Maize Yield Estimation in Intercropped Smallholder Fields Using Satellite Data in Southern Malawi
Maize Yield Estimation in Intercropped Smallholder Fields Using Satellite Data in Southern Malawi
复制标题
DOI：
10.3390/rs14102458
复制
发表时间：
2022-05-01
期刊：
影响因子：
5
通讯作者：
Dash, Jadunandan
中科院分区：
文献类型：
--
作者：
Li, Chengxiu;Chimimba, Ellasy Gulule;Dash, Jadunandan
卫星数据为估计作物产量提供了很大的潜力，这对于了解产量差距的决定因素，从而提高粮食生产，特别是撒哈拉以南非洲地区的粮食生产至关重要。然而，准确评估作物产量及其空间变化是具有挑战性的，因为小的领域大小，广泛的间作做法，以及不充分的实地观察。本研究的目的是首先评估卫星数据在估计玉米产量在间作小农领域的潜力，其次评估如何卫星数据的空间和时间分辨率，在田间变异性，字段大小，收获指数和间作做法等因素影响模型的性能。在收集了现场数据（田地大小、产量、间作发生、收获指数和叶面积指数）之后，开发了统计模型以根据多光谱卫星数据预测产量（即，Sentinel-2和PlanetScope）。根据上述因素评估模型准确度和残差。在150个调查的领域，我们的研究发现，近一半是间作豆类，平均地块大小为0.17公顷。尽管间作造成了混合像素，但基于Sentinel-2红边植被指数（VI）的模型可以中等精度（R-2 = 0.51，nRMSE = 19.95%）估计玉米产量，而更高空间分辨率的卫星数据（例如，PlanetScope 3 m）仅显示出性能的边际改善（R-2 = 0.52，nRMSE = 19.95%）。季节性峰值VI值提供了更好的准确性比季节性平均/中位数VI，这表明峰值VI值可能会捕捉到占主导地位的上部玉米叶层的信号，可能会受到林下间作效应的影响较小。尽管如此，间作的做法降低了模型的准确性，因为模型的残差是较低的纯玉米（1吨/公顷）相比，间作领域（1.3吨/公顷）。这项研究提供了一个参考业务玉米产量估计间作小农领域，利用免费卫星数据在马拉维南部。它还突出了利用卫星图像估计间作田产量的困难，并强调了充分的卫星观测对监测撒南非洲间作做法的重要性。
Satellite data provide high potential for estimating crop yield, which is crucial to understanding determinants of yield gaps and therefore improving food production, particularly in sub-Saharan Africa (SSA) regions. However, accurate assessment of crop yield and its spatial variation is challenging in SSA because of small field sizes, widespread intercropping practices, and inadequate field observations. This study aimed to firstly evaluate the potential of satellite data in estimating maize yield in intercropped smallholder fields and secondly assess how factors such as satellite data spatial and temporal resolution, within-field variability, field size, harvest index and intercropping practices affect model performance. Having collected in situ data (field size, yield, intercrops occurrence, harvest index, and leaf area index), statistical models were developed to predict yield from multisource satellite data (i.e., Sentinel-2 and PlanetScope). Model accuracy and residuals were assessed against the above factors. Among 150 investigated fields, our study found that nearly half were intercropped with legumes, with an average plot size of 0.17 ha. Despite mixed pixels resulting from intercrops, the model based on the Sentinel-2 red-edge vegetation index (VI) could estimate maize yield with moderate accuracy (R-2 = 0.51, nRMSE = 19.95%), while higher spatial resolution satellite data (e.g., PlanetScope 3 m) only showed a marginal improvement in performance (R-2 = 0.52, nRMSE = 19.95%). Seasonal peak VI values provided better accuracy than seasonal mean/median VI, suggesting peak VI values may capture the signal of the dominant upper maize foliage layer and may be less impacted by understory intercrop effects. Still, intercropping practice reduces model accuracy, as the model residuals are lower in fields with pure maize (1 t/ha) compared to intercropped fields (1.3 t/ha). This study provides a reference for operational maize yield estimation in intercropped smallholder fields, using free satellite data in Southern Malawi. It also highlights the difficulties of estimating yield in intercropped fields using satellite imagery, and stresses the importance of sufficient satellite observations for monitoring intercropping practices in SSA.