Location, biophysical and agronomic parameters for croplands in northern Ghana
Location, biophysical and agronomic parameters for croplands in northern Ghana
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DOI：
10.5194/essd-14-5387-2022
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发表时间：
2022-12
影响因子：
11.4
通讯作者：
J. Gómez-Dans;P. Lewis;F. Yin;K. Asare;P. Lamptey;K. Aidoo;D. MacCarthy;Hongyuan Ma;Qinglin Wu;Martin Addi;Stephen Aboagye-Ntow;C. Doe;R. Alhassan;I. Kankam-Boadu;Jianxi Huang;Xuecao Li
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作者：
J. Gómez-Dans;P. Lewis;F. Yin;K. Asare;P. Lamptey;K. Aidoo;D. MacCarthy;Hongyuan Ma;Qinglin Wu;Martin Addi;Stephen Aboagye-Ntow;C. Doe;R. Alhassan;I. Kankam-Boadu;Jianxi Huang;Xuecao Li
抽象的。小农农业是撒哈拉以南非洲粮食生产系统的基石。由于若干原因，非洲的产量大大低于可能达到的产量，而且特别容易受到气候变化的影响。需要对这些高度异质性的景观进行监测，以满足农民的需求，制定适当的政策并确保粮食安全，地球观测（EO）必须是这些努力的一部分，但在西非缺乏开发和测试EO方法的地面数据，在本文中，我们提供了（i）作物位置，（ii）生物物理参数和（iii）作物产量的数据，2020年和2021年在加纳收集了生物量，本文对此进行了报道。2020年，在加纳三个不同的农业生态区（几内亚萨凡纳、过渡区和落叶区）的1800多块农田中调查了作物类型。2021年，在几内亚萨凡纳区调查了少量农田，此外，对一组56块玉米田进行了叶面积指数（LAI）和叶片叶绿素浓度的重复测量。收获时还对产量和生物量进行取样。取样田的叶面积指数在0.1至5.24 m2 m − 2之间，而叶片叶绿素浓度在6.1至60.3 µ g cm − 2之间。产量在190至4580 kg ha − 1之间变化，具有重要的田间变异性（平均每田标准差381 kg ha − 1）。本文使用这些数据来（i）评估数字地球非洲2019年农田掩模，其中61%的2020/21年采样农田被数据集标记为农田，（ii）开发和测试来自地球观测行星表面反射率数据的LAI检索方法（验证相关系数R = 0.49，均方根误差（RMSE）0.44 m2 m − 2），（iii）为加纳创建2021年玉米分类数据集（在测试区域内的整体精度：0.84），和（iv）探索最大叶面积指数和作物产量之间的关系，使用线性模型（相关系数R = 0.66和R = 0.53，分别为原位和行星派生叶面积指数）。在地球观测小组全球农业监测倡议范围内提供的这套数据集对了解小农耕作系统中的作物演变和分布作出了重要贡献，并将有助于研究人员开发/验证利用地球观测数据监测这些系统的方法。本文中描述的数据可从www.example.com获得（Gomez-Dans等人，2022年）。
Abstract. Smallholder agriculture is the bedrock of the food production system in sub-Saharan Africa. Yields in Africa are significantly below potentially attainable yields for a number of reasons, and they are particularly vulnerable to climate change impacts. Monitoring of these highly heterogeneous landscapes is needed to respond to farmer needs, develop an appropriate policy and ensure food security, and Earth observation (EO) must be part of these efforts, but there is a lack of ground data for developing and testing EO methods in western Africa, and in this paper, we present data on (i) crop locations, (ii) biophysical parameters and (iii) crop yield, and biomass was collected in 2020 and 2021 in Ghana and is reported in this paper. In 2020, crop type was surveyed in more than 1800 fields in three different agroecological zones across Ghana (the Guinea Savannah, Transition and Deciduous zones). In 2021, a smaller number of fields were surveyed in the Guinea Savannah zone, and additionally, repeated measurements of leaf area index (LAI) and leaf chlorophyll concentration were made on a set of 56 maize fields. Yield and biomass were also sampled at harvesting. LAI in the sampled fields ranged from 0.1 to 5.24 m2 m−2, whereas leaf chlorophyll concentration varied between 6.1 and 60.3 µg cm−2. Yield varied between 190 and 4580 kg ha−1, with an important within-field variability (average per-field standard deviation 381 kg ha−1). The data are used in this paper to (i) evaluate the Digital Earth Africa 2019 cropland masks, where 61 % of sampled 2020/21 cropland is flagged as cropland by the data set, (ii) develop and test an LAI retrieval method from Earth observation Planet surface reflectance data (validation correlation coefficient R=0.49, root mean square error (RMSE) 0.44 m2 m−2), (iii) create a maize classification data set for Ghana for 2021 (overall accuracy within the region tested: 0.84), and (iv) explore the relationship between maximum LAI and crop yield using a linear model (correlation coefficient R=0.66 and R=0.53 for in situ and Planet-derived LAI, respectively). The data set, made available here within the context of the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) initiative, is an important contribution to understanding crop evolution and distribution in smallholder farming systems and will be useful for researchers developing/validating methods to monitor these systems using Earth observation data. The data described in this paper are available from https://doi.org/10.5281/zenodo.6632083 (Gomez-Dans et al., 2022).