Along the Vetrivier (Vet River) in South Africa, a patchwork of circular and rectangular fields spreads across what is otherwise a semi-arid part of the Free State province. The water brings life to an array of crops, contributing to the agricultural productivity of the wider Maize Triangle .
沿着南非的维特里维耶（维特河），一片圆形和矩形的田野遍布自由州省的半干旱地区。这些水为多种农作物带来了生命，为更广泛的玉米三角地区的农业生产力做出了贡献。
The agricultural area shown in this image lies about 110 kilometers (70 miles) north of Bloemfontein. The scene is reminiscent of a modern abstract painting. Colorful circles mingle with straight-edged fields in combinations of red, green, and blue. But each color carries physical meaning, providing clues about crop types and revealing how they changed over the course of the Southern Hemisphere’s growing season.
该图像中显示的农业区位于布隆方丹以北约 110 公里（70 英里）处。这个场景让人想起现代抽象绘画。彩色圆圈与红色、绿色和蓝色组合的直边字段混合在一起。但每种颜色都具有物理意义，提供有关农作物类型的线索，并揭示它们在南半球生长季节过程中的变化。
Data for the visualization were acquired by the NISAR (NASA-ISRO Synthetic Aperture Radar) satellite during 10 passes over the area between November 2025 and March 2026. L-band radar observations, which can “see” vegetation’s structure instead of its color, were analyzed to produce per-pixel statistical measures across the scene. By combining radar scattering behaviors observed across multiple dates into a single composite, scientists built a compact summary of seasonal agricultural activity and change.
用于可视化的数据是由 NISAR（NASA-ISRO 合成孔径雷达）卫星在 2025 年 11 月至 2026 年 3 月期间 10 次飞越该地区时获取的。L 波段雷达观测可以“看到”植被的结构而不是颜色，经过分析，可以生成整个场景的每像素统计测量值。通过将多个日期观察到的雷达散射行为合并为一个组合，科学家们构建了季节性农业活动和变化的紧凑摘要。
“It’s a pretty picture, but there are also important things that it communicates to us,” said Paul Siqueira, a scientist at the University of Massachusetts Amherst, and ecosystems lead of the NISAR science team . “With NISAR, crops like maize and sunflower appear differently than forests because of their size differences and period of growth.”
“这是一幅美丽的图画，但它也向我们传达了一些重要的信息，”马萨诸塞大学阿默斯特分校的科学家、NISAR 科学团队的生态系统负责人保罗·西凯拉 (Paul Siqueira) 说。 “通过 NISAR，玉米和向日葵等作物由于大小和生长周期的不同而显得与森林不同。”
In this false-color composite, green indicates a vegetated area; red represents an unvegetated surface; and blue indicates how rapidly a vegetated area changed over the season. For instance, stable vegetation—such as forested areas—display a light blue component. Plants that change structure throughout the season, such as wheat and maize (corn), have a darker blue component.
在这个假色合成图中，绿色表示植被区域；红色代表未植被的表面；蓝色表示植被面积在整个季节变化的速度。例如，稳定的植被（例如森林地区）显示出浅蓝色成分。在整个季节改变结构的植物，例如小麦和玉米，具有深蓝色成分。
In practice, most pixels contain a mix of these colors, producing the visualization’s rich and varied palette. For example, plants that grow rapidly (contributing some green) and are harvested early (contributing a large red component) make fields appear orange. Sunflowers are known to exhibit this pattern in the region, though ground validation would be needed to confirm their presence in any given field.
实际上，大多数像素包含这些颜色的混合，从而产生可视化丰富多样的调色板。例如，生长迅速（贡献一些绿色）且收获较早（贡献大量红色成分）的植物会使田地呈现橙色。众所周知，向日葵在该地区表现出这种模式，但需要进行地面验证来确认它们在任何特定领域的存在。
The processing behind the visualization is relatively straightforward, but it is based on a large amount of data. NISAR sends radar signals to Earth and measures how they bounce back; the orientation of the returned radar waves (cross-polarized or co-polarized) carries information about the structure of vegetation and surfaces. By combining radar measurements from multiple satellite passes and calculating statistics for each pixel, scientists built the detailed map of the landscape’s characteristics throughout the growing season.
可视化背后的处理相对简单，但它基于大量数据。 NISAR 向地球发送雷达信号并测量它们如何反射回来；返回的雷达波的方向（交叉极化或共极化）携带有关植被和表面结构的信息。通过结合多个卫星经过的雷达测量值并计算每个像素的统计数据，科学家们绘制了整个生长季节景观特征的详细地图。
The technique provides a repeatable way to monitor crop development, the impacts of irrigation, and land-use change across large regions. As NISAR collects more data, researchers will be able to compare seasons, track field-to-field differences in growth patterns, and better understand how agricultural systems respond to water availability and climate variability.
该技术提供了一种可重复的方法来监测大区域的作物发育、灌溉的影响和土地利用变化。随着 NISAR 收集更多数据，研究人员将能够比较季节、追踪田间生长模式的差异，并更好地了解农业系统如何应对水资源供应和气候变化。