Abstract

Accurate monitoring of croplands helps in making decisions (for insurance claims, crop management and contingency plans) at the macro-level, especially in drylands where variability in cropping is very high owing to erratic weather conditions. Dryland cereals and grain legumes are key to ensuring the food and nutritional security of a large number of vulnerable populations living in the drylands. Reliable information on area cultivated to such crops forms part of the national accounting of food production and supply in many Asian countries, many of which are employing remote sensing tools to improve the accuracy of assessments of cultivated areas. This paper assesses the capabilities and limitations of mapping cultivated areas in the Rabi (winter) season and corresponding cropping patterns in three districts characterized by small-plot agriculture. The study used Sentinel-2 Normalized Difference Vegetation Index (NDVI) 15-day time-series at 10 m resolution by employing a Spectral Matching Technique (SMT) approach. The use of SMT is based on the well-studied relationship between temporal NDVI signatures and crop phenology. The rabi season in India, dominated by non-rainy days, is best suited for the application of this method, as persistent cloud cover will hamper the availability of images necessary to generate clearly differentiating temporal signatures. Our study showed that the temporal signatures of wheat, chickpea and mustard are easily distinguishable, enabling an overall accuracy of 84%, with wheat and mustard achieving 86% and 94% accuracies, respectively. The most significant misclassifications were in irrigated areas for mustard and wheat, in small-plot mustard fields covered by trees and in fragmented chickpea areas. A comparison of district-wise national crop statistics and those obtained from this study revealed a correlation of 96%.

摘要

对农田的准确监测有助于在宏观层面做出决策（保险理赔、作物管理和应急计划），尤其是在由于天气条件不稳定而导致种植变异性非常高的旱地。旱地谷物和豆类是确保生活在旱地的大量弱势人口的粮食和营养安全的关键。有关此类作物种植面积的可靠信息构成了许多亚洲国家粮食生产和供应国民核算的一部分，其中许多国家正在使用遥感工具来提高对耕地面积评估的准确性。本文评估了在以小块农业为特征的三个地区绘制拉比（冬季）耕地和相应种植模式的能力和局限性。该研究采用光谱匹配技术 (SMT) 方法，以 10 m 分辨率使用 Sentinel-2 归一化植被指数 (NDVI) 15 天时间序列。SMT 的使用基于充分研究的时间 NDVI 特征和作物物候学之间的关系。印度的狂犬病季节以非雨天为主，最适合应用这种方法，因为持续的云层覆盖将阻碍生成清晰区分的时间特征所需的图像的可用性。我们的研究表明，小麦、鹰嘴豆和芥末的时间特征很容易区分，总体准确率达到 84%，小麦和芥末的准确率分别达到 86% 和 94%。最严重的错误分类是在芥末和小麦的灌溉区，在被树木覆盖的小块芥菜田和零散的鹰嘴豆地区。对地区国家作物统计数据和从本研究中获得的数据进行比较，发现相关性为 96%。