Intercepted photosynthetically active radiation (Ipar, MJ m^-2 d^-1) is a key biophysical variable governing plant photosynthetic rate and net primary productivity (NPP, g m^-2 d^-1). Under optimal growth conditions, Ipar scales proportionally to NPP by a factor termed ‘optimum radiation use efficiency’ (RUE_opt). The Carnegie-Ames-Stanford approach (CASA) considers temperature and moisture constraints to RUE_opt and has been widely used in remote sensing studies to estimate productivity of various ecosystems. However, scale effects on the CASA have not been sufficiently investigated nor quantified. In this study, data at scales of field (Rapidscan reflectance data), air- (unmanned aerial vehicle (UAV) imagery) and spaceborne (Sentinel-2 imagery), as well as for several environmental constraints, were utilized to develop and test the CASA. The test plant was potato grown for multiple years on sandy soils in Denmark. The results showed that data from all scales provided comparable estimates of daily fraction of Ipar (f_Ipar) and Ipar. Maximum air temperature and diffuse radiation were the most important environmental factors constraining RUE_opt of potato. Taking these into account considerably improved the prediction of NPP with the CASA (R² increase of 25–32 % compared to no constraints), whereas stress effects due to soil moisture and vapor pressure deficits were less important (relative improvement in R² of 2–3 %). Hence, RUE_opt was 4.66, 4.19 and 4.98 g MJ^-1 for Rapidscan, UAV and Sentinel-2, respectively, which was about 47 %–59 % higher than the estimates of the observed actual radiation use efficiency. This study offers an operational method for deriving RUE_opt at plant species level since remote sensing and meteorological data are both readily available, and demonstrates that environmental constraints of the CASA does vary, depending on the study region. The method can be used to predict dry matter production in-season for other crops as well, with potential for extending the approach to determine fertilization and irrigation demands.

截获的光合有效辐射（Ipar，MJ m ^-2 d ^-1）是控制植物光合速率和净初级生产力（NPP， gm ^-2 d ^-1）的关键生物物理变量。在最佳生长条件下，Ipar通过称为“最佳辐射利用效率”（RUE _opt）的因数与NPP成比例。卡内基-埃姆斯斯坦福的方法（CASA）考虑温度和湿度约束RUE_选择并已广泛用于遥感研究中以估计各种生态系统的生产力。但是，对CASA的规模影响尚未得到充分研究或量化。在这项研究中，利用田间尺度的数据（快速扫描反射率数据），空中（无人机）图像和太空（前哨2图像）以及一些环境约束条件来开发和测试卡萨 测试植物是在丹麦的沙质土壤上种植多年的马铃薯。结果表明，来自所有规模的数据都提供了Ipar（f _Ipar）和Ipar每日比例的可比估计。最高气温和扩散辐射是限制RUE的最重要环境​​因素_选择马铃薯。考虑到这些因素，使用CASA可以显着改善NPP的预测（与无约束相比， R ²增加25–32％），而土壤水分和蒸汽压不足引起的应力影响则不太重要（ R ²相对提高2） –3％）。因此，Rapidscan，UAV和Sentinel-2的RUE _opt分别为4.66、4.19和4.98 g MJ ^-1，比观察到的实际辐射使用效率的估计值高约47％–59％。这项研究为推导RUE _opt提供了一种操作方法在植物物种层面上，因为遥感和气象数据都很容易获得，并且证明了CASA的环境限制确实有所不同，具体取决于研究区域。该方法也可用于预测其他作物在季节内的干物质生产，具有扩展确定施肥和灌溉需求的方法的潜力。