Comparing Landsat7 ETM+ and NAIP imagery for precision agriculture application in small scale farming: a case study in the south eastern part of Pittsylvania County, VA

Small scale farming identify farms with less than 300 acres of agricultural land and represent a large population of producers in the US, thus the interest in procedures such as Precision Agriculture Application in productivity cycles. This study compares publicly available Landsat7 ETM+ imagery, at nominal 30 meters pixel resolution, and National Agricultural Imagery Program’s (NAIP) imagery, at nominal 1 meter pixel resolution, to evaluate their use in Precision Agriculture (PA) applications for small‐scale farming. The selected study area was determined based on crop characterization and land size criteria identified in the South Eastern part of Pittsylvania County, VA. The selected agricultural fields within the study area, 14 in total, were of varying shapes, ranging from 7.5 to 150 acres in size, and characterized by a specific crop type such as non‐alfalfa hay. ❧ The methodology for this study consisted in the computation and analysis of four vegetation indices (VIs) to evaluate the effect of imagery resolution to depict vegetation maturity in the selected 14 sites. The VIs used consisted of: Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Soil‐Adjusted Vegetation Index (SAVI). In addition to the Vis analysis, a pixel Percent Error estimate was derived from the low‐ and high‐resolution VIs products to evaluate the amount of variance between Landsat7 ETM+ and NAIP data. ❧ As expected, NAIP’s VIs results provided more detail about the study sites compared to the Landsat7 ETM+ VIs products. This was evident as NAIP’s ability to locate and visualize vegetation and non‐vegetation features within the study sites, which is of particular importance for PA applications. In contrast, Landsat7 ETM+ imagery were not able to provide adequate identification and monitoring capabilities when used in limited areal extent, specifically required for small scale farming PA applications. Spectral mixing of land features smaller than the 30 meters pixel resolution imagery were causing vegetation differences to be diluted across the fields rather than being isolated and identifiable like in the NAIP’s VIs results. ❧ Results from the PE analysis confirm the VI results and show a great difference between VI values derived from the low resolution Landsat7 ETM+ and high resolution NAIP imagery. The majority of the sites contain a high percentage of pixels error above the acceptable percentage, which outline that VI values derived from low resolution imagery do not provide results comparable to the high resolution imagery. Moreover, the size of the sites do have an effect on the amount of acceptable PE within each field, with larger fields containing higher percentages of Acceptable PE than smaller sites. Therefore, due to the use of reduced size fields in small scale farming, the use of low resolution imagery might not be appropriate to adequately represent the actual ground conditions necessary for reliable PA use.

小型农场指农业用地不足300英亩的经营主体，其在美国农业生产者群体中占据较大比例，因此精准农业（Precision Agriculture）在生产周期中的应用流程受到广泛关注。本研究对比了公开获取的Landsat7 ETM+影像（标称像素分辨率30米）与美国国家农业影像计划（National Agricultural Imagery Program，NAIP）影像（标称像素分辨率1米），以评估二者在小型农场精准农业应用中的适用性。研究区域选定于弗吉尼亚州皮特西尔瓦尼亚县东南部，选取标准涵盖作物特征与土地面积要求。研究区域内共选取14块农田，形状各异，面积介于7.5至150英亩之间，种植有非苜蓿干草等特定作物类型。
本研究的方法包括对四类植被指数（Vegetation Indices，VIs）进行计算与分析，以评估影像分辨率对14个样地植被成熟度的表征效果。本次选用的植被指数包括：比值植被指数（Ratio Vegetation Index，RVI）、归一化差异植被指数（Normalized Difference Vegetation Index，NDVI）、绿色归一化差异植被指数（Green Normalized Difference Vegetation Index，GNDVI）以及土壤调节植被指数（Soil-Adjusted Vegetation Index，SAVI）。除植被指数分析外，本研究还基于高低分辨率的植被指数产品计算了像素百分比误差（Percent Error，PE）估算值，以评估Landsat7 ETM+与NAIP影像数据之间的方差差异。
正如预期，相较于Landsat7 ETM+的植被指数产品，NAIP的植被指数结果能为研究样地提供更丰富的细节信息。这一点可从NAIP可清晰识别并可视化样地内的植被与非植被特征得到印证，而这对精准农业应用而言尤为关键。与之形成对比的是，当应用于小型农场精准农业所需的有限区域范围时，Landsat7 ETM+影像无法提供足够的识别与监测能力。由于该影像的像素分辨率为30米，小于该尺寸的地表特征会发生光谱混合，导致农田内的植被差异被整体稀释，无法像NAIP植被指数结果那样被单独识别与区分。
百分比误差分析结果验证了植被指数分析的结论，同时显示基于低分辨率Landsat7 ETM+与高分辨率NAIP影像提取的植被指数数值存在显著差异。多数样地的像素误差占比超出可接受阈值，这表明基于低分辨率影像提取的植被指数结果无法与高分辨率影像的结果相提并论。此外，样地面积对每块农田内可接受的百分比误差占比存在影响：面积较大的农田，其可接受误差占比高于面积较小的样地。因此，鉴于小型农场多采用面积较小的农田，使用低分辨率影像或许无法准确表征可靠精准农业应用所需的实际地表状况。