A new digital cover photography dataset and processing tool for SMAPVEX19-22: How siting and sky condition impact plant area index retrievals in continuous measurement set-ups

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology Pub Date : 2025-08-13 DOI:10.1016/j.agrformet.2025.110767

Simon Kraatz , Michael H. Cosh , V. Kelly , Laura Bourgeau-Chavez , Jisung Geba Chang , Chris Cook , Victoria A. Walker , Paul R. Siqueira , Andreas Colliander

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引用次数: 0

Abstract

Satellite remote sensing is widely used for Leaf Area Index (LAI) retrievals, but calibration and validation efforts require ground Plant Area Index (PAI) values that may be converted to LAI. Digital Cover Photography (DCP) presents an affordable means for covering large regions (∼2 × 33² km²) and multiple years. The Soil Moisture Active Passive Validation Experiment conducted from 2019–2022 (SMAPVEX19–22) utilized DCP data to study vegetation impacts on soil moisture retrievals in forests. This work reports on the DCP tool “EzPAI”, its outputs for SMAPVEX19–22, and clear sky PAI bias identification and its correction. EzPAI features sky condition tracking, multi-tier data screening, data quality flagging, and two-corner thresholding. We found that PAI is overestimated in clear conditions, and our bias correction approach reduced this by 0.2 on average. Massachusetts (‘MA’) and New York (‘MB’) networks attained comparable results for cloudiness (∼67 %) and poor data quality (21 %). Benchmark comparisons to other approaches (DCP tools, Sentinel-2 LAI, LAI-2200c) showed good agreement and performance. EzPAI showed similar results for in situ and Sentinel-2 LAI comparisons, achieving R∼0.9, and some bias (MD= <0.53). For comparisons to the coveR DCP tool the correlation was 0.73 and bias 0.26. For summer PAI totals, CoveR, coverPy, EzPAI and LAI-220c obtained nearly identical results: 4.08±0.35 4.08±0.33, 4.12±0.33 and 4.16±0.77. Differences may be explained in part due to image quality issues (noted at needleleaf canopies), the LAI-2200c data being noisy (σ_spring=0.43, σ_summer=0.72), the different measurement modalities used (satellite, handheld hemispherical, DCP), and our assumption of a constant extinction coefficient (k = 0.65) across 144 site-years. PAI values ranged from 2.73 to 5.16 (3.92 average) and 2.44 to 4.96 (3.80 average) for MA and MB, respectively. Processing time on a Dell Precision Laptop 7560 was 0.87 per image, which was about 3x speedier than coveR.

查看原文本刊更多论文

SMAPVEX19-22新的数字封面摄影数据集和处理工具：在连续测量设置中，选址和天空条件如何影响植物面积指数检索

卫星遥感广泛用于叶面积指数（LAI）的检索，但校准和验证工作需要将地面植物面积指数（PAI）值转换为LAI。数字封面摄影（DCP）提供了一种经济实惠的方法，可以覆盖大面积（~ 2 × 332平方公里）和多年。2019-2022年土壤水分主被动验证试验（SMAPVEX19-22）利用DCP数据研究植被对森林土壤水分回收的影响。本工作报告了DCP工具EzPAI及其SMAPVEX19-22的输出，以及晴空PAI偏差的识别和校正。EzPAI具有天空条件跟踪，多层数据筛选，数据质量标记，和两个角阈值。我们发现，在明确的条件下，PAI被高估了，我们的偏差校正方法将其平均降低了0.2。马萨诸塞州（“MA”）和纽约（“MB”）网络在云量（约67%）和数据质量差（21%）方面取得了类似的结果。与其他方法（DCP工具、Sentinel-2 LAI、LAI-2200c）的基准比较显示出良好的一致性和性能。EzPAI在原位和Sentinel-2 LAI比较中显示出相似的结果，达到R ~ 0.9，并且存在一定偏差（MD= <0.53）。与coveR DCP工具比较，相关性为0.73，偏倚为0.26。对于夏季PAI总数，CoveR、coverPy、EzPAI和LAI-220c得到的结果几乎相同：4.08±0.35、4.08±0.33、4.12±0.33和4.16±0.77。差异的部分原因可能是由于图像质量问题（针叶冠层），LAI-2200c数据有噪声（σspring=0.43, σsummer=0.72），使用的不同测量方式（卫星，手持半球，DCP），以及我们假设144个站点年的消光系数恒定（k = 0.65）。MA和MB的PAI值分别为2.73 ~ 5.16（平均3.92）和2.44 ~ 4.96（平均3.80）。戴尔精密笔记本电脑7560每张图像的处理时间为0.87，比coveR快了大约3倍。

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来源期刊

Agricultural and Forest Meteorology 农林科学-林学

CiteScore

10.30

自引率

9.70%

发文量

415

审稿时长

69 days

期刊介绍： Agricultural and Forest Meteorology is an international journal for the publication of original articles and reviews on the inter-relationship between meteorology, agriculture, forestry, and natural ecosystems. Emphasis is on basic and applied scientific research relevant to practical problems in the field of plant and soil sciences, ecology and biogeochemistry as affected by weather as well as climate variability and change. Theoretical models should be tested against experimental data. Articles must appeal to an international audience. Special issues devoted to single topics are also published. Typical topics include canopy micrometeorology (e.g. canopy radiation transfer, turbulence near the ground, evapotranspiration, energy balance, fluxes of trace gases), micrometeorological instrumentation (e.g., sensors for trace gases, flux measurement instruments, radiation measurement techniques), aerobiology (e.g. the dispersion of pollen, spores, insects and pesticides), biometeorology (e.g. the effect of weather and climate on plant distribution, crop yield, water-use efficiency, and plant phenology), forest-fire/weather interactions, and feedbacks from vegetation to weather and the climate system.