Modifying NISAR’s Cropland Area Algorithm to Map Cropland Extent Globally

• Published in: Remote sensing (Basel, Switzerland) Vol. 17; no. 6; p. 1094
• Main Authors: Sharp, Kaylee G., Bell, Jordan R., Pankratz, Hannah G., Schultz, Lori A., Lucey, Ronan, Meyer, Franz J., Molthan, Andrew L.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2025
• Subjects: Accuracy; Agricultural land; Agriculture; Algorithms; Artificial satellites in remote sensing; Atmospheric conditions; backscatter; C band; Cereal crops; Classification; Coefficient of variation; Corn; Crops; Datasets; Land cover; Machine learning; Measurement; NISAR; Optimization; Pixels; Radiation; Remote sensing; Rice; SAR; Soybeans; Synthetic aperture radar; Thresholds; Time series; Vegetables; Wheat; Myanmar; United States; China; Ukraine; Brazil; Thailand; France; India; United States > US
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs17061094

Synthetic aperture radar (SAR) is emerging as a valuable dataset for monitoring crops globally. Unlike optical remote sensing, SAR can provide earth observations regardless of solar illumination or atmospheric conditions. Several methods that utilize SAR to identify agriculture rely on computationally expensive algorithms, such as machine learning, that require extensive training datasets, complex data pre-processing, or specialized software. The coefficient of variation (CV) method has been successful in identifying agricultural activity using several SAR sensors and is the basis of the Cropland Area algorithm for the upcoming NASA-Indian Space Research Organization (ISRO) SAR mission. The CV method derives a unique threshold for an AOI by optimizing Youden’s J-Statistic, where pixels above the threshold are classified as crop and pixels below are classified as non-crop, producing a binary crop/non-crop classification. Training this optimization process requires at least some existing cropland classification as an external reference dataset. In this paper, general CV thresholds are derived that can discriminate active agriculture (i.e., fields in use) from other land cover types without requiring a cropland reference dataset. We demonstrate the validity of our approach for three crop types: corn/soybean, wheat, and rice. Using data from the European Space Agency’s (ESA) Sentinel-1, a C-band SAR instrument, nine global AOIs, three for each crop type, were evaluated. Optimal thresholds were calculated and averaged for two AOIs per crop type for 2018–2022, resulting in 0.53, 0.31, and 0.26 thresholds for corn/soybean, wheat, and rice regions, respectively. The crop type average thresholds were then applied to an additional AOI of the same crop type, where they achieved 92%, 84%, and 83% accuracy for corn/soybean, wheat, and rice, respectively, when compared to ESA’s 2021 land cover product, WorldCover. The results of this study indicate that the use of the CV, along with the average crop type thresholds presented, is a fast, simple, and reliable technique to detect active agriculture in areas where either corn/soybean, wheat, or rice is the dominant crop type and where outdated or no reference datasets exist.