI fR estimates from 22.7% to 4.7%, and SINDRI fR estimates from 6.0% to 2.2%. The mean and variance of the probability density distribution of fR estimated from residue indices, before and after moisture correction, were greatly reduced in the partially irrigated fields, but only slightly in fields with uniform water distribution. The estimation of fR should be based on SINDRI if appropriate bands are available, but fR can be reliably estimated by combining NDTI with a water content index to mitigate the uncertainty caused by variable moisture conditions.

中文翻译：

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通过耦合残留物和水分的光谱指数获得改进的作物残留物覆盖率估计

摘要 作物残留覆盖率 (fR) 和耕作强度的遥感评估可以提高对农业实践环境影响的预测并促进可持续管理。估算 fR 的光谱指数对土壤和作物残留物含水量敏感，因此当水分条件变化时，fR 估算的不确定性会增加。我们的目标是评估基于短波红外区域 (SWIR) 的光谱残留指数用于估计 fR 的稳健性，并减轻由可变湿度条件对 fR 估计造成的不确定性。在为马里兰州（美国）的一个研究地点获取的 Worldview-3 卫星图像中确定了 10 个具有中心枢轴灌溉系统的田地（8 个部分灌溉和两个均匀干燥的田地）。采集图像时田地处于灌溉中期，允许比较干燥和潮湿条件下的残留物覆盖。场在每个场的干湿部分被细分为大致相同大小的楔形，并为每个像素提取 SWIR 波段。计算了两个作物残留指数（归一化差异耕作指数（NDTI）；短波红外归一化差异残留指数（SINDRI）和水分指数（WI）。根据湿楔和干楔之间的 WI 差异对每个波段的反射率进行水分调整, 并计算了更新的 NDTI 和 SINDRI。最后，计算了每个场的残留指数估计的 fR 的概率密度分布。SINDRI 在估计 fR 方面比 NDTI 更稳健。光谱带的水分校正降低了均方根误差NDTI fR 估计从 22.7% 到 4.7%，和 SINDRI fR 估计从 6.0% 到 2.2%。在水分校正前后，由残留指数估计的fR概率密度分布的均值和方差在部分灌溉的田地中大大降低，但在水分分布均匀的田地中仅略微降低。如果有合适的波段可用，fR 的估计应基于 SINDRI，但可以通过将 NDTI 与含水量指数相结合来可靠地估计 fR，以减轻由可变水分条件引起的不确定性。